The way I see it, artificial intelligence (or AI), really leapt into the zeitgeist in late-2022 or early-2023 with the public introduction of DALL-E2 and ChatGPT. Since then, developments in AI have progressed at a breathtaking pace.

We’re thick in the action of the latest earnings season for the US stock market – for the second quarter of 2026 – and I thought it would be useful to collate some of the interesting commentary I’ve come across in earnings conference calls, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here
• 2024 Q4 – here, here, and here
• 2025 Q1 – here and here
• 2025 Q2 – here and here
• 2025 Q3 – here, here, and here
• 2025 Q4 – here, here, here, and here
• 2026 Q1 – here, here, here, and here

With that, here are the latest commentary, in no particular order:

Alphabet (NASDAQ: GOOG)

Alphabet’s management recently announced new AI models, Gemini 3.6 Flash, 3.5 Flash-Lite, and  3.5 Flash Cyber; the Gemini Flash models are Alphabet’s workhorse models and management is seeing lots of demand for them because of their performance and cost; Gemini 3.5 Flash Cyber has the same performance as other larger, frontier cyber models; Alphabet is testing Gemini 3.5 Pro; Alphabet has started pre-training for Gemini 4 and management is excited by its progress; developers and enterprises have strong token usage of Alphabet’s models; 9 million developers are building with Alphabet’s models each month; Alphabet’s model APIs (application programming interfaces) processed 22 billion tokens per minute in 2026 Q2 (was 16 billion tokens per minute in 2026 Q1); management launched the Omni video model in May and it has led to a 40% increase in daily users creating videos on the Gemini app; Alphabet’s Gemma family of small open models that can run locally on devices, have been downloaded over 900 million times, with the latest Gemma 4 been downloaded 300 million times since launching in April; the Gemini app now has 950 million monthly active users (was 750 million in 2025 Q4), with daily users up 3x from a year ago; Gemini Spark, the personal AI agent within the Gemini app, is now available globally; the Gemini Flash models are great for enterprise needs;

Yesterday, we announced new models, Gemini 3.6 Flash and 3.5 Flash-Lite, which are cost-effective and highly efficient. We are seeing tons of demand for our workhorse Gemini Flash series because it hits the sweet spot of performance and cost. We also launched Gemini 3.5 Flash Cyber, which I’m really excited about. Paired with our CodeMender agent, it finds and fixes vulnerabilities and delivers performance at the frontier comparable to far bigger cyber models. Gemini 3.5 Pro is currently in testing, and our team is already building the next generation of models. We have started our most ambitious pre-training run yet for Gemini 4 and are excited by the progress we are seeing at the frontier.

Demand for our models is translating to strong token usage across developers and enterprise customers, and we continue to be supply constrained, a sign of momentum and rapid adoption. More than 9 million developers are building each month with our models across our APIs and key developer products. Our model APIs are now processing approximately 22 billion tokens per minute. That’s up from 16 billion just a quarter ago.

This quarter, we launched Omni. It allows users to create anything from any input, starting with video. Since launching at I/O in May, there’s been a 40% increase in daily active users creating videos on the Gemini app. Our Gemma family of open models, small enough to run on local devices, are hugely popular. These models have been downloaded over 900 million times, and our latest Gemma 4 models have been downloaded over 300 million times since launching in April…

…The Gemini app, which now has 950 million monthly active users with daily active users tripling in the last year. Users love new agentic features like Daily Brief and our personalized agent, Gemini Spark, which is now available in the U.S. and internationally…

…If you think about an area like customer service, you need really good voice quality, you need live streaming, you need the ability to reason on that. If you’re a professional services firm, you need high-quality summarization, content generation, et cetera. Flash does very well on all of that. 

Antigravity, Alphabet’s 1st-party agentic coding solution, has more than 2.4 million weekly active users; Antigravity has helped a team in Google Chrome compress a 2-year delivery timeline into 3 months; 83% of Alphabet’s sales team are using Gemini-powered tools every weeks, and achieving a 20% higher win rate; Gemini-powered agentic solutions are addressing 75% of support queries

Our agentic development platform, Antigravity, allows anyone to build in the agent-first era. It has more than 2.4 million weekly active users. Antigravity is a powerful tool for users and enterprises, and it’s completely accelerated how we build internally. As just one example, a team in Chrome is now on track to accelerate delivery by 8 times, compressing a 2-year timeline into 3 months through model-driven refactoring…

…83% of our sales team uses Gemini-assisted tools weekly, driving up to a 20% higher win rate when using customized pitch narratives. Our ads customer support teams use Gemini-powered agentic solutions that now autonomously address 75% of support queries, freeing them to solve ur customers’ most complex challenges. 

AI continues to be expansionary for Google Search; Google Search usage hit an all-time high during the recent World Cup; management recently combined AI Overviews and AI Mode into a seamless Search experience; management is infusing Google Search with frontier capabilities such as AI agents; Alphabet’s AI features are driving higher usage of Google Search; AI Mode in Search has surpassed 1 billion monthly active users since launching in October 2025 and is driving incremental growth in overall Search queries; Alphabet’s AI features are now sending billions of clicks per week to websites; Alphabet has lowered the cost of AI Mode responses to its lowest level since launch despite introducing more AI capabilities; management is encouraged with the monetisation on even commercial queries that show AI Overviews; users are asking more detailed questions in AI Overviews and AI Mode, leading to more opportunities for more relevant ads; management is deploying and testing new advertising formats in AI Mode, including Text Ads, Direct Offers, and Highlighted Answers; Highlighted Answers is showing early user traction

With Search, AI continues to drive an expansionary moment with new experiences resonating with users and driving growth in queries. As a big football fan, I was particularly excited to see Search usage hit an all-time high during the World Cup this year…

…We recently brought together AI Overviews and AI Mode into one seamless Search experience that combines our frontier capabilities with the best of the web. We are continuing to incorporate more frontier capabilities into Search with agents, personal intelligence, and notebooks. Our AI-powered features are driving increased Search usage. Since expanding AI Mode globally last October, we have surpassed 1 billion monthly active users. Just like AI Overviews, AI Mode is driving an incremental increase in Search queries overall, and we are now sending billions of clicks to websites every week through AI features in Search…

…Thanks to our engineering and hardware optimizations, this quarter, we reduced the cost of AI Mode responses to its lowest level since launch, even as we have brought more advanced AI capabilities…

…We continue to be encouraged with monetization performance on queries that show AI Overviews, even as we’ve expanded AI Overviews to more commercial queries. Across AI Overviews and AI Mode, people are asking more specific and detailed questions, providing opportunities for more relevant ads. Within AI Mode, we continue to test and deploy a range of new ad formats. For Text Ads, we improve performance by adding contextual site links based on the conversation. Direct Offers is gaining momentum, with partners like IHG Hotels & Resorts soon surfacing special offers during trip planning. In Highlighted Answers, our latest experience, placing clearly marked sponsored links inside list responses is showing early user traction. 

Alphabet’s management recently launched Ask Youtube, which is powered by Gemini and allows users to ask complex questions about Youtube videos; early engagement with Ask Youtube is encouraging, with 140 million users in June 2026; management is widening the Ask experience to Youtube’s search experience; Youtube introduced creator shows and custom sponsorships, where AI is used to dynamically surface videos tailored to a brand’s desired moment; Kate Spade worked with Youtube creators and saw a 3.25% brand lift in purchase intent; Youtube’s direct response growth is fueled by brands’ usage of Demand Gen; Arc’teryx used Demand Gen to achieve a 70% better return on advertising spend

We’re also bringing the power of conversational AI directly into the YouTube experience. Ask YouTube uses our Gemini models to let people ask complex questions about individual videos, get quick takeaways, and jump straight to moments in those videos. The early engagement is encouraging. More than 140 million users engaged with Ask YouTube on the Watch page in June 2026. We are bringing that Ask experience to the wider search experience on YouTube…

…Over 550 million watched them on their televisions. This made the FIFA World Cup 2026 the most viewed World Cup in YouTube history. Advertisers connected with fans through FIFA channel takeovers, Gemini-powered soccer-themed sponsorships, and game day mastheads… 

… More broadly, on YouTube, we introduced an exclusive slate of creator shows making it easier for brands to tap into the fandom of creators. We launched custom sponsorships to put brands at the heart of the world’s biggest moments as they unfold on YouTube, using AI to dynamically surface videos tailored to a brand’s desired moment. Brands continue to partner with YouTube creators to engage new audiences. Kate Spade reached Gen Z through a first-of-its-kind YouTube creator campaign and partnered with creators Ellie Thumann and Hannah Meloche. By leveraging multiple format videos on YouTube, the brand drove a 3.25% brand lift in purchase intent… 

SMBs continue to fuel our direct response growth through campaigns like Demand Gen. They leverage our AI to scale visual storytelling across YouTube, Shorts, and now Google Maps. To attract high-value shoppers across North America and Europe, outdoor brand Arc’teryx used Demand Gen to achieve a 70% better return on their ad spend compared to other paid channels.

Google Cloud’s momentum is driven by Alphabet’s vertical integration of the entire AI technology stack; the Gemini models are a key driver of Google Cloud’s growth and they are integrated into all of Google Cloud’s products; Google Cloud is seeing strong, diversified demand across products, customers, geographies, and industries; Google Cloud is enjoying growth from winning new customers (customer acquisition doubled year-on-year in 2026 Q2), expanding with existing customers (existing customers are exceeding commitments by 50% in 2026 Q2, an acceleration from 2026 Q1), and partners (Google Cloud Marketplace transactions were up 7x year-on-year in 2026 Q2); the Gemini Enterprise platform is seeing rapid adoption, with its Agent Development Kit reaching nearly 70 million total downloads in 2026 Q2; nearly 90% of the Fortune 100 companies are using Gemini Enterprise; businesses are using Gemini to build custom agents, automate processes, and more; 500 Google Cloud customers processed over 1 trillion tokens each over the last 12 months (was 330 in 2026 Q1), with 2,000 customers consuming over 100 billion tokens each; Booking Holdings has expanded a multi-year commitment with Google Cloud; it’s still early days for AI-native and AI-enabled workloads on the cloud, and it’s also early days even for overall workloads that have shifted to the cloud 

Next, Cloud. Our continued momentum is driven by our integrated AI portfolio consisting of chips, models, data, security, and agent platforms, all designed to work together. Gemini continues to be a key driver of growth and is deeply integrated across all of our cloud products, including Gemini Enterprise, data analytics, cybersecurity, and Google Workspace. We are seeing strong, diversified demand across products, customers, geographies, and industries. Our product differentiation is driving expansion in three ways. We are winning new customers, more than doubling our acquisition velocity year-over-year. We are deepening our relationships with existing customers who are expanding their usage and exceeding their commitments by more than 50%, also, an acceleration over last quarter. We are driving growth with partners, with transactions on Google Cloud Marketplace growing over seven times year-over-year.

One of the strongest parts of our growth comes from the rapid adoption of our Gemini Enterprise platform. It’s differentiated with easy-to-use tools to build agents and automate processes, connectivity to enterprise systems, cost management, and governance tools. In Q2, Agent Development Kit, our framework for building and deploying enterprise AI agents, reached nearly 70 million total downloads. As I said, nearly 90% of Fortune 100 are using Gemini Enterprise. We have customers like PepsiCo for AI and analytics solutions, Intel to streamline core processes, HSBC for wealth management, Bell Canada for customer engagement, Macy’s for commerce experiences, and SIGNAL IDUNA for knowledge management. More broadly, Gemini is transforming how millions of businesses use AI to build custom agents, automate processes, improve cybersecurity, manage customer relationships, streamline data analytics, collaborate effectively, and more.

All of this momentum is driving growth in our paid token usage. Nearly 500 Cloud customers have each processed more than 1 trillion tokens in the last year, and usage is so much deeper than that. Over the last 12 months, more than 2,000 enterprises consumed over 100 billion tokens…

…Booking Holdings expanded a multi-year cloud commitment and are partnering closely

to advance our AI-powered ad formats. It’s also deploying Google’s AI technology to enable new

customer experiences like agentic dining reservations on OpenTable, helping restaurants get

discovered and booked right when it matters most…

…We used to talk about cloud itself, very small percentage of overall workloads and enterprises have shifted to cloud. Think about what percentage of workloads are really AI native and AI enabled. It again feels very early. 

Alphabet’s management is seeing strong interest for Google Cloud’s AI-powered security offerings; 90% of Fortune 100 companies are Google Cloud Security users; 90% of Wiz customers are using AI-powered security features; in 2026 Q2, the number of AI workloads protected by Google Cloud’s security platform saw a 45% sequential increase; Alphabet has a new security offering called Google AI Threat Defense

We’re also seeing strong interest in our AI-powered security platform, which is differentiated because it integrates threat intelligence, cyber response prioritization with Wiz, AI-automated scanning, code remediation, and monitoring. Today, 90% of Fortune 100 are Google Cloud Security users, nearly 90% of Wiz customers are using AI-powered security features, and we have seen a more than 45% quarter-over-quarter increase in the number of AI workloads scanned and protected by our security platform. Our security tools are being used to protect critical infrastructure, including financial services organizations such as Morgan Stanley, telecommunication providers such as Telus, healthcare organizations such as Texas Children’s Hospital, software companies such as Atlassian, and several government agencies. With our new Google AI Threat Defense, we are really excited to bring our new cyber model and CodeMender to help our customers defend against AI threats.

Alphabet’s management believes Google Cloud offers the industry’s widest range of AI accelerators, including Alphabet’s latest TPU 8t and 8i, and NVIDIA’s latest Vera Rubin GPU platform; Google Cloud’s Virgo Network connects a million AI accelerators across multiple data center sites into a unified supercomputer; Google Cloud has native support for AI accelerator programming languages, which enables workload portability across GPUs and TPUs; Google Cloud’s agent-optimised Axion CPU has 30% better performance per dollar than other CPUs; Google Cloud’s AI infrastructure is seeing strong demand from a wide variety of companies, such as AI labs, technology companies, financial services providers, pharmaceuticals, and robotics companies

We offer the industry’s broadest range of accelerators from Google and NVIDIA, including the new NVIDIA Vera Rubin platform and TPU 8t and 8i, which delivers strong price performance…

…First, our Virgo Network, which is designed to meet the needs of modern large-scale AI workloads. It allows customers to connect a million AI accelerators across multiple data center sites into a unified supercomputer. Second, our software stack has native support for JAX, PyTorch, vLLM, and SGLang, enabling workload portability across GPUs and TPUs. Third, our new agent-optimized Google Axion CPU provides 30% better performance per dollar compared to peer offerings. 

We are seeing strong growth and demand for our AI infrastructure offerings from leading labs such as Ineffable Intelligence, next-generation AI builders, including Kakao, financial services like Deutsche Börse Group, pharmaceutical companies such as Pfizer and Roche, and robotics and spatial intelligence companies such as World Labs. 

Waymo has introduced a new vehicle, Oasis, to riders; Oasis is the 1st vehicle powered by the 6th generation Waymo Driver

Waymo introduced its newest vehicle, Oasis, to public riders. This is the first vehicle powered by the sixth-generation Waymo Driver and will welcome more riders in the coming months. 

Alphabet’s management continues to accelerate the deployment of Gemini across the company’s entire advertising infrastructure; Gemini improves Search Ads’ ability to find relevant ads for longer searches that were previously difficult to monetize; in 2026 Q2, Gemini drove a 20% improvement in showing highly relevant ads in Shopping ads; 500,000 advertisers have adopted AI Max; users of AI Max enjoy 15% more conversions or value on Search at similar ROAs; AAA Auto Club Enterprises used AI Max to drive a 17% improvement in conversion volume and an 11% decrease in cost per lead; half of Alphabet’s SMB (small, medium business) advertising customers use its generative AI tools for creative development

We continue to accelerate the deployment of Gemini across our entire ads infrastructure to boost performance in three areas mentioned before: ads quality, advertiser tools, and AI user experiences.

First, ads quality. At Google Marketing Live, we showcased how Gemini improves query understanding, allowing us to find relevant ads for longer searches previously difficult to monetize. The core engine of our Search Ads relies on a dual prediction, delivering immediate utility for the user while maximizing measurable value for the advertiser. Gemini completely supercharges this capability. We use Gemini’s advanced reasoning to decode the nuances of longer, more detailed queries. With Shopping ads, for instance, we drove a 20% improvement in showing highly relevant ads, helping shoppers immediately find the best match. 

Second, advertiser tools. Take AI Max. It’s out of beta, and 500,000 advertisers have already adopted it. Those who adopt our AI-powered campaigns like AI Max or Performance Max, see an average of 15% more conversions or value on Search at a similar ROAs. AAA Auto Club Enterprises used AI Max to personalize creative assets and capture growth from increasingly detailed insurance searches. This led to a 17% improvement in conversion volume and an 11% decrease in cost per lead. We see strong adoption of our generative AI creative tools, which makes creative development easier, especially for SMBs. In fact, over half of our SMB customers globally use AI to create or optimize their creatives…

Merchants are rapidly adopting Alphabet’s UCP (Universal Commerce Protocol) for agentic commerce

In collaboration with the retail industry, we established the open-source Universal Commerce Protocol, UCP, as the new standard for agentic commerce. Merchants are rapidly adopting UCP, with Target and Steve Madden now live, while new members have joined the UCP Shopping and Food Tech Councils to help steer its vision.  

Google Cloud had 82% revenue growth in 2026 Q2 (was 63% in 2026 Q1) driven by growth in GCP; GCP grew at a higher rate than Google Cloud’s overall growth; Google Cloud’s growth was driven by core GCP, AI solutions, and AI infrastructure; Google Cloud started recognising revenue for the first time in 2026 Q2 from selling TPUs; Google Cloud’s revenue growth still accelerated materially even after excluding TPU sales; Google Cloud operating margin was 35.6% (was 32.9% in 2026 Q1 and 20.7% in 2025 Q2); Google Cloud backlog grew 11% sequentially to $514 billion in 2026 Q2 (was $462 billion in 2026 Q1); most of Google Cloud’s backlog are GCP contracts and just over 50% of the backlog is expected to be recognised as revenue in the next 2 years; when external TPU sales agreements are signed, they are reflected in Google Cloud’s backlog; Google Cloud’s $514 billion in 2026 Q2 primarily consists of GCP contracts; external TPU sales impact Alphabet’s operating cash flow because the company needs to build ahead of selling the systems; the use of TPUs in Google Cloud brings better margins

Cloud revenues were up 82% to $24.8 billion, driven primarily by GCP, which grew faster than cloud overall. Core GCP, AI solutions, and AI infrastructure were all important drivers of growth. We also began to recognize revenues from TPU system sales, which we delivered to customer data centers for the first time in Q2. Cloud revenue growth accelerated meaningfully even after excluding the impact of TPU system sales. Cloud operating income was $8.8 billion, more than tripling year-over-year, and operating margin increased from 20.7% in the second quarter last year to 35.6%. Google Cloud’s backlog increased by more than $50 billion sequentially, reaching $514 billion in the second quarter. The increase was driven by strong demand for our enterprise AI offerings. The majority of the backlog is related to typical GCP contracts from a broad mix of customers, and we expect to recognize just over 50% of the total backlog as revenue over the next 24 months…

…In terms of revenue recognition and how we look at the TPU system sales. The way to think about it is the following. When we sign the agreements that I’ve mentioned in the prepared remarks, they would be then reflected in the cloud backlog. The vast majority of the $514 billion of cloud backlog is the GCP agreements, but the TPU system sales are reflected in that backlog. We start building inventory to be able to sell those systems. You see that impact on the cash from operations because we built ahead, obviously, as we’re building that business and ramping up. Once we start delivering the sales, generally that’s when we start recognizing revenue…

…On the TPU margins, we don’t break out margins for any specific products or infrastructure component. Certainly, there are benefits from designing and manufacturing our own chips.   

Alphabet’s management is seeing significant demand within Google Cloud; management continues to expect to recognise most of the revenue of external TPU shipments in 2027; management expects to use 3rd party centers in 2026 Q3 as a bridge while building internal compute capacity, as Google Cloud remains constrained by supply, and the use of 3rd-party data centers will result in near-term margin pressure; management has raised capex guidance for 2026 to $195 billion to $205 billion (was previously $180 billion to $190 billion; 2025’s capex was $91.4 billion, which was itself up 65% from $55.4 billion in 2024, and 2024’s capex was up 69% from 2023); the higher capex guidance is because of an acceleration in the delivery of capacity to meet growing demand (i.e. Google Cloud is pulling-forward capex); management continues to expect 2027’s capex to be much higher than 2026’s; management expects Alphabet’s free cash flow to remain under pressure because of AI-related capex; management continues to see attractive returns on Alphabet’s capex; the supply constraint of Google Cloud comes from strong demand from both external customers and internal use cases; the use of 3rd-party data centers is to support large customers at a high near-term cost, but there’s still high ROI (return on investment) over the entire life-time of the deals with these customers; when Alphabet’s input costs for capex goes up, management is able to raise prices for its services; Alphabet’s level of capex in 2027 is driven by strong demand indicators that management is seeing, and the dynamics in the market look even better today compared to a year ago

In Google Cloud, we are seeing significant demand for products and services, which we expect to drive strong growth. As I mentioned earlier, we started delivering TPU system to customer data centers in the second quarter. We continue to expect to recognize a relatively small portion of the revenues from our existing TPU system sales agreements this year, ramping as we exit 2026. We anticipate the vast majority of the revenues from these agreements will be realized in 2027. Given the supply-constrained environment, we plan to expand the use of third-party capacity in Q3 as a bridging strategy while we build out more internal capacity. This strategy allows us to keep growing our customer base and capture greater overall value. However, it will create modest margin pressure in the near term as we utilize this capacity…

…We are updating our full-year 2026 CapEx guidance range to $195 billion-$205 billion, up from our previous estimate of $180 billion-$190 billion. The increase in the range is primarily due to an acceleration in the delivery of capacity to meet growing demand. As we previously shared, we continue to expect our CapEx to increase significantly in 2027 and will provide more details at a later date…

…We expect the free cash flow will remain under pressure, driven by our investments in technical infrastructure, which enables us to capitalize on the AI opportunity and continue to drive attractive returns. Q2 represented another strong quarter…

…It feels like we are in very early innings of what feels like secular shift across multiple areas. In our core information businesses, just the possibilities when I see what all you can do with the absolute frontier capabilities, there’s still a lot of work ahead to translate all that into experiences for our consumer users. You can think about end-to-end agentic experiences to really meaningfully do a lot more for them. All of that looks like extraordinary opportunities with extraordinary returns for executing well on those opportunities… 

…We are seeing very strong demand, both from external cloud customers as well as across the business…

…I think on the bridge deal, the main thing I would say is, look, on the margin, there are very, very large customers of ours on Cloud who we are trying to support them through this extraordinary moment. The incremental opportunities they are bringing to us, while a short-term cost over a few months may be very high, in the lifetime of the deal, as we bring more capacity on, is highly ROI positive…

…To the extent that our input cost is going up to us, we reflect that in our ability to price our solutions and see returns there. All of that is factored into how we are planning…

…Our compute capacity investments in 2027, to the first question I answered, I think we are seeing strong demand indicators, including long-term deals, the existing deals which we have, which are renewing with exceptional demand on a moving forward basis. We are using all that to plan and invest accordingly. I think if anything, the dynamics look healthier than where we were about a year ago, that’s what gives us the confidence to undertake those investments.  

Alphabet’s management is aware that the company does not have frontier capabilities in coding and agentic coding and the teams are working on it; 3.6 Flash is already showing improvement in coding compared to 3.5 Flash; management is very confident that Alphabet will be at the frontier again and they are being very ambitious with Gemini 4; Gemini 4 will be a much larger base model compared to the previous generation; management expects Alphabet to pick up the pace to almost a monthly-cadence when it comes to model releases; management wants Gemini 4 to compete at the frontier level of where the frontier will be when Gemini 4 is released

There are areas where we’ve acknowledged we need to improve. Coding and agentic coding is an example of that, and the teams are very focused on it…

…In terms of agentic coding, we are iterating, and you will see us make continued iterations. 3.6 Flash, for example, compared to 3.5 Flash, jumped over 10 points in DeepSuite as a benchmark, and it is more token efficient doing so. We are using it internally. We are testing it with many customers in coding. We see the progress just in six weeks from the prior version to the new version, and you will see more continued iterations on that as well. 

In terms of the frontier, we are both very committed and very confident of being at the frontier. For the next generation of frontier, you’re going to need much larger base models. We are now training Gemini 4, and we’re being very ambitious with it…

…We will need Gemini 4 as a larger base model to compete at that frontier level, so we are focused on executing on that well…

…On the speed of model releases, I do think you will see us continue to pick up pace…

…Picking up pace and releasing models almost at a monthly cadence is part of our roadmap as we are building Gemini 4 as well…

…We want to compete at the frontier level of where the frontier will be when Gemini 4 comes out, we are applying a lot of our compute and effort in that direction.

Alphabet’s management’s priority with TPUs is to allocate to internal teams for frontier model development; to balance demand for TPUs, Alphabet is using both TPUs and GPUs to serve its models

In terms of allocating our TPUs, look, our first priority is making sure we are allocating what we need to compete at the frontier in terms of AGI development…

…Given the extraordinary demand to balance the external demand, even for most cloud customers, we are using both TPUs and GPUs mainly for serving our models. Think Vertex AI, Gemini Enterprise, the momentum we see. We’re using it or agentic workloads, et cetera. We are using it for those purposes…

Amazon (NASDAQ: AMZN)

AWS grew 36.7% year-on-year in 2026 Q2 (was 28% in 2026 Q1) and is now growing at its fastest pace in 18 quarters; AWS added $4.6 billion in revenue sequentially, 80% more than the largest increase; AWS’s backlog is $496 billion in 2026 Q2 (was $364 billion in 2026 Q1), up triple-digits year-on-year; AWS’s run rate has reached $169 billion (was $150 billion in 2026 Q1); AWS’s AI business revenue run rate is $25 billion in 2026 Q2 (was $15 billion in 2026 Q1), up triple digits year-on-year; management thinks customers are choosing AWS for AI for 4 reasons, namely, (1) AWS’s broad capabilities, (2) customers want their AI inference to be at where their other applications and data reside, and this happens to be in AWS, and (3) AWS has the strongest security and operational performance; AWS is seeing growth in both AI and non-AI workloads, and growth in one is driving growth in the other; AI workloads drive non-AI workloads because (1) post-training reinforcement learning and agent tool use are mostly run on CPUs, and AWS’s Graviton CPU has 30%-40% better price performance than competitors, and (2) AI workloads need databases, and this is an AWS strength too; management sees enterprises as being very early in using inference at scale; management now believes AWS can be a trillion-dollar annual revenue business in time, up from the previous view of a “few hundred billion dollar revenue business”; management is seeing customers who want to benefit from AI accelerate their migration to the cloud; management is seeing a strong correlation in customers’ AI spend and core growth in AWS; AWS is on track to doubling its 2025 power capacity by 2027 

Revenue growth of 36.7% year-over-year, accelerating for the fifth straight quarter, our fastest growth in 18 quarters back when AWS was less than half its current revenue size. We added over $4.6 billion in revenue quarter-over-quarter, about 80% more than our largest increase ever. Our backlog stands at $496 billion, growing triple digits year-over-year. AWS is now a $169 billion annualized revenue run rate business, which, for perspective, would place it 24th on the Fortune 500 list if it was a standalone company…

…Our AI revenue run rate climbed significantly quarter-over-quarter, and is now also over $25 billion, growing triple-digit percentages year-over-year.

Customers choose AWS because we offer the broadest capabilities. They want their AI inference to reside near their other applications and data, and more of it resides in AWS than anywhere else. Because AWS has the strongest security and operational performance, we’re seeing strong growth across both AI and non-AI, what we call core, and growth in one is driving growth in the other. Growth in AI drives core because post-training reinforcement learning and agent tool use is mostly done on CPUs versus AI accelerators. This is an advantage for AWS, as our Graviton chip is the strongest CPU chip, offering up to 30%-40% better price performance than other options. You need a place to store this AI data and to run vector databases, which are also emblematic of a meaningful edge for AWS because we have the broadest and most capable functionality by a fair bit in these core infrastructure areas. We feel similarly about the AI stack, top to bottom. We have a unique offering that customers are excited about…

…Remember, enterprises are still very early in using inference at scale in their current production applications. We long believed AWS could become a few hundred billion-dollar revenue business and now believe it’ll be at least double that, and very possibly be a trillion-dollar annual revenue business for us in time, with very appealing accompanying free cash flow and return on invested capital…

…Increasingly, customers seeking the full benefits of AI are accelerating their transition to the cloud. We see a strong linkage between AI spend and core growth. As customers invest in AI, we see a corresponding increase in core consumption…

…We’re on pace with the capacity build that we talked about a few quarters ago, where we said we expect to have double the power capacity by the end of 2027 that we had in 2025, and we continue to be on that track.

AWS’s chips business is now at a $25 billion annual revenue run rate in 2026 Q2 (was $20 billion in 2026 Q1), up triple-digits year-on-year; management thinks AWS’s Tranium AI chip and Graviton CPU both have leading price-performance; OpenAI and Anthropic, the 2 highest-profile AI labs, have multi-year, multi-gigawatt commitments to Trainium; a growing number of AI startups, and larger technology companies, are also adopting Trainium; Graviton is used by 98% of AWS’s top 1,000 EC2 customers; Graviton revenue commitments are up 3x sequentially in 2026 Q2; Graviton 5 is growing nearly 2x faster than Graviton 4 did; AWS continues to have a deep partnership with NVIDIA, as management knows that customers want choice; management sees incredible demand for Trainium and some customers who are interested in obtaining Trainium chips outside of AWS; management thinks there’s a real chance AWS will start selling Trainium chips to 3rd party data centers

Our chips business now has an annual revenue run rate of over $25 billion, growing triple-digit percentages year-over-year…

…We are unusually well-positioned for this AI inflection, given our leading price-performance chips in both AI with Trainium and CPU with Graviton. In addition to the two leading AI labs in the world, Anthropic and OpenAI, making multi-year, multi-gigawatt commitments to Trainium, an increasing number of AI startups are also adopting Trainium, including unicorns like Neurorobotics and Odyssey, joining startups like Twelve Labs, Descartes Labs, Poolside AI, Karakuri, Metagenomi, NetoAI, and Splash Music, and larger companies like Uber and Pinterest all adopting Trainium.Graviton is used by 98% of our top 1,000 EC2 customers. The revenue commitments have increased nearly three times quarter-over-quarter, and Graviton5 is growing nearly 2x faster as Graviton4 did.

We also continue to have a deep partnership with Nvidia, and we’ll continue making AWS the best place to run Nvidia chips, as we have customers who will run on Nvidia for as long as we can foresee, and we believe strongly that customers want choice…

…We just have an incredible amount of demand for Trainium. There are a lot of customers who are very excited about using it in the form that we’re providing right now. We do have an increasing number of customers who are interested in us providing the Trainium chips to them, separate from our cloud, and we’re actively having those conversations and exploring, and I expect there’s a real chance we’ll do that in the future.

Amazon’s management continues to think that technical companies will build their own foundation models, and AWS’s SageMaker AI service helps them do that; Bedrock, AWS’s fully-managed service for companies to build upon frontier models, provides high-performance and cost-effective inference; Bedrock provides the best selection of leading models at superior performance and with governance and security; Bedrock continues to grow rapidly; building agents at production scale is hard, so Amazon Bedrock Agents provides the building blocks for organisations to build and manage agents; management thinks most companies will use turnkey agentic services; AWS’s coding agent Kiro is 50% more cost-effective than competitors, and usage has tripled sequentially in 2026 Q2; Amazon Q is an AI work companion that lets users manage leading SaaS tools and take action for users; Amazon Q was recently made more capable with autonomous agents and more integrations with SaaS tools; Amazon Q’s customers include large multinational companies; AWS has agentic services such as Amazon Connect for call centers and AWS Transform for software migration; management recently released AWS Continuum, an agentic service leveraging frontier models for cybersecurity; management expects AWS Continuum to grow quickly; all 5 major airlines, and many leading banks and healthcare companies, are users of Amazon Connect; Amazon Connect is growing very quickly; 

As we’ve been saying for 18 months now, technically competent companies are going to build their own foundation models. Not the really big frontier models, but smaller models that leverage their proprietary data. There is no easier service for this than our SageMaker AI service. Customers also need a high-performance, cost-effective inference service, and that’s what Amazon Bedrock provides. Bedrock not only provides the best selection of leading models at superior performance and with the governance and security controls that companies need, it’s also continuing to grow incredibly quickly…

…After you’ve built an agent, you have a lot of muck to worry about. A production agent needs somewhere secure to run, memory so it holds context, an identity so it can act on a user’s behalf, tools and data to connect to, and a way to watch what it’s doing once real traffic hits. Stitching all that together reliably is hard, and it’s stalled many production deployments. It’s why we’ve built Amazon Bedrock Agents. It provides building blocks as managed infrastructure, and our teams keep iterating, recently adding features like policies which give companies deterministic controls over what agents can do, payments so agents can execute transactions autonomously, web search to ground agents’ knowledge without having to leave AWS, and a new harness that further speeds up how fast customers can put this all together, including creating the agent with Strands Agents. While companies will construct their own purpose-built agents from the ground up, most will also use turnkey agentic services…

…Our own spec-driven Kiro, which is up to 50% more cost-effective than others and tripled in usage quarter-over-quarter…

…Amazon Q, an intelligent AI work companion that helps you manage, search, and automate your digital workload across email, calendar, local or cloud files, and custom workflows. Unlike other offerings in this space, Q also lets you manage across leading SaaS tools like Slack, Salesforce, Jira, Teams, and ServiceNow. Q enforces a company’s existing access controls so each person sees only what they’re cleared to see. Then it takes action: scheduling meetings, drafting and sending email, updating a CRM record, building a dashboard, and more. In Q2, we made Q even more capable, adding autonomous agents that customers set up in plain language to run continuously in the background and carry out multi-step tasks, a personalized activity feed that pulls email, messages, calendars, and tasks into one prioritized view, and 16 new integrations, including Adobe, Moody’s, and Snowflake. Q has momentum, with 3M, Allianz, AstraZeneca, Autodesk, BMW, Exxon, FINRA, Hyundai, Intuit, Mondelēz International, Moody’s, the NBA, the NFL, Sun Life, and Southwest Airlines all using it.

We also have services like Amazon Connect, our call center service, and AWS Transform, which automates software migration growing quickly…

…We recently released AWS Continuum, which discovers, prioritizes, validates, and remediates code vulnerabilities. It starts by ingesting the backlog of vulnerabilities a team already has and then leverages the new frontier models to run comprehensive scans. Continuum uses agents in each company’s own business context to prioritize what matters, reasoning through questions like, “Is the affected component deployed? Is it reachable? Is it in a production path? What’s the impact if it’s exploited?” Then it validates vulnerabilities in a sandbox so teams aren’t chasing false positives. Finally, it recommends the fix. It is hard to talk with enterprises about AI right now without their mentioning security. We expect Continuum to grow quickly…

…Amazon Connect, which is our call center service, which is used by all five major leading airline providers, as well as many of the leading banks and healthcare companies, continues to grow very quickly.

Amazon’s management has clear line of sight to strong financial returns on the company’s originally-planned capex of $200 billion, or higher, for 2026; Amazon’s capex has 2 components, the data centers, and servers; spending on data centers is done 2 years before servers are slotted in to start monetisation; once a data center has servers in place, AWS immediately starts generating significant revenue; a data center can be monetised for 30-plus years without needing startup capital again; servers are typically purchased months before they are put into service; management will only buy servers when they see strong demand signals; servers take slightly less than 3 years to breakeven and have 5-6 years of useful life; most of AWS’s AI capacity is contracted for at least 5 years, so AWS earns significant free cash flow on servers in the 2-3 years after breakeven; AWS has a strong track record of pulling forward breakeven periods for servers; AWS typically gets 5-6 generations of server economics from data centers, with subsequent generations after the 1st having better overall economics because the upfront data center spending is not needed; when there’s demand for many data centers, Amazon has to spend ahead of time before the data centers can come online and be monetised, and management sees very compelling revenue, free cash flow, and ROIC (return on invested capital) a few years after the data centers are being monetised; management has gone through the same monetisation cycle during the 1st era of cloud computing; cloud computing’s demand-build was more gradual than AI; management is seeing margins and returns for Amazon’s AI build tracking slightly ahead of the cloud computing build at the same point of evolution; management has raised Amazon’s capex for 2026 to $220 billion (capex was $128 billion in 2025, and $83 billion in 2024 because of higher cost of memory chips; even with the higher capex guidance, management sees AWS as being supply-constrained in 2026 and 2027, and possibly 2028, with 2028 demand being striking; the lion’s share of AWS’s capacity in 2027 is already reserved, and quite a bit of capacity for 2028 is also already reserved

Earlier this year, we said we plan to invest approximately $200 billion in cash CapEx in 2026, the majority of which to support AI and AWS. At this level of spend and higher, we have clear line of sight to strong financial returns…

…There are two major parts of the investment, the data centers and the servers and networking equipment that go into them. These have different capital cycles. Data center capital is spent starting two years before we can put servers into them to start monetizing. Once a data center opens with servers plugged in, we start generating significant revenue right away and then get to monetize these data centers for 30-plus years without having to spend that startup capital again. Servers and networking equipment operate on a shorter cycle. We typically purchase these a few months before putting them into service, so we have strong visibility into customer demand before we trigger the spend. If the demand isn’t there, we won’t spend the capital.

For servers and networking equipment, on average, it takes a little less than three years to break even on that investment. The servers currently have a useful life of at least five to six years, and most of our AI capacity these days is being contracted for at least five-year terms. That means that we’re driving significant free cash flow on the servers and networking equipment in the two to three years after we break even. It’s also worth noting that AWS has a strong track record of pulling forward break evens on server equipment where we’ve already made meaningful progress and finding ways to extend the useful life of this equipment without sacrificing customer experience. For our data centers, which have 30-plus-year useful lives, we should get at least five to six generations of server economics, like I explained earlier, with subsequent generations after the first having even better overall economics because we don’t have to repeat that upfront data center investment I mentioned earlier.

This means in the short term, when demand is necessitating so many data centers being built simultaneously in advance of when we can start monetizing them, we’ll spend a lot of CapEx and encounter free cash flow headwinds until these data centers come online, can be monetized, and we get a few years into these servers being utilized. As we get a few years out and the revenue growth outpaces the incremental CapEx growth, which will happen at some point, the resulting revenue, free cash flow, and return on invested capital is very compelling. We’ve done this before in the first era of cloud computing, just over a longer time horizon, where demand built more gradually than it has in AI. We see the margins and returns in AI tracking what we saw with Core at the same point of evolution, actually a little ahead.

We now believe we will spend approximately $220 billion in cash CapEx in 2026. The higher cost of memory pushing this number up from our prior estimate of about $200 billion. Even at that amount, we will still not have enough capacity to meet all the demand we have in 2026, and I believe this dynamic will also be true in 2027, too. In fact, the demand we already have for 2028 is striking…

…We have so much demand right now. Apart from what we’ve talked about in 2026, the lion’s share of capacity in 2027, we’re adding a lot of capacity, as I mentioned just a few minutes ago, is largely reserved, and we have quite a bit of capacity that’s already been reserved for 2028. 

Over 350 million Amazon customers have used Alexa for Shopping, Amazon’s agentic shopping assistant, in the last 12 months; in 2026 Q2, active users of Alexa for Shopping was up nearly 100% year-on-year, and interactions was up 5x; management expanded Amazon Lens to 10 additional countries and it’s now available in 21 countries; in the US, customers who use Alexa for Shopping spend 40% more per order than those who don’t; customers who’ve tried Alexa+ are signing up for Prime at 25% higher rates

Customers love Alexa for Shopping, our agentic AI shopping assistant. It offers personalized recommendations, product comparisons, price history, and the ability to automate shopping through features like price alerts and auto-buy. Over 350 million customers have used it in the last 12 months, and engagement accelerated in Q2, with active users nearly doubling and interactions up over 5x year-over-year. We also expanded Amazon Lens, which lets customers take a photo of anything they see and instantly find the same or similar items on Amazon, to 10 additional countries, and it’s now available in 21 countries around the world…

…We find that everywhere Alexa goes, it drives momentum for the business. For example, in the U.S., customers who use Alexa for Shopping spend an average of over 40% more per order than those who don’t. Customers who’ve tried Alexa+ are signing up for Prime at nearly 25% higher rates.

Ads Agent is one of Amazon’s AI-powered advertising tools and it lowers campaign setup time from hours to minutes; advertisers using Ads Agent see 8% lower cost per impression and 6% lower cost per acquisition; management has expanded Ads Agent to 11 new countries in 2026 so far

We make it easy to create, launch, and optimize full-funnel campaigns using AI-powered tools, including Ads Agent, which turns hours of setup and targeting into minutes. Advertisers using Ads Agent targeting see 8% lower cost per impression and 6% lower cost per acquisition, and we’ve expanded it to 11 new countries this year.

Amazon’s management thinks that AWS can be wildly successful even if Amazon does not have its own frontier model, because there is not going be just one model to rule the world; management still wants Amazon to pursue building frontier models because it gives Amazon more control over costs and model-features; management thinks there will be at least 6 frontier models that are equally good over the next few years, and Amazon’s model will be among the mix

AWS and Amazon can have a wildly successful business without its own frontier model. A lot of that is because there is not going to be one model to rule the world. You already see that right now. You see it. It’s not just Anthropic, or it’s not just OpenAI. You see increasingly more and more companies being interested in the open models as well…

…All that said, we are pursuing our own frontier model, and we’re doing it for a few reasons. First of which is it just gives us additional control over cost. Cost for our own consumer applications, also we’re trying to drive costs down for customers. Having a player like ourselves that’s always focused on trying to take the price performance and the cost down for customers all the time, we think will help keep the models more cost effective for customers. I think also it allows us to have more control over prioritization on what models focus on. We have, both from our own external customers as well as our internal customers inside the company, certain priorities that matter that we want the models trained especially well for, then it gives us some control on speed. My view of it is that within the next few years, you’re going to have at least a half dozen models that are comparably good to each other.

In the 2025 Q4 earnings call, Amazon’s management said market demand for AI compute looked like a barbell with AI labs on one end spending a lot on compute for just a handful of applications, and with enterprises on the other end using AI for productivity purposes; now, the adoption curve for AI still looks like a barbell, with the middle being enterprise production workloads that are mostly not using inference pervasively; management thinks the middle of the barbell will become the largest AI workloads, and that AI will change every customer experience and lead to the invention of brand-new experiences; management does not know if the trajectory of the middle of the barbell will be as steep as seen currently with the ends of the barbell

We see this adoption curve in AI right now is very barbellled. There is, on one end of the barbell, the AI labs are consuming gobs and gobs of compute, and there are a few runaway successful generative AI applications like Claude Code and ChatGPT. On the other end of the barbell are enterprises who are getting real value from AI in cost avoidance and productivity. These are things like automating customer service or business process automation or fraud or things like that. In the middle of the barbell is all of the current enterprise production workloads, some of which are using inference in a pervasive way, but most of which aren’t. That is going to change very significantly over time. In my opinion, that will be the largest absolute segment, the existing production workloads in the enterprise and new businesses and workloads that startups build too. I think we’re still in the relative early stages of how much demand there’s going to be for AI. I think it’s going to change every customer experience that we know. I think that it will invent all sorts of new ones that we never imagined. I don’t know if the trajectory of that middle part of the barbell will be the same wildly steep trajectory that we’ve seen with the current barbell AI labs piece.

It seems that most of AWS’s contracts do not have built-in protections for cost inflation

[Question] Could you talk about how your pricing strategy at AWS incorporates future cost inflation? Do your longer-term contracts allow for stable return profiles despite cost inflation?

[Answer] What I would say is that most of the deals that you sign, there’s a certain amount of your demand that is on demand, where there aren’t contracts. A large amount of it tends to be deals and agreements that you’ve signed. The deals that you sign, those will be the prices and those will be the agreements that we have over the duration of that contract. New agreements that you sign, you always take into account what your costs are and how you ultimately build a price that you agree to with your customers. I think it’s no secret right now to any company in the world that there are inflated prices right now on some of the components like memory and hard drives and SSDs.

Apple (NASDAQ: AAPL)

Apple’s management recently unveiled the new Siri AI, and they are thrilled with the response from early users; Apple’s work on Apple Intelligence are done in a way that’s personal and private, with AI models that are running on-device and on servers using private cloud compute; management thinks Apple’s differentiating factor with AI is its massive unified memory bandwidth, industry-leading power-efficient performance, and deep on-device intelligence; management thinks Apple’s products are the best hardware for users to experience AI; Apple recently unveiled new AI-powered accessibility features; one AI-powered accessibility feature is for power wheelchair users to control drive systems with just their eyes, using the Apple Vision Pro; Siri AI had a public beta a few weeks ago, and the feedback has been great; Siri AI is private, based on a user’s personal context, and integrated into iOS; management is unsure what the compute costs will be for the new Siri AI, but they do see upgrade possibilities on iCloud+ for heavy users; management is still unclear if the introduction of Siri AI will result in a step-change in Apple’s cost structure; Siri AI’s initial roll out will not include China and Europe for regulatory reasons, but management is working with the relevant authorities to solve the problems

This year’s WWDC was a wonderful showcase of our latest innovations. We were tremendously excited to unveil the all-new Siri AI, a completely reimagined version of Siri that is profoundly capable, deeply personal, and integrated seamlessly across our platforms. We’ve been absolutely thrilled by the response from people who’ve been using Siri AI in the developer and public betas. The reviews from early users have been phenomenal, it’s been so wonderful to hear from people who are excited about the capabilities we’ve built. It underscores our philosophy that building AI that is private and based on personal context can change how users find information and get things done with our products in a way that truly enriches their lives…

…We’re excited about the work we’re doing on the next generation of Apple Intelligence, including Siri AI and the AI features we’re developing across our platforms. These experiences are intuitive and useful, while also deeply integrated in a way that’s personal and private with the latest models running on-device and on servers using private cloud compute…

…What sets Apple apart is the unique combination of massive unified memory bandwidth, industry-leading power-efficient performance, and deep on-device intelligence, all built around the customer experience from the ground up. The result is that Apple has created the world’s best hardware to experience AI, whether using Apple Intelligence, including Siri AI, or third-party offerings…

… In honor of Global Accessibility Awareness Day, we unveiled new features to help users get more out of the products they use every day. New intelligent capabilities are coming to VoiceOver, Magnifier, Voice Control, and Accessibility Reader to make them more useful and intuitive. We’re also using on-device speech recognition to generate subtitles for video content without captions. Apple Vision Pro is adding a feature for power wheelchair users to control drive systems using just their eyes…

…[Question] Just on iOS 27 and Apple Intelligence, went into public beta earlier this month. Could you talk about learnings from the public beta? Will the new Siri AI be a demand driver for iPhones this holiday?

[Answer] We released it to the public for a public beta a few weeks ago, the continued feedback is really, really great. I think it’s a very big idea to have AI that’s private, that’s based on your personal context, and that’s integrated across the operating system… In terms of what it means for compute cost, it’s obviously early going for us. I don’t want to say that we have a complete plan for that. We do believe there will be people that want to use it a lot. We will have some kind of upgrade possibilities on iCloud+ where people can buy up the stack on iCloud+. We’ll see how the pickup for that is…

…[Question] Is it right to think that the capital intensity of Apple will change in the future because of Siri AI?

[Answer] We use some third-party cloud, and we do our own data centers. There will be a mix. Generally speaking, as you know, we have been growing our OpEx and spending more in AI in general and quite a bit more. There are other locations on the P&L other than OpEx, like COGS etc., that also have AI expenditures. We’ll see what Siri AI does from the cost side of it. There’s also the ability when people use it a lot for them to move up on an iCloud Plan as well. What the balance of that is a bit uncertain at the moment…

…[Question] When you announced Siri AI, you also did mention, along with the rollout, that probably we won’t have the initial rollout in China and Europe. Just wanted to get your updated thoughts on that front.

[Answer] You look at the EU, we’re working closely with the commission. Obviously, our complete desire is to launch everything everywhere at the same time. That’s always the philosophy that we have. We have not been able to do that in the European Union, but we’re working closely with them to try to get to something that would allow us to offer Siri AI there. It is offered or will be offered for the Mac there, because the Mac is not covered by the same regulations as the iPhone and the iPad. Net-net, we’re working with them and hope to reach some sort of solution. You look at China, last week we received approval to ship sort of the original features of Apple Intelligence, things like cleanup and so forth. We’re working now through the rollout of those, and there will be more work required down the road for Siri AI. We’re at the front end of that.

Apple’s management thinks the Mac is an AI powerhouse, with excellent on-device inference and creation capabilities; management is seeing customers use the Mac Mini for agentic AI, and deploy clusters of Mac Studios to run frontier models locally; more companies are choosing Macs for their on-device AI advantages, including Disney and Credit Agricole; Credit Agricole is using on-device AI on MacBook Pros to reduce manual processing time of regulatory workflows by 80%

Mac delivered its best June quarter yet with $10.4 billion in revenue, growing an impressive 29% from a year ago despite significant supply constraints. This revenue growth was driven by the incredible strength of our latest lineup with MacBook Pro and the all-new MacBook Neo. According to IDC, we gained share globally. We also set a June quarter revenue record in developed markets and an all-time record in emerging markets with particular strength in Greater China, where we had an all-time revenue record. In addition, we achieved all-time records for upgraders and customers new to Mac.

With the power of Apple silicon, the Mac lineup delivers outstanding power-efficient performance, massive memory bandwidth, and next-level AI capabilities. Mac continues to be the ultimate AI powerhouse, excelling at high throughput, on-device inference, and creation across a broad range of AI workloads. We’re seeing customers increasingly put those capabilities to work, from using Mac Mini as a powerful platform for agentic AI to deploying clusters of Mac Studio systems to run frontier class models locally…

…More companies are choosing Mac for on-device AI advantages, including lower costs, better performance, and enhanced privacy and security. At Disney, creative teams are increasingly turning to Mac for on-device AI workflows that reduce overall cloud token costs and keep their IP secure. Crédit Agricole, France’s leading retail bank, is using on-device AI on MacBook Pro to streamline regulatory workflows, reducing manual processing time by over 80%.

Users of AirPods are using live translation, powered by Apple Intelligence

Meanwhile, we continue raising the bar across our AirPods lineup, whether it’s the immersive listening experience of AirPods Pro 3 or the premium listening experience and exceptional active noise cancellation of AirPods Max 2. With live translation powered by Apple Intelligence, people are crossing language barriers and connecting like never before.

Apple’s management will reinvest tariff refunds into the US; Apple recently announced a new agreement with Broadcom to design custom silicon and wireless connectivity technologies; management expects the new agreement with Broadcom to exceed $30 billion, and it is part of Apple’s $600 billion manufacturing commitment to the US, and the largest commitment to-date; Apple will soon open the Apple Advanced Manufacturing Center in Houston; the Apple Advanced Manufacturing Center is in a facility where Apple is currently assembling AI servers and will soon manufacture Mac Minis; the Apple Advanced Manufacturing Center will be imparting the processes Apple uses to make its products to strengthen the USA’s entire advanced manufacturing ecosystem; Apple will be sourcing 100 million components from TSMC’s Arizona fab in 2026 and management is really pleased about the fab; Apple’s use of the TSMC Arizona fab is part of Apple’s $600 billion commitment

 Last year, we made a $600 billion commitment to the U.S. over four years, and now, as we said before, we plan to reinvest the tariff refunds we’ve received into the U.S. We’re pleased with the progress we’ve already made advancing the American supply chain. Earlier this month, Apple announced a new agreement with Broadcom to design and produce custom silicon components and cutting-edge wireless connectivity technologies. The new multi-year agreement with Broadcom, which is part of Apple’s American Manufacturing Program, is expected to exceed $30 billion. This marks our largest-ever American manufacturing program commitment. It’s also an important step forward in our work to build an end-to-end silicon supply chain here in the U.S.

We’re excited for the upcoming opening of the Apple Advanced Manufacturing Center in Houston. The center is located in a facility where we currently assemble advanced AI servers. Later this year, we’ll make Mac Mini there, too. The center will teach students, supplier employees, and business of all sizes the same innovative processes we use to make our products. The goal is to empower American manufacturers to take their work to the next level and strengthen the entire advanced manufacturing ecosystem…

…In Arizona, we do source over 100 million components this year out of Arizona, it is part of our $600 billion commitment to the U.S., and we could not be more pleased with how that fab has ramped and is producing for us.

Apple’s management thinks it will be a great idea if there were more memory chip suppliers; management had to reluctantly raise the prices of Apple products to deal with a 100-year flood on memory pricing; it’s still unclear to management if Apple’s price hikes will impact demand

In terms of the sources of supply, primarily the DRAM market has three suppliers. Obviously if there were more suppliers, that would be good, and it would help us on the supply side and perhaps the pricing side. It’s unclear on the pricing side, it could help on the supply side…

…We reluctantly raised prices, I would say. We did it because we’re in what I would characterize as a 100-year flood on the memory pricing, with exponential increases in memory prices…

…Obviously, we’ve now had to increase prices on iPad and Mac — and the price elasticity there, it’s just too early to come to a definitive conclusion of what happens there, because it takes a little while for the channels to adjust since there’s channel inventory, and it takes a while for the consumer to respond. And so we’ll understand that more in the weeks ahead.

ASML (NASDAQ: ASML)

ASML’s management is seeing strong end-market demand that is motivating its customers to raise their capex; management is keen to support its customers’ demand; management is seeing both Logic and DRAM customers entering long-term agreements with their customers and having unprecedented visibility on future demand; management is seeing Logic customers add a lot of capacity on existing advanced nodes because of AI-related demand; management is seeing Logic customers aggressively ramp the 2nm node and even start ramping the 1.4nm node; the dynamics in the Logic segment are driving both an increase in litho intensity and more demand for litho; management sees a clear need for more supply of memory chips; management is seeing Memory customers accelerate capacity plans; management is seeing advanced Memory nodes calling for higher lithographic intensity, for both EUV (including low-NA EUV) and DUV (immersion belongs to DUV); management is seeing a perfect storm for ASML on DRAM (memory chip) in 2026 and beyond

The end market demand, this has motivated our customers to increase their CapEx but also accelerate all their plans. This really creates a need for more systems basically starting this year. We are doing the same on our side, extending output, extending basically our teams so that we can support them moving forward…

…What we also see for both Logic and DRAM is that our customers are getting long-term agreements with their own customers, which really invites them to commit for the long term. Because they have what I would call a quite unprecedented visibility on what will happen to the market…

…Quite a bit happening with Logic. So first, if we look at the existing advanced nodes 5nm, 4nm, 3nm, we see that our customers are trying to add a lot of capacity there now. This is because there is a huge demand on those technologies coming from AI. At the same time, the 2nm ramp is done as aggressively as possible. We see customers adding capacity, accelerating their plans and even start basically to look at the 1.4nm ramp…

…These dynamics in the logic segments are driving both an increase in litho intensity and greater demand for advanced lithography…

…If we look at the price of Memory today, either for DDR or for HBM, there is a clear need for more supply. This is translating into, again, acceleration of capacity plans from our customers. So this is happening with all customers. On top of that, as we discussed previously, the latest nodes are calling for more litho, for higher litho intensity, both on EUV but also advanced immersion…

…DRAM lithography intensity is rising as customers migrate to advanced nodes. This includes both EUV and deep UV immersion with EUV Low-NA growth driven by the increased replacement of multi-patterning with more cost-effective single-exposed EUV…

…HBM will require more wafers. So there’s a volume effect again. So that’s one element. The second element is, of course, the number of EUV and immersion layers, which has increased basically on the nodes that are ramping very, very strongly right now. So the 1c node, for example, which is going to be an enormous node, or even 1b are using more EUV layers. So this is really this combination, which creates a bit the perfect storm for ASML on DRAM this year and most probably the next few years to come.

ASML’s management is having very constructive discussions with customers for long-term business; ASML has already received nearly all the EUV orders needed for 2027, despite the company adding 30% more EUV capacity for 2027 compared to 2026; ASML has already received large orders for EUV for 2028, and this has driven ASML to increase EUV capacity in 2028 by another 30%; the capacity additions for EUV for 2027 and 2028 are for low-NA EUV systems; ASML can achieve the capacity additions by optimising existing cleanroom space, and has space to increase capacity by even more if needed; the 30% capacity increase in EUV for 2027 refers to tools, while the actual wafer capacity will be increased by 45%; the cleanroom optimisation does not require ASML to sacrifice any of its high-NA EUV supply; management thinks it’s possible that ASML’s anticipated capacity additions will increase further; the current planned capacity addition for 2028 is an act of preemption by ASML’s management, but they are based off strong customer signals; management sees plenty of operating leverage to come down the road for ASML

We are having very constructive discussions with our customers on the long term. Their own visibility to their business allows them to share with us also, longer than, I would say, usual visibility on their business. We are talking of course about next year, but also beyond that. Now this has also translated practically into very strong order bookings through the first half of 2026.

If we look into more detail starting with 2027, there we are pretty much already close to receive all the EUV orders we need for 2027. This is with us adding about 30% capacity for EUV in 2027 versus 2026. When we look at 2028, we have received already a large number of orders from our customers for EUV. This has also invited us very strongly to investigate another 30% increase in our EUV capacity for 2028. Now, of course, when EUV grows DUV grows as well. Immersion is going to be important. Also for 2027 and for 2028 we are going to look into a 30% increase of our capacity for both years…

… For 2027, we are now close to being fully covered with orders for Low-NA EUV, and we are planning to increase our Low-NA EUV capacity by around 30%. Looking ahead to 2028, we have already received a significant number of Low-NA EUV orders. Strong demand forecasts from our customers have led us to investigate a further 30% capacity increase for that year…

…[Question] The 30% increase in ’28, which implies 110 tools. And I mean, Christophe, you mentioned investigating. The word is precise. Do you need a new cleanroom for that?

[Answer] The number we are mentioning, we can achieve basically by optimizing the existing cleanroom space in the right way. So this is also why we can create basically that improvement in the short term…

…The balance between demand and supply as we see it today gets us to the 30%, right? So that’s the way we do it. If customers are going to come to ASML and say, “Hey, ASML, we need considerably more.” Then just as we’ve been doing it in the past couple of months, we need to look ourselves in the eye, we need to look at all the supply chain and just see what can further be done…

…When it comes to EUV in particular, right, the tool mix that we’re going to ship next year will be Es and Fs, while this year, it’s a combination of Ds and Es. And if you recognize the difference in output, then in essence what you’re looking at is not 30% improvement of wafer capacity that we’re adding, but approximately 45%…

…The optimization I was referring to before is really basically across all products. Now I think everyone understands that, of course, a lot more is being done today on Low-NA and immersion, for example, than High-NA. But we are not sacrificing, I would say, any of our High-NA supply by doing the rest of the optimization…

…[Question] The capacity increase to 85 and 110 units, you are meeting the demand, not undershipping. Is that correct?

[Answer] I think that as you have noticed in the last few months, I don’t think we have reached yet a stable state on what the demand will be for ’27, certainly not for ’28. So we keep on revising basically with our customers what that demand is. And again, the whole goal of our supply is to follow that demand. So I would not say that we are done with this discussion…

…We’re investigating the 110 scenario for EUV in Low-NA by 2028. Of course, we don’t have orders for 110 EUV Low-NA at this stage. So we’re not waiting. We’re preempting…

…The demand signals that we’re getting from customers also when it comes to ’28 are sufficiently strong for us to seriously investigate this 110 number and the related number on immersion that we signaled to you…

…In the past, we increased the headcount of R&D quite substantially. I would say that today, we believe that with the team that we have today, we can really entertain a very aggressive roadmap going forward. So all in all, I think you will continue to see us manage both R&D and SG&A quite nicely. And as a result of that, the operating leverage that you imply, I think the operating leverage will indeed become better in the quarters and the years to come.

Intel is now using ASML’s high-NA EUV systems in production for its most advanced nodes; management expects to soon enter discussions with all of ASML’s customers on how/when high-NA EUV systems will be inserted into their high-volume manufacturing flows; the maturity of the high-NA EUV systems is maturing to the level required for high-volume manufacturing; management believes that the high-NA EUV system, at single exposure, will bring cost benefits to customers; management thinks the high-NA EUV system’s cost will have more advantages over low-NA EUV once the high-NA EUV system matures; management thinks the partnership with Intel is the clearest example of the progress high-NA EUV is making in terms of its maturity; Intel was the first to get the high-NA EUV systems, so it became the first to implement in production; management thinks both logic and DRAM are good candidates for high-NA EUV systems; there’s very little fungibility in the optical tools used in low-NA EUV and high-NA EUV systems 

As you may have read it, Intel is basically now using High NA in production on their most advanced products. So it means that some of the products you buy today from Intel have been created with an High NA machine. So this is, of course, a very important milestone. This is the proof of the maturity of the tool. We talked a lot about that in the last quarter. We are seeing that happening with all customers and therefore expect to enter that discussion with all our customers on how exactly and when exactly the tool will be inserted in high volume manufacturing…

…Intel Foundry is using ASML High-NA EUV technology on the Intel 18A process node to produce a subset of its Intel Core Ultra Series 3 processors…

… We are continuing to work very closely with our customers to prove the value of High-NA technology for their process technology road maps. In parallel, the maturity of the platform is improving towards the level required for insertion into high-volume manufacturing…

…Every new generation of lithography system ASML ever brought to market was with a strong intention to reduce the cost of patterning. So when you look at High-NA single expose, the design of the tool, the performance of the tool will be such that it provides a cost benefit to our customer…

…We are still basically working on bringing the High-NA platform to the level of maturity of Low-NA. And when you achieve that, this is practically the time where the cost of High-NA is going basically to provide an advantage versus the existing technology…

…I think the key again for High-NA to be cost-effective, to beat the cost of Low-NA plus immersion multi-patterning is to bring High-NA to the right maturity…

…We are very happy with the press release this morning about Intel, because this is, I would say, maybe the strongest sign so far that we’re getting there…

…We talked about Intel today. I think you know that Intel was first to get the technology. So they are first to implement it in production…

…The opportunity for DRAM is significant also because the volume is also significant. But there’s no real change there. I think we still see both logic and DRAM being a good candidate for High-NA. The reason for that is both DRAM and advanced logic will be shifting more and more towards multi-patterning Low-NA over time. So that applies to both…

…In terms of the fungibility of equipment for High-NA and Low-NA, particularly when it comes to ZEISS, because that’s the way, C.J., I interpret your question, that really isn’t there. It’s totally different tools that you need that ZEISS needs to produce a High-NA optic versus a Low-NA optic. So it’s not that there is fungibility that you can use High-NA tools to get more Low-NA output.

Intel (NASDAQ: INTC)

Intel’s management sees demand outpacing supply for the company (despite growing its supply) as well as the semiconductor industry; the supply shortage is expected to last for some time; management sees a rapid and sustained build-out of compute infrastructure for the semiconductor industry; Intel’s customers are signalling a strong and sustainable spending environemnt 

Strong demand for our products continue to outpace our growing supply…

…Industry is facing one of the most severe supply constraints in its history across leading-edge logic silicon wafers, memory and substrates. These shortages will persist for the foreseeable future…

…Intel is uniquely positioned to benefit from the overwhelming demand for compute as the entire industry continue a rapid and sustained build-out of compute infrastructure…

…Customers continue to signal a strong and sustainable spending environment driven by the unprecedented demand for AI compute. Industry-wide supply constraints across wafers, memory and substrates remain the dominant challenge our customers are facing to support the AI infrastructure build-out.

Intel’s management sees the transition to agentic AI systems driving CPU density higher in AI data centers; management is seeing customers place growing importance to Intel’s x86 CPUs in AI data centers; Intel’s Xeon 6 server CPU product continues to be one of the company’s fastest ramping products ever; Intel’s design services business (where Intel helps customers build custom AI chips) saw revenue grow 3x year-on-year in 2026 Q2, driven with the help of the x86 CPU franchise; management’s outlook for server CPU demand has increased yet again and management expects strong growth for the industry for 2026-2028; management thinks the CPU-to-GPU ratio in AI systems is now at parity, and could even skew towards CPUs in the future; management sees significant growth in the CPU market

As AI expands from training to inference and increasingly to agentic and multi-agent systems, general purpose server CPU density continue to increase, and our core server CPU franchise is growing faster than ever…

…Demand accelerate across cloud and enterprise as customers increasingly recognize the critical role that CPUs in general and x86 CPUs, in particular, play in the AI infrastructure…

…Xeon 6 continue to be one of the fastest ramping products in Intel history, reflecting improving execution and strong customer demand…

…We continue to make steady progress in our newly announced design services business with revenue growing nearly 3x year-over-year. We see tremendous opportunities to leverage our strong x86-based general purpose computing franchise to build more purpose-built computing products for the AI era…

…Our outlook for server CPU demand has improved again since our last earnings report and we’re forecasting strong double-digit unit growth for the industry this year and next, with momentum extending into 2028…

…Lip-Bu has talked in the past about the ratio of CPU to GPU going up. And we now believe we’re almost in parity at this point and could eventually even skew more to CPUs on a unit basis…

…From all the inputs we’re getting from our customers in terms of the level of spend and also the long-term agreements we put in place and the visibility we’ve gotten, we feel like the growth is going to be significant. 

Intel’s management is more confident than ever of the company’s foundry roadmap for leading-edge chips; in 2026 Q2, Intel’s leading-edge 18A node exceeded internal volume and yield expectations, and saw output increase meaningfully; Intel is now ramping multiple new products on the 18A node while supporting demand for existing products; the ramp up of 18A for Intel’s internal products provides validation as the company engages external customers; management has started risk-production for the 18A-P node, which has performance and power improvements over 18A; Intel’s 14A node is outpacing the 18A node’s development; customer engagements for 14A has increasing momentum and management is increasingly confident that the node will be a highly competitive node in performance, power, density, cost, and schedule; Intel remains on track for 14A risk production for internal products in 2027 H2; management has decided to fully commit to a high volume ramp for 14A in 2028; the 18A node is now in volume production across commercial and consumer products in the PC client segment; the 18A node’s output was 25% over target in 2026 Q1, and up 50% sequentially; nearly all of Intel Foundry’s revenue in 2026 Q2 was from internal demand; Intel Foundry has lowered the cost of Intel’s Panther Lake SKU by 50% year-to-date in 2026 Q2, with further reductions expected for the rest of 2026 and 2027; the 18A node’s quarter-to-date yield for 2026 Q3 are ahead of targets set earlier this year 

My confidence in our foundry process road map has grown significantly since joining over a year ago. I am more confident than ever of the strategic in significant and unique value proposition of Intel Foundry. During Q2, our factories across Intel 7, Intel 3 and Intel 18A exceeded internal volume targets, driven by improving yields, better cycle times and increasing wafer starts. 18A output increased meaningfully in the quarter. Yields continue to track ahead of expectations. We are now ramping multiple new products on 18A, while supporting growing demand for our lead products, including Panther Lake and Wildcat Lake. I keep raising the bar on the internal targets, and the team continues to meet the challenge. The successful volume ramp of 18A for our internal products provide important validations as our Intel Foundry engaged with external customers. We also began risk production of 18A-P, providing additional performance and power advantages, while maintaining IP and design compatibility with Intel 18A, positioning 18A-P as a competitive node for external customers…

…I’m encouraged by our progress on Intel 14A. Defect density and transistor performance are all outpacing 18A development. PDK 0.5 is now complete, and PDK 0.9 is on track for October. We continue to build out and validate the IP portfolio for 14A as we position the 14A family for broad-based adoptions across a wide range of customers. I’m pleased to see the increasing momentum on customer engagements for Intel 14A, and I’m increasingly confident that the 14A will be a highly competitive process offering across key vectors of performance, power, density, cost and schedule. With encouraging external customer progress and increased demand for our internal products, we remain on track for 14A risk production for our internal products in second half of 2027, and we make the decision in Q2 to fully committed to high-volume ramp in 2028…

…In our core PC client segment, Intel 18A is now in volume production across multiple commercial and consumer products. Our factory output continues to increase sequentially every month. The successful high-volume ramp of 18A for our internal products provides important validation as Intel Foundry engaged with external customers…

…Our Client Group has now brought 18A to full scale with 400-plus designs for Series 3 across consumer and commercial…

…Turning to Intel Foundry, revenue of $5.8 billion was up 6% sequentially on higher fab volumes driven by strong growth in Intel 18A with output approximately 25% above target and up more than 50% quarter-over-quarter. External foundry revenue was $293 million in the quarter…

…Intel Foundry has driven down the cost of our primary Panther Lake SKU by roughly 50% year-to-date and is on track for an additional 20% this year with further meaningful reductions planned in 2027…

…Our wafer output across our major nodes exceeded expectations from 90 days ago, and Q3 quarter-to-date 18A yields are trending ahead of targets set in March.

Customer interest in Intel’s advanced packaging technology, EMIB-T (Embedded Multi-die Interconnect Bridge with Through-Silicon Vias) remains very high; EIMB-T has a growing backlog, and yields are hitting targets; management wants to ramp EMIB-T into high volume production in 2027

On advanced packaging, customer interest for EMIB-T continue to be very high. Technology is compelling, providing capabilities for advanced AI silicon solutions, which are not possible with today’s mainstream offerings. We continue to have a growing EMIB-T backlog, yield and reliability are hitting targets, and we focused on ramping the technology into high volume and high quality to support customer ramps in 2027.

Intel’s management sees a growing opportunity for AI with edge devices; management thinks the market for edge and physical AI will match that of enterprise AI; Intel had 130 Series 3 design wins for edge AI in 2026 Q2

We recently renamed our PC business to our Client Computing and Physical AI Group or CCPG. We did this to recognize the growing opportunity for AI at the edge…

…We expect enterprise adoption of AI to be a long-term tailwind for CCPG, but our AI-driven market prospects don’t stop there as the edge and physical AI opportunity is likely to at least match the client TAM over time. CCPG showcased this growing opportunity with 130 Series 3 design wins for edge AI applications, including brain and control deployments for robotics.

Intel has a multi-year collaboration with SambaNova, an AI inference chip startup

We also extended our heterogeneous AI strategy through multiyear collaboration with SambaNova. We are pleased with their growing momentum as we work with them to drive performance and power improvements with disaggregated inference.

Intel’s management thinks the company is the only one that can build the whole of compute solutions for AI, from traditional CPUs and GPUs, to custom AI chips

We are the only company that can design, manufacturing, build the entire range of computing solutions from general purpose, traditional CPUs and GPUs to more purpose-built ASICs and CPUs optimized for agentic AI, as we increasingly move from compute dominant by system on chip towards system in package.

Intel’s vPro mangeability software had 1,500% growth in the last 4 quarters, demonstrating that manageability and enhanced security are critical for agentic deployments

On the commercial side, activations for our market-leading vPro manageability software have surged 1,500% over the last 4 quarters, underpinning that manageability and enhanced security are critical must-haves in the agentic workplace.

Intel’s management now expects capital expenditure to be more than $20 billion in 2026, which is up from the previous expectation of around $18 billion; management is aggressively locking in tool purchase orders from vendors; management expects Intel’s capital expenditure in 2027 to be much higher than 2026’s level; Intel’s total capital expenditure on tools and space in the U.S. is approaching $100 billion for 2021-2026; management is committed to have capital expenditure match customer demand; Intel’s capital expenditure is fairly broad-based and includes advanced packaging, although more is allocated for front-end manufacturing than advanced packaging; the higher capital expenditure for 2026 is a signal of management’s confidence in Intel’s customers, and the higher visibility management now has on the long-term demand outlook; management sees significant returns from Intel’s capital expenditure; Intel’s external customers became excited when they started to see the 14A node’s PDK (process design kit yield, and this gave management comfort to invest more in capital expenditure; most of Intel’s capital expenditure in 2026 will be for tools for Intel’s leading edge nodes; management thinks Intel has sufficient liquidity on the balance sheet for now to fund its capital expenditure, while prepayments from customers are also helpful, although the company may need to tap the capital markets if the business becomes really successful and more capital expenditure is needed to fulfil demand; management is willing to commit capital expenditure only if there are customer commitments

Due to strong customer demand signals, we’re raising our outlook for 2026 and now expect our CapEx to be more than $20 billion, which is up significantly versus our expectations entering the year. We’re also aggressively locking in tool purchase orders from our vendors, accelerating our clean room build-outs and actively securing supply of substrates and memory. As a result, we’re forecasting 2027 capital expenditures to be significantly above the 2026 levels with the vast majority spent across our U.S. network. In fact, as we look back from 2021 through 2026, our total capital spending in tools and space in the U.S. is approaching $100 billion, significantly higher than any other semiconductor company over that time frame. We remain committed to tightly matching our expenditures with customer demand and remain financially disciplined as we capture the growth ahead…

…The CapEx is fairly broad-based. It’s going to include advanced packaging. . As Lip-Bu talked about, we’re pretty excited about our prospects on EMIB-T. And so we will be investing in that. That said, the cost of a fab for the front end is much more expensive than a packaging facility. So it will be skewed towards the front end…

…This increased investment is a signal of our confidence in customers across all of our business units. We feel very confident, particularly in places where we’ve gotten long-term agreements that we now have the signal to be able to kind of forecast out what the outlook looks like for the next few years in terms of demand, and we’re putting forth the capacity in anticipation of that across all of our businesses…

…That’s why you see the CapEx going up next year. But over time, they generate significant return. And particularly now as we migrate towards a model where we keep these processes on longer, the returns are quite significant…

…The engagement with the customer, the feedback have been very positive, tremendous demand for our own products and also external foundry customer engagement that give me the confidence the moment they’re starting to see the 0.9 PDK the yield, they’re starting to get excited about what kind of product they want to run that and how much capacity we can provide them. So those are very positive signs that they are really serious about going forward. And that’s why, as I mentioned earlier, I don’t put CapEx unless I see the yield performance, the IP is ready to serve the customer and also customer engagement, the level of engagement I see…

…Most of the CapEx dollars are going to at this point is tooling. We’ll increase tooling in ’26 by 40% relative to ’25. So we’re investing a significant amount in tooling. And it’s where you might expect, it’s Intel 3, it’s 18A, it will be 18A-P…

…[Question] As you are planning these investments for the back half of the year and into next year, how are you thinking about the balance sheet?

[Answer] We feel like we’re in a really good place from a balance sheet perspective. We have over $30 billion of cash. We have a $10 billion revolver. So we’ve got $40 billion of liquidity… We have seen, by the way, our customers willing to invest with us. And we’ve had prepays from customers that we’ve been — that has enabled us to unlock capacity that’s helped us. That said, if we’re super successful, which we’re driving to, we may need to tap the capital markets to drive some more investment…

…We’re just going to be very careful around making bets ahead of customer commitments. I think that’s the most significant change with Lip-Bu is until we really know that we’ve got the customers, we don’t want to put a significant amount of capital.

Intel’s management thinks the ASIC (application specific integrated circuit) for AI business has a market opportunity of more than $100 billion; management expects the annualised revenue run rate for Intel’s ASIC for AI business to increase rapidly

[Question] On the ASIC business. I guess based on what was disclosed last quarter, it’s about a $1.2 billion run rate business now growing well for the company. How do we think about the diversity of that business? And just you’ve announced Fortinet. I’m just curious of how you’re thinking about the growth profile of that business?

[Answer] This is a massive opportunity. I think potentially it’s over $100 billion TAM market…

…Growth rates of ASICs. I mean I would say that today, we’re probably running at about a $2 billion run rate or at least approaching a $2 billion run rate for that business. We think in the not-too-distant future, we’ll be at a $4 billion run rate for that business.

Intel’s management sees memory chips having a severe supply shortage; Intel is collaborating with all 3 major memory chip companies; management sees memory chips as a bottleneck in AI currently, and so Intel is working on memory, with the recent hire of SK Hynix’s CEO (SK Hynix is one of the 3 major memory chip companies)

Memory becomes a big supply constraint challenge. And we’re collaborating with the 3 big memory vendors. That’s very important to serve our customers as our #1 priority. The next thing, as you recall, Intel has a rich history in the memory. And recently, we hired Seok-Hee Lee to join us. He used to be the CEO of SK Hynix. And clearly, memory, it becomes the bottleneck, a lot of AI infrastructure and pain point for customers. And we’re also looking at how other areas that we can integrate compute and memory and also how the stacking and then how can we use the memory more — utilization more efficiently. So I think there’s a lot of areas we are working on. Stay tuned, and we will work on that. 

Mastercard (NYSE: MA)

Mastercard’s management is seeing higher demand for the company’s security solutions; Mastercard is able to identify threats before they materialise through Recorded Future’s capabilities (Recorded Future was acquired by Mastercard in 2024 Q4 and it provides AI-powered solutions for real-time visibility into potential threats related to fraud); Mastercard Threat Intelligence was launched in 2026 and in its first 3 quarters, it has identified more than 7 million card-testing transactions in 192 countries and prevented $172 million in fraud; Mastercard Merchant Trust Services is a new suite of AI-powered capabilities to help identify fraudulent merchants

We’re seeing increased demand for Mastercard’s robust and unique security solutions as clients navigate the ever-expanding threat landscape. Today, we have differentiated capabilities that span cybersecurity, identity, and fraud…

…We’re identifying threats before they materialize. How? We’re bringing Recorded Future’s market-leading intelligence capabilities to our clients globally and seeing strong engagement across sectors. This quarter, we partnered with Wipro, a global information technology and consulting company, to further scale our capabilities. Building up on our acquisition of Recorded Future, we also launched Mastercard Threat Intelligence specifically for payment fraud. In its first three quarters, Threat Intelligence has identified more than 7 million card testing transactions across 192 countries. Stopping that activity prevented an estimated $172 million in fraud linked to malicious domains. That’s real value to us, our customers, and of course, our cardholders…

…Mastercard Merchant Trust Services is a new suite of AI-powered capabilities to help identify fraudulent merchants. Keeping the scammers from setting up shop will reduce fraud, cut out disputes, and provide greater security. The goal, real transactions for real purchases from real merchants.

Mastercard’s management believes agentic commerce is the next evolution in payments and is a significant opportunity for the company as it leads to incremental transactions and services; Mastercard Agent Pay helps power secure and trusted agentic transactions across Mastercard’s global acceptance network; management expects cards to prevail in an agentic world; early engagement with Mastercard Agent Pay is encouraging; management sees agentic commerce giving rise to a new class of payments in machine-to-machine (M2M) payments; management believes M2M payments expands Mastercard’s addressable market; management recently announced Mastercard Agent Pay for Machines to allow AI agents to purchase low-value digital services at machine speed; Mastercard is currently the only network enabling M2M payments; there were already 30 industry leaders participating in Mastercard Agent Pay for Machines at its launch; management sees agentic transactions in consumer-oriented use cases happening through existing card networks, and these transactions also bring the opportunity for services; Mastercard Agent Pay has a capability called Verifiable Intent, developed together with Google, that allows consumers to challenge a transaction; management sees agentic transactions in B2B use cases happening through existing card networks; management sees agentic transactions in M2M as possibly requiring different types of payment infrastructure, such as stablecoins

Agentic commerce is the next evolution in payments, where the importance of security, transparency, and control only increase. Agentic commerce creates a significant opportunity for Mastercard. It leads to incremental transactions and even more opportunity for our services. Through Mastercard Agent Pay, we’re helping power secure and trusted agentic transactions across our global acceptance network using tokenization, zero liability protections, and unique dispute resolution capabilities. These capabilities are just a few of the reasons why we expect cards will prevail in an agentic world, both in consumer and commercial use cases. It’s early days, but engagement across the globe is energizing.

The rise of agentic commerce also brings about an entirely new class of payment use cases, machine-to-machine payments. This is an expansion of our addressable market and one that we are at the forefront. We recently announced Mastercard Agent Pay for Machines, which enables AI agents to purchase low-value digital services such as APIs, compute, data, content at machine speed. With on-chain permissioning and off-chain settlement, Mastercard is the only network enabling machine-to-machine payments. An ecosystem is rallying behind us. At launch, we had more than 30 industry leaders participating, including Adyen, Ant International, BVNK, Checkout.com, Cloudflare, Coinbase, and OKX. We are a first mover in this space and one with credibility and trust to deliver…

…There’s the consumer-oriented use cases in agentic commerce where our keyword search turns changes, and we may use agents for that. That could be an LLM, that could be a first-party agent by a large retailer. There are transactions that are now then delegated to agents, and that can happen very well through the existing card networks. What you need for that is, that’s what merchants always need. They need reach, they need predictable user experiences. That’s the same, that’s true for consumers. We really believe that cards will prevail in that world. This is a tremendous opportunity for us also on the services side through tokenization, inside tokens and so forth…

…We have additional capabilities that we put into the Agent Pay protocol from us, and one of them is Verifiable Intent, which allows you to basically challenge a transaction, say, “I never wanted to buy this,” and then the chargeback process can kick back in. This was innovated together with Google…

…If you look on the B2B side, you can see there’s a range of agentic commerce transactions that can happen where you have an agent that does purchasing for a company. That can very well happen on the card ecosystem, very similar to what I just said on the consumer side. Amounts, speeds, purposes, they will need the protections, they need the global reach, all of that can apply, and we believe that’s a continued opportunity for us, particularly on the services side again…

…Machine-to-machine payments. That is low-ticket, micro-ticket transaction that happens at very high velocity. For that, we can see a world emerging where different kind of underlying infrastructure is required. For that, we’ve put out our protocol, which is an evolution of Agent Pay, which is Agent Pay for Machines. This is the only network protocol that’s out there today to facilitate that. Now, the underlying infrastructure for that, we’ve mapped it out. You can start to see that there is a transaction that is recognized from one agent to another, these machines talking to each other, but the settlement happens through different kinds of rails. That could involve stablecoins, but it also could involve different types of settlements. We’re actually quite open to that. What it needs is the immediacy of these agents to recognize that transaction, and that is what Agent Pay for Machines actually does.

Meta Platforms (NASDAQ: META)

Meta’s management sees the company’s AI investments accelerating all parts of the core business; the AI investments are improving users’ experiences, performance for advertisers, and the speed of shipping new experiences; management is optimistic about the integration of LLMs (large language models) into Meta’s recommendation systems for Instagram and Facebook because the models the company to show more relevant and engaging content; management thinks the new Muse Image and Muse Video models will lead to a huge expansion of new and personalised content to show people; Meta is using LLMs in its advertising systems to (1) improve the prediction and ranking of ads that are shown, and (2) drive significant increases in advertising relevance and conversions on both Instagram and Facebook; management sees clear signs of Meta’s AI investments paying off because Meta’s advertising business is reporting faster year-over-year revenue growth than any other competitor; 9 million small businesses are now using at least one of Meta’s Gen AI advertising creative tools (was 8 million in 2026 Q1); management is rolling out end-to-end creative solutions for advertisers and Muse Image will be important for this as it can produce better ad variations; Muse Image is getting great feedback so far; management has launched Meta One, a subscription that provides more tools and AI features; management will explore different tiers and pricing options for Meta One as demand grows; recent new experiences Meta has launched include Instagram Instants, Forum, and Seller; management will use recommendation systems to scale new experiences; adoption of Image Generation more than doubled among advertisers in 2026 Q2

We are now at a point where our investments in AI are accelerating every major part of our core business. They’re improving the experience for people using our apps, driving better performance for advertisers, and helping our teams build new experiences and ship faster…

…In Instagram and Facebook, I am very optimistic about our work to integrate large language models into our recommendation systems. LLMs add a first principles understanding of what the content is about and why it is compelling, as well as a deeper understanding of what people are interested in and what their goals are when they are using our apps. This means that we can show more relevant and engaging content that better reflects people’s goals and interests. Our new Muse Image and Muse Video models will also dramatically expand the universe of content that people can discover across our platforms. There are already two large sets of content to draw from. First, from your friends and the people you follow, and second, from creators that you don’t follow. Now there’s going to be a whole new and nearly infinite universe of personalized content. This is going to make our services a lot more useful and engaging for people.

For ads, we are using LLMs to improve how our systems predict and rank the ads that we show. We’ve expanded the context that we can take into account around a person’s organic and ads activity to determine an ad’s relevance, driving significant increases in relevance and conversions on both Facebook and Instagram. On a dollar basis, our ads business is reporting faster year-over-year revenue growth than any other company’s reported ad business. These AI investments are paying off.

We are also seeing a lot of demand for our new AI-powered creative tools. 9 million small businesses on our platforms are now using at least one of our AI ad creative tools, and we’re rolling out new end-to-end creative solutions that help advertisers translate performance data into their creative decisions. Muse Image is going to supercharge this. The model can analyze images, improve its own work, and produce better ad variations based on advertiser input. We’re getting great feedback on this so far.

We also just launched Meta One, a new subscription offering that provides more tools and AI features across our apps. As demand grows, we’re going to offer a variety of different tiers and pricing options there as well…

…Earlier this year, we shipped Instagram Instants. We also just launched Forum, a standalone groups app, and Seller, a standalone marketplace app. I expect it to become a lot easier to ship new apps, we are planning to build out more ideas and use our recommendation systems to scale them to the people who will find them interesting, as we’ve done with Threads…

…Image Generation, which now lets advertisers produce more creatives at scale from existing content, including a new ability to create images from video assets, saw adoption more than double this quarter. 

Meta’s management is building new personal agents that will be the foundation for new products and revenue streams in the future; Muse Spark will be a base for Meta’s personal agents; management thinks personal agents will need to work right out of the box, compared to enterprise agents where software engineers are more willing to make them work; management thinks WhatsApp will become an important surface for individuals to interact with multiple agents; WhatsApp is already the leading surface where people engage with Meta AI; management wants to deliver a private and secure AI experience for individuals; Meta recently launched incognito mode on WhatsApp and the Meta AI app for users to have private conversations with their assistant; management believes personal agents will be a massive market a few years down the road

We are developing new personal agents that will be the foundation for our next wave of products and revenue lines in the months and years ahead…

…One reason that we are so focused on making Muse Spark great at agentic capabilities is that we think that there’s a very big opportunity to ship a few types of agents that are aligned with our mission and business. The first is personal agents. Soon, we will have agents that can work 24/7 on your behalf to help you achieve your goals and improve your life, your health, your relationships, your finances, whatever you want…

…The first domain that agents have really taken off in is coding. Engineers are more technical and willing to spend time making those agents work. To build great personal agents, this needs to be a great consumer product that just works out of the box and is easy enough for billions of people to adopt and use…

…As we move towards a future where we’re all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important. WhatsApp is already the leading surface where people engage with Meta AI. As we build out a platform for more agents across our messaging apps, we’re going to innovate on how to deliver a private and secure AI experience. We launched incognito mode this quarter on WhatsApp and the Meta AI app, allowing people to have private conversations with their assistant that even Meta can’t see. We’re planning to make strong privacy and security a fundamental part of the agents that we’re building as well…

…We think that consumer personal agents is going to end up being an extremely important and massive market. I think that it’s extremely unlikely if you look out five years from now, for example, that whatever period of time you want, that you don’t have billions of people with a personal agent that understands your goals and that is just working on your behalf 24/7 to achieve your goals in whatever the domain is that you care about, whether it’s helping you with your health or your hobbies or your personal finances or your productivity in running your home better or improving and enhancing your relationships, helping with your career.

Meta’s management sees a large opportunity to sell to businesses, including business agents and compute; Muse Spark 1.1 is an agentic coding model that is available through Meta’s new public API (application programming interface); management is ramping up distribution of Muse Spark and is making it easier for enterprises to adopt the model; management recently made Meta Business Agents available globally on WhatsApp and Messenger, and will soon roll it out on Instagram; 1 million businesses are already using Meta Business Agents; Meta Business Agents can learn from daily conversations with customers and bring insights to businesses; management’s goal is to build a business-in-a-box service for an individual to start and run a business on Meta’s platforms; management intends to monetise Business Agents through subscriptions and volume-based pricing, and eventually results-based pricing; management thinks results-based pricing will allow Meta to run efficient auctions over the company’s compute capacity; management recently introduced Meta Business Agent Platform for enterprises to build and manage agents at scale on WhatsApp; Brazilian car rental company Movida used a business agent on WhatsApp and saw a 44% increase in daily bookings compared to a year ago, with 85% of conversations in WhatsApp resolved entirely by the AI agent without human assistance

We see a large enterprise opportunity to sell to businesses, including APIs, Business Agents, potentially selling Compute directly and other services that we’re building for large customers…

…Muse Spark 1.1 is a strong agentic encoding model that is very efficient and excels at computer use, tool use, and multimodal understanding. It’s available through our new public API. We are ramping up distribution through partner channels and more coding agents over the coming weeks. We are also building out features to make it easier for enterprises to adopt Muse Spark…

…We made Meta business agents available globally this quarter on WhatsApp and Messenger. There are already more than 1 million businesses using them to talk to their customers or complete sales every week. We’re rolling business agents out on Instagram now, too. One interesting thing about having an agent talk to your customers every day is that it learns over time and can bring all of those insights back to you. We’re building more agentic capabilities to summarize all these conversations, digest what happened overnight, and surface what customers are asking for. Soon it’ll go further, including suggesting ways to grow your business, giving you competitive intelligence and real-time insights into what’s working and what’s not. Over time, we’d like to build this into a business-in-a-box service that can help you start and run a whole business using Meta’s platforms. In terms of how we will monetize these, we have a mix of subscriptions, volume-based pricing. I expect that we’re going to continue to evolve more of these products to be like our ad systems, where businesses only pay us when we achieve results for them. Over time, that will let us run an efficient auction over our compute, similar to how we do that for advertisers today…

…Earlier this month, we also introduced the Meta Business Agent Platform, which gives enterprises the infrastructure to build, customize, and deploy their business agent at scale on WhatsApp. The platform provides larger businesses with enterprise-grade controls, guardrails, and measurement built in so they can define rules and offer personalized experiences, starting within the messaging apps that their customers already use. Movida, one of Brazil’s largest rental car companies with nearly 400 locations, deployed a business agent on WhatsApp to handle the entire booking flow, from vehicle selection and pricing to payment, in a single conversation. Returning customers could complete a reservation in as few as three messages. In a one-month period, Movida reported a 44% increase in daily bookings through WhatsApp when compared to the same period in the prior year, that 85% of conversations in the channel were resolved entirely by the AI agent without human assistance.

Meta’s management thinks the trajectory of Meta Superintelligence Labs (MSL) is strong; MSL recently shipped the Muse Spark 1.1 and Muse Image models; Meta AI has seen a 60% increase in daily users since Muse Spark was integrated with it; Muse Spark 1.1 is an agentic coding model that is available through Meta’s new public API (application programming interface); management still believes in open sourced models, and expects MSL to release open source models soon; management thinks that closed models can be more jagged in intelligence than open sourced models; some experiences on Meta’s apps are not possible to be built by others because Meta has always taken a full-stack approach to its technology stack and this is why management wants control over the models Meta is using, and hence why the company is building its own models; management thinks current open source models are not as good as frontier models, so they do not want to use them; management thinks companies who invest in building models will be rewarded well over time

It’s been a little more than a year since we launched Meta Superintelligence Labs, our trajectory is strong. In the last month, we shipped Muse Spark 1.1 and Muse Image. Since we rebuilt Meta AI and integrated Muse Spark, we have seen a 60% increase in the number of people interacting with the assistant each day, that continues to grow quickly week-over-week…

…On open source, I think we have always felt like open source was an important part of the ecosystem, it’s good for the world…

…In ramping up Meta Superintelligence Labs, in some ways, actually counterintuitively, it takes some more work to do open source models because if you’re doing something as a closed system that you’re only building for your own use cases, it can be a little more jagged. Whereas if you release it as open and it’s going to be used for a lot of things, you want to make it more well-rounded…

…We expect that we will get back to releasing some open source models at some point soon…

…The question is, do we think that because there are some open-weight models that we can just rely on those? Right now, the open-source models are not as strong as the frontier models, so no is the basic answer…

…Some qualitative experiences are just not even possible for others to build because we go all the way down the stack. It just seems to me pretty clear that having kind of sovereignty over building your own models is going to be an important part of that stack going forward, which is why it is important for Meta, but is also why other people care about open source and why open source matters overall…

…I get that this is a big investment and it’s a big bet. We see the technology working. We’re happy with the trajectory of the lab. I’m excited about the products that are coming we believe that this is going to be a big thing. I get that this is sort of a big bet across the industry. My personal bet is that the people who invest in this are going to be rewarded and feel very good over time.

Meta’s management continues to see glasses as the ideal form factor for hardware that people use to interact with AI because they can be worn throughout the day and can provide insights while people remain present in the moment; Meta recently released its own line of Meta glasses in collaboration with EssilorLuxottica, and these glasses come with Muse Spark out of the box; early sales for the Meta glasses have exceeded management’s expectations

As we get closer to personal superintelligence, we are also going to need hardware that allows you to seamlessly interact with it. Glasses are the ideal form factor since they can be with you throughout the day and they can assist you without pulling you away from the moment. Our glasses remain one of the fastest-growing consumer electronics of all time. We continue adding to the lineup. We just released our own line of Meta glasses in collaboration with EssilorLuxottica, including a style that we designed with Kylie Jenner. They’re the first glasses to ship with Muse Spark out of the box so that they can understand what you’re seeing and give even more helpful answers. Early sales have been strong, exceeding our expectations.

Meta’s management sees the company as the only one in the world building AI with the main goal of putting it in people’s hands and distributing it widely

We’re the only major company building AI with the primary goal of putting superintelligence directly into people’s hands. Rather than centralizing superintelligence, we are focused on distributing it widely and giving everyone the ability to direct it towards what matters to them. That’s the way that society has always made progress. I think that these are the right values for building a positive AI future, and if we help build this, then I think that we will continue to build a very strong business as well.

Improvements to Meta’s Feed and Reels recommendations drove a double-digit year-on-year increase in global time spent on Instagram; improvements to rankings drove a 9% year-on-year increase in video time spent on Facebook, and over 10% in US and Canada

On Instagram, global time spent this quarter grew double digits year-over-year this quarter, largely driven by improvements to our Feed and Reels recommendations. On Facebook, video time spent increased 9% globally year-over-year and over 10% within the U.S. and Canada, where it was driven by ranking improvements.

LLMs improve Meta’s ranking and recommendation systems by (1) making existing systems smarter by understanding content and generating better training data, (2) helping with engineering development by evaluating content quality, detecting trends, and testing ranking changes, and (3) increasing the level of personalisation in recommendations; on engineering development, Meta recently had every public Reels and Feed post on Instagram being automatically analysed by an LLM, and management is working to include more surfaces on Facebook; on engineering development, management recently began using the Muse family of models to understand content and early results are positive; on increasing the level of personalisation, management recently shipped the largest single-release ranking improvement to date on Reels, which drove a 15 basis point increase in sessions on Instagram, and this ranking improvement is now being brought to Feed; on increasing the level of personalisation, Meta’s largest ranking models can now identify high-quality new Reels at creation, and over half of all recommended content on Instagram Feed is now less than one day old, more than double from 2025 Q2; on increasing the level of personalisation, users can now use natural language prompts to tune their recommendations on Instagram and Facebook, and early users have an 80% retention rate; Meta is building its next-generation recommendation systems, and the effort includes building foundation models for simultaneously recommending organic content and advertising, and developing LLM-native recommender systems; on building next-generation recommendation systems, in 2026 H1, Meta had continuously pre-trained a large-scale model with recommendations data, and observed healthy scaling laws; managment sees room to continue improving Meta’s recommendation systems into 2027

We are finding that LLMs are increasingly capable of delivering ranking and recommendations gains.

First, they make our existing systems smarter by understanding what the content is actually about and generating better training data.

Second, LLM-powered agents are also helping with engineering development by evaluating content quality, detecting trends, and testing ranking changes. Earlier this year, we reached a milestone of every public Reels and Feed post on Instagram being automatically processed through an LLM and analyzed across dimensions from topics to tone, and we’re working towards including more surfaces on Facebook as well. These signals can then be passed to downstream applications across ranking, recommendations, and content policy enforcement, which is a key building block toward greater personalization. This quarter, we also began using our Muse family of models to conduct content understanding across signals like video topic classification and summarization, and we’ve seen positive early results.

Finally, our recommendations are also becoming more personalized, surfacing more fresh content while giving people more direct control over what they see. On Reels, we shipped our largest single-release ranking improvement to date, combining faster inference with a new architecture that draws on deeper user history to improve predictions. This drove a 15 basis point increase in sessions on Instagram, with particular strength in reshares and time spent, which are both strong indicators of better content to user matching. We are now bringing this to Feed, where early results look comparable. We are also getting new content to people more quickly. Investments we’ve made in more real-time infrastructure and modeling improvements on new videos are allowing our largest ranking models to now identify high-quality new Reels at creation. On Instagram Feed, over half of all recommended content is now less than one day old, more than double from a year ago. We’re also giving people more direct control of the content they see. Today, Instagram users can visit the Your Algo page, which lets users write natural language prompts to tune their recommendations. Similarly, on Facebook, we launched Shape Your Feed. Early results show over 80% retention among users who engage with it.

Looking forward, we’re executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that are designed to power organic content and ads recommendations simultaneously, as well as developing LLM native recommender systems. We hit our first research milestone this half by continuously pre-training a large-scale model with recommendations data, and observing healthy scaling laws in the process. We’re encouraged by this milestone and expect continued progress in the second half of the year…

…We certainly see further headroom to continue improving recommendations over the rest of the year and into 2027. We expect that will help us drive additional gains on both engagement on Facebook and Instagram.

Meta continues to enhance its systems to show advertising to users at the optimal time and location; management recently introduced Meta Generative Recommender, which uses LLMs to reason about ad content and user preferences simultaneously; early pilots of the Meta Generative Recommender architecture drove a 1% increase in app event conversions on Instagram; in 2026 Q2, management improved Meta’s user understanding models, which when combined with GEM (Generative Ads Model), drove an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook; Advantage+, Meta’s suite of AI-powered advertising automation tools, is now at a $75 billion revenue run rate (was $60 billion in 2025 Q3); advertisers who use multiple tools within Advantage+ see compounding gains; Indian apparel company Underneat adopted Advantage+ and saw a 13% incremental lift in purchases and a 16% increase in add-to-cart conversions 

The first part of this work is optimizing the level of ads within organic engagement. Here, we continue to enhance our systems to show ads at the optimal time and location…

…Within our ad systems, we’re delivering performance gains as we deploy more complex and predictive models. This quarter, we introduced Meta Generative Recommender, a paradigm shift in how our ad system works. Rather than scoring every possible ad individually, we are now using LLMs to reason about ad content and user preferences together and predict the best ad for each person. This makes our ad matching more intelligent and more precise, which compounds performance gains for advertisers. We deployed the first generative model into our ads retrieval system and saw notable improvements in ads performance. Early pilots using LLMs to better understand user preferences drove a 1% increase in app event conversions on Instagram. In Q2, we also advanced our user understanding models to analyze ads and organic activity, simultaneously improve both user experience and advertiser performance. Combined with our GEM model for ads ranking and sequence learning, these advancements generated an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook…

…Our AI-powered Advantage+ end-to-end solutions continue to grow, reaching over $75 billion in annual revenue run rate this quarter. We’re working to deepen adoption as advertisers who leverage multiple tools see compounding performance gains…

…Underneat, an online apparel brand in India, had been setting up each campaign manually across Facebook and Instagram. After adopting Advantage+ sales campaigns layered with Advantage+ audience placements and budget optimization, they saw a 13% incremental lift in purchases and a 16% increase in add-to-cart conversions.

Meta’s management continues to invest aggressively in AI infrastructure to meet growing AI usage; management recently announced a new strategic venture with BlackRock for a 1 GW data center; management expects a significant part of Meta’s compute to go towards training models, growing the core business, and building AI agents and new products; Meta is getting a lot of offers for its compute at a significant premium over what it paid; management’s approach to building compute capacity has 3 key elements, (1) the industry has historically under-built for AI adoption, (2) management has high confidence in Meta’s ability to utilize the capacity effectively, and (3) industry capacity will remain tight for some time; management’s current plans are to maximise capacity for 2026 and 2027, and to have flexibility to grow compute in 2028 and beyond if needed; the flexibility comes from the long-lived nature of the compute assets and Meta’s efforts in developing custom AI chips; management thinks that Meta’s models, consumer experiences, and enterprise offerings will be the best and highest ROI use of its compute infrastructure; a substantial amount of Meta’s compute goes towards training models; management believes there’s a much higher margin on selling intelligence rather than on selling compute directly; management believes that near-term compute capacity is more valuable than long-term capacity; Meta is compute-constrained today, so much so that there are many ROI-positive areas that management would put compute toward if there was any capacity

As AI usage in our products and businesses continues to ramp, we continue to invest aggressively in infrastructure to meet the demand. Yesterday, as part of our Meta Compute effort, we announced a new strategic venture with BlackRock to develop a new one gigawatt data center in El Paso, Texas. Overall, we expect that a significant portion of our compute is going to go towards training our models, growing our core business, and delivering personal agents and new products. We also expect to grow a large business serving large customers as well…

…We’re getting a lot of offers for compute at a significant premium over what we paid for it, and we have more coding and productivity tools on our roadmap as well…

…Our approach to building capacity is strongly influenced by several key elements. First, the broad environment for building infrastructure is dynamic and uncertain in both near-term and longer-term time horizons. The industry has under-built historically for the wave of AI adoption, making existing capacity, including our own, extremely valuable. Longer-term, the supply chains need to be built out to support the capacity that we anticipate we and others will need for AI-powered experiences. Second, we have high confidence in our ability to utilize capacity to scale and build on top of our existing experiences, as well as continue to invest in foundational models that will create substantial new opportunities. Consequently, our current plans are geared towards maximizing 2026 and 2027 capacity. When we have had incremental capacity in the past, it has proven extremely valuable in scaling experiences like Reels, and we are confident that this will be true in this timeframe as well. Longer-term, it’s harder to predict the exact usage scaling curves…

…We believe that being on the frontier will unlock new markets and opportunities for which we may need additional compute. Therefore, our longer-term capacity strategy aims to give us the flexibility to continue growing compute in 2028 and beyond by laying down data center and network foundations to accommodate future server decisions. The long-lived nature of these assets inherently provides the flexibility that will make it possible to adjust our investment to the pace of AI adoption. In addition, we have been making strategic investments in areas like our internal custom silicon effort, which will provide long-term strategic flexibility and supply chain leverage…

…Finally, we believe that overall industry capacity is going to remain tight for the foreseeable future. As we’ve said earlier, we strongly believe that the models, consumer experiences, and enterprise offerings that we are building will be the best and highest ROI use of our infrastructure. Those enterprise offerings have the potential to take multiple forms, as Mark mentioned. Agentic tools, our API, or monetizing compute directly, given outsized market demand…

…A substantial amount of the compute goes towards training models to be a leading lab, and I think that’s an important investment…

…We believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly…

…Generally, we believe near-term capacity is more valuable than long-term capacity…

…We are today and expect to be in the sort of foreseeable future demand-constrained. That really includes our core business too, where we still have numerous ROI-positive places that we would put compute toward if we had it.

Meta’s management believes that the company’s distribution advantage will give the company the opportunity to serve AI products to many users, even if the company’s models are not on the frontier

We believe that our distribution advantages will give us the opportunity to serve AI products that are valuable for everyone, both our 3.6 billion users and millions of businesses. This should be true regardless of whether our models are on the frontier.

Microsoft (NASDAQ: MSFT)

Microsoft’s management added 31 new data centers across 5 continents in 2026 Q2 (FY2026 Q4), or 1 gigawatt of compute capacity; Microsoft is bringing capacity online faster than ever; Microsoft has reduced dock-to-live times for new GPUs by nearly 50% over the last 12 months; Microsoft is on track to double its overall compute footprint in 2 years; 

We added 31 new data centers across five continents this quarter, bringing the total to 88 this year as we expand our footprint in response to accelerating demand. We’re also bringing capacity online faster than ever. Over the last fiscal year, we’ve reduced dock-to-live times for new GPUs in our largest regions by nearly 50%. All up, we added another gigawatt of capacity this quarter and remain on track to roughly double our overall capacity in just two years.

Microsoft’s management is optimising silicon across systems and software to get more from the company’s compute infrastructure; Microsoft has increased the throughput for Copilot workloads by 4x since the start of FY2026; Microsoft’s AI infrastructure utilises chips from NVIDIA, AMD, and itself (Maia); Microsoft’s Maia 200 chip continues to scale, has 30% better tokens per dollar compared to other leading AI chips, and now supports both OpenAI and MAI (Microsoft AI) models; Microsoft will be among the first cloud providers to provide the latest generation GPUs from AMD (Helios) and NVIDIA (Vera Rubin); management sees CPUs as being just as important as GPUs when running agentic workloads; Microsoft’s Cobalt CPUs are powering 1st-party and 3rd-party workloads; management expects to have Microsoft’s own Cobalt 200 racks in over 25 data centers around the world by the end of July

We’re also getting more from the infrastructure we already have by optimizing across silicon systems and software. For example, we increased the throughput for Copilot workloads 4x since the start of the year…

…We also continue to modernize our fleet with our own silicon innovation alongside the latest from NVIDIA and AMD. Maia 200 continues to scale. It delivers 30% better performance per dollar than the latest generation hardware in our fleet and is now supporting both OpenAI and MAI models. And we will be among the first cloud providers to deploy next generation rack-scale AI infrastructure based on AMD Helios and NVIDIA Vera Rubin.

When it comes to running agents, CPUs are just as important as GPUs. Our Cobalt VMs are powering both our own first-party workloads as well as workloads for customers, including Adobe, Arm, Elastic, OpenAI, Sprinklr, and TomTom. By the end of this month, we expect to have our Cobalt 200 racks in over 25 data centers around the world as we rapidly expand capacity.

Microsoft’s management thinks customers want the right model for the right task; management thinks Microsoft offers the broadest selection of models, more than 11,000, among the cloud hyperscalers; the number of customers building with models from different providers has increased by 5x since the start of FY2026; Levi Strauss is using Foundry to bring together models from OpenAI and Anthropic, and 1,000 domain-specific agents; management is building Foundry to be the complete app and agent stack, which gives agents access to the IQ layer, tools, durable state and memory, sandboxes, rubrics, and evals; there are more than 100,00 Foundry customers and revenue doubled year-on-year in 2026 Q2 (FY2026 Q4); Telefonica is using foundry for its first wave of agents tackling mission-critical network operations; the number of Foundry customers at an annualised run rate of 1 trillion tokens was up 4x year-on-year in 2026 Q2 (FY2026 Q4)

Every customer wants the right model for each task based on quality, latency, cost, and compliance. We offer the broadest model catalog in the cloud with over 11,000 models, including the latest from OpenAI, Anthropic, Mistral, xAI, as well as our own MAI family. Since the start of the year, we have seen 5x increase in the number of customers building with models from multiple providers. Levi Strauss & Co., for example, is using models from OpenAI and Anthropic on Foundry as it brings more than 1,000 domain-specific agents into a unified enterprise AI platform…

…Beyond model choice, data, and context, we are building Foundry as the complete app and agent stack. It gives agents access to the IQ layer, the tools they use, along with durable state and memory, secure sandboxes, rubrics and evals, and even their own self-improvement loops. We now have 100,000 Foundry customers and revenue more than doubled year- over- year. Telefónica, for example, adopted Foundry as the foundation of its corporate agentic platform with its first wave of agents tackling mission-critical network operations. All up, the number of Foundry customers at 1 trillion tokens annualized run rate increased 4x year- over- year.

Microsoft recently announced more than a dozen new models across modalities, and all offer cost-efficient inference; Microsoft’s models are co-designed with its own AI chips; MAI (Microsoft AI) models have 40% better performance per watt when they are run on Maia 200 chips; management is building a new model system where the harness, context, memory, and action space are separate from the models, meaning every model is substitutable; Microsoft is using the new model system in its products; developers using MAI-Code-1-Flash on GitHub Copilot are getting higher code acceptance rates and 10% lower median token usage while still getting frontier capabilities; MAI-Code-1-Flash is delivering comparable quality to GPT-5.6 in Excel for common tasks with much lower costs; MAI-Cyber-1-Flash achieves better security performance than much larger models but at half the cost; MAI-Voice-2-Flash has led to an 89% reduction of GPU costs in Dynamics 365; MAI-Image-2.5 has led to an 84% reduction in GPU costs in PowerPoint; any company can use Microsoft’s new model system in Foundry; management thinks that it will be increasingly clear that organisations want AI system providers who will help them with outcomes and knowledge creation, and not simply extract knowledge, and this direction of travel has led to the architectural design of separating the harness from the model, so that the model layer is swappable; management thinks that the recent Hugging Face breach by an unreleased OpenAI model is a good example of why enterprises cannot depend on just one model; MAI-Cyber-1-Flash can achieve better security performance than much larger models but at half the cost because 90% of the tasks are done by MAI-Cyber-1-Flash whereas the remaining 10% is done by the frontier model; the work done by Microsoft on model diversification also helps with margin improvement

We are also accelerating our own model development. We announced more than a dozen new models across image, voice, transcription, coding, security, including our first reasoning model, MAI Thinking-1, all with cost-efficient inference at the core for the enterprise use cases. We are co-designing these models with our silicon. We are seeing 40% better performance per watt when running MAI models on Maia 200. More importantly, we are building a new model system where the harness, context, memory, and action space are separate from any one model family, thereby moving the frontier on the cost-to-outcome curve. It’s not just about cost. It also has the added benefit of business continuity and resilience because every model is substitutable.

This is the system we are using in our products with great results. For example, millions of developers have used MAI-Code-1-Flash on GitHub Copilot, achieving higher code acceptance rates and 10% lower median token usage while still having access to frontier capabilities from OpenAI and Anthropic. In Excel, MAI-Code-1-Flash is delivering comparable quality to GPT-5.6 for the most common tasks while operating at significantly lower costs. In security, MAI-Cyber-1-Flash achieves better performance than much larger Mythos model, but at half the cost when combined with our multi-agent security harness. More broadly, across our model implementations, we are seeing significant efficiency gains, including 89% reduction of GPU costs in Dynamics 365 with MAI-Voice-2-Flash and up to 84% reduced GPU costs in PowerPoint with MAI-Image-2.5. This system is available to any company to use as part of Foundry…

…Every firm is going to evaluate who are the providers who are helping them with their outcomes and their knowledge creation. I think that that is now fairly clear, and it’s going to become clearer by the day. This is not going to be about, come in and take all my knowledge and benefit yourself, whereas I am not getting anything out of it. Given that direction of travel, we are very clear about the architectural design of the platform, which is you’ve got to keep your harness separate from the model. The harness will ensure that your memory, your context, all of that is external. That means any given model at any given time is swappable…

…If you look even at the Hugging Face incident, the biggest thing that we should take away from that is you can’t depend on any one model. You will maybe need multiple models to even remediate some challenges that get caused by one model. That’s the way to think about it, which is you can’t be subject to the refusal of the one model…

…Essentially you can have Mythos level performance, with 50% less cost because of this MAI-Cyber-1-Flash. The reason is because 90% of the tasks are done by the MAI-Cyber-1-Flash model, and 10% of the tasks, you still go to the frontier. This is sort of that mixing of the right model for the right task in what is essentially a pipeline job, is a super important characteristic…

…The work, frankly, on model diversification also is a margin improvement opportunity. Being able to serve the best possible outcome with a more efficient, or both efficient in terms of token usage and efficient in terms of cost structure, are also margin levers.

Microsoft’s management sees the data estate shifting from primarily supporting human users to supporting agents; Microsoft’s customers are adopting the company’s AI-optimised databases; PostgreSQL revenue was up 55% in 2026 Q2 (FY2026 Q4); the number of PostgreSQL customers who also use Foundry was up 80% in 2026 Q2 (FY2026 Q4); customers are increasingly choosing PostgreSQL as the database for their AI workloads; Microsoft has Horizon DB, a fully-managed PostgreSQL service that has 3x the throughput of self-managed deployments;  Fabric customers grew 60% year-on-year in 2026 Q2 (FY2026 Q4) to 40,000 (was 35,000 in 2026 Q1); 17,000 customers now use both Fabric and Foundry, up 60% year-on-year (was 15,000 in 2026 Q1); management recently introduced the agent-fore Rayfin SDK (software development kit) for building apps in Fabric; tens of thousands of customers, including 90% of the Fortune 500, are grounding their agents; management recently introduced Web IQ, which allows agents access to the web; Web IQ is already being used by popular AI assistants, including ChatGPT; Agent 365 is a control plane for managing agents’ governance, identity, and security; tens of thousands of companies are already using Agent 365 to manage 40 million agents in 2026 Q2 (FY2026 Q4) (was “tens of millions of agents” in 2026 Q1)

The data estate is evolving from primarily supporting apps used by people to supporting agents. Customers are rapidly adopting our AI-optimized databases like Cosmos DB and PostgreSQL to give agents fast, secure access to real-time data and context they need for memory and retrieval.PostgreSQL revenue was up 55%, accelerating for the third consecutive quarter. Also, the number of PostgreSQL customers also using Foundry increased 80% as customers increasingly choose it as the database for AI workloads. We are going further with Horizon DB, our new fully managed PostgreSQL service on Azure, which delivers three times the throughput of self-managed deployments…

…We now have over 40,000 paid Fabric customers, up more than 60% year-over-year, and over 17,000 customers now use Foundry and Fabric, up 60% year-over-year as enterprises connect agents to real-time operational, analytical, and unstructured data in Fabric…

…Tens of thousands of customers, including nearly 90% of the Fortune 500, are already grounding their agents in enterprise context with Foundry, Fabric, and Work IQ. This quarter, we introduced Web IQ, which gives agents access to real-world intelligence from across the web. It is already being used by the most popular AI assistants, including ChatGPT…

…With Agent 365, we offer a control plane that extends companies’ existing governance, identity, security, and management frameworks to agents they build. Just two months in, Agent 365 now has nearly 40 million agents registered across tens of thousands of companies.

There are over 30 million Microsoft 365 Copilot seats in 2026 Q2 (FY2026 Q4) (was “over 20 million” in 2026 Q1), with net seat adds up more than 100% sequentially; management made Copilot generally available in June 2026 to help customers complete multi-step tasks; management recently introduced Autopilots, which are autonomous long-running agents powered by OpenClaw; in 2026 Q3 (FY2027 Q1), management will bring all of Microsoft’s Copilot experiences together in one super app that spans both consumer and commercial experiences; management has been improving Copilot and recent customer feedback has been good; Copilot’s user satisfaction scores have doubled over the last 3 quarters; Copilot’s latency was reduced by 25% in 2026 Q2 (FY2026 Q4); usage intensity of Copilot is at a record, with conversations per user up nearly 100% year-on-year in 2026 Q2 (FY2026 Q4), and average weekly engagement on par with Outlook and Teams; the time taken for Copilot to move from deployment to high usage has fallen from months to just days over the last 12 months; the number of Copilot customers with more than 50,000 seats was up 7x year-on-year in 2026 Q2 (FY2026 Q4); the number of Copilot customers deploying Copilot to the majority of their knowledge workers was up 75% sequentially in 2026 Q2 (FY2026 Q4); Copilot has saved NHS England employees 43 minutes per year, and NHS England is rolling out Copilot to 505,000 employees; KPMG is rolling out Copilot to 276,000 employees; HSBC has committed to 200,000 Copilot seats; many customers have bought 60,000 or more Copilot seats; management recently added usage-based billing to Copilot Cowork; the super app will have Chat, CoWork, Autopilot, and Code in it; management thinks the addition of usage-based billing to Copilot Cowork has increased Microsoft’s addressable market

We now have over 30 million paid Microsoft 365 Copilot seats with net seat adds more than doubling quarter-over-quarter…

…Last month, we made Copilot generally available, helping customers complete multi-step tasks grounded in their work data while meeting enterprise security and compliance requirements. This quarter, we also introduced Autopilots, autonomous long-running agents with full enterprise compliance, including always-on personal agent powered by OpenClaw.

This quarter, we are bringing these Copilot experiences together, including code in one super app spanning both consumer and commercial experiences. This is a major step forward and I look forward to sharing more soon. More broadly, we have steadily been improving the quality and performance of Copilot and have been delighted by the recent customer feedback. Over the last three quarters, user satisfaction scores have doubled and are now at an all-time high. This quarter alone, we cut latency by 25%. These quality improvements, together with continued product innovation, are driving record usage intensity. The number of conversations per user nearly doubled year-over-year. Average weekly engagement is on par with Outlook and Teams. The time from deployment to what we think of as high usage, meaning monthly active usage, about 80% across a customer’s user base, has fallen from months to just days over the past year. The number of customers with more than 50,000 seats increased over 7x year-over-year, and the number of enterprise customers deploying Copilot to the majority of their information workers grew nearly 75% quarter-over-quarter, a signal of how central Copilot has become to their operations.

NHS England, for example, is rolling out Copilot to 505,000 clinicians and staff, the largest healthcare deployment of its kind after a trial showed it saved employees an average of 43 minutes per day. KPMG is expanding its deployment across its global workforce of more than 276,000 professional, HSBC committed to 200,000 seats to accelerate its workforce transformation. AstraZeneca, Boeing, Infosys, Coke Inc., Procter & Gamble, Stellantis, Tata Consultancy Services, University of Pittsburgh Medical Center, Wells Fargo, and Wipro each purchased 60,000 or more…

…Earlier this month, we added usage-based billing to Copilot Cowork with thousands of customers already paying for and actively using it…

…We now have Chat, CoWork, Autopilot, Code all coming to essentially what is going to become this flagship super app that various roles can use it…

…If I think about historically Office compared to what Microsoft Copilot is much more narrower. This is the first time where you really have an enterprise-wide tool, which has a both per seat and usage-based pricing. The TAM is much more expansive.

Microsoft’s management has added consumption plans to its business model; customer service within Microsoft Dynamics 365’s MCPs (model context protocols) have seen the highest usage-based credit consumption, up 4x sequentially in 2026 Q2 (FY2026 Q4); management recently introduced usage-based billing to GitHub Copilot; after the introduction of usage-based billing, GitHub Copilot had significant consumption revenue and continued to have business and enterprise seat growth

In addition to this, we are also evolving our business model beyond per seat to per seat plus consumption, further expanding our TAM and delivering more customer value. Earlier this month, we added usage-based billing to Copilot Cowork with thousands of customers already paying for and actively using it…

…In Biz Apps, we have been reinventing Microsoft Dynamics 365 for an agent-first world. We are exposing over 650,000 MCP actions across sales, finance, supply chain, HR, and customer service so that agents can now access business context and take action using the same data models, rules, permissions, security guardrails, and audit trails as any application user. We are also moving from seats to seats plus consumption model. Customer service is at the forefront of this transformation with usage-based credit consumption in this category up 4x quarter-over-quarter with customers like Northern Trust using our tools to drive proactive intelligence…

…GitHub Copilot now has 50 million users. This quarter, we introduced usage-based billing and have continued to see business and enterprise seat growth and also significant consumption revenue after the new model went into effect. Copilot revenue accelerated over 60% quarter-over-quarter.

Microsoft’s management recently launched Microsoft Frontier Company for customers to build AI systems that learn and improve with usage, through customers’ workflows, domain knowledge, and judgment; Microsoft will embed 6,000 industry and engineering experts with customers as part of Microsoft Frontier Company; Microsoft has completed over 330 of such projects with 164 customers; Microsoft’s teams worked with Novo Nordisk to build an agent that analyses clinical data under strict compliance requirements; Microsoft worked with LSEG to embed AI into LSEG Workspace

There is a tremendous opportunity to turn customers’ workflows, domain knowledge, and accumulated judgment into AI systems that learn and improve with every usage. To help customers capture that opportunity this month, we launched Microsoft Frontier Company, the largest outcome-driven engineering organization in the industry. We will embed 6,000 industry and engineering experts with customers to co-design, co-innovate, and continuously improve AI systems at scale. We’ve been testing this model over the past year, completing over 330 projects across 164 customers, including many of the world’s leading companies across industries. For example, our FD teams worked with Novo Nordisk to build an agent that helps analyze clinical data while meeting its strict compliance requirements. We partnered with LSEG to embed AI into LSEG Workspace, helping finance professionals ask complex questions and quickly find answers across structured and unstructured financial content.

Microsoft’s management sees a big opportunity for Windows to become a place for unmetered intelligence by combining on-device compute with enterprise-grade security

We see significant opportunity for Windows to become the offload for unmetered intelligence, combining powerful on-device compute with enterprise-grade security.

1/3 of Microsoft’s cloud and AI-related capex in 2026 Q2 (FY2026 Q4) are for long-lived assets that will support long-term monetisation, while the other 2/3 are for CPUs and GPUs; management is able to easily slowdown Microsoft’s capex, both for the short-lived and long-lived assets, depending on the demand environment; in thinking about Microsoft’s capex, management is studying history, such as the railroad boom-and-bust described in the book 1873

Capital expenditures were $41 billion, including the impact from higher component pricing as noted in our guide. Roughly two-thirds of our CapEx was for short-lived assets, primarily CPUs and GPUs, as customers increasingly build solutions that leverage both AI and non-AI infrastructure. The remaining spend was for long-lived assets. This quarter, total finance leases were $5.6 billion and were primarily for large data center sites, and cash paid for PP&E was $35.8 billion…

…You’ve seen our CapEx really pivot toward what I would call and do call short-lived assets, which really, right, that’s CPUs and GPUs that have relatively shorter lead times. If the demand environment changes, you just slow down what is, in fact, the largest component, right, and the driver of COGS. The investment into land and data center builds is actually quite flexible, right? It’s a smaller percentage of the overall cost structure, and timing can be changed on much of that, especially on the builds. You can stagger the timing of the build-out of, as I was saying, some of the GPUs and CPUs that you plan to put in. When you think about being able to manage through that, hyperscalers have been doing that for quite a long time in terms of having the flexibility and the understanding of manage those changes in demand…

…All of us are reading this, “1873” is the book to be read. So in my mind, I think you’ve got to get the product shape right. That’s sort of a lot of what we are focused on. You have to get the portfolio right. Amy talked about what we are doing, whether it’s in Copilot or the super app, bringing all the form factors or all the way to Azure and the agent first sort of primitives in Azure. You kind of have to really get that portfolio to all come together. The mix of customers is super important. You have to recognize the breadth, the geo mix, the segment mix, the workload mix, and you got to really think about all of those when you’re even building capacity. You’ve got to run an efficient railroad.

Azure grew revenue by 43% in 2026 Q2 (FY2025 Q4) (was 40% in 2026 Q1); Azure’s revenue growth was better than expected because of efficiency gains in the compute fleet and the earlier delivery of new capacity; Azure continues to be constrained by capacity; the new capacity was quickly monetised; Azure’s revenue in 2026 Q2 also benefitted from stronger than expected GitHub Copilot consumption following the June business model change to usage-based pricing; Azure’s compute infrastructure is fungible when it comes to running any model family; Azure has a very diverse book of business; Azure has been able to adjust pricing to counter a rise in hardware costs 

In Azure and other cloud services, revenue grew 43% against a prior year that included accelerating growth. Customer demand continues to exceed available capacity. Revenue growth was ahead of expectations, driven by efficiency gains across our CPU and GPU fleet, as well as process improvements to enable earlier delivery of new capacity. That additional in-quarter capacity for Azure was quickly monetized. Results also benefited from stronger than expected GitHub Copilot consumption following the June business model change to align pricing with usage and value…

…Given that we continue to see growing demand, no matter what model is chosen or what model family or whether it’s run a model of your own, the Azure platform’s quite efficient at delivering that. Think about that infrastructure as being pretty fungible…

…The other thing is, that’s important, Mark, is you just have an incredibly diverse book of business, by geo, by segment, by industry…

…[Question] How do you manage through the hardware price increases that we’re seeing, the component prices?

[Answer] We’re adding this capacity, to your point, but a lot of this obviously is also being sold in newer contracts, and we’re able to have the pricing reflect it, but keep value.

From FY2027 onwards, management will extend the useful life of Microsoft’s data centers and offices from 15 years to 25 years; the extension of the useful life will only have minimal benefit to FY2027’s operating income; the extension of the useful life will have greater impact to capital expenditures, as the extension will shift Microsoft’s future data center leases from finance leases to operating leases (finance leases are included in capital expenditures, while operating leases are not); management now expects 2026’s total capital expenditure to be $175 billion (was previously $190 billion) because of the shift from finance leases to operating leases

Effective at the start of FY 2027, we are extending the estimated useful life of our data centers and office buildings from 15- 25 years, reflecting our operating history and expected use of these assets. The impact of this update is reflected in today’s guidance. This change affects only the timing of future depreciation and is expected to have a minimal benefit to FY 2027 operating income. The greater impact is on capital expenditures, as more of our future data center leases will shift from finance leases to operating leases as a result of this update. Finance leases are included in capital expenditures, while operating leases are not.

Outside of this useful life impact, our calendar year 2026 CapEx investment expectations remain unchanged. However, the shift from finance to operating leases adjusts our expectation to approximately $175 billion…

…At the company level, with strong commercial momentum, we continue to expect another fiscal year of double-digit revenue and operating income growth. Operating expenses should grow in the mid to high single digits, reflecting continued investment in R&D compute capacity, talent, and data. We expect FY 2027 capital expenditures will grow year-over-year, given demand signals across our portfolio. Even as we invest to meet growing demand, full fiscal year operating margins should be down less than a point. In addition, we expect to remain free cash flow positive in FY 2027.

Netflix (NASDAQ: NFLX)

Netflix’s management is using LLMs (large language models) to improve title discovery and understanding of member preferences; management is using AI to improve Netflix’s search function; in 2026 H1, GenAI (generative AI) workflows were used in 300 of Netflix’s titles, with the workflows mostly in post-production; the use of GenAI helps Netflix to deliver higher quality output faster and at lower cost, and also produce sequences that otherwise would have to be skipped; it’s still early days for Netflix’s acquisition of InterPositive (a startup focused on providing AI tools for filmmakers) but Netflix is already seeing the impact of genAI on its productions; Netflix has other genAI tools outside of InterPositive; management believes that the presence of AI tools does not change the fact that it takes great artists to create great content; Netflix is using AI tools for set references, pre-vis, VFX, sequence prep, and shot planning; management is seeing AI use cases in content production scaling faster and faster; a recent Netflix documentary, American Experiment, features 17 minutes of AI-enhanced footage that were produced twice as fast and at half the cost compared to non-AI options

We are leveraging LLMs to improve title discovery and to better understand member preferences. We’re also enhancing search for our members with new voice search functionality and AI-powered natural language search…

…In 2026, GenAI workflows have been used in roughly 300 of our titles, with the largest concentration of work in post-production. We are increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost than traditional methods. In some cases, productions would have had to leave out key shots and sequences in the absence of GenAI technology. For example, Glory (India), Brasil 70: A Saga do Tri (Brazil), and The American Experiment (US) utilized GenAI tools to create highly complex sequences (e.g., enhanced crowds, historical battle sequences, and worldbuilding establishing shots)…

…It’s early days for InterPositive, but we’re broadly seeing that gen AI is starting to have an impact across hundreds of our productions. So important to note that we have other gen AI tools in addition to InterPositive…

…On the content side, we believe it takes great artists to make something great, and AI is not changing that. AI will give creators better tools to bring their visions to life. Movies are being made by people who make movies. AI provides them with better tools to make them even better…

…So today, our talent leverages tools for things like set references and pre-vis and VFX and sequence prep and shot planning, which all makes the production itself so much more smooth and efficient and fast. And that’s just the beginning. We’re seeing it across the entire production life cycle and AI — those use cases are scaling faster and faster. So our documentary series we just released called American Experiment. That series features 17 minutes of AI-enhanced footage. It enabled us to expand the scope of the series in ways that just wouldn’t have been feasible before. Those 17 minutes, they were produced twice as fast and at half the cost of previous options.

Netflix’s management has expanded Netflix’s AI-powered advertising tools across its full advertising lifecycle

In Q2, we expanded our AI-powered tools across the full advertising lifecycle, from planning and creative production to campaign management, optimization, and reporting. 

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s management is seeing very strong demand for TSMC’s leading edge nodes; TSMC’s capital expenditure is always in anticipation of growth in future years; management has raised capex guidance for 2026 to US$60 billion to US$64 billion partly because of agentic AI (previous guidance is for US$52 billion to US$56 billion; capex growth at the high end of the new guidance would be 56% from 2025’s capex of US$41 billion); management does not foresee any bottlenecks to TSMC’s capacity expansion plans; most of TSMC’s capex for 2026 will be for advanced process technologies; management now expects TSMC to grow revenue by above 40% in USD terms in 2026 (previous guidance was for growth to above 30%); TSMC’s capex in the last 3 years was ~US$100 billion, and the next 3 years is now expected to be much, much higher (previous guidance was for it to be “much higher”), although management does not have a specific 3-year capex outlook to share for 2026-2028; management now thinks the AI accelerators business will have an even stronger CAGR for 2024-2029 than the high-end of the mid-to-high-50% CAGR communicated in the 2026 Q1 earnings call; it’s very likely for TSMC’s capex guidance for 2026 to continue increasing; management thinks the next few years will look really good for TSMC; management now thinks the CAGRs for 2nm, 3nm, and 5nm nodes will be even higher than the very strong CAGRs mentioned during a recent symposium; TSMC’s capex guidance for 2026 was raised partly because customers are willing to cooperate with TSMC (hinting at pre-payments?), and because of price inflation of tools

Demand for our leading-edge technologies is very strong…

…At TSMC, a higher level of capital expenditures is always correlated to higher growth opportunities in the following years…

…Given the continued strong structural demand from our customers, including the newly emerging Agentic AI market, we have decided to raise our full year 2026 capital budget to be between USD 60 billion and USD 64 billion as we continue to invest heavily to support our customers’ growth. We always collaborate closely with the tool suppliers well in advance to prepare the capacity, whether it is a strong up cycle or down cycle, just like our customers collaborate with us well in advance to plan our capacity. Thus, we do not foresee any bottlenecks to our capacity expansion plans.

About 70% to 80% of the 2026 capital budget will be allocated for advanced process technologies. About 10% will be spent for specialty technologies and about 10% to 20% will be spent for advanced packaging, testing, mask-making and others…

…Supported by our robust technology differentiation and the broad customer base, we now expect our full year 2026 revenue growth to be slightly above 40% year-over-year in U.S. dollar terms…

…We do not have a number to share with you. But as you know, we invest CapEx this year for the future business opportunity. And as long as there are business opportunities, we will not hesitate to invest. As you can hear from our prepared remarks that we — our conviction in the megatrend, AI megatrend multiyear is very strong, and we are stepping up the CapEx, including increasing this year’s CapEx. Last time, we said our CapEx in the next 3 years will be significantly higher than the CapEx in the past 3 years. Now is the — the CapEx in the next 3 years will be even more significantly higher than the past 3 years…

…If you read our message that we continue to invest more. We increased the CapEx with a good reason. So if you’re asking about the AI’s CAGR, let me give you not a number, but it’s stronger and stronger and stronger. So we don’t give you the number today because it continue to increase. So we don’t know how to answer this question, but stronger than what we said before…

…This year, we say we increased the CapEx from $52 billion to $56 billion, now $60 billion to $64 billion. And you bet, that will continue to increase…

…Because of the revenue corresponding to our investment, right, because we know we forecast our demand, and then we make an assessment, and then we do the CapEx. Next few years is going to be a very good business for TSMC. That’s all I can say…

…[Question] I noticed that during your symposium that you actually mentioned about 2-nanometer family capacity growth will be growing at around 70% CAGR from ’26 to ’28 and N3 plus and N5 to grow by 25% CAGR from ’22 to ’27. So I was just wondering, are those numbers still right assumptions today?

[Answer] We showed the chart. Okay. Now it’s bigger. That’s what I say…

…[Question] From year-to-date, so TSMC raised the CapEx guidance by almost USD 10 billion. So can you give me some color where is the upside from? How you guys see the difference from 6 months ago?

[Answer] The most important reason is because of the demand continued to increase, and we feel the pressure from the customer to drive TSMC, not drive actually, to cooperate with TSMC for the capacity increase. That’s one of the major reasons. The second reason is inflation. Now we buy the tools with inflation price.

TSMC’s management sees very robust AI-related demand; management continues to see very strong signals and positive outlooks from TSMC’s customers’ customers, who are the cloud service providers; management’s conviction in the AI megatrend remains very high; management thinks demand for chips, driven by AI, will be incredibly strong to at least 2030, but is unsure if there will be a dip in-between; management thinks the collective trend of AI is so robust it is creating a new industry altogether

AI-related demand continues to be extremely robust. The AI megatrend continues to drive the need for more and more computation, which supports the robust demand for leading-edge silicon. Our customers and customers’ customers, who are mainly the cloud service providers, continue to provide us with a very strong signal and positive outlook. Thus, our conviction in the multi-year AI megatrend remains very high…

…I believe from this day on all the way to probably 2029, 2030, the demand is very strong. Whether in between there’s a dip or not, I’m not very sure. But the trend is so robust that I believe we are witnessing a kind of a new industry. I would like to say the new industry called AI industry, which is so common in our daily life because it’s going to affect our automotive, affect the humanoids, robot, and also impact to all the industry. So by the amount of money we put in, I mean, including all the CSPs, this alone is a very important new industry to the world. And so the demand will be there. And the fundamental thing is semiconductor chips, and most of them in TSMC.

TSMC’s management sees the emergence of agentic AI leading to a resurgence of the importance of CPUs (central processing units) in AI data centers; management thinks the resurgence of CPUs is positive for TSMC because nearly all the companies behind the major CPU architectures are customers of TSMC

The emergence of Agentic AI is leading to a resurgence in the role of CPUs in AI data centers, which drives more silicon demand in addition to AI accelerators. We believe this is positive for TSMC as no matter what CPU approach is taken, whether it’s x86, ARM-based, or RISC-V architecture, they are almost all TSMC’s customers. We are already collaborating closely with our CPU customers and working to support them with the most advanced technologies and necessary capacity, so they can capture the Agentic AI market opportunities.

TSMC’s management collaborates closely with its customers, and customers’ customers, when planning the company’s capacity; management is aware that while the CEOs of TSMC’s customers’ customers are telling their own truths, the combined picture is not the truth, and management is adjusting for that in thinking about true end-demand so that TSMC does not end up with over-capacity

To address the structural increase in overall long-term semiconductor market demand profile, TSMC collaborates closely with our customers and our customers’ customers to plan our capacity. Given the fundamental complexity of leading-edge technologies and the design-in and lead time involved, we also have a very good idea of their multiyear product road map and production plans. This is important because it takes more than 5 years to develop the technology and product, prepare the capacity, and ramp it up to high-volume production. Internally, TSMC employs a disciplined capacity planning system to assess the market demand from both a top-down and bottom-up approach. This is a continuous and ongoing process. Based on our assessment, we are stepping up our CapEx investment to increase our capacity, to support our customers’ future growth…

…Now remember that I believe every customer tell me the truth, everyone. You put all the truths together, it’s not the truth. So we have to make some of the judgment. You know what I mean, since you are laughing. Because all the customers are very aggressive, right? That’s the CEO’s job. CEO got to be aggressive. So they give me the number of their demand, and I believe they try their best to tell me the truth. So I put all together, all the truths together is not a truth. Mark down that word. So yes, we do a very careful judgment. May not be correct, may not be correct, but we did carefully and because this is a big money, right? 

TSMC’s management has announced an additional US$100 billion investment in Arizona for fabs for nodes of 2nm-and-below, in partnership with TSMC’s US customers and the US government; management believes the latest Arizona investment will strengthen the US’s semiconductor supply chain; the additional US$100 billion investment in Arizona will see TSMC build at least 4 additional fabs, with both front-end and back-end fabs; TSMC is also receiving government support in the USA, similar to Intel, but TSMC does not announce the support; there’s no firm timeline for the additional US$100 billion in investment, as it depends on the market situation and customers’ demand, but management wants to move fast

With a strong collaboration and support from our leading U.S. customers and the U.S. federal state and city governments, we would like to announce an additional USD 100 billion investment in Arizona. This is to build several more semiconductor logical wafer fab for 2-nanometer and below technologies as well as advanced packaging fabs to support the strong multiyear demand from our leading U.S. customers. We believe this investment will help to further foster the development of the U.S. semiconductor ecosystem, strengthen the supply chain, and support an increasing number of high-tech, high-paying jobs in the United States…

…We announced additional $100 billion investment in Arizona. How many fabs? Many. So actually, let me say that, say probably, additional 4 more fabs will be built.

[Question] And that’s combining front- and back-end?

[Answer] Yes…

…The other one in the U.S., they got a very strong U.S. government support. We also got the government support, by the way, although we don’t announce it…

…[Question] Do you have any schedule or time frame to share about this additional $100 billion?

[Answer] Most of the time, it depends on the market situation and our customers’ demand. So if you ask me to give you a firm schedule, no, we don’t have it today. But we do have a plan. And we speed it up. We try to speed it up as fast as possible.

TSMC’s management is building new fabs in Taiwan; management is continuing to convert 5nm tools for 3nm capacity in Taiwan; management is focusing on flexible capacity support among the N7, N5, and N3 nodes; the upcoming A14 node has 10-15 speed improvement at the same power compared to N2, or 25-30 power improvement at the same speed, and a nearly 20% chip density gain; the A14 node is on track and progressing well; management has introduced the A13 and A12 extensions, which are both superior to A14; A13 and A12 are scheduled for volume production 2029; management believes the A14 family will be an even larger and long-lasting node than N2; management wants to move as fast as possible in Taiwan and other countries

We are building 13 leading-edge and advanced packaging fab in Taiwan over the next several years, and we will continue to further invest in Taiwan…

…We continue to convert 5-nanometer tools to support 3-nanometer capacity in Taiwan…

…We are also focusing on capacity optimization across node, which including flexible capacity support among N7, N5 and N3 nodes…

…Our A14 technology representing the second generation of nanosheet transistors and deliver another full node stride from N2 with performance and power benefit to address the incessant need for high-performance and energy-efficient computing. Compared with N2, A14 will provide 10 to 15 speed improvement at the same power or 25 to 30 power improvement at the same speed and close to 20% chip density gain. A14 technology development is on track and progressing well…

…We also introduced A13 and A12 as extension of the A14 family. A13 represents a further advancement of A14, achieving an over 6% die area saving through an innovative 97% optical shrink. Through continuous design technology co-optimization, A13 also drive further performance and power efficiency improvement. A13 design rule are backward compatible with A14 to ensure smooth IP migration. We also introduced A12, which will bring our innovative superpower rail technology to the A14 platform for superior performance, power, and area benefit. Both A13 and A12 are scheduled for volume production in 2029. We believe A14 and its derivative technologies will propel our A14 family to be an even larger and long-lasting node for TSMC than N2. Just like 2-nanometer technology is a larger and longer-lasting node than 3-nanometer, and here further extend our technology leadership position well into the future…

…We’re also moving the new fabs and the facilities in Taiwan as fast as possible. And the same thing, we try to bring up a new fab in the Japan as fast as possible. Because of the situation today is the demand and the supply, the gap is so big. So we are working very hard to narrow the gap.

TSMC’s management has demonstrated 90% device performance and 90% SRAM yield; management is seeing strong customer interest and engagement from smartphone and HPC AI applications

Internal product-like vehicle demonstrated close to 90% device performance and close to 90% 256 megabits SRAM yield. We are observing a strong level of customer interest and engagement from both smartphone and HPC AI applications and customer now tape-out activity is ongoing and ahead of schedule. Pre-production will start in 2027 and volume production is scheduled for 2028.

When dealing with foundry competition, TSMC’s management thinks 3 things are the most important, namely, technology, manufacturing, and customer trust; management is not that concerned about foundry competition because they think choosing a foundry partner requires deep work to understand the foundry’s technological road map and it’s not as simple as changing a brand of milk from a convenience store; management is welcoming competing advanced packaging technologies because it lessens TSMC’s load and provides more flexibility for customers; advanced packaging is a back-end business, and it’s where TSMC is less worried about competition

The most important thing as we continue to say is the technology, manufacturing, and customer trust. These 3 fundamental never change. For my 30-some years, 40 years career, it’s always the most important thing. And that’s always the TSMC’s secret recipe to win the business. So from my – from the competition point of view, choosing a technology, ramping it up is not buying a milk from 7-Eleven. Well, I’m using that — I’m quoting the sentence for my customer, anyway. It says that you’re choosing a kind of a technology partner, it is no shortcut. You need to understand the technology. You need to really utilize it using the test chip, and then something, and work together, and then prepare the capacity and ramp it up. That’s why I would say, it takes about 5 years. It’s not that today, you think this milk is better, you go to the next store, it’s a 7-Eleven. You don’t like it, you go to another store. No…

…[Question] My question is regarding the new advanced packaging technology. We noticed that especially the EMIB-T is gaining traction. So how will TSMC react this request?

[Answer] Our packaging capacity is so tight that now it’s limited by customers’ growth. So we welcome that additional flexibility in the market. And so that will help TSMC’s front-end wafer business growth, which is a majority part of TSMC’s business. The technology looks good, according to the newspaper. And we hope they will be successful and so that share some of the loading from TSMC. Today, we’re working very hard to shorten the gap between the demand and the capacity. And so as I said, we welcome to have this additional alternatives, and so the flexibility for my customer…

…[Question] If these technologies have some small problem, and then ask our company to support. So how our company accommodate it?

[Answer] Our #1 is to support our customers’ success. So whatever that we can hear about our customers’ business, we want to win…

…The front-end’s wafer business and the back-end’s business are 2 different things, right? If they are the same, then you can expect ASE become the front-end competitor also. It’s 2 different things. And I also say that since our capacity in the back-end is so in shortage mode, the gap is bigger. And so, I welcome that the competitor offers some of the flexibility to my customer so that their front-end wafer can be put into the package, and that help TSMC’s front-end wafer business. 

TSMC’s management is diligently checking to make sure its AI chips do not end up in its customers’ inventory and are actually being put into production

Are we sure that we deliver the chips to our customer, and they were not put into inventory? So we — actually, we are checking the AI data centers progress, the building, the location, the demand, the racks, we’re checking all that to make sure that TSMC chips will not be put in inventory.

TSMC’s management is not worried about customer concentration for AI-related demand; management sees a lot of new players in the AI industry

[Question] Could I ask about the risk that you see around customer concentration, as AI demand continues to significantly outgrow other end markets? I think your exposure to your top 5 customers is becoming meaningfully larger than at any point in your history.

[Answer] No, that’s not our concern. Besides what you say the customers are growing bigger and bigger, we are very happy about it. And some of the customers also growing very fast. So it’s not — Jim, it’s not what you said that the bigger customer is growing bigger and bigger. No. I mean that’s — there’s a lot of new player in the AI industry.

TSMC has started production for its COUPE (Compact Universal Photonic Engine) platform; COUPE is TSMC’s silicon photonics platform designed to send data using light instead of copper wire; management believes AI data centers will need to lower power consumption while increasing communication bandwidth, and these are important functions of the COUPE platform; management believes COUPE’s demand will grow in the next few years and it will become a fairly important technology for TSMC

[Question] When should we expect the COUPE platform to have a material contribution to your top line?

[Answer] We start the production right now, and it will be ramped up. As time goes by, I think the AI data center need to lower down the power consumption and increase the bandwidth of the communication channel. So I believe the COUPE will continue to increase the demand, and then will become a fairly important technology in the next few years.

All the silicon-roads for AI lead to TSMC

[Question] You mentioned about the Agentic AI and the CPU growth potential. But can you give us more update among that AI, different kind of chips between GPU, accelerators or CPU? What you see the growth potential and your visibility?

[Answer] I don’t think I can give you a very specific number. But let me share with you. All of them are in TSMC. And they’re also using the same kind of leading-edge technologies. We’re working with our customers to allocate the wafer, the supply to balance the CPU, GPU, XPUs ratio.

In mature nodes, there is a shortage for nodes that are related to AI, such as for power management ICs (integrated circuits); the demand for mature nodes from consumer products is not high

The mature node cover a lot of different segments. Only the one which related to AI is in shortage, which is the most important one, is the #1 is power management IC, because all the AI data centers need a lot of power management. And those are the mature node technology like 0.18 micron, 90-nanometer or something like that. Those are in shortage definitely. And also the sensor portion because of — you need a lot of sensor to detect the environmental information and put into the AI data center to analyze it. Other than that, other area, just like you pointed out, the consumer product is not in a high demand. And so other segment is not so strong demand. And as I pointed out in my statement, other area, no, it’s not so much of, say, in a lot of shortage, not at all.

Tesla (NASDAQ: TSLA)

Tesla’s management is seeing very high take rate of FSD in locations where it is approved; management thinks consumers are purchasing FSD primarily, instead of buying Tesla vehicles; management thinks Tesla will enjoy similar uptick in demand as it gets approval for FSD in different countries; 55% of Tesla’s vehicle deliveries in North America in 2026 Q2 had FSD subscriptions enabled; FSD now has 1.5 million paid customers globally (1.3 million in 2026 Q1), 55% of which are paid upfront and 45% are subscriptions; management has turned off upfront payments for FSD, so they expect FSD monetisation to come from subscriptions in the future; the Robotaxi fleet is currently running v15 (version 15) of FSD; v15 has 7 major parallel tracks of improvement over v14; Robotaxi’s v15 already has 40% of the tracks merged together; management thinks v15 will further improve the safety profile of the Robotaxi fleet; management thinks it makes sense to upgrade all Tesla vehicles to at least Hardware 4 

We’re seeing in locations that have FSD approved, we’re seeing a very high take rate of FSD. In fact, I think for a lot of people, they’re actually buying Tesla Full Self-Driving with a car attached, as opposed to a car with FSD. They’re coming into our stores in the U.S. and telling me they want the Full Self-Driving and with whatever car it comes with, essentially. Clearly this is a significant demand driver and as we get approval for FSD in different countries, I think we’ll see a similar uptick in demand…

…In Q2, we had, in North America, about 55% of our deliveries had FSD subscription at the time of delivery enabled. Overall, FSD attach rates continue to improve, reaching nearly 1.5 million paid customers globally, of which 55% is upfront purchases and the remaining 45% is subscriptions. We expect that the bulk of the growth in FSD monetization will come from subscriptions as we’ve removed the purchase option in most markets…

…The currently operating Robotaxi fleet is already running early versions of the V15 FSD software that we had referred to in the past. For V15, we had planned roughly about seven major improvement tracks, and they’re all happening in parallel. The early V15 builds that are running on Robotaxi have already 40% of those tracks merged together, and that’s what’s running in the fleet right now. As we continue to complete our work on V15, we will see that the car is going to be ridiculously safe and capable…

…I think it’s going to make sense to upgrade all cars that have less than Hardware four. Any cars that have cameras, basically, because otherwise it would be probably too many modifications. Anything that’s set up for cameras, it’ll be financially sensible at some point to upgrade them. I think we’d want to upgrade them to the next generation of AI board.

Tesla’s management thinks the company’s energy business will be crucial for scaling up AI data centers; power constraints are a major issue for the deployment of AI data centers; power can cycle dramatically in AI data centers, especially for training runs, so the data centers require fast-acting power electronics to smooth out the huge changes in power 

The energy business is also growing incredibly fast and, I think will be crucial for the scale-up of artificial intelligence data centers…

…We think power constraints are going to be, they already are a major issue for AI. Just turning on the AI computers, the AI compute demand is so high that even the hyperscalers are having trouble turning on their AI compute and finding the power, and then smoothing the power, especially for the training runs where the power cycles dramatically in a very short period of time. You can have, during a training run, the power consumption can drop by 70% for 100 milliseconds. You really need fast-acting, advanced power electronics to be able to smooth out the massive changes in power, especially during their training runs. That’s why SpaceX has bought so many Megapacks for the data centers. It’s actually mostly for smoothing out the power for the training runs.

Tesla’s management said during the 2026 Q1 call that the company will increase capital expenditure significantly, partly for AI-related investments; management is confident that the capex will yield massive returns; the higher capex in 2026 Q2 caused free cash flow to become negative; management continues to expect capex for 2026 to be more than $25 billion; management is tapping on the debt markets to help fund Tesla’s capex

This is a massive CapEx year. I’m confident that all the things that we’re investing in will yield incredible returns. Really, maybe the best CapEx returns that we’ve ever seen…

…Our free cash flow ended up being negative for the quarter. Most of the reason for it going negative is because CapEx more than doubled sequentially. We expect it to increase further in the second half of 2026. We continue to expect that CapEx for this year will be more than $25 billion. CapEx will grow for the next two or three years as we expand our Robotaxi fleet, expand our production capacity for Optimus, make investments for semiconductor fab, install solar manufacturing capacity, and AI compute infrastructure, in addition to all the other expansions we’ll do for other manufacturing for automotive. In addition to using our cash for such investments, we are being opportunistic in securing certain debt facilities that will give us the capacity to borrow up to $30 billion to help accelerate such investments.

Tesla’s management will soon start production for Optimus, the company’s autonomous humanoid robot; management continues to think Optimus will be the biggest product ever; no one has ever built an autonomous humanoid robot that can do tasks without any programming; there are many challenges in the electromechanical design of Optimus; management foresees very substantial challenges in scaling the production of Optimus, as the degree of difficulty is proportionate to the newness of a robotic part, and there is no existing supply chain for Optimus; Tesla has in-house a significant amount of Optimus’ production; Tesla has an Optimus production line in Fremont, California; management expects the initial part of the Optimus production S-curve to be long and flat; management thinks a lot of videos of robots on the internet are pre-programmed or remote controlled; management believes Optimus will be the first humanoid robot capable of doing generalised tasks; Optimus will come with hands that have the same dexterity as the human hand, and then eventually have superhuman dexterity; management is confident that the same AI technology for self-driving cars can be used for a digital version of Optimus; the development of Digital Optimus is in partnership with SpaceX’s (an Elon Musk company) Grok models; management thinks having Optimus take on the human form-factor gives the robot access to huge amount of learning-data as the form factor allows the robot to learn directly from humans; workers at Tesla factories are providing the data for Optimus to learn from; Optimus benefits from a reinforcement learning loop when a large number of robots practice their tasks; management has the same end-to-end AI approach for Optimus’s AI as they do for FSD; management sees Optimus 4 as having much higher vertical integration than Optimus 3; management sees 10 million units a year for Optimus 4, compared to 1 million units annually for Optimus 3

We will soon start production with Optimus…

…I think Optimus will be the biggest product ever…

…It’s one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that if you simply ask it to do something or show it a video, it can do the task without any programming. No one’s ever achieved this. There are many challenges in the electromechanical design of the robot to achieve sufficient dexterity, also to be very reliable and have long wear and tear. Meaning, it needs to be out in the field and not break down…

…The production scaling challenge is very substantial. This is going to be the hardest product to scale manufacturing that we’ve ever made at Tesla, because everything on the robot is new. The difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot…

…With Optimus, there is no supply chain. We’ve had to build up a supply chain in its entirety, or in-house the production. We actually have in-housed a tremendous amount. The Optimus production line that we’re building out in Fremont, in place of what used to be the Model S, X production, it looks incredible…

…Optimus will follow the normal S-curve of a manufacturing ramp, but the initial portion of the S-curve will be quite flat and long because of the newness of the parts in the robot. 

You’ve probably seen lots of impressive demonstrations of robots on the internet, but those demonstrations you’re seeing are pre-programmed or remote controlled. There is no humanoid robot that is actually able to do generalized tasks. Optimus will be the first one that is capable of doing that, where it’s not just a demo, it’s genuinely useful in day-to-day life. And Optimus is designed to have full human dexterity. A hand that has the same level of dexterity, if not higher, than a human hand. The human hand is an incredible thing. The more you study the human hand, the more you realize how amazing hands are. It’s more than just opposable thumbs. The nuances of how human hands work are amazing. The closer you look, the more amazed you are. Optimus will have that capability. It will have human and then superhuman dexterity…

…We have Digital Optimus, which is basically driving a computer screen, I guess you could call it computer use or something like that, but driving a computer screen in the same way you drive a car. For the car, it’s pixels in or photons in and controls out. The same thing is true for Optimus and Digital Optimus. It’s photons in, controls out. We feel confident we can adapt the same Tesla AI technology that we developed for self-driving cars to have a self-driving computer screen or self-driving computer, essentially. The self-driving computer, where the very low cost, Tesla AI computer can handle all of the sort of real-time tasks. They’re doing real-time video control of the screen of the computer. It’s not like screenshots type of thing, it’s real-time video, at high frame rate. Digital Optimus will be important, obviously, for physical Optimus because physical Optimus needs to be able to operate computers. It can’t come up to a touch screen and not know what to do. It’s got to have a generalized touch screen and computer use capability.

That’s looking promising and this is in partnership with SpaceX. SpaceX’s Grok, sort of the big model that is the manager of Digital Optimus and tells Digital Optimus what to do, provides it with a series of tasks, then Digital Optimus goes and does those tasks…

…Optimus has been designed to not just match the appearance of human beings, but also the functionality and dexterity of humans. One of the main reasons for this approach is that having the human form factor and function allows us to learn from humans on how to perform a wide variety of tasks. This opens up the entire world to provide data for training Optimus. Just like FSD, we have access to a broad fleet of humans giving us data from all of the workers at our factory…

…When we have a large number of Optimus robots practicing their tasks in what we call the Optimus Academy. The data from the bots experiencing the task themselves will be invaluable and help us close any minor form factor gaps that may exist between the bot and the humans. This is also when the reinforcement learning loop kicks in, where the bot initially attempts some tasks, fails sometimes, learns from both the successes and failures of those tasks, and eventually learns to master those tasks at perhaps a superhuman level. Our AI strategy for Optimus is aligned the same. It’s the same end-to-end strategy that drives FSD, pixels in, controls out. Just like FSD, we expect it to work broadly. In FSD, you can get in the car, type in an address, and then hit start, and it just handles all of driving from park to park. The same thing is going to be true for Optimus as well. You’re going to just ask it to do anything, and then it should just perform the entire task on its own without you having to do anything along the way…

…For Optimus 4, which will be built in Austin, that will be a much more vertically integrated supply system for Optimus 4. That would aim to have an order of magnitude more production of Optimus 4 than Optimus 3. Sort of aspirationally 10 million units a year versus 1 million units a year of Optimus 3. With all the caveats there, which is insanely difficult to scale production.

Tesla’s management is trying to scale Robotaxi as fast as possible, but they are doing so while being very careful not to harm anyone; Robotaxi has been introduced in a number of cities in Florida, Texas, and the Bay Area; management expects Robotaxi’s miles driven to increase by more than 10% a week; Robotaxi is in 7 markets in the US currently; management expects to accelerate the ramp of Robotaxi throughout the year and expand into new US markets; Robotaxi has driven more than 380,000 miles unsupervised with zero incidents; Robotaxi did not require LIDAR, radars, HD maps and more to drive safely; management thinks Robotaxi’s current safety record is validation of the company’s entire AI approach to autonomy; Robotaxi’s number of unsupervised miles has been growing double-digits every week since the start of 2026; it has been taking management relatively less effort to launch Robotaxi in new cities and management expects the time to launch in a new city to trend towards zero; management expects the Robotaxi business to be fully vertically integrated; management thinks Robotaxi will not face any demand challenges; the Robotaxi fleet is still small (only in the dozens) but management wants to expand into new cities to test the generalisability of Robotaxi’s autonomy technology; vehicles in the Robotaxi fleet are driving 24/7, so Tesla can get a lot of miles from them

There are, I think, 30,000 – 40,000 automotive deaths per year in the U.S. alone, most of those do not generate any press or maybe, you never really read about almost any of those. If we injure even one person, it’ll be worldwide headline news, and regulators will immediately clamp down on our activities. We don’t want to injure anyone. We’re going as fast as humanly possible in scaling Robotaxi, but while trying to ensure that we do not harm anyone at all, and ideally do not even run over a pet. That’s really the constraint is we want to grow as fast as possible with Robotaxi without harm to anyone…

…We’ve opened up in a number of cities in Florida and in Texas, obviously in the Bay Area. We’ll continue to scale, I think, very rapidly with more than 10% a week in terms of miles driven…

…We continue to grow the Tesla Robotaxi fleet and have expanded to a total of seven markets in the U.S. We expect the ramp of the fleet to accelerate throughout the year, along with expansion into new U.S. markets…

…In terms of safety, the program has had an impeccable safety record. We have driven more than 380,000 miles of unsupervised Robotaxi, now across six cities in two different states. We have had zero notable incidents. Any reports have been of other actors impacting us when we were stationary…

…Historically, the so-called experts have always claimed that you need LiDARs, radars, HD maps, and the entire kitchen sink to drive safely. Here we show that such is not true. You can have safe, comfortable, and affordable autonomy with just cameras. This record should be a huge validation of Tesla’s entire AI approach…

…Since the beginning of this year, we have grown at double-digit growth rates to the number of unsupervised miles that the fleet drives every week…

…For expanding to new cities, it has been relatively less effort on our front. We expect that the time to launch to a new city will continue to trend towards zero, towards an end where we operate in entire states as a whole instead of going city by city…

…[Question] Would you ever consider third-party distribution partnerships, such as with rideshare providers, to increase utilization? Is the plan for now to keep Robotaxi fully vertically integrated?

[Answer] We expect to be vertically integrated with Robotaxi as we are in the rest of our business. I don’t think we’re going to have any demand challenges with Robotaxi. The economics will be so compelling that I think we will really have a lot more desire to use the service than I think demand will outstrip our ability to service the demand…

…[Question] On Robotaxi. If I look at the launches, you’ve been adding cities, the number of units is still, it looks like based on media reports, sort of in the dozens as opposed to hundreds. Why not just sort of scale up Austin or one or two cities before adding cities, what do you need to sort of get that higher volume numbers in a major city? What sort is the roadblock to start adding more vehicles on the ground?

[Answer] The reason we have been expanding across different cities instead of just doubling down on a single city, is that we want to make sure that our stack is a very general one. It is a general one. We just want to both prove to ourselves and to other folks that it is working across a lot of different cities without too much effort per city…

…In terms of miles versus vehicles, since these vehicles operating the Robotaxi fleet drive basically continuously as opposed to human drivers who use vehicles for maybe a couple of hours a day or something like that. These vehicles are in mostly continuous operation, which means that even for a few vehicles, you can get a lot of miles out of them, that’s why we are tracking the amount of unsupervised miles.

Tesla’s management expects to announce the location for the TeraFab soon; management believes TeraFab is a necessary project for Tesla because the company will be constrained by the supply of AI chips, otherwise, for the scaling of Optimus; management has placed semiconductor manufacturing orders for the development fab in Austin; the development fab will have lithography mask production, logic, memory, packaging, and chip testing all under one roof, which is unique among fabs, and gives it a rapid iterative cycle to test high-risk ideas

The Terafab, we expect to announce a location soon, and provide more details about our plans in that regard…

…I do think Terafab is going to be an amazing initiative and a necessary one, and one without which we will be constrained in our ability to scale Optimus production, because we simply won’t have enough AI chips. It’s crucial to solve that, and we’ll have to solve memory, logic, and packaging in order to scale Optimus…

…We’ve placed equipment orders for our development fab in Austin. That development fab, I think, is pretty cool because it’s intended to have lithography mask production, and then logic, memory and packaging and chip testing all under one roof. You can have a very fast iterative cycle, and try out new chip designs very quickly and see if they work. I don’t think such a building exists anywhere on Earth. This is really going to be super helpful as we try some exciting, adventurous, high-risk, high-payoff bets on AI chips.

Tesla’s management is building a Megapod design that combines an AI computer with an x86 CPU; the Megapod comes in a box that can be placed anywhere, and thus can allow Tesla to scale AI compute with disaggregated electricity production, such as its Supercharger network; Tesla’s Supercharger network has 7GW of power, and growing

We’re also building out a Megapod design that has Tesla AI computer with x86. It’s a pairing, an x86 computer with a Tesla AI computer in a box, and it’s got this Digital Optimus in a box, and in a Megapod, kind of like the Megapack packaging. Where we put just a large number of AI full plus x86 combos in a giant box, essentially. These boxes can be placed anywhere in the country or outside the country. This allows us to scale AI compute using disaggregated electricity production. Because there’s lots of places all around the world, including at our Superchargers. I think we’ve got something like seven gigawatts of power at our Superchargers and growing. We can place Megapods at many of these Superchargers and have distributed power for AI.

Tesla’s management thinks the Cybercab, which is the autonomous vehicle for the company’s Robotaxi fleet, is a phenomenal product; the manufacturing targets for Cybercab are now roughly aligned with the growth of Robotaxi’s unsupervised miles; v15 of FSD will also work on Cybercab; the Cybercab is a new form factor, so Tesla needs to accumulate driving data that is specific to the Cybercab before the company can put a lot of them on the road

Regarding the Cybercab, first of all, it’s a phenomenal product. Anyone who rides in it instantly falls in love with the experience. We have aligned our manufacturing targets to roughly match the projected growth rate of the unsupervised miles. The same V15 models that power the Model Y and other platforms will also work on Cybercabs…

…Because it is a new vehicle chassis, we need to accumulate driving data that is specific to the Cybercab before we can put a lot of them on the road.

Tesla’s management is seeing Tesla’s suppliers making investments to support Optimus; Samsung and Micron are providing good support to Tesla

Our suppliers have been great, they have made and are making tremendous investments in support of Optimus and Robotaxi and whatnot. Samsung and TSMC, in particular, are building fabs, TSMC in Arizona and Samsung in Texas, and putting in tens of billions to build AI compute for Optimus and Robotaxi. Panasonic has also invested many billions in increasing battery cell production…

…The Samsung fab, that’s going to be pretty significantly dedicated to future projects. That’s a massive investment, multi-billion dollar. We’re seeing the same level of investment going to memory and also new specific items like metal injection molded parts, flexible printed circuits, and all sorts of nonlinear technologies that are more based for the robot as opposed to the traditional vehicle supply chain we’ve had…

…I’d actually also like to thank Micron for giving us memory allocation. They’ve got to make some very tough decisions on memory allocation. We really appreciate Micron making room for Tesla in the years to come and giving us actually a very significant allocation on reasonable terms given the pretty insane pricing of memory these days.

There are increasing levels of collaboration between Tesla and SpaceX, and management appears to be hinting at some form of combination; Starlink, in particular, is important for Tesla because Starlink ensures that Robotaxis have constant internet connectivity

As you can tell from all the many collaborations on so many fronts with SpaceX, there’s more and more overlap, especially with Terafab, that’s really going to be a gigantic project. Obviously, we can’t talk about combining companies and that kind of thing on an earnings call. It’s got to be done with the appropriate process…

…We continue to benefit from our relationship with SpaceX, they’ve been a great partner, we have numerous beneficial transactions with them. Earlier this year, we deepened our relationship through an investment and a framework agreement. This will allow us to continue to work with them on projects that Elon mentioned, like Terafab and Digital Optimus…

…You’ve got Grok in the car. Grok helping drive Digital Optimus. You also got Starlink being integrated into the Cybercab, Starlink will be integrated into all our car vehicles, at least for markets that Starlink is active. Because for a Robotaxi situation, you need to have coverage everywhere. There are many places, even in Silicon Valley, where the cellular coverage is terrible or sometimes non-existent, which is surprising for Silicon Valley. I know when I drive to work, those first 10, 15 minutes, I can’t actually do any calls because the cellular connectivity is so bad. We can’t have Robotaxis getting stuck in these Bermuda Triangles of lack of cellular connectivity. Starlink with its ability to do connectivity anywhere is actually quite important, so we don’t have Robotaxis missing in action. Obviously if people are sitting in the car, they go, “Blip,” and want to do high productivity stuff or want entertainment. With Starlink, you can watch 4K live sports in the car and with very low cost per gigabyte of data, as that’s really not feasible via the cellular system.

There are a shortage of truck drivers in the USA and a fully autonomous Tesla Semi will be important to address the shortage; management’s focus at the moment is on solving autonomy for Tesla’s high-volume vehicles before doing to for the Semi, which is a low-volume vehicle; management thinks autonomy for the Semi will come in 2027, and in time for the scaling up towards volume-production of the Semi

There is a really serious shortage of truckers. There just aren’t enough people around who want to drive trucks, which are crucial for transport throughout America. An autonomous Semi is actually going to be very important to address the shortage of truck drivers. Obviously will be great for improving safety, and making it easier on truck drivers to have a self-driving Semi is going to dramatically improve safety and comfort for truck drivers that use the Tesla Semi. Since the total number of units of the Tesla Semi is still low, and will continue to be a very small percentage, even by the end of this year, of our total vehicle fleet. It makes sense for us to focus our self-driving efforts on our high-volume vehicles, Model 3 and Y, and solving self-driving for, and really getting to the point where it’s generalized, unsupervised self-driving for those vehicles, and Cybercab. We expect to get self-driving working on the Tesla Semi probably around the end of this year or early next year. I just don’t want it to be a distraction on the march of nines of safety for self-driving on three, Y, and Cybercab. It’s taking a bit of a backseat for the next six months or so for autonomous Semi. It will definitely be working next year and in time for the scale-up to high production of the Tesla Semi.

The Tesla chip team is making fast progress on the AI5 chip; management is very excited about the design of the AI6 chip as they think it’s going to be the best edge computing chip in the world

The Tesla chip team is really doing great work and making incredibly fast progress on AI5. I’m very excited about the design of the Tesla AI6 chip. I think it’s going to be the best edge computing chip in the world.

Visa (NASDAQ: V)

When generative AI first emerged, Visa’s management quickly deployed the technology to engineering, client service, and model orchestration; with the emergence of agentic AI, management has used the technology to build end-to-end pipelines with human oversight and autonomous capability, leading to a reduction in the size of product development teams from 10 or more people to 2 or 4; teams in Visa that use agentic AI have showed 80% more code commits and a 65% increase in the pace of feature development; Visa now has more than 150 AI-powered applications; in the last 12 months, Visa has shipped more than 300 major product releases with the help of AI; management has eliminated certain roles in Visa to fund AI-related investments; Visa has an AI financial assistant, which allows banks to provide white-lablled AI insights to cardholders; Visa has the Vulnerability Agentic Harness to allow users to use frontier models to fix problems; Visa has increased the velocity of its consulting projects with the help of AI; management thinks Visa still has enormous opportunity ahead to deploy AI within the company

AI is changing how work gets done at Visa. With the dawn of the generative AI era, we moved quickly to deploy AI across our enterprise to assist us in areas like engineering, client service, and model orchestration. As we enter the era of agentic AI, we are going beyond AI assistance and harnessing the power of AI to execute work and tasks with our supervision. We have progressed materially in product development and engineering, deploying new tools, plugins, agent skills, and persistent sessions to create an end-to-end pipeline with human oversight and autonomous capability. As a result of the unlocks we can realize with this new tooling, we are reforming our product development teams that used to be 10 or more into smaller and more nimble agentic squads of two to four. The results are meaningful for those teams that are using the agentic tool chain, with 80% more code commits and 80% plus improvement in requirement definition from 30 days to five days, which has translated to 65% plus faster feature development…

…We now have more than 150 AI-powered applications, and over the last 12 months, we have shipped more than 300 major product releases…

…Today, we announced that we are eliminating roles, with the majority being in our technology and product teams, to ensure that we are continuing to position Visa for future growth…

…The investments that we have in front of us are enormous. I think what we’ve shown over the last couple of years is that we can drive efficiencies, we can take the savings that we generate from those efficiencies, and we can invest those savings against the strategic levers that we laid out at our strategy at our Investor Day and deliver accelerated performance as a result….

…We continue to develop new products, including our AI financial assistant, enabling banks to white label our AI-powered financial insights from their data and Visa’s network data for their cardholders right in the bank’s own app and website…

…We built the Visa Vulnerability Agentic Harness, an orchestration layer that allows us to use models like Mythos to find and fix issues at AI speed. It is available now on GitHub to our clients, along with a technical blueprint, remediation, and validation agents…

…For our advisory and other portfolio, in addition to the strong marketing services revenue growth, we’ve also increased the velocity of our consulting projects through the help of AI. Just this past quarter alone, for over 700 clients across 100-plus countries and territories, we delivered 1,200 consulting projects, which is more than we delivered for all of 2019…

…I think we still have enormous opportunity ahead of us there, and we’re just going to continue to lean in to using these tools to drive efficiency and effectiveness, shipping products better, faster, and ultimately, better serving our clients in the ecosystem.

Visa’s new agentic-AI-driven way of working has made big impacts on its initiatives for stablecoins and agentic commerce; management believes that agentic commerce will expand Visa’s addressable market; Visa recently enabled new seller capabilities and infrastructure for agentic commerce; Visa is partnering with OpenAI to enable secure payments in agentic commerce; Visa is partnering with Meta Platforms to enable consumers to transact in Facebook and Instagram seamlessly and securely with Visa tokens; management thinks the world of commerce is still only in the very early stages of a major adoption curve for agentic payments; management thinks the adoption curve of agentic payments will follow the same patterns as previous technological cycles; management thinks that trust will be the ultimate accelerant for adoption of agentic payments; Visa’s products within agentic commerce have all been about building trust for consumers

As a result of this new way of working, we are able to design, build, and ship products at an increased velocity with continuous innovation and improvement. I want to touch on two areas where we are deploying our new way of working with great impact, stablecoin and agentic commerce…

…If stablecoins are reshaping the back end of commerce, we see AI as transforming the front end. We believe agentic commerce will expand our addressable market and drive future growth for Visa. This quarter, we continued to work across the ecosystem by enabling new seller capabilities such as our Agent Score and Agent Directory and building infrastructure such as our Token Assurance Framework to ensure agent-initiated transactions are transparent and trusted…

…We are excited to be partnering with OpenAI to enable secure Visa payments within agentic commerce. Through the partnership, Visa will provide its global network, credentialing capabilities, and security infrastructure to support agentic commerce experiences, helping consumers and businesses interact and transact with confidence. As part of our partnership with Meta, Visa is enabling new ways to pay across Facebook and Instagram, powered by Visa Intelligent Commerce, allowing consumers to transact seamlessly and securely with Visa tokens…

…We believe that AI and agentic commerce will expand our addressable market. We believe we’re in the very early stages of what’s going to be a major adoption curve in payments. I think to get a sense of how this progresses from here, it’s instructive to look at other kind of major cycles that we’ve been through, whether it was e-commerce or mobile commerce, tokenization, tap-to-pay. These innovations and these kind of major forces, they followed a similar pattern, right? You have an early period where Visa and other players are establishing standards, we’re announcing, launching, and shipping new products, as you mentioned. You migrate into the early adoption period of the curve, which ultimately then leads to growing consumer momentum and ultimately broad scale. All of those kind of ones that I mentioned previously have gone through that, and they’ve achieved that broad scale. We don’t think agentic commerce will be any different…

…I think the ultimate thing that’s going to accelerate that adoption is going to be trust. Trust that the payment is secure, trust that the agent is authorized, trust that the transaction reflects the consumer’s actual intent, and then the protections exist if something goes wrong. If you look at the products that we’ve announced over the last several quarters, they’re all directly intended to address that trust and ensure that our users have trust in using Visa credentials to make agentic commerce transactions. That’s where we’re investing.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Alphabet, Amazon, Apple, ASML, Mastercard, Meta Platforms, Microsoft, Netflix, TSMC, and Visa. Holdings are subject to change at any time.

Mastercard (NYSE: MA) and Visa (NYSE: V) are two of the largest payments companies in the world. As a result, they have a great view on consumer spending that’s taking place. With both companies reporting their earnings results for the second quarter of 2026 earlier this week, the bottom line is that consumer spending remains strong in the USA and other parts of the world. Here’s what they are seeing.

*What’s shown in italics between the two horizontal lines below are quotes from Mastercard and Visa’s management teams that I picked up from their earnings conference calls.

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From Mastercard

1. Mastercard’s management sees consumers and businesses being healthy and continuing to spend and they are supported by positive job growth, low unemployment, and purchasing power; management is monitoring geopolitical risks; management is seeing economies around the world adapting to changing conditions; the fundamentals of consumer and business spending and travel remain healthy

Consumers and businesses are healthy and continue to spend, supported by positive job growth, low unemployment, and real purchasing power in many major economies. At the same time, we continue to monitor geopolitical uncertainty and its potential economic impacts…

…Around the world, economies are adapting to changing conditions, with consumers and businesses continuing to demonstrate resilience…

…Overall, the underlying fundamentals of consumer and business spending and travel remain healthy.

2. Worldwide GDV (gross dollar volume) was up 8% year-on-year in 2026 Q2 in constant-currency basis; cross-border volume was up 12% globally in constant-currency, driven by both travel and non-travel cross-border spending (cross-border volume growth was 13% in 2026 Q1); switched transactions was up 9% year-on-year in 2026 Q2; card growth was 5% in 2026 Q2, with Mastercard ending the quarter with 3.7 billion cards in circulation (there were 3.7 billion cards in 2026 Q1, and year-on-year growth was 5% then); domestic assessments were up 10%, cross-border assessments were up 20% and transaction processing assessments were up 12%

I’ll speak to the growth rates of our key volume drivers for the second quarter on a local currency basis. Worldwide gross dollar volume, or GDV, increased by 8% year-over-year. In the U.S., GDV increased by 6%, with credit growth of 10% and debit growth of 1%. As a reminder, the Capital One debit portfolio migration was basically complete in Q1. Excluding the impacts from that migration, our U.S. debit GDV growth would have been 8%. Outside of the U.S., GDV increased 9%, with credit growth of 9% and debit growth of 10%. Cross-border volume increased 12% globally for the quarter, reflecting continued growth in both travel and non-travel related cross-border spending…

…Switched transactions grew 9% year-over-year in Q2…

…Card growth was 5%. Globally, there are 3.7 billion Mastercard and Maestro-branded cards issued…

…All growth rates are described on a currency-neutral basis unless otherwise noted. Looking quickly at each key metric. Domestic assessments were up 10%, while worldwide GDV grew 8%. The two PPT difference is primarily driven by pricing. Cross-border assessments increased 20%, while cross-border volumes increased 12%. The eight PPT difference is driven primarily by pricing in international markets and mix. Transaction processing assessments were up 12%, while switched transactions grew 9%. The three PPT difference is primarily due to favorable mix and pricing, partially offset by lower revenue from FX volatility, and other network assessments were $326 million this quarter.

3. In 2026 Q2, Mastercard’s operating metrics had good year-on-year growth and were stable sequentially; in July 2026 so far, Mastercard’s operating metrics continue to be strong with worldwide switched volume growth of 9% (6% in the USA, and 11% outside of the USA), switched transactions growth of 9%, and cross-border volume growth of 11%; card-not-present ex-travel’s sequential decline in growth rate was driven by timing; Mastercard’s US business had some benefit from the World Cup in 2026 Q2, but it was hard to quantify; Mastercard’s US business continues to have healthy consumer and business spending trends; management is seeing strong spending trends in both mass and affluent consumers in the US and around the world, although affluent consumers have higher growth in spending

Let me comment on the operating metric trends for Q2 and the first four weeks of July. Switched metrics were generally in line with Q1, and underlying spend remained stable. Of note, excluding Capital One debit, on a like-for-like basis, U.S. switched volume growth was 10%, or two PPT higher sequentially. This increase was driven by higher spend on fuel and overall strong consumer and business spending.

Moving to our cross-border metrics, our overall cross-border volume growth remained healthy at 12% in the second quarter. Cross-border card-not-present ex-travel remained strong at 20%, benefiting from increased card-not-present spend from Venezuela and the timing of large retail promotional events. While cross-border travel was down sequentially, relative to the April metrics we discussed on our last earnings call, we saw improved growth in the quarter due to lower impacts from the developments in the Middle East and timing of holidays.

As we look at the first four weeks of July, our metrics remain relatively stable and strong. Looking specifically at card-not-present ex travel, let’s focus on July compared to June. The sequential decline is primarily driven by timing, including the large retail promotional events that happened in June this year as compared to July last year, and by mix of days…

…The strong underlying consumer and business spending, which we’re seeing in the U.S. There has been a tailwind which has come on account of higher fuel prices, so let’s recognize that. You probably have some impact coming through from the World Cup as well, as it relates to the second quarter in particular. Hard to really quantify what that is just because we can’t really identify exactly what that is. I would tell you the underlying consumer and business spending trends continue to hold up well in the U.S. To your point, if I look at it’s broad-based. We see it across credit and debit. We see it across consumer and commercial…

…I would say they’re holding up well, both across mass and affluent. Certainly in the U.S., but across the world as well. We try and track the best we can as it relates to what we’re seeing in spending patterns based on the product codes that are out in the market, which serve the different categories of customers. When we look at that, we’re seeing generally strong trends, across both mass and affluent. What you do tend to see is higher growth in the affluent side of spending. That’s kind of not a new phenomenon. That’s been with us for some time now.

From Visa

1. US payments volume growth was good at 10% in 2026 Q2 (FY2026 Q3), a growth rate not seen since FY2019; there was good growth in both US credit and debit volumes; growth across consumer spend bands improved sequentially, with the highest spend band continuing to grow the fastest; both discretionary and non-discretionary spend remained strong; management did not see a deterioration in spend in the lower bands

U.S. payment volume grew 10% year-over-year, up about two points from Q2, a growth rate not seen since fiscal 2019, excluding the post-COVID recovery, with both card present and card not present growth accelerating strongly. U.S. payments volume growth was the result of several factors, including higher tax refunds, the cost of fuel, retail, including the timing of promotional shopping events, strong Visa Direct growth, and FIFA-related spend. U.S. credit rose 11% year-over-year, up more than a point from Q2. Debit accelerated by more than two points from Q2 to grow 9% year-over-year. Growth across consumer spend band saw incremental improvement from Q2, with the highest spend band continuing to grow the fastest. Across our volume, both discretionary and non-discretionary spend remained strong. We do not see signs of the lower spend consumer weakening in our volumes.

2. Visa’s cross-border volume growth remained strong in 2026 Q2 (FY2026 Q3) at 12%, up from 11% in 2026 Q1

Q3 total cross-border volume grew 12% year-over-year, up more than a point from Q2. Cross-border e-commerce volume was up 16%, three points above Q2, primarily driven by retail, including the timing of promotional shopping events. Travel-related cross-border volume was up 10%, consistent with Q2. While the conflict continued to be an offsetting factor, commercial and U.S. inbound continued to improve, and in June, the FIFA World Cup boosted inbound North America and Latin America volume.

3. Payments volume on Visa’s network continues to grow in July 2026, with US payments volume up 9%, cross-border volume up 14%, e-commerce volume up 18%, and processed transactions up 9%

Now, let’s look at drivers through July 21st, with volume growth in constant dollars. U.S. payments volume was up 9%, with both credit and debit up 9% year-over-year. A step down from June, primarily due to retail, including the timing of promotional shopping events, a lack of a day’s mix benefit that helped June, and the change in the cost of fuel. For cross-border volume excluding transactions within Europe, total volume grew 14% year-over-year, with e-commerce up 18% and travel up 12%. Processed transactions grew 9% year-over-year.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I currently have a vested interest in Mastercard and Visa. Holdings are subject to change at any time.

JPMorgan Chase (NYSE: JPM) is currently the largest bank in the USA by total assets. Because of this status, it is naturally able to feel the pulse of the country’s economy. The bank’s latest earnings conference call – for the second quarter of 2026 – was held earlier this week and contained useful insights on the state of American consumers and businesses. The bottom-line is this: the US economy remains resilient, but the risks to the global and US economy are shifting, with the consequences unknown.

What’s shown between the two horizontal lines below are quotes from JPMorgan’s management team that I picked up from the call.

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1. The US economy remained resilient in 2026 Q2, as businesses continued to invest and hire; the economy’s resilience is driven partly by AI-related capital investments; the risks to the US and global economy are shifting, and it’s anybody’s guess as to how the risks will eventually play out; consumers and small businesses in the USA continue to show resilience, with strong employment driving spending; management thinks it’s really hard to untangle AI-related and non-AI-related capital investments; consumer spending remains robust across income segments; consumer delinquencies are lower than expected; management does not see a K-shaped economy in the USA; management thinks the US economy is in a slightly higher than normal inflationary environment

The U.S. economy has demonstrated notable resiliency this year, with stronger business investment and hiring. This strength is being supported by several tailwinds, including AI-driven capital investment, fiscal stimulus and the benefits of more efficient regulation. However, several risks are shifting below the surface like tectonic plates, including geopolitical tensions and wars, sticky inflation, large global fiscal deficits and elevated asset prices. We cannot predict how these forces will ultimately play out. They may remain manageable, but they could also cause meaningful disruptions when they shift or collide…

…Consumers and small businesses continue to show resilience despite elevated gas prices and inflation with higher tax refunds and a solid labor market contributing to strong spend growth…

…We do see some decent kind of CapEx and associated loan growth across the franchise. And at least on the surface, some of that does not appear to be AI related. However, I was a little reluctant to draw that conclusion too strongly just because the AI theme has started to proliferate in so many different parts of the economy, right? It’s like the comments about data centers wind up creating a lot of demand for like plumbers and electricians, right? So you wind up seeing it in sort of slightly nonobvious places. And so any given bit of loan growth or CapEx that you see that doesn’t superficially look like it’s AI-related might still be…

…Spend is kind of fine robust and across income segments. It seems like a bit of a tailwind there from tax refunds. Delinquencies are a little lower than we expected. And again, that’s a better performance. You see pretty much across the board by kind of FICO score. There’s some of that economic heterogeneity data came out from the Fed recently, which also I think doesn’t give a lot of support to the K-shape narrative essentially…

…From our perspective, through all the various dimensions, there’s not like that much there in terms to support the K-shape narrative…

…We are in a slightly higher than normal inflationary environment.

2. Net charge-offs for the whole bank (effectively bad loans that JPMorgan can’t recover) was flat at US$2.4 billion compared to a year ago (charge-offs was $2.3 billion in 2026 Q1)

Credit costs were $2.5 billion, with net charge-offs of $2.4 billion and a net reserve build of $149 million.

3. JPMorgan’s investment banking fees were up 27% in 2026 Q2 from a year ago because of strong performance in equity underwriting and mergers & acquisitions (M&A); management still sees a robust pipeline for capital markets activities; management thinks there’s some pull-forward in investment banking fees; management thinks the capital markets environment is close to as good as it gets, but they do not know how long it will last

IB fees were up 30% year-on-year, reflecting double-digit growth across all products with particularly strong performance in equity underwriting. While this quarter’s performance was supported by both some large ECM deals and the acceleration of the closure of some M&A transactions, the pipeline remains quite robust. And the current activity levels seem to be encouraging more activity. As a result, while conversion will obviously be dependent on market conditions, we expect activity levels to remain healthy…

…To what extent would this quarter’s results like particularly elevated as a result of some of the large high-profile IPOs and other capital raisings in particular. And I think clearly, there was some pull forward. And clearly, the large deals contributed meaningfully to this quarter results…

…It’s getting close to as good as it gets. We just don’t know how long it’s going to last… 

…I just think we’re in a very healthy active exuberant market with very high prices and very high volumes, and we benefit from that. We just don’t know how long it will continue. Could it get a lot better than this? It can get better. But how much better? I don’t know.

4. Management now expects credit card net charge-offs for 2026 to be 3.2% (previous expectation was 3.4%; was around 3.3% in 2025)

We now expect Card net charge-off rate to be approximately 3.2% and reflecting better-than-expected consumer credit performance.

5. Management sees the market as being extremely risk-on

The market is clearly extremely risk-on and we’re kind of takers of that. And we’re trying to strike the right balance between supporting all our clients and being appropriately cautious in an environment that has some complicated dynamics in it.

6. Management is seeing some credit deals for data center development that they think are questionable

For whatever reason, I think the data center underwriting space is one that resonates with me as a kind of bellwether for what people are doing. And we passed on some deals that — obviously, because when you look at the data center stuff, the key question is like what happens with power supply, what happens with tenants, what happens with — it’s a well-discussed thing. And we have a pretty precise framework to govern what we’re willing to do and what we’re not willing to do in that space across those types of risks. And we saw some deals come through where we were just like, “Yes, we’re not doing that.”

So it’s normal, I guess, it’s competitive, and people are eager to be involved. And in some cases, there’s ironically some element of like relationship lending that’s happening through the data center space, when it’s kind of a start-up entity that’s building the data center. So that’s part of the story a little bit, too. But I don’t think we’re screaming from the rooftops that underwriting is — underwriting standards have collapsed, but I think you see normal pressures, and we’re navigating those in the way that we do, which is we do flex in some moments for particularly important clients in situations where we feel like it’s the right thing to do. But in general, we try to be the one that holds the line and make sure that we’re guided by our own risk appetite and a kind of appropriately skeptical view of the environment.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I don’t have a vested interest in any company mentioned. Holdings are subject to change at any time.

For many years, I have shied away from investing in stocks whose underlying businesses are closely linked with commodities. I find it really difficult to assess their business fortunes over a multi-year period because their business results are closely entwined with the prices of the relevant commodities, and I do not have any ability to predict these prices. A recent review of a company Jeremy and I looked at two years ago illustrates this difficulty really well.

Back in October 2024, Jeremy and I came across a company named Beaver Coal which owns land that it leases out to third parties for the extraction of timber and coal in exchange for royalty payments. At the time, around 70% of Beaver Coal’s revenue came from royalties linked to the extraction of coal, and the company had a high double-digit dividend yield that looked attractive on the surface. Here’s an edited reproduction of our conversation on Whatsapp about the company at the time:

[1:36 am, 25/10/2024] Jeremy :

Beaver Coal sounds interesting. At 12% dividend yield – 9% after tax, quite a decent return.

[5:12 am, 25/10/2024] Ser Jing:

I don’t find it interesting enough. The company owns land that it leases out to 3rd parties to extract timber and coal in exchange for royalty payments. About 70% of Beaver’s revenue comes from coal-extraction royalty, so it’s still very dependent on coal prices. Very similar to this company called Natural Resource Partners.

[10:59 am, 25/10/2024] Jeremy :

Actually quite interesting as the income likely gonna be quite stable barring some small fluctuations from coal prices. But yeah I guess the risk is if coal prices collapse but likely won’t anytime soon. The other problem is the reserve running out in 28 years so the share price will likely degrade over time as the cash cow dries up.

[11:03 am, 25/10/2024] Ser Jing:

I’ve yet to plot a chart of Beaver’s revenue against coal prices, so can’t tell how stable the revenue actually is. I did look at such data for Natural Resource Partners, and it’s not stable – still cyclical

[11:17 am, 25/10/2024] Jeremy :

Ya I cant see beaver’s historical revenue on TIKR. Have to look through the annual reports to see the past financial data.

[11:27 am, 25/10/2024] Ser Jing:

Haha yea, some of these obscure stocks have data that’s hard to find

[11:39 am, 25/10/2024] Ser Jing:

Beaver’s revenue and net profit fell in 2023

https://www.otcmarkets.com/stock/BVERS/financials

Natural Resource Partners had the same thing, and management said it was because of a decline in coal prices. So seems like Beaver Coal is in the same situation, where swings in coal prices will affect its revenue and net income. 

Coal prices have continued falling in 2024, and Natural Resource Partner’s revenue and net income have declined double digits. So Beaver’s trailing numbers are not very useful.

[12:03 pm, 25/10/2024] Jeremy :

Thanks. I see. Yeah I guess these swings in prices can have quite a big impact

[12:24 pm, 25/10/2024] Ser Jing:

I keep waiting for a commodity-related company whose business is not affected by commodity price swings, but yet to find one. Even the royalty-based ones can’t cut the mustard.

[1:00 pm, 25/10/2024] Jeremy :

Haha but guess that’s the nature of it. That’s why trade at such nice valuations. When you add the low valuations plus the swings, I guess you still can make a decent return.

[1:14 pm, 25/10/2024] Ser Jing:

Haha still find it hard to make such a call, coz I can’t come to a view on whether the business can be larger in 5-10 years.

[1:20 pm, 25/10/2024] Jeremy :

Ah I see.. I see it as a diminishing business but the cash taken out of it will more than make up for the diminishing value of the asset. 

A bit like real estate with a 28 year lease. Haha. You can just keep collecting rent, which will more than offset the cost of purchasing the property, whose value will degrade to 0 at the end of 28 years. But you must buy cheap haha.

Can probably do an IRR calculation based on the cash flow. The nice thing about Beaver is the capital is distributed. If it’s reinvested into lousy projects, then it’s hard to gauge.

[3:59 pm, 25/10/2024] Ser Jing:

What makes it difficult here is that Beaver’s net income can fluctuate wildly. The company distributes all earnings as dividends, so we can take the dividend per share to be equivalent to its net income per share.

Its trailing dividend yield is 12%, based on a trailing dividend of $400 per share. But this is based on 2023 financials. In 2017-2019, its dividend was around $225 per share. Coal prices in 2024 are already lower than in 2023, so the forward dividend yield is lower than $400 per share. If coal prices fall further from 2024 levels, then the dividend is likely going to be even lower. 

This shows coking coal futures prices (coking coal is metallurgical coal) over the last 10 years. 2024’s prices for coking coal futures are similar to the period in 2017-2019. 2015-2016 prices for coking coal futures are about 50% lower than today’s level. 

https://www.investing.com/commodities/coking-coal-futures-streaming-chart

I think it’s just very hard to make an accurate IRR calculation if we have no view on where coal prices go.

[4:04 pm, 25/10/2024] Jeremy :

Ah I see.. yeah very wild profit fluctuations. 2023 earnings don’t look sustainable. Maybe probably better to take the last 10 year average earnings as a gauge.

[4:09 pm, 25/10/2024] Ser Jing:

Yea, 2021 and 2022 were bumper years for Beaver Coal because coking coal prices rocketed. In 2023, coking coal prices started coming down, then continued falling in 2024.

[4:14 pm, 25/10/2024] Ser Jing: 

Coking coal futures were around $1000-$1500 for 2017-2020. In 2021 and 2022, the futures reached a high of nearly $4000

[4:19 pm, 25/10/2024] Jeremy :

Big swing in the price. Seems like 2023 dividends was an anomaly. Probably closer to to 2017-2019 dividend of $225.

[4:19 pm, 25/10/2024] Jeremy :

In that case the valuation is still too steep. Especially after tax.

[4:24 pm, 25/10/2024] Ser Jing:

That’s my guess for now – but we’ll know in time when Beaver reports 2024 financials! Still worth keeping an eye on the company.

[4:25 pm, 25/10/2024] Ser Jing:

Yea, hence my earlier statement that I’m still waiting for a commodity-related company whose business is not affected by commodity price swings. Haha

I recently reviewed Beaver Coal after it reported its financials for 2025. It turned out that the company’s revenue, net income, and dividend had fallen materially since 2023, as shown in Table 1 below. 

The culprit for the big declines was lower coal prices. According to the Coal 2025 report from the IEA (International Energy Agency): 

“Coal prices averaging lower in 2025 than in previous years…

…After unprecedented prices in 2021 and 2022 amid the energy crisis, coal prices continued to be higher than the pre-Covid levels throughout 2023 and 2024…

…Met coal prices have followed a distinct trajectory since mid-2023, with significantly higher volatility compared with high-CV thermal coal. Prices exceeded USD 350/t in the third quarter of 2023, driven by rising demand from China and India. Market tightness eased in the second quarter of 2024, supported by increased exports from Mongolia to China. Since then, prices continued to decline, averaging USD 186/t in the first eight months of 2025.”

Beaver Coal’s arc in 2023-2025 is yet another important reminder to me of the significance a commodity’s price movement has on the business fortunes of a company whose revenues are linked to said commodity. This significance in turn makes it really difficult for me to assess the long-term future of a commodity-linked company when I have no ability to predict the price of the commodity in question. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I don’t have a vested interest in any company mentioned. Holdings are subject to change at any time.

Last year, I shared my notes on investing in thrift conversions from investing legend Peter Lynch’s lesser-known book, Beating The Street. I credited Beating The Street as an important part of my education on thrifts.

There’s actually another book, from another investing legend, that also taught me about thrift conversions: Seth Klarman’s Margin of Safety. Klarman is the founder of Baupost Group, an investment firm that has generated a mid-teens annual return over more than four decades.  

Because Margin of Safety is a rare book, and because I’m fascinated with thrift conversions from an investing angle, I thought it would be useful to share my notes from Margin of Safety. 

What’s shown between the two horizontal lines below, besides the section-headers, are direct quotes from Klarman’s book. 

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Thrift IPOs have attractive economics for investors

A thrift institution with a net worth of $10 million might issue one million shares of stock at $10 per share. Again ignoring costs of the offering, the proceeds of $10 million are added to the institution’s preexisting net worth, resulting in pro forma shareholders’ equity of $20 million. Since the one million shares sold on the IPO are the only shares outstanding, pro forma net worth is $20 per share. The preexisting net worth of the institution joins the investors’ own funds, resulting immediately in a net worth per share greater than the investors’ own contribution…

…So long as the thrift has positive business value before the conversion, the arithmetic of a thrift conversion is highly favorable to investors. Unlike any other type of initial public offering, in a thrift conversion there are no prior shareholders; all of the shares in the institution that will be outstanding after the offering are issued and sold on the conversion. The conversion proceeds are added to the preexisting capital of the institution, which is indirectly handed to the new shareholders without cost to them. In a real sense, investors in a thrift conversion are buying their own money and getting the preexisting capital in the thrift for free.

Insiders in a thrift participate in the IPO at the exact same terms as public shareholders

Unlike many IPOs, in which insiders who bought at very low prices sell some of their shares at the time of the offering, in a thrift conversion insiders virtually always buy shares alongside the public and at the same price.

Thrifts that stray far from traditional mortgage lending are risky

Thrifts incurring high risks, such as expanding into exotic areas of lending or venturing far from home, should simply be avoided as unanalyzable. Thrifts speculating in newfangled instruments such as junk bonds or complex mortgage securities (those based on interest or principal only, for example) should be shunned for the same reason…

…This does not mean that investors could not profit from investing in risky institutions but rather that the potential return is not usually justified by the risk and uncertainty. Owing to the high degree of financial leverage involved in thrifts, there can be no margin of safety from investing in the shares of thinly capitalized financial institutions that own esoteric or risky assets.

The book value of a thrift is a low estimate of what an acquirer would pay

In evaluating such thrifts, book value is usually a low estimate of private-market value; most thrift takeovers occur at a premium to book value.

An example of a thrift conversion that looked attractive to Klarman

In June 1990 Jamaica Savings Bank converted from mutual to stock ownership through a newly formed holding company, JSB Financial (JSB)…

…At the time of the JSB conversion, the United States had experienced a nationwide real estate downturn. Estimates of the total cost of the thrift industry bailout were reaching as high as $500 billion…

…Organized in 1866 in New York, it had on December 31,1989, total assets of $1.5 billion and retained earnings of $197.1 million, a ratio of tangible capital to total assets of 13.5 percent prior to conversion. This was among the highest ratios in the country. Two-thirds of the assets of JSB were held in U.S. Treasury and other federal agencies’ securities or cash equivalents, while only 30 percent was in loans, virtually all residential mortgages…

…The economics of a thrift conversion are such that even with JSB’s obvious merits, the shares were offered to investors at only 47 percent of book value and a pro forma price/earnings multiple of ten times…

…One interesting way to evaluate the risk of investing in JSB was to consider that half the proceeds from the stock conversion, or $80 million, were to be retained at the holding company. This cash represented excess capital that could be used to repurchase JSB shares subsequent to the public offering. If the cash had been used in its entirety to repurchase JSB shares at two-thirds of book value (a 40 percent premium to the Ira price), the company could have repurchased one-third of the shares of JSB that had just been issued. While most shareholders might have chosen not to sell at that price, the effect of such a program would almost certainly have been to raise the price of JSB shares. In fact, the pro forma book value per share, adjusted to reflect this hypothetical repurchase, would have increased from $21.12 to $25.00, an 18 percent increase. This illustrates the opportunity to investors of owning a thrift that is financially capable of and willing (as JSB indicated it was) to repurchase its shares cheaply. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I don’t have a vested interest in any company mentioned. Holdings are subject to change at any time.

Earlier this month, I published Even More Of The Latest Thoughts From American Technology Companies On AI (2026 Q1). In it, I shared commentary in earnings conference calls for the first quarter of 2026, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

A few more technology companies I’m watching hosted earnings conference calls for 2025’s fourth quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here
• 2024 Q4 – here, here, and here
• 2025 Q1 – here and here
• 2025 Q2 – here and here
• 2025 Q3 – here, here, and here
• 2025 Q4 – here, here, here, and here

With that, here are the latest commentary, in no particular order:

Adobe (NASDAQ: ADBE)

Adobe’s management sees AI changing customer behaviour at unprecedented speed and this means Adobe needs to change its strategy; management now thinks the immediate opportunity for Adobe is to accelerate new user acquisition and lifetime value through freemium offerings; Acrobat and Express MAU (monthly active users) has increased from 700 million a year ago to 850 million in 2026 Q1 (FY2026 Q2); Business Professional and Consumer traffic on adobe.com is up 35% year-on-year in 2026 Q1 (FY2026 Q2) and management wants to serve this traffic without immediate paywalls; management has increased creative freemium MAU from 50 million a year ago to 90 million in 2026 Q1 (FY2026 Q2); management wants to expand the Firefly freemium experience to acquire users; the shift to freemium will have a negative short-term impact on Adobe’s ARR but will build the foundation for long-term growth; Firefly freemium users who convert to paid users display early signs of significant credit consumption; Adobe’s products feature highly in intent-based search, so management thinks it’s better for the company’s long-term growth to allow users to experience Adobe for free first; management has plenty of prior experience converting freemium users to paid users

Relative even to the beginning of fiscal 2026, AI is accelerating customer behavior at an unprecedented speed, and we need to evolve our strategy and execution to address these changing expectations. Much like our developers have embraced and expanded the AI coding market, there’s a transformation underway for how consumers are discovering, experiencing, onboarding and purchasing products across all categories, including creativity, productivity, gaming and entertainment. As it relates to creativity and productivity, there is an unprecedented demand across additional surfaces for the combination of content consumption and content creation. Conversational interfaces and agents now orchestrate across tools to achieve outcomes faster. The proliferation of media generation models is reshaping and democratizing content workflows from ideation through delivery. AI-first applications that will serve broader audiences need to provide free, intuitive onboarding that drive usage and monetization through paywalls. Big picture, the immediate opportunity for Adobe is to accelerate new user acquisition and lifetime value through a freemium offering.

As it relates to Business Professionals and Consumers, we have dramatically increased Acrobat and Express MAU from greater than 700 million to greater than 850 million year-over-year. The opportunity is to serve billions of Business Professionals and Consumers through a comprehensive freemium funnel, building on the success of the Adobe Reader model…

…Business Professional and Consumer traffic on adobe.com seeking Adobe capabilities is growing 35% year-over-year. We believe this traffic is better served through a customized, friction-free onboarding experience without immediate pay walls and will result in greater customer acquisition and deeper engagement over time…

…For next-generation creators, the opportunity is to deliver an AI production studio across web and mobile that seamlessly integrates with the power and precision capabilities of Creative Cloud. We have increased our creative freemium MAU from 50 million to 90 million year-over-year. The opportunity is to attract hundreds of millions of additional creators through a freemium funnel based on the early success of Firefly…

…The new personalized journeys for creators drove approximately 50% increase in Firefly ARR quarter-over-quarter through Firefly apps and credit packs. Based on this early success, we are confident that we should expand the Firefly freemium experience to acquire and delight the next generation of creatives…

…While we continue to attract strong traffic to adobe.com, which grew over 40% year-over-year, our traditional direct-to-pay journeys may not always fulfill visitor intent as a growing number of new users are first looking to quickly complete their intended task as they begin their relationship with Adobe. Given products like Adobe Firefly, Express and Acrobat AI Assistant have friction-free onboarding and significant adoption, we can now rebalance our journeys to better serve this new generation of users rather than send them predominantly to direct-to-pay journeys. This shift will come at the cost of short-term ARR, but will accelerate user acquisition in MAU, while building the foundation for long-term growth by removing friction from user onboarding, enabling deeper user engagement and driving stronger lifetime value…

…Firefly freemium users who convert to our paid plans are highly engaged with early indications of significant credit consumption…

…What we see is a shift in — or an emergence in terms of LLM usage, and that is driving a lot more intent-based search. So what is an intent-based search? Someone might type into a search engine, summarize this PDF, right? And what we do is we are using SEO and SEM and some of Anil’s Semrush capabilities now to make sure that we’re ranking high when someone types in something like summarize PDF. When the user clicks on our link, we take them instead of taking them to adobe.com and talking to them about Acrobat, we’re now taking them directly into Acrobat web with a single call to action, which is upload your PDF and then we summarize it for them. And when we summarize it for them, we then introduce them to this idea that they can use AI Assistant to even to have — and ask some questions. And we use this process to let them build habit before we start giving them paywalls. So that’s an evolution. If we just took that traffic direct to a paid flow to buy Acrobat and download Acrobat, it wouldn’t produce as much opportunity long term for Adobe. Similarly, in Firefly, we see things like a growth in terms like generating pixel art for social media posts. Again, we have ranked really high in SEO SEM, then we take them directly into Firefly so they can upload an image of themselves, create this pixelated version, maybe introduce them to this idea that you can convert that to video, but it’s a very different flow. And that’s where the world is going…

…We found in that process, things like Edit PDF or Redact PDF inquiries are a great opportunity to take a user that’s built up a habit, using these products and convert them to a long-term paid customer. a lot of that same learning that infrastructure that we have in place for Acrobat that we’ve developed over the years, that same infrastructure applies to everything we’re doing, as you said, with Express, with Firefly, with Acrobat AI Assistant. And the foundation of how we’re taking that 90 million of creative freemium MAU and converting that is identical.

Adobe’s management is seeing massive growth in content creation for marketing use cases; management is seeing an enormous opportunity in marketing use cases; management is seeing enterprises increasingly bringing marketing capabilities in-house because of AI, and they are looking to Adobe for headless and agentic capabilities with pricing models that address outcomes as well as AI usage; Adobe’s AI-first ARR (annual recurring revenue) for Customer Experience Orchestration grew 4x year-on-year in 2026 Q1 (FY2026 Q2); the acquisition of Semrush has helped to improve Adobe’s Customer Experience Orchestration offering by allowing Adobe to offer a brand visibility product; Adobe’s GenStudio ARR grew 25% year-on-year in 2026 Q1 (FY2026 Q2); Semrush added $480 million of ARR to Adobe; management thinks the upcoming brand-visibility product will become a must-have for chief marketing officers (CMOs)

Content creation designed specifically for marketing use cases is exploding. New AI coworkers and agents offer organizations the ability to deliver automation and outcomes powered by context, data, MCPs and skills. These address the dual needs of enterprises to expand consumer centricity and cost savings in the era of AI. Business models are expanding to include consumption and outcome-based pricing along with subscriptions. The total marketing opportunity across people, software, agency and channel spend is enormous.

AI is changing enterprise behaviors as they’re increasingly bringing more marketing capabilities in-house through their adoption of software platforms and the creation of custom models that uniquely capture their brand intelligence. IT organizations are looking to Adobe to accelerate their provisioning, deployment and customization to serve their consumers through the availability of headless and agentic capabilities with pricing models that address outcomes as well as AI usage. Customer Experience Orchestration, AI-first ARR grew 4x year-over-year, reflecting how Adobe is the leader in both the traditional marketing category and the emerging Customer Experience Orchestration category. The introduction of Adobe CX Enterprise and CX Enterprise Coworker at Adobe Summit expands the vision and delivery of our category-defining CXO solutions.

The successful acquisition of Semrush unifies our search engine optimization, generative engine optimization and AEM solutions to further extend our CXO offering. We will deliver this integrated offering that addresses brand visibility at the Cannes Lions Festival of Creativity later this month. This combination of creativity and marketing uniquely differentiates Adobe. No other company brings together what creators and marketers can do across our applications and delivery platforms.

Adobe GenStudio ARR grew over 25% year-over-year, reflecting enterprise demand for an end-to-end solution that spans workflow and planning, creation and production, asset management, activation and delivery and reporting and insights…

…Semrush added $480 million ARR to our book of business and expands our ability to serve marketers of every scale. We are rapidly integrating Semrush into Adobe, uniting Semrush’s discoverability intelligence with Adobe’s agentic web apps. We look forward to unveiling a comprehensive brand visibility solution, combining Semrush with Adobe at the Cannes Lions Festival of Creativity later this month…

…Every brand across the world wants to have the right placement and regardless of which LLM consumers are using. They want to have the right message, and they want to have their messages show up on LLMs, on social media and all the other new platforms that consumers are going to. And we believe that the best way to do that is to take their content that they have already within their content management system like Adobe Experience Manager, and make sure it gets out there, whether it’s the bots and the agents that the LLMs have or third-party sites, which have credibility with these LLMs, making sure that all the brand visibility shows up in the right places. That requires the integration of what Semrush brings, which is the outside in knowledge of how — what is actually being prompted for what’s being searched for and that the database that they have of all of the prompts and search queries and so on and combine it with the inside-out intelligence that we have with all the content, marrying those two provides us the opportunity to bring the most comprehensive brand visibility solution in the market, and that’s what we’re introducing it Cannes later this month. So we are super excited about that, and we believe that this is going to be a must-have for every CMO.

Adobe’s AI-first ARR (annual recurring revenue) tripled year-on-year in 2026 Q1 (FY2026 Q2), after also tripling in 2025 Q4 (FY2026 Q1); Adobe’s AI-first ARR is now $500 million

Adobe’s AI innovation has driven an impressive 3x year-over-year increase in AI first ARR to greater than $500 million.

Under the Business Professionals and Consumers group, Adobe’s management recently introduced the Adobe Productivity Agent, which shifts Acrobat from a static document tool to an interactive experience; users can now share branded PDF Spaces with customisable AI assistants tailored to specific audiences; Acrobat AI Assistant paid MAU was up 150% year-on-year in 2026 Q1 (FY2026 Q2)

This quarter, we introduced the Adobe Productivity Agent, shifting Acrobat from a static document tool to an interactive experience. The Productivity Agent is an AI experience built into Acrobat that draws on Adobe Acrobat’s document intelligence and Adobe Express’ AI-first creation capabilities to help business professionals understand, create and share information. It can turn documents into rich outputs like presentations, podcasts and social content, support conversational PDF editing and power the new sharing capabilities in PDF Spaces. Customers get the agent through Acrobat AI plans.

Users can also now share branded PDF Spaces with customizable AI Assistants tailored to a specific audience, whether for sales prospecting, content marketing or research delivery. Early adopters of PDF Spaces, including Vice Media, Kid Cudi, Jessica Yellin and Mindy Weiss are using PDF spaces to move audiences from passive reading to interactive engagement…

…Acrobat AI Assistant paid MAU grew over 150% year-over-year and lifetime AI users in Acrobat tripled year-over-year, showing both monetization traction and broad-based engagement.

Under the Creative and Marketing Professionals group, generative credit consumption is growing strongly; traffic from the Creative and Marketing Professionals group was up 50% year-on-year in 2026 Q1 (FY2026 Q2); Firefly ARR was up 50% sequentially in 2026 Q1 (FY2026 Q2); management has launched Adobe Creative Agent beta; Adobe Creative Agent will be monetised through Adobe’s existing credit consumption model; Adobe Creative Agent is available in the major chatbot products; Firefly’s ending ARR in 2026 Q1 (FY2026 Q2) is approaching $300 million; the number of generated assets in Firefly Enterprise was up 4x year-on-year in 2026 Q1 (FY2026 Q2); Adobe has a partnership with NVIDIA for Firefly Foundry

Demand for AI content creation is exploding across ideation, generation and semantic editing, and generative credit consumption continues to show strong growth…

…In Q2, C&CP traffic to adobe.com grew over 50% year-over-year…

…This immense volume of traffic drawn to the Adobe brand, includes users seeking to purchase Creative Cloud, Photoshop and other CC apps and an increasing number of new users who are looking for Adobe Magic to complete a creative task with a friction-free experience…

…Firefly ARR grew approximately 50% quarter-over-quarter through Firefly apps and credit packs. We were excited to launch the Adobe Creative Agent beta in Q2. The agent is available as part of Creative Cloud and Firefly subscriptions and provides a conversational experience to achieve complex and repetitive creative tasks. Agent usage will be monetized through our existing credit consumption model. The Adobe Creative agent is also available in Claude, ChatGPT and soon, Copilot and Gemini…

…In Premiere, we launched a brand-new color mode, a first-of-its-kind color grading experience built specifically for video editors. We continue to deepen AI capabilities across our flagship Creative Cloud applications Photoshop added Rotate Object and Illustrator released Turntable, both enabling subscribers to turn 2D photos and illustrations into 3D renditions they can rotate and harmonize into their work. Capabilities like these drove record AI usage within our flagship applications.

Firefly continues to support third-party models now with Kling 3.0 and Kling 3.0 Omni. Firefly ending ARR across Firefly App, Firefly credit packs and Firefly Enterprise is approaching $300 million exiting Q2. Firefly Enterprise spanning Firefly Services, Adobe Firefly Foundry and Brand Intelligence is helping the world’s largest brands industrialized content production with brand-safe custom models. The number of generated assets grew more than 4x year-over-year making it an AI content engine for marketing at scale.

Our announced NVIDIA partnership will bring accelerated computing to Adobe Firefly Foundry for faster, higher-performing custom models across image, video, audio, vector and 3D, plus a cloud-native 3D digital twin built on Omniverse and OpenUSD.

Adobe’s management is focused on 3 AI-first solutions to target the marketing automation and customer experience orchestration opportunities, namely, Adobe Experience Platform (AEP), Adobe GenStudio, and Adobe Experience Manager (AEM); GenStudio ARR was up 25% year-on-year in 2026 Q1 (FY2026 Q2); subscription revenue for AEP was up 30% year-on-year in 2026 Q1 (FY2026 Q2); AEP delivers 70 billion profile activations and 35 trillion segment evaluations daily, and 1 trillion experiences annually; more than 80% of AEP and AEM customers are now using Adobe’s agentic capabilities; there are 1,500 customer trials happening for Adobe’s agentic web offerings; management recently launched Adobe CX Enterprise, which is an agentic system for enterprises to manage their entire customer life cycle; CX Enterprise has a feature called CX Enterprise Coworker, which is a specialised AI agent that executes tasks based on business goals; CX Enterprise Coworker has seen great customer interest since launch, with 150 enterprises in early adoption; management recently launched Adobe Brand Intelligence, which helps enterprises create and validate on-brand content; Adobe Brand Intelligence is headless, so it can integrate with other apps outside of Adobe; in 2026 Q1 (FY2026 Q2), Adobe announced native integrations on major AI platforms; CX Enterprise Coworker capabilities are integrated into NVIDIA’s NemoClaw platform; global agencies are standardising on Adobe partly for its AI capabilities

The opportunity for AI-powered marketing automation and customer experience orchestration is large and growing, and we are continuing to gain market share and expand our leadership. We are focused on 3 critical AI-first solutions: Adobe Experience Platform and native apps for customer engagement; Adobe GenStudio for content supply chain; and Adobe Experience Manager agentic web apps for brand visibility…

• …GenStudio ending ARR grew over 25% year-over-year as leading brands and agencies continue to standardize on Adobe to power their content supply chain;
• Subscription revenue for AEP and native apps grew over 30% year-over-year. AEP delivers over 70 billion profile activations and 35 trillion segment evaluations per day, as well as more than 1 trillion experiences per year;
• Over 80% of AEP and AEM customers are now using agentic capabilities built into our products. 
• Over 1,500 customer trials are underway for our agentic web offerings — Adobe LLM Optimizer, Sites Optimizer and Brand Concierge…

…We launched Adobe CX Enterprise, a new end-to-end agentic AI system that simplifies how enterprises manage their entire customer life cycle, from acquiring and engaging prospects to driving conversion and lasting loyalty. Adobe CX Enterprise brings together AI agents, agent skills and Model Context Protocol endpoints with an intelligence and governance layer to deliver reliable and auditable agentic workflows that enable highly personalized, differentiated customer experiences. Over 20,000 global brands have built their business on Adobe and CX Enterprise will help usher them into the era of agentic AI. As part of CX Enterprise, we announced CX Enterprise Coworker, a specialized AI agent that executes tasks based on business goals, dramatically increasing productivity and campaign execution. CX Enterprise Coworker has garnered tremendous customer interest since launch, with over 150 leading enterprises in the early adoption program prior to general availability this week…

…We also introduced Adobe Brand Intelligence, a continuous learning system that helps enterprises create and validate on-brand content faster and with less effort. Adobe Brand Intelligence learns from creative and marketing team feedback, approvals and rejections in real time. It is a headless platform exposed through APIs, so it can integrate with existing first and third-party apps rather than running as a separate app…

…In Q2, we announced native integrations with major enterprise AI platforms, including Microsoft Copilot, Anthropic, OpenAI and Google Gemini. Our partnership with NVIDIA brings CX Enterprise Coworker capabilities into the NemoClaw enterprise agent platform, enabling brands to deploy Adobe’s customer experience intelligence within NVIDIA’s secure policy-governed OpenShell run time. Leading global agencies, including Dentsu, Havas, Omnicom, Publicis, Stagwell and WPP are standardizing on Adobe, combining our AI-powered capabilities with their unique IP and industry expertise to co-develop innovative, differentiated solutions for joint clients.

Adobe’s management has seen AI driving companies to add to all the capital that’s already being spent on coding, and they think a similar dynamic will happen with the creative industry; management wants Adobe to be the AI platform for all creativity across all surfaces 

I like to also characterize this much like what’s happened with the code opportunity. If you think about what’s happened with the code opportunity across AI, it’s just completely being turned upside down. And every company is thinking about how they can add to all of the billions that is already spent in code. The same opportunity exists, I think, in every single category, whether that’s gaming, entertainment and creativity. And this is an opportunity for us not just to focus on creative pros and communicators who’ve traditionally been the strength of this company, but to actually become that AI platform for all creativity across every single surface. The success that we’ve seen associated with what we have done on these new products. We talked about the MAU, we’ve talked about the ARR that’s coming. We want to just have a singular focus right now to make sure that we go capture that immense opportunity with a singular focus and a clear marketing message.

Adobe’s management thinks the company is uniquely suited to tackle creativity solutions, in relation to possible competition from the AI platform companies

Whether it’s Amazon, Microsoft or Google, we are huge users of their cloud services, which at the end of the day is a significant revenue stream for them. So we have great partnerships with all three of them. I think with Google specifically, we also partner on how we can jointly go to media and entertainment. We are a big user of their Nano Banana within our applications. So I think there’s a lot of synergy associated with that. 

I think with OpenAI and with Anthropic, they are looking to say, how can they become more of a sort of platform of choice and provide us. I think all of their focus right now, I would say, Brad, is on code. And that’s where everybody is doing a [indiscernible] left on that. And I think creativity is an area that we not only have a passion for that we’re uniquely qualified, and so this is our time and our opportunity to leverage everything that they are providing. And so with every one of them, we have a great partnership. But I think as it relates to the consumer side of creativity, which is where this is going after, we’re, I think, a company of one in terms of the focus that we can have on that particular business.

Oracle (NYSE: ORCL)

Oracle had very strong year-on-year revenue growth of 93% for its Cloud Infrastructure business in 2026 Q1 (FY2026 Q4), driven by AI demand

Cloud infrastructure revenue grew 93%, reflecting strong demand for both AI workloads and our database services, and cloud apps was up double-digit at plus 10%.

Oracle’s gross margin for FY2026 has declined as it builds out its AI infrastructure business; the buildout has also caused free cash flow to be negative; management expects Oracle’s capex to be more than $70 billion for fiscal 2027; management sees strong returns on the capex Oracle is deploying; Oracle will be raising $40 billion in debt and equity in fiscal 2027 to support its capex; Oracle’s capex is creating near-term pressure on gross margins, but management expects rapid improvement in the margins once Oracle’s data centers reach full contractual revenues; management actually wants to accelerate Oracle’s capex; management sees the returns on Oracle’s capex to be in the high 20s percentage at steady state, with even higher returns for capex that support bring-your-own-hardware contracts

For the full year, our gross margin stepped down around 5 points as expected as we start to see the impacts from the build-out of our infrastructure business and the acceleration in its revenues, primarily offset by lower operating costs as a percentage of revenue, driven by operating efficiencies. All of this translated into strong cash flow from operations of $32 billion, up 54%. We did continue with our program of capital investment tied to unlocking the strong growth opportunities in front of us. Our net cash outlay for capital expenditures for the full year was $48 billion, taking into account equity payments and timing impacts of around $8 billion…

…We’ll continue those investments in our fiscal year 2027, with an expected net cash outlay for capital expenditures of around $70 billion. This includes customer prepayments and timing impacts expected at around $20 billion to $25 billion, so our reported CapEx will be higher by this amount. Importantly, these investments are being driven by committed customer demand reflected in our record RPO, giving us confidence in our long-term outlook as well as strong returns on the capital we’re deploying…

…To support our capital investment program, we expect to raise around $40 billion in debt and equity in our fiscal year ’27 and that includes our already announced $20 billion at-the-market equity issuance. We don’t anticipate raising additional debt funding in calendar year 2026…

…While these investments are creating pressure on the near term to gross margins in our infrastructure business, we expect margin performance in infrastructure to improve rapidly as we reach full contractual revenue levels at our data centers…

…Part of my job is to figure out ways to actually accelerate CapEx. Hilary has a tough life. My job is starting to spend the money a little bit faster, so I can get ramped revenue sometimes…

…The way I think about return from that business model is in return on invested capital. And what we see is return on invested capital in the high 20s at a steady state. So once the revenues have ramped for large projects at the project level. And that doesn’t take into account upside like who knows if the GPUs don’t need to be replaced over the long term and things like that. Just purely in the steady state, when we’re at the steady state of the contracts that we have. And as we’re generally able to preserve and improve margins in the case of things like bring-your-own-hardware, the ROIC structures, the ROIC for those types of structures will be even higher. And again, that back of envelope, I’m just calculating return on invested capital is after-tax operating margin plus depreciation divided by gross investments, so total gross CapEx at the project level.

Oracle’s remaining performance obligation (RPO) in 2026 Q1 (FY2026 Q4) was up 363% year-on-year to $638 billion (was $553 billion in 2025 Q4), driven by demand for AI infrastructure

Our remaining performance obligations, or RPO, finished at $638 billion, up 363%. This unprecedented level of RPO provides exceptional visibility into our future revenue growth, all supported by long-term contractual customer commitments and reflects the strong customer demand we see across both AI infrastructure and cloud services.

Oracle’s management sees customers wanting to use AI to increase productivity quickly, and within budget; Oracle’s customers are now past the experimental stage with AI and are looking to implement enterprise-grade agentic solutions; Oracle’s customers are looking to leverage their proprietary data with AI; management is seeing customers wanting to achieve a positive ROI from AI quickly

Our customers are now focused on how to leverage AI in their own businesses. They want AI to increase productivity, enhance customer service, and create real competitive advantages. But they want to do it quickly and within their existing budget envelope…

…Our customers have moved past the experiment stage with AI. They are ready to implement enterprise-grade, complete agentic solutions to help run their businesses…

…I’m also having very interesting conversations with our customers around leveraging their own proprietary data sets with AI. Much of this data already sits in an Oracle database or is generated by Oracle applications. For many enterprises, inferencing against decades of rich operations data is where the benefits of AI compound exponentially…

…One of the things we’re increasingly hearing from customers is how much are we going to spend on AI? And how do I get ROI very quickly?

Oracle’s management sees Oracle having a unique advantage in AI by providing the entire suite of applications, data, infrastructure, and AI tooling; Oracle has delivered over 1,000 AI agents over the past year; management sees Oracle as being the fastest, most affordable way for customers to consume AI; Oracle’s customers are looking to leverage their proprietary data with AI, and much of this data is already in an Oracle database; management thinks inference against proprietary data is how enterprises can benefit from AI; Oracle’s full stack allows customers to quickly leverage AI with their private data; Claro, National Health Service, Lojas, and QXO are examples of customers using all or parts of Oracle’s full stack for AI

Oracle’s unique advantage is that we deliver the applications, the data, the infrastructure, the AI tooling, and the industry expertise together. That combination invariably puts us at the center of customer conversations, whether they’re existing Oracle customers or not…

…Over the past year, we have delivered more than 1,000 AI agents across our application suites. These agentic-based offerings can reason, decide, and execute work across processes. So the quickest, most affordable and most productive way customers can begin consuming AI is just to continue using Oracle’s applications. Since every 3 months, they get more and more of the AI features built for them and ready to go. This is a major shift in enterprise software, and Oracle is uniquely positioned to lead it…

…I’m also having very interesting conversations with our customers around leveraging their own proprietary data sets with AI. Much of this data already sits in an Oracle database or is generated by Oracle applications. For many enterprises, inferencing against decades of rich operations data is where the benefits of AI compound exponentially. Oracle’s full stack offerings allow customers to get up and running quickly, leveraging AI together with their private data sets.

This is why Claro, a major telecommunications provider in Latin America, chose OCI, field services applications and our AI data platform to automate customer service for their 30 million subscribers this quarter. U.K. National Health Service’s Shared Business Services; Lojas, the Brazilian retailer; and QXO, the fastest-growing building products distributor in the United States, combined AI-ready Oracle infrastructure or database products with Oracle applications to move their businesses forward.

Oracle’s management recently launched Oracle AI Agent Memory for developers to build agents that can remember and utilise enterprise context; management recently launched Oracle Deep Data Security that precisely limits what an AI agent can see or act upon; management has added vector database search and other features into Oracle’s database product

Last quarter, we also released a long list of major new AI functionality in the Oracle database. Here are just 2 examples. The Oracle AI Agent Memory is a library that helps developers build agents that can remember, reason and act with enterprise context. Oracle Deep Data Security has data access rules at the database level. This protects against both unauthorized access and it limits precisely what data a user and any AI agent acting on their behalf can see or act upon…

…The innovation in the database, I mentioned a couple of Deep Data Security and Agent Memory that we put into the database, things like vector database search and features that we’ve been adding into the database are part and parcel to the companies’ AI strategies.

Oracle’s management is simplifying how customers consume and pay for AI agents; customers can purchase additional tokens on top of the AI innovation they are getting from Oracle for free; management is introducing outcome-based pricing models, such as interview agents that are priced based on the number of candidates screened; management had a limited roll out of Oracle’s token bundle in 2026 Q1 (FY2026 Q4); the limited roll out already saw 33 customers repurchase tokens; it can be tricky to price on outcomes if the company offering the agentic service is not the entity that’s creating the outcome, but in Oracle’s case, it has a full stack service, so it’s easy to measure outcomes; management expects the initiative to simplify how customers consume and pay for AI agents to resonate with customers and boost Oracle’s growth

We are simplifying how customers consume and pay for agentic capabilities. Our new agentic pricing aligns with customer value. Now much of our AI innovation in our core applications continues to be included at no extra charge. However, customers can also purchase additional agentic capacity in a simple, predictable way by purchasing bundles of tokens that can be used across our application suites. We’re also introducing outcome-based commercial models that align pricing directly to the value derived. For example, interview agents that are priced based on the number of candidates screened or hospitality upsell agents priced on the percentage of end consumer upsell transactions. In Q4, we started a limited rollout of our token bundles and had 33 customers, like Aon Services Corporation and Liberty Energy, repurchase tokens to have access to more advanced reasoning and models…

…In health care, in our new AI-based automated agents where we’re automating doctors’ notes, we’re automating lab orders. We’re able to measure and actually price based on patient throughput, which is what the providers — one of the things providers care about is how many people can we get through a health care system, reduce waiting queues, give better service to patients…

…The sort of difficult thing is that you’re not creating the outcome in the first place, that’s a tricky thing to price in. But since we’ve made this full stack investment and since we’re able to very easily take the best of the output from the large language models to our customers, pair that with our — both our horizontal applications and our industry applications, we have a very easy way to measure outcomes for our customers…

…We’re allowing as much flexibility and as much aligned with the value in our pricing models across our entire application suite as we possibly can. And I expect that, that will continue to resonate well with customers as it did in the quarter. And as we roll it out across our entire fleet, it certainly should be helpful for our growth story as well.

Oracle’s management thinks the AI infrastructure market dwarfs the existing cloud infrastructure market; management sees the AI infrastructure market as being trillions of dollars per year

Cloud infrastructure has become a very large market because of the ever-growing demand for server-side computing. AI infrastructure makes the existing cloud infrastructure market look small. Everything we see shows this market size is trillions of dollars per year.

Most of Oracle’s AI infrastructure contracts signed in 2026 Q1 (FY2026 Q4) are either bring-your-own hardware or prepaid; bring-your-own hardware and prepaid contracts have similar margins as Oracle’s other contracts; Oracle delivered 1.2 gigawatts of AI infrastructure to customers in FY2026, with 2026 Q2 (FY2027 Q1) deliveries already approaching 1 gigawatt; management thinks there will be many winners in AI and they want all of them as Oracle customers; Oracle’s AI infrastructure business has many tenants; Oracle had 35,000 GPUs from 59 customers come up for renewals in 2026 Q1 (FY2026 Q4) and 49% of those customers renewed for 92% of the GPUs, with the remaining 8% sold to other customers; Oracle’s global GPU utilisation is 97.5%; Oracle’s Abilene, Texas AI data centre has delivered 42% of its total capacity, with 35% of further capacity to be delivered in the next 90 days; Oracle’s Shackelford, Texas AI data center will begin delivery to customers in 2027 H1; Oracle’s Dona Ana County, New Mexico AI data center will start customer delivery in 2027 H1; Oracle’s Saline, Michigan AI data center will start customer delivery in 2027 H2; Oracle’s Port Washington, Wisconsin AI data center will start customer delivery in 2027 H2; management thinks the propensity for customers to renew AI infrastructure contracts with Oracle depends on the company’s ability to maintain massive GPU clusters; management sees a path for Oracle’s AI infrastructure business to earn higher margins over time even as it lowers prices for customers; for the bring-your-own-hardware AI infrastructure business, Oracle is providing data centers that are properly constructed and designed, the appropriate networking technologies, and every other thing necessary apart from the AI accelerator chips; it’s not easy to operate the bring-your-own-hardware AI infrastructure business

We signed $67 billion in AI infrastructure contracts this quarter, the majority of which was either bring-your-own-hardware or prepaid. This increases our combination of bring-your-own-hardware or prepaid customer contracts to $75 billion, with those contracts having no degradation in margin compared to our other contracts…

…Q4 finalizes an impressive FY ’26 where we delivered more than 1.2 gigawatts to customers. Our pace of delivery continues to accelerate with our FY ’27 Q1 delivery approaching 1 gigawatt, nearly the same capacity as we’ve delivered in the previous 4 quarters combined.  There will be many winners named, and our strategy is to have them all as customers. We continue to diversify across our largest customers with 4 customers contracting for more than $8 billion this quarter.

Our infrastructure is fundamentally multitenant, and we continually allocate capacity between customers. In Q4, 35,000 GPUs from 59 separate customers were up for renewal. 49% of those customers renewed for 92% of those GPUs. That doesn’t mean, though, that 8% of those GPUs were idle. Most of those GPUs themselves were subsequently sold to other customers in the same quarter. Our global GPU utilization rate is 97.5%…

…Abilene, Texas today has delivered 42% of the total capacity. An additional 35% of capacity will be delivered in the next 90 days, with the remainder delivering in the subsequent quarter. Moving forward to Shackelford, Texas. We contracted this in August of 2025. Customer delivery begins in the first half of FY ’27 — sorry, first half of calendar year ’27. 115 megawatts of power capacity is already available online, more than 1 month ahead of schedule. If we take a look at Doña Ana County, New Mexico. We contracted this in September of 2025. Customer delivery begins in the first half of calendar year ’27 as well. Power design is based on gigawatts of clean, energy-efficient Bloom fuel cells. If we look at Saline, Michigan, we contracted this in October of 2025. Customer delivery begins in the second half of 2027. The network core is ahead of schedule and delivered at the end of this calendar year. And then to the final site I want to touch on, Port Washington, Wisconsin. This was contracted in September of 2025 and delivery begins in the second half of calendar year ’27…

…I find that largely what affects future renewals is that several years of relationship that we’re going to have between now and then. And we’re fundamentally in the service business. If you think that you’re just buying something and then you’re done with it, it’s not the way it works, right? These people are relying on what we do at Oracle to run and maintain these massive clusters every day…

…As the market continues to mature, and we deploy more and more of our research and development dollars and making things more efficient, I think there’s ways that Oracle gets higher and higher margins, but we actually can offer lower and lower prices to our customers….

…One of the things that Oracle can provide to our customers is that we can go out and put upfront capital and then depreciate that over a period of time and help finance the customers’ usage of that. But that’s not the only thing we provide and for a lot of customers it’s not even the most important thing to provide. What they contract with us for is the ability to go out and get the data centers constructed, design them properly, secure them, design networks that go inside of them, install a cloud, give them a complementary set of services around the specific hardware because it turns out that a set of these accelerators on their own is not functioning cloud. You need general purpose compute, you need general purpose storage, you need load balancers, you need security function, you need identity. You need all of that to actually make this stuff usable and Oracle provides all of that…

…Anyone that thinks that these things are easy to operate is very confused. So you’re not just buying a single rack and putting it into your data hall. These are extremely complex clusters that require constant care and feeding, constant maintenance across the network and the hardware itself.

Oracle’s management sees agentic coding as the most obvious and valuable use case of AI; Oracle’s internal demand for agentic coding is not slowing down and the same goes for the company’s customers; management sees enormous demand for agentic coding

AI is delivering value on multiple fronts, but the most clear and obvious is agentic coding. This is an area where we have a front row seat as both the provider and as a consumer. Agentic coding tools has completely changed how Oracle operates, and we see no slowdown in our own demand for such capabilities. The same is true for all the customers and partners we work with. The demand for AI infrastructure in this domain alone is enormous, ignoring the many, many other growth areas.

Oracle’s management sees demand for AI infrastructure to be massively higher than supply for at least a few years ahead

I think there’s clearly several years in, there’s still a massively higher demand than there is supply.

Oracle’s management thinks the SaaSpocalypse does not apply to mission-critical software systems, as customers realise that AI that’s built into existing SaaS solutions is a good approach

As far as impact of SaaSapocalypse, I would say maybe a couple of quarters ago, there were some delayed decision cycles out there as customers saw through that. But really, particularly in the mission-critical systems space, which is where we play at Oracle, people have quickly moved on to that and realized that enterprise software, particularly when you have AI built into our SaaS solutions is certainly a very good approach and is necessary to move forward for the modernization and protection of their businesses.

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Last month, I published More Of The Latest Thoughts From American Technology Companies On AI (2026 Q1). In it, I shared commentary in earnings conference calls for the first quarter of 2026, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

A few more technology companies I’m watching hosted earnings conference calls for 2026’s first quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here
• 2024 Q4 – here, here, and here
• 2025 Q1 – here and here
• 2025 Q2 – here and here
• 2025 Q3 – here, here, and here
• 2025 Q4 – here, here, here, and here

With that, here are the latest commentary, in no particular order:

MongoDB (NASDAQ: MDB)

MongoDB’s management sees 2 dimensions to the growth opportunity ahead, namely (1) organisations running core workloads on MongoDB, and (2) organisations moving agentic applications into production and choosing MongoDB as the core database; the 2 dimensions reinforce each other, as agentic applications are built on the data already residing within MongoDB

These conversations reinforce my conviction in both what we have built and the scale of the opportunity ahead. That opportunity has 2 dimensions. The first is core workloads where large customers run their most demanding, mission-critical workloads on MongoDB across on-prem, public clouds and hybrid environments. The second is AI, where enterprises, digital natives, frontier labs and AI natives alike are moving agentic applications into production and choosing MongoDB as the data platform to power them. As you heard from other software companies, these 2 opportunities are not distinct and, in fact, reinforce each other. Enterprises are starting to build agentic application on top of the very data already running on MongoDB.

MongoDB’s management is seeing accelerating AI adoption across the company’s base, with MCP (Model Context Protocol) server usage growing significantly; Voyage customers have doubled sequentially in 2026 Q1 (FY2027 Q1); Vector Search adoption is far outpacing MongoDB’s overall growth; Voyage AI embeddings entered public review in 2026 Q1 (FY2027 Q1) and it allows developers to deliver semantic search in minutes; MongoDB has delivered 10-plus integrations with LangChain for Vector Search and more

AI adoption of MongoDB technologies across our customer base continues to accelerate. MCP server usage is growing significantly. Voyage customers have more than doubled quarter-over-quarter and Vector Search adoption is far outpacing overall company growth…

…This quarter, automated Voyage AI embeddings entered public preview, removing weeks of infrastructure work and enabling developers to deliver semantic search in minutes…

…LangChain is the world’s most widely adopted agent framework with over 1 billion downloads. We delivered 10-plus native integrations with LangChain for Vector Search, hybrid retrieval, semantic caching and agent memory.

Several frontier AI labs have selected MongoDB for mission-critical use cases; it’s still early days for MongoDB regarding the frontier AI labs’ workloads, but management is optimistic about expanding with the labs over time; AI-native companies are choosing MongoDB as the foundation for their data layer and the right choice for the data layer is important because it is a chokepoint on rapid scaling; it’s still early, but MongoDB’s management is starting to see enterprises shift from experimenting with AI to deploying AI in production; customers are choosing MongoDB as the memory layer for AI agents; MongoDB’s current results are driven by core workloads, but management is seeing growing moment from AI and agentic workloads, and MongoDB is ready for agentic deployment at scale whenever it happens; management is seeing the frontier AI labs realising that MongoDB is a great data platform, after trying out alternatives such as Postgress; the frontier AI labs are using MongoDB for multiple use cases

Turning to AI. This opportunity spans 3 distinct segments. First is the frontier labs. Several of these have selected MongoDB for use cases that are mission-critical to the deployment of their products among the most demanding data workloads in the industry. The depth of engagement varies by lab and by workload, and it is still early. But we feel great about the use cases we are winning and the ability to expand within these customers over time.

Second is AI-native companies. These customers are choosing MongoDB as the foundation for their AI products from day 1 because the data layer determines if you can scale to support rapid growth…

…Third is enterprise deploying AI. It is still early here, but we are beginning to see customers move from experimentation into production, building AI application on top of the operational data layer already running their business…

…Customers choosing MongoDB as the memory layer for AI agents themselves, agentic workloads need memory, that’s transactional, high velocity and able to retrieve the right context at the right time…

…Our results today are driven primarily by core workloads, but we are seeing real and growing momentum from AI and agentic workloads and believe MongoDB is purpose-built to be generational data platform for the agentic era…

…I’m seeing it’s still early, Matt, just to be clear, because the security governance, observability, there are many, many aspects to the agents and what kind of outcomes they deliver if it is agents at scale. But we feel that we are ready…

…[Question] In your prepared remarks, you mentioned frontier labs and it sounded like it was labs plural. I know you choose your words very carefully in the prepared remarks. I guess, did I pick that up correctly, that Mongo might now be working with multiple frontier labs?

[Answer] Yes, it is plural, and it was chosen carefully. Thank you for noticing… As we work with them, and as they have tried, whether it’s a Postgres alternative or others, they have come to realize that. And these are truly at the forefront of innovation in AI space or driving innovation that MongoDB is just a great data platform for some of the workloads. And the point around — of course, we cannot go into specific details with our agreements with them on type of use cases, but they vary and there are multiple use cases depending on the lab, that we’re working with them, and it’s early, but we will continue to expand.

MongoDB’s management sees the company as the generational data platform for agentic AI for 5 reasons, namely (1) MongoDB is architecturally built for AI because rigid relational data schemas are not suitable for agentic coding and LLMs , whereas they play well with unstructured document databases, (2) MongoDB is a high-performance data platform that allow agents to read and write in real time, (3) MongoDB delivers retrieval accuracy that agents require for customer-facing applications, (4) MongoDB can run on-premises, on the cloud, on a hybrid format and (5) MongoDB is embedded in the tools that developers and agents are using; management sees 3 legs of the stool for an agentic workload, namely, the harness, the LLM (large language model), and the data layer; customers of MongoDB appreciate the integration with LangChain because this means the data layer works really well with the harness layer; MongoDB’s database was not designed with AI workloads in mind, but it turns out that the architecture is perfectly suited for AI workloads

We are seeing real and growing momentum from AI and agentic workloads and believe MongoDB is purpose-built to be a generational data platform for the agentic era. Built natively into the platform, MongoDB’s innovations in the core database, embeddings and vector capabilities are moving us beyond a system of record to becoming the real-time system of intelligence. That shift comes down to 5 core strengths.

Number one, MongoDB is architecturally built for AI in 2 key ways. First, our flexible schema is uniquely suited to how applications get built in the agentic era. A growing share of software is now created through prompt-driven development, natural language iteration rather than line-by-line authorship. Whether the prompt comes from a developer or an agent, the shape of the application shifts with each prompt and a rigid relational schema becomes a tax on every iteration compromising agility. In addition, LLMs are the lingua franca for AI, and they speak in unstructured documented shape data, the exact form MongoDB was built around…

…Second, MongoDB is a transactional, high-performance data platform built for how agents actually work. Agents don’t behave like traditional applications. They read, write and act continuously across multiple simultaneous threads with a single agent spawning subagents that each make independent reads and writes in real time. Analytical systems built for off-line processing weren’t designed for this, and it shows in the performance when you run agents on top of them. MongoDB 8.3 released this month takes that step one further, delivering up to 45% more reads, 35% more writes and 15% more ACID transactions over 8.0 without changing a line of application code.

Third, MongoDB is a data platform that delivers the retrieval accuracy agents need to be trusted while optimizing tokens and cost in production. For internal tools, occasional errors may be tolerable. But for customer-facing application such as clinical decision support, fraud detection, financial transaction, insurance transaction, accuracy is nonnegotiable. MongoDB delivers best-in-class retrieval through integrated Vector Search and Voyage embeddings and reranker models, purpose built to surface the most relevant context when agent needs it…

…Fourth, MongoDB runs wherever the agent needs to run across all 3 major clouds, on-prem and in hybrid environments…

…Fifth, MongoDB is embedded in the tools, developers and agents actually use to build agentic applications…

…The simplicity when we talk to customers is 3 legs of the stool for any agentic workload is harness, LLM and data layer. And if they are being used as in LangChain, they have significant traction. Even when I talk to some of the large banks, whether it’s on-prem or in the cloud, there’s significant traction on the harness layer. And then they say, okay, what about the data layer and data layer, MongoDB being a choice for the data layer just makes sense. So we have done many integrations with them, and we are seeing this being played out at some of the large enterprise customers who say, hey, CJ, I’m glad that the data layer as in MongoDB really works with the harness layer. And of course, we can choose whichever LLM we want…

…I would say that architecture, it is almost — our founder calls it really well that. We would rather be lucky than smart. And when we created MongoDB — this is from Dwight. We didn’t have AI workloads in mind, but this architecture is perfectly suited for AI workloads.

MongoDB’s management recently announced MongoDB Checkpointer for LangSmith; MongoDB Checkpointer for LangSmith collapses a dedicated Postgres instance per agent into a single, shared Atlas cluster; the MongoDB Plugin and agent skills on Claude Code’s marketplace was recently launched

We recently announced that MongoDB Checkpointer for LangSmith deployment, which collapses what used to be a dedicated Postgres instance per agent into a single, shared Atlas cluster, state, memory and operational data unified in one place. Last month, we also launched the MongoDB Plugin and agent skills on the Claude Code marketplace, where we are already seeing strong early traction with developers.

Endor Labs, an AI-native application security platform, chose MongoDB Atlas as its default database; Endor Labs is using Atlas and Atlas Search for mission-critical security workflows; MongoDB Atlas is lowering Endor Labs’ operational friction

For example, Endor Labs is an AI-native application security platform, protecting over 7 million applications across both human written and AI-generated code. Endor selected Atlas as its default database to support 225% year-over-year revenue growth. Endor uses Atlas and Atlas Search to power its mission-critical security workflows, including AURI, its new security intelligence layer for AI coding agents, allowing the company to reduce operational friction and accelerate delivery of its differentiated offerings.

Food delivery company Zomato has 25 million monthly active users; Zomato is using MongoDB Atlas to sell its AI-native customer support platform, Nugget, to other enterprises; Zomato chose MongoDB Atlas over DynamoDB and DocumentDB for its aggregation pipeline, right consistency and flexible schema; MongoDB Atlas has lowered Nugget’s support cost by 55% and raised human agent productivity by 40%

Zomato is a great example. The world’s second largest food delivery company with 25 million monthly active users built Nugget, an AI-native customer support platform, they are now selling to other enterprises on Atlas. After evaluating DynamoDB and DocumentDB, they chose Atlas for its aggregation pipeline, right consistency and flexible schema. Nugget now orchestrates 15 million conversations per month on MongoDB’s platform, reducing support cost by 55% and improving human agent productivity by 40%.

Adobe’s Journey Agent is using MongoDB Atlas for long-term memory; Atlas Search and Atlas Vector Search enables Adobe to achieve sub-100 millisecond hybrid search for Journey Agent to act in real time

Adobe’s Journey Agent is a clear example. A composite multimodal AI agent that unifies Adobe’s marketing suite and orchestrates end-to-end customer journeys for their global B2C user base with MongoDB as the agent’s long-term memory and reasoning layer. Adobe leverages the MongoDB platform, Atlas Search and Atlas Vector Search together to power the sub-100 millisecond hybrid search the agent needs to act in real time.

The growth of AI startup ElevenLabs was being choked by its data layer, and made the decision to move to MongoDB recently; Postgres databases are choking the growth of AI native companies that have adopted it

I shared the example of somebody like ElevenLabs at .local London a few weeks ago, they were using first-party database for operational data. They were using another software for search. And basically, most of those product lines were really choking as ElevenLabs was growing significantly, right? They are now at a $500 million ARR. So when I asked the team technically, the engineer who made that decision saw that the growth of the company as in that AI native company, ElevenLabs was being held up by the data layer. And us having Search, Vector Search and operational data in a single platform, they are — they made the decision to move to MongoDB not too long ago. And 2 things they said that really resonated with me, Ryan. Number one, they are like, gee, we should have done this a lot sooner. Otherwise, we would have not to deal with all these outages and other things they dealt with the previous platform. And number two, now choosing MongoDB even though they have scaled significantly on their ARR as an AI native company gives them peace of mind.

I’m hearing them from other AI native companies who also chose maybe a Postgres or something and Postgres completely choked on the performance. So that just gives me a lot of confidence that if AI native company where AI is the business or agentic layer is the business and they feel that they can scale with MongoDB.

Nu Holdings (NYSE: NU)

Nu Holdings is seeing AI-driven productivity gains, with engineering through up 50% year-on-year in 2026 Q1, weekly token consumption up 10x from the start of 2026 to March, and testing cycles becoming 90% faster; nearly 100% of Nu Holdings’ employees are utilising AI tools

AI is driving productivity gains across the company, with engineering throughput up 50% YoY, weekly token consumption nearly ten times higher than at the start of the year, and testing cycles 90% faster…

…We’re reaching close to 100% utilization of AI tools among our employees across all functions of the organization.

Nu Holdings’ management expects to launch new AI-native experiences to customers in 2026; Nu Holdings’ AI Private Banker functionalities currently have 15 million active users

Customer journeys are being rebuilt end-to-end, with new AI-native experiences expected to reach customers during 2026…

… Nu’s AI Private Banker functionalities — financial insights, payments, credit advice, and debt resolution — are now serving more than 15 million monthly active users. 

Nu Holdings’ proprietary foundation models, NuFormer, is already in production to make lending decisions for credit cards in Brazil and Mexico, and unsecured lending in Brazil; NuFormer can make a decision for each personal loan request in under a second

NuFormer, Nu’s proprietary set of foundation models, is in production today for credit card decisioning in Brazil and Mexico, and for unsecured lending in Brazil, with real-time AI valuation now pricing and approving every personal loan request individually based on its predicted NPV in under a second. 

Nu Holdings’ management sees 3 structural advantages the company has in AI, namely, (1) proprietary data from 135 million customers, (2) a cloud-native technology stack that’s built internally, and (3) a strong talent base

Nu’s AI Transformation is anchored by three structural advantages: first-party data at scale from 135 million transacting customers generating one of the largest and most differentiated financial datasets in the world; a proprietary cloud-native technology stack with core banking systems built internally and data unified across the company; and a world-class talent base of ten thousand employees from more than 50 nationalities across six countries. 

NVIDIA (NASDAQ: NVDA)

NVIDIA’s management capitalised on an inflection in inference demand by ramping its Blackwell systems; NVIDIA’s Data Center revenue again had very strong growth in 2026 Q1 (FY2027 Q1), driven by strong demand for Blackwell systems; the Blackwell systems are the fastest product ramp in NVIDIA’s history; management sees Blackwell systems as having the lowest token generation cost for inference; every hyperscaler, cloud provider, and model maker is using Blackwell; OpenAI’s latest GPT-5.5 model was trained with and is being served by Blackwell systems; Microsoft’s latest largescale AI data center, Fairwater, is powered by Blackwell GPUs; Amazon’s AWS will be adding more than 1 million Blackwell and Rubin (the next generation GPU) GPUs; Google Cloud will be offering Blackwell systems; Blackwell Ultra delivered the highest throughput in MLPerf inference results; management has improved the GB300 Blackwell system’s throughput by 2.7x and cost by 60% in just 6 months; management has line of sight to $1 trillion in Blackwell and Rubin revenue for 2025-2027

We capitalized on the inflection in inference demand by ramping Blackwell systems across our diverse end customer base. from hyperscalers to model makers to AI cloud providers and sovereign customers. In Q1, we also allocated capital effectively across R&D, investments in our ecosystem and share repurchases…

…Data Center revenue of $75 billion was up 92% year-over-year and 21% sequentially, driven by sustained strength in our Blackwell architecture and demand for GB300 NVL72 was particularly strong with frontier model builders and hyperscalers each having cumulatively deployed hundreds and thousands of Blackwell GPUs, marking the fastest product ramp in our company’s history. Grace Blackwell is the fastest training system as well as the lowest token generation cost at inference…

…Our Blackwell architecture is everywhere, adopted and deployed by every major hyperscaler, every cloud provider and every major model maker. Last month, we celebrated OpenAI’s launch of GPT-5.5, codesigned for, trained with, and served on Blackwell, currently positioned at the top of artificial analysis leaderboards. Microsoft’s Fairwater, the world’s most powerful AI data center is now live, ahead of schedule, powered by hundreds of thousands of Blackwell GPUs. Starting this year, AWS will add more than 1 million Blackwell and Rubin GPUs and are collaborating on Spectrum Networking. At Google, Blackwell will be offered to customers in the cloud, including confidential computing capability, a new foundation for secure high-performance AI…

…MLPerf inference results are in, and once again, we swept every benchmark as Blackwell Ultra delivered the highest throughput across the broad set of models and deployment scenarios. Full stack innovations drove the 2.7x increase in throughput and a 60% reduction in the cost per token on GB300 compared to just 6 months ago…

…We are continuing to work vigorously on our supply chain ecosystem to address the incredible demand we see ahead of us, giving us full confidence in the $1 trillion in Blackwell and Rubin revenue we foresee from 2025 through calendar 2027.

NVIDIA’s ethernet networking product, Spectrum X, is now larger than all ethernet peers combined; NVIDIA’s other networking product, Infiniband, grew 4x year-on-year in 2026 Q1 (FY2027 Q1), driven by XDR technology

Spectrum-X, our end-to-end Ethernet platform purpose-built for AI, is now larger than all Ethernet network peers combined. InfiniBand has also had a very strong quarter, growing more than 4x year-over-year, driven by deployments of our next-generation XDR technology.

Half of NVIDIA’s Data Center revenue comes from hyperscalers, and the other half comes from ACIE (AI Clouds, Industrial, and Enterprise) customers, including sovereigns; ACIE customers grew 31% sequentially in 2026 Q1 (FY2027 Q1), with AI Cloud revenue tripling year-on-year; the number of partner data centers in the AI Cloud business exceeding 10 megawatts is now over 80, up nearly 100% year-on-year; Sovereign revenue was up 80% year-on-year in 2026 Q1 (FY2027 Q1); NVIDIA’s AI systems are now in nearly 40 countries

Back to our Data Center results. Hyperscale revenue of $38 billion was approximately 50% of Data Center revenue and increased 12% quarter-over-quarter. ACIE revenue was $37 billion and grew 31% quarter-over-quarter, including AI cloud revenue that more than tripled year-over-year. Our customers have enabled rapid stand-up of AI compute capacity. The number of partner data centers exceeding 10 megawatts has nearly doubled in just 1 year, now surpassing 80 sites. Sovereign revenue increased more than 80% year-over-year. NVIDIA AI infrastructure is now deployed across nearly 40 countries, representing $50 trillion in GDP.

NVIDIA’s management is seeing rising prices for renting the company’s previous Hopper and Ampere generations of GPUs

The value of NVIDIA AI infrastructure is rising. The price of renting an H100 has risen 20% year-to-date, while A100 cloud pricing is up nearly 15%. Benefiting from the versatility of our platform and continuous performance enhancements enhanced by our software stack, customers are generating profitable revenue beyond the depreciable life of their GPUs.

NVIDIA’s management is seeing the largest hyperscale workloads, across search, advertising, recommendation systems, and content understanding, continue to transition from CPUs to GPUs

First, from search and advertising to recommender systems and content understanding, the largest hyperscale workloads continue to transition from CPU to GPU-based accelerating computing.

NVIDIA’s management is seeing an inflection in the adoption of AI-native products and services, led by a transition to agentic AI; management is seeing incredible momentum with the AI model builders, with OpenAI’s Codex being a standout; there are a few hundred thousand AI agents today, but management sees a future world with billions of agents and they will all be using tools; management sees AI agents spinning off sub-agents, and each spin requires inference; management sees agents as having lower patience than humans

The adoption of products and services native to AI is inflecting. Since the advent of ChatGPT, we have witnessed mainstream AI transition from one-shot inference to reasoning and to now agentic…

…Growth in the model layer, particularly at Anthropic and OpenAI has been incredible with momentum continuing to accelerate, including breakout growth in OpenAI’s Codex since the launch of GPT-5.5…

…My sense is that the world is going to have billions of agents. Not today, I mean, we’re going to grow into it, but we’ll have billions of agents. And those billions of agents will all use tools. And those tools can be like PCs, just like us humans using PCs today. In the future, you’ll have an agent using PC and so if you kind of think along the lines of in the future, you pick your favorite number of agents at the moment. At the moment, call it, a few hundred thousand, but in the future, call it, eventually a few billion…

…Every one of those agents are going to spin off subagents. And every time they spin these off, you’re going to need to do inference…

…Agents use these tools and have — they have lower patience and tolerance than humans, and they want things to happen quickly.

NVIDIA’s management sees a $3 trillion to $4 trillion AI infrastructure opportunity by the end of 2029, driven by hyperscalers’ forecasted capex of over $1 trillion in 2027; management expects NVIDIA’s business to be growing faster than the growth in the hyperscalers’ capex; management expects hyperscalers’ capex to continue growing from here, because in the age of AI, compute equates to revenue, unlike in the SaaS (software-as-a-service) era

With analysts now forecasting hyperscale CapEx to exceed $1 trillion in 2027 and Agentic AI beginning to proliferate all industries, AI infrastructure spending is on track to reach $3 trillion to $4 trillion annually by the end of this decade…

…We should be growing faster than hyperscale CapEx. And the reason for that is illustrated by the segmentation that I just described. Our data center business has 2 large parts. It has more parts than that, but we combined it into 2 large parts for simplicity’s sake…

…The hyperscale CapEx that you were just talking about. And there are $1 trillion this year. I have every expectation it is going to grow from here for fundamentally good reasons. This is the way computing is going to work in the future. And if they don’t have the compute, they won’t have the revenues. It is very clear, compute is revenues, compute is profit. And so the world is changing. Software didn’t use to use — SaaS didn’t use to use as much compute, but AI requires a tremendous amount of compute.

NVIDIA has deepened its collaboration with Anthropic and will serve Anthropic’s AI compute needs through multiple cloud providers; management sees NVIDIA’s share of frontier AI models growing significantly; NVIDIA is the only platform that runs every frontier AI model

We have deepened our collaboration with Anthropic and are delighted to be a strategic partner to expand their compute capacity. We will support the company’s growth trajectory through AWS, Azure, CoreWeave, SpaceXAI and more. Now with the addition of Anthoropic too, OpenAI, Gemini, SpaceXAI, Meta MSL, Microsoft AI, TML, Reflection, Perplexity, Cursor, and other major frontier labs already building on NVIDIA. Our share of frontier AI models will grow significantly…

…NVIDIA is the only platform that runs every Frontier AI model.

NVIDIA’s management thinks the right metric to analyse the economics of NVIDIA’s GPUs is not the price paid, but the lifetime cost of the GPU in producing intelligence

Customers do not buy GPUs. They build AI factories and the right economic metric is not the purchase price of the GPU. It is the lifetime cost of an AI factory producing intelligence. Token per watt, tokens per dollar, uptime, utilization, time to production, software durability and asset life. NVIDIA excels at all of them.

NVIDIA’s management sees agentic AI as a growth opportunity for CPUs; NVIDIA’s Vera CPU can deliver 1.5x faster performance per core, 2x performance per watt, and 4x density per rack compared to x86-based CPUs; CPUs are a market NVIDIA has never addressed prior to Vera; management sees a total addressable market of $200 billion for CPUs in agentic AI; management has visibility to $20 billion in total CPU revenue in 2026 (FY2027); management sees 4 different use cases for Vera, which are Vera with the Rubin GPUs, Vera as a standalone CPU, Vera with CX-9 for storage, and Vera with CX-9 for security; the $200 billion CPU addressable market for Vera is specifically for Vera as a standalone CPU; management sees the Vera CPU as being supply constrained throughout the life of a Vera Rubin; an AI agent is a harness around an AI model, and this harness runs on a CPU, and the tools the harness utilises also runs on a CPU; Vera was designed to be an agentic CPU; traditional CPUs have many cores that are rentable, but agentic CPUs are designed to generate and process tokens and this is a strength of the Vera CPU; management sees the Vera CPU as the second largest driver of NVIDIA’s revenue beyond the $1 trillion in revenue-visibility management has for Rubin and Blackwell

Agentic AI and reinforcement learning represents new growth opportunities for CPUs. Building on the success of our Grace CPU, Vera is arriving just in time to meet this inflection. Built on custom ARM cores and codesigned end-to-end with Rubin GPUs and NVLink, Vera will deliver up to 1.5x faster performance per core, 2x performance per watt and 4x density per rack compared to x86-based alternatives. Vera CPU opens a brand-new $200 billion TAM for NVIDIA, a market we have never addressed before, and every major hyperscale and system maker is partnering with us to get it deployed. We have visibility to nearly $20 billion in total CPU revenue this year, setting us up to become the world’s leading CPU supplier…

…4 ways — let me just start with the one that you already know. The first way is Vera Rubin. And we’ll sell millions of Rubins, and every 2 of them is connected to a Vera. And of course, we price those 2 and they’re properly priced. And so that’s #1 use case. The second use case is Vera standalone CPU. The third is Vera with CX-9 and the software stack for storage. And then Vera in a — with CX-9 with a software stack for security and compute isolation and confidential computing. Okay, so each one of those use cases is built on Vera. And my sense is that we’ll be supply constrained throughout the entire life of Vera Rubin. There are 4 different use cases of it. And — but anyhow, the answer to your question is — of the $20 billion is a stand-alone…

…An agent is essentially what people call a harness. The agent has a harness that does the — and the harness could be OpenClaw, it could be Hermes, code — Claude Code is essentially a harness around Claude around the Opus model. OpenAI’s Codex is a harness around the GPT-5.5 model. And so these are harnesses. And these harnesses provide for things like IO, orchestration, memory management, tool use connected to tools, for example, browsers and things like that, C compilers, python compilers. And so the harness runs on CPU. And the tool use runs on CPUs. So for example, if the AI were to do a search or do a browser, use a browser that would run on the CPU…

…Vera was designed to be an agentic CPU. The CPUs of the past were designed to have many cores so that it could be easily rentable. People rented cores. Well, agents don’t rent cores. They just want the work to be done fast. The economics of the past was dollars per core. That’s the economics of cloud computing of the past. The economics of the AI of the future is tokens per dollar or dollars per token. And so what we need to do in the future is to generate tokens, process tokens as fast as possible, and that’s what Vera does incredibly well…

…[Question] Back at GTC, I believe you discussed $1 trillion visibility into both your Rubin and Blackwell platform revenue. But I believe that excluded things like LPX, Rubin, CPX and the Vera CPU racks. Can you maybe give us a sense about whether the Vera CPUs are going to be the biggest source of upside above and beyond that $1 trillion?

[Answer] In terms of incremental above the $1 trillion, I would say, one, the continued growing of share of the Frontier AI models. I’m expecting to grow more share. And so I’m expecting that to grow. Number two, we didn’t include any Vera CPU, stand-alone CPU in that number. And so I expect that to be the second largest. The TAM is, of course, quite large in agentic systems, and all of our customers are quite excited about Vera and we’re going to sell a whole bunch of Veras. And then third would be LPX, because as I explained earlier, LPX is designed as a — because of its SRAM architecture, it has the benefit of very low latency and very, very high interactivity, but it’s — also its throughput, its context processing ability is also quite limited.

NVIDIA’s next-generation GPU system, the Vera Rubin, is on track for shipment in 2026 Q3 (FY2027 Q3); Vera Rubin can deliver 35x higher inference throughput and 10x greater AI factory revenue compared to Blackwell systems; Google Cloud will be supporting 960,000 Rubin GPUs across multiple sites for customers; management thinks every single frontier AI model company will be adopting Vera Rubin once it’s launched, and that Vera Rubin will be even more successful than Blackwell even though they are unsure if Vere Rubin will ramp as quickly as Blackwell

We are on track to commence production shipments of Vera Rubin in the second half of this year starting in Q3. By integrating 7 purpose-built chips across 5 accelerated racks, Vera Rubin will deliver up to 35x higher inference throughput and up to 10x greater AI factory revenue compared with Blackwell. As an early adopter, Google’s A5X bare metal instances, which can support up to 960,000 Rubin GPUs across multiple sites can enable customers to run their largest AI workloads on NVIDIA’s optimized infrastructure…

…Every single frontier model company will jump on Vera Rubin from the get-go, and that wasn’t true before on Blackwell. And so Vera Rubin is off to a tremendous start and will surely be more successful than even Grace Blackwell…

…[Question] You mentioned GB300 is sort of the fastest ramp in the company’s history. How should we think about Vera Rubin against this benchmark. It’s obviously a new architecture at the silicon level, but in similar rack. Does that mean we should expect a similar slope to the Vera Rubin ramp as the GB300?

[Answer] It’s hard to say at this point what will be a faster ramp. But again, we have demand already planned, we’ve got POs. We’ve got almost all of our major customers ready to go, and these are very complex systems that we need to put together. So I think it’s just about the timing that it’s going to take for us to get that into market. Nothing else other than getting from production of all of the different systems that we have ready for order.

NVIDIA is yet to generate revenue from China and management does not know if the company’s AI chips will ever be allowed into China

While the U.S. government has approved licenses for H200 to be shipped to China-based customers, we have yet to generate any revenue, and we are uncertain whether any imports will be allowed into the country.

NVIDIA’s Physical AI revenue has exceeded $9 billion in revenue in the last 12 months; NVIDIA will power Uber’s robotaxi fleet in 30 cities and 4 continents by 2028; companies building industrial, surgical, and humanoid robotics are using NVIDIA’s technology; management thinks physical AI encompasses industries that have been untouched by IT (information technology) for the past 30 years, but they will soon be impacted by AI

Our physical AI continues to gain momentum, exceeding $9 billion in revenue over the last 12 months. Our partnership with Uber will power the robotaxi fleet across nearly 30 cities and 4 continents by 2028. And in robotics, leading companies across a range of industrial, surgical and humanoid applications are building on NVIDIA’s technology to develop and deploy at scale…

…When I talk about physical AI, and I talk about how the rest of the $100 trillion industry that has not been affected by — impacted by IT in the last 30 years. It’s about to be impacted by AI.

NVIDIA has increased inventory purchase commitments to $145 billion; NVIDIA is facing supply challenges

 In Q1, we increased total supply, inclusive of inventory purchase commitments and prepaid to $145 billion. While we are not immune to supply challenges, we remain confident in our ability to support the growth opportunity ahead with our intense focus, scale and long-standing partnerships with critical suppliers continuing to serve us well.

NVIDIA’s management thinks every base station in the future would be an AI-powered radio network

In the future, every single base station, every single radio network would become an AI-powered radio network.

Frontier AI companies are growing revenues in 1 month what older SaaS companies took a decade to achieve

Frontier AI companies, both Anthropic and OpenAI growing at an incredible pace. The fact that they can grow within 1 month, what some of the SaaS companies would have taken a decade to grow tells you something.

NVIDIA’s management thinks industrial AI will likely not be delivered via the cloud; the hyperscalers were happy to adopt AI first because they focused mostly on consumer applications where the stakes are lower but for industrial applications, AI needs to be really capable, safe, and productive before adoption can happen; right now, industrial AI has developed slower than consumer AI, but management thinks industrial AI will be even larger than consumer AI in the future

Many industrial companies, there’s no choice, but to put the computer where the context is, where the action is, you can’t put that in the cloud. It has to respond reliably, quickly every single time, can’t imagine a chip plant, a chip fab being connected to a cloud service provider, doesn’t make any sense…

…Hyperscale developed AI first for a lot of reasons. They have great computer science. They have excellent data center capability. And they also focus largely on consumer applications, which, if not perfect, is not the end of the world. It enhances the service — so long as it enhances the service. And so for many of the other applications, industrial applications, enterprise applications, until the AI is very capable and does really productive work and does it safely, and it could do it in a way that can actually generate impact and income, it doesn’t really get used. And so you expect the second category to develop slower than hyperscale, and you could see that in the numbers. However, long term, if you look at industrial and enterprise, clearly, that’s where future economics is going to be because it represents some $50 trillion, $80 trillion of the world’s economy. And so — and it’s going to be larger than that because of AI.

NVIDIA’s management thinks sovereign AI clouds will not want to use custom or semi-custom AI chips

The sovereign AI clouds. And so there’s a whole category of data centers that semi-custom chips just don’t apply because these data centers want to buy systems, they want to operate systems, they don’t want to design, they don’t want to build it themselves.

NVIDIA’s management sees the company taking market share in inference really quickly partly because of its new partnership with Anthropic; management thinks most of the inference taking place in AI data centers outside of the hyperscalers will be on NVIDIA

we are growing share in inference, and we’re growing share in inference very, very quickly. And the reason for that is this year, the number of frontier model companies grew. And so there’s Cursor and Perplexity and there’s some new model companies, TML and Reflection and the list goes on. And so the number of frontier model companies has grown, and we added Anthropic to our partnership this year. They’re expanding incredibly fast. We’ve partnered with them to secure computing capacity across Azure, AWS, CoreWeave, I forget who else we’ve already announced, but there’s a whole list of others that we are bringing online for them. And so the amount of capacity that we’re going to bring online for Anthropic this year and next year is going to be quite significant, very significant. And so we’re growing and our coverage of Anthropic has been largely 0 until just recently. And so we’re gaining share tremendously fast in inference…

…Everything that I’ve just explained in the inference question is really focused on hyperscale. Remember, there’s a whole second category of AI data centers that we serve almost uniquely. Now this segment is very fragmented. It requires a fairly integrated — a really well-integrated platform solution and a very large go-to-market. And that segment, all of the inference, 100% of that — the vast majority of that is NVIDIA.

NVIDIA’s management sees the LPX server rack as a specialty rack designed for low latency and high token rate but with low throughput

The LPX is designed for a low latency and high token rate. But its throughput is low. Its throughput is low. Its model size capacity is low. And its context processing, its ability to absorb a lot of context, for example, for software coding, for agentic workloads, its ability to absorb a great deal of context is lower. And so the challenge is simply, and I’ve explained before that the use case for LPX is not broad. It’s intended for somebody who has a fairly large portfolio of different types of token services. And for the high token rate, maybe these services are quite premium and the number of customers is not significant, but the token rate is very high.

Okta (NASDAQ: OKTA)

Okta’s management sees each AI agent in an organization as a new identity; AI agents are a rapidly-growing identity category, but they are the least governed; Okta brings agents under control by treating them as identities that can be managed and governed by existing identity management systems; management thinks that there will be more AI agents than humans over time, so the identity becomes increasingly important; all of Okta’s top 100 customers are deploying AI agents, but they are mostly doing it in a haphazard way in terms of security; management is seeing companies start to realise the importance of security for AI agents; management believes that companies will be getting their agentic capabilities from many different platforms; 90% of Okta’s customers have agents in production, but only 22% are confident in the governance of the agents

The future of technology is agentic. For Okta,, this represents a tremendous opportunity and an even greater responsibility. Every agent inside an enterprise is a new identity. Today, AI agents are the fastest-growing identity in the enterprise but also the least governed. Okta helps bring agents under control by treating them as first-class identities that can be managed and governed by their existing identity management system. We believe, over time, most large enterprises will have more agentic identities than human ones. This shift broadens the attack surface because every agent comes with credentials privileges, and the ability to act on a user’s behalf. In turn, this raises the strategic value of the identity layer because governing autonomous systems requires the kind of control, audit, continuous intent-driven authorization and real-time enforcement only an identity platform can deliver…

…I’ve spent the last 6 months, I’m on this goal to talk to in-person face-to-face with our top 100 customers, about 75 customers in. And when you mix that with a bunch of other conversations, here’s what’s going on, everyone is deploying agents in some way, shape or form. But they’re really just starting to think about and put in programs in place to lay out the rails of governed managed adoption. So a concrete example is you’ll have a development team that is using cloud code, but it’s connected to GitHub and their JIRA system with static tokens in the local developer box. So that company is viewing agents, but they’ve really done it in a haphazard nonsecure way. And what’s happening now is they’re figuring out those rails. They’re figuring out how they’re going to have secure connections, have a system to monitor where all the agents are, have the ability to support it for multiple platforms…

… I think what I’m seeing is that Boards and CEOs are saying, we know this agentic thing is real. We’ve got to put the guardrails in place for that. and we know that security is real, and we’re going to spend money on that. And it’s, the reality of it, Brian, is that it’s the fundamentals. It’s identity. 80% of breaches are go through identity. And you know you have to patch your systems. You know you have to have a good multilayered defense and Zero Trust so you can defend for multiple ways…

…there’s a few fundamental truth right that are going to play out. I think, one is that they’re going to get agentic capabilities from many, many companies. They’re going to have different platforms. They’re going to have hyperscaler platforms. They’re going to have Foundation model platforms. They’re going to have open source platforms. They’re also going to get agentic capability from apps. Salesforce is going to have there. Workday is going to have their ServiceNow is on and on…

…Customers have a problem today. They have a problem today where over 90% of them have agents in production, and only 22% of them are confident to have them governed.

Okta’s management sees 3 advantages the company has in securing AI agents, namely, (1) distribution, where Okta can extend its identity system to AI agents, (2) product breadth, where Okta is the only vendor that address both sides of the agent security problem, and (3) neutrality, where Okta allows customers to choose whichever cloud provider and agentic platform they want; Okta’s 3 advantages in securing AI agents are mutually reinforcing; Okta as a neutral identity layer, can help customers avoid vendor lock-in for agentic capabilities

To help our customers confidently secure this shift, we’re building on 3 unique advantages, each with powerful network effects: distribution, product breadth and neutrality…

…In the agentic era, identity becomes even more foundational. When a customer secures their agents with Okta, they are not taking on a new platform; they are extending the trusted foundation they already rely on with Okta. We’ve already seen how our customers benefit from this expansion in other parts of our business. Customers are finding value in Okta’s unified identity system as Okta in governance was once again the leading contributor among our new products. This distribution flywheel is evident in our results…

… Our second unique advantage is product breadth. We are the only vendor with solutions that address both sides of the agent security problem…

… The third unique advantage is neutrality, which is more important than ever. The AI landscape is opting rapidly. Customers need an identity solution that frees them to choose whatever technology serves their business best without fear of vendor lock-in. As the leading independent and neutral identity platform, Okta gives organizations the flexibility to do exactly that. In the same way, enterprises run workloads across multiple clouds, they are deploying agents across various platforms like OpenAI, Anthropic, Google, Microsoft, Salesforce and a growing set of open source frameworks. Managing and securing an autonomous workforce requires a neutral, independent identity layer that others can’t provide. In practice, cloud providers, model providers and agent platforms are partnering with Okta to securely manage agent identities as they continue to proliferate across the enterprise…

…These 3 advantages are unique and mutually reinforcing. The more organizations use Okta to secure their agents, the more identity signals flow into our platform and the stronger our governance and detection becomes, and our neutrality allows us to secure current and future agent frameworks for customers, allowing Okta to capture more of the addressable market…

… I think, one is that they’re going to get agentic capabilities from many, many companies. They’re going to have different platforms. They’re going to have hyperscaler platforms. They’re going to have Foundation model platforms. They’re going to have open source platforms. They’re also going to get agentic capability from apps. Salesforce is going to have there. Workday is going to have their ServiceNow is on and on. Everything is going to be agentic — have agentic capabilities. But we know they’re going to have a directory of these things or roster everything, a policy layer and they’re going to have to make sure they can connect to things. And so we’re seeing our customers — it’s a kind of a no-regrets move to pick this independent and neutral identity layer that can solve those fundamental problems without locking them in 

Okta has two product categories to address both sides of the agent security problem; Okta for AI Agents became generally available in April 2026 and provides enterprises with centralised visibility into agents with identity governance capabilities; Auth0 for AI Agents is for developers building AI agents and it helps developers ship secure agents inside their products; Okta had strong pipeline generation in 2026 Q1 (FY2027 Q1), driven partly by Okta for AI Agents and Auth0 for AI Agents; the opportunity for Okta for AI Agents is not limited to existing workforce customers, and it extends to every enterprise with a multi-platform AI strategy; Okta for AI Agents is integrated with ServiceNow and Amazon Bedrock; there is a lot of interest in Okta for AI Agents and Auth0 for AI Agents, but they are still early and are currently not contributing materially to the business; management believes Okta for AI Agents and Auth0 for AI Agents will become really big products; Okta can give agents specific access to different apps based on access management; the pipeline for Okta’s agentic products is bigger than anything management has ever seen; the pipeline for Okta for AI Agents is bigger than that for Auth0 for AI agents because companies are further along with deploying internal agents than building agents into products; management is already starting to see some pull-through of demand for Okta’s non-AI products because of Okta for AI Agents

Okta for AI agents, which became generally available last month, gives enterprises a single control plane to discover, govern and manage agents across their organization. It is the first and best implementation of the blueprint for the secure agentic enterprise, an industry framework for bringing agents under control by answering the three questions that have dominated my customer conversations over the past several months. Where are my agents, what can they connect to and what can they do? Enterprises need to maintain visibility and control over their sprawl of agents, ensuring they have governed identities, consistent access policies and ways to shut them down to secure every agent into end. Okta provides customers with centralized visibility into agents with identity governance capabilities, including ownership assignment and life cycle management while giving IT and security teams, critical security controls to deactivate rogue agents. For developers building AI agents, Auth0 for AI agents provides the identity foundation to ship secure agents inside their products. Auth0 for AI agents secures agents, APIs and users effortlessly for B2B, B2C and internal apps, all backed by the enterprise grade Auth they already trust. In tangible terms, pipe generation in Q1 was strong, driven in part by these 2 new products…

…Okta is the only modern identity platform purpose-built to sit above the agent ecosystem, and it federates with whatever identity provider a customer runs. That means the opportunity for Okta for AI agents is not limited to our existing workforce customers. It extends to every enterprise with a multi-platform AI strategy…

…We’ve entered into a partnership with ServiceNow that integrates their AI control tower product with Okta for AI agents…

…Okta for AI agents now integrates with Amazon Bedrock Agent core to provide customers with identity governance capabilities for their agents…

…They’re figuring out how they’re going to have secure connections, have a system to monitor where all the agents are, have the ability to support it for multiple platforms. And that’s why you’re seeing the record interest and the record pipeline for what we do with Okta for AI agents and Auth0 for AI agents. The reality is of these products, it’s still early. They’re not materially contributing to the business in Q1. In fact, we’re still being prudent in our guide. They’re not even — they’re a little bit in the guide, but not significant in the guide but it’s going to be big…

…So it’s very natural to say, who can really manage these connections and give me these governed rails for all these secure connections, where my agents are, what they’re doing, what can they do? It’s a natural fit for us. So I think as they build out this infrastructure, we’re in this really great position to have to be a super, super meaningful part of the business and TAM over the next several quarters and several years…

…We tell you who your agents are. There’s a directory of agents. We can scan multiple platforms and multiple systems and give you that source of truth of where your agents are and we can help you set a policy on what they can connect to. Agents can this from teams and they can read this from Slack, and they can read this information from Snowflake and they can you read this from GitHub. So it’s like a single sign-on or access management…

…[Question] You mentioned a building pipeline on AI. I wonder if you might hope with the size of this maybe relative to other products in the past

[Answer] The pipeline is bigger than anything we’ve ever seen…

…[Question] The difference between AI for agents in Auth0 versus Okta, the 2 different platforms. Maybe just help us appreciate the technology aspect of that? And is there like a big difference in size of pipeline between the 2? 

[Answer] They’re both healthy, the Okta pipeline is bigger. And I think that’s because it’s a little bit of a — I think the companies that are figuring out how to manage and deploy internal agents are further along than people building agents into their products and into their websites…

…We’re seeing that the products we’ve offered for AI agents in this blueprint, this vision we have for the industry and agents is raising the strategic level of conversations, which is pulling in other products and helping us displace legacy faster and sell more of our existing products and our newer products into new customers in the base than we would be otherwise. I say that because to make it clear that the AI agent products are still, is still immaterial, the contribution with Okta for agents going GA in April. They had a good quarter, but it’s still a small base. So the pull-through is real already though.

Okta’s management believes that no single company can address the agentic security market; Okta has entered into partnerships with AI leaders ranging from ISVs (independent software vendors) to AI vendors and hyperscalers; the ISVs include ServiceNow, while the AI vendors include Anthropic and OpenAI; Okta is partnering with Anthropic for its Project Glasswing cybersecurity initiative

Neutrality becomes even more important when it comes to technology partnerships and integrations, like the traditional cybersecurity landscape, no single company can address the agentic security market alone. That’s why we’ve partnered with AI leaders from ISVs to hyperscalers to frontier AI vendors, and I’d like to highlight a few of those partnerships today.  We’ve entered into a partnership with ServiceNow that integrates their AI control tower product with Okta for AI agents. Our partnership with Google brings centralized identity guidance and access control to Google’s agent gateway. Okta for AI agents now integrates with Amazon Bedrock Agent core to provide customers with identity governance capabilities for their agents. We were a launch partner for OpenAI’s release of GPT 5.5 trusted access for cyber. And finally, we’re collaborating with Anthropic in a number of ways to testing Anthropic’s preview model as part of Project Glasswing to a new integration between Okta Identity Security Posture Management and the Cloud compliance API.

Okta’s management is pricing agentic products as an increase to a user’s monthly price because (1) management is seeing customers want to consume agentic products via this pricing model, and (2) agents are currently mostly deployed on behalf of users; management thinks pricing models for agentic products will evolve over time and the software industry is still figuring it out; management is seeing that the average deal size for AI-specific deals is much larger than the average deal size for other types of deals; Okta does not have unlimited-consumption AI deals

And so the way we’ve done pricing for our products is exactly in line with how our products have been priced in the past. They’re priced on, it’s an uplift to a named user or it’s an uplift to a monthly active user. Now you might say, “Hey, Todd, but agentic — agents are this new thing and why are you pricing them on an active user or a named user price?” And that’s for two reasons. One reason is that’s the way customers want to consume it right now. And two, the majority of concrete use cases in the world right now for agents, it’s on behalf of the user. It’s an agent working on behalf of a software developer. It’s an agent working on behalf of a support rep. It’s an agent working on behalf of someone in accounting. So it’s very natural how they want to buy it and how they’re actually being used. So it’s an uplift on a named user, and it’s uplift on an active user.

Now we fully understand that, that’s going to evolve. And there will be more autonomous agents that have to be priced not by user base or not an extensive user. They have to be the unit has to be the number of agents. It’s a little bit tricky because it’s very hard to define the number of agents because some person might say, “Oh, I have 1,000 agents, but it’s really kind of 1,000 copies of the same agent or 1,000 instances of the same agent. In other cases, it might be literally 1 instance of an agent acting for many, many different use cases. So the industry is kind of figuring that out, and we’ll figure that over time how to monetize and price that now…

…The average deal size for these AI-specific deals is significantly larger than the average deal size for the rest of the company…

…[Question] You guys are doing deals where basically the contract is for an unlimited number of agents. The good thing is in those deals, I’m hearing that the spend is very, very high relative to your existing spend and other products. But the risk there is what if the customer doesn’t get to unlimited agents, so there’s downside renewal or other things that could happen. So how are you approaching that dynamic with customers in factoring in the contracts?

[Answer] There’s no unlimited. If there is unlimited, it’s time bound. So there have been some deals where we’ve done like a year, and then it’s like we’re going to figure out after a year what the — how the use case really unfolded and how to snap it back to the kind of normal pricing model. But there’s no — it’s not unlimited in the sense of time and volume.

Okta’s management is seeing the leaders of AI companies being worried about the durability of their revenues

If you look at the — particularly the AI landscape, I was having dinner with a bunch of CEOs of companies, different sizes, and everyone is super worried about their spend in their products and their revenue in their products being not durable because it’s token spend, and they worry about the products being used and then and maybe someone is going to look at the spin and stop spin the token spend

Okta’s governance-related product portfolio is still performing well; Okta’s privileged access product is not as mature as the governance portfolio

We’re very excited about our AI products. But governance continues to be a strength for us. We talked over the past couple of quarters about how governance has evolved from being primarily a cross-sell add-on product to also now being a land product. And we are seeing sizable land opportunities, starting with governance at some companies that are displacing systems that they’ve had in place. So we’re very excited about the enterprise readiness and robustness of our governance product and the rural deployments…

…Privileged access is further behind governance on that maturity curve. It came to market a little bit later. We’re continuing to invest heavily in it and we did an acquisition back Q3 at Axis to add capabilities to that. And we’re continuing to invest in that breadth of portfolio, kind of rounding out the identity security fabric in addition to all the momentum that we’re seeing with our great success in the AI product.

ServiceNow wanted kill switches for rogue AI agents; Okta can help sever connections and access that any rogue AI agent has

ServiceNow is, as you mentioned, super interesting. They are — their product strategy is they want to be the control tower for all AI agents. And what is, what they were really interested in was this kill-switches capability. When agents go awry and agents aren’t following the policy, how do you shut them down, and that can mean a lot of different things. That can mean actually stopping the running of the agent that can mean quarantining the agent at a network level, there’s many different strategies. The one thing we do really well and that they wanted from us is the ability to sever the connections, the access tokens, the actual logical connection at the authorization layer to the back-end resources, and we’re really good at that. That’s kind of the core of our product. What can these things connect to, what can they do.

Okta’s management thinks that cybersecurity in the future will take multiple companies to secure, and the large AI model providers cannot do it by themselves

I think in terms of the model providers, how they’re going to play in the broader cyber ecosystem, it’s going to take a village. I think we’ve seen that in cyber forever. I think consolidation in cyber never seems to work. All seems to be — gets to a certain point and then new threats emerge, and the companies that are trying to consolidate cyber have such a hard time integrating amongst themselves. It kind of fractures a part. And I think that will continue. I think cyber in the agentic world is going to take a village, and we’re going to have to make sure it’s integrated together and make sure we have layered defenses. And that’s why I think it’s really healthy to be coming into this conversation with this open mindset of, hey, we have our lane, we’re going to try to provide the best identity foundation in the world and then connect around that in a standard way that helps customers get great outcomes.

The cost of inference is real at Okta, and management thinks more companies using AI models will be scrutinising their inference costs in the near future; management is optimistic that Okta can manage inference costs and drive positive ROI (return on investment)

The cost thing you’re talking about is real, the inference costs and the AI tooling and what it’s driving in terms of expenses. And I think you’re going to see at Okta, and then over the whole industry over the next 6 to 12 months, you’re going to see a little bit more scrutiny in terms of what are you getting from all this, the inference cost you’re spending, how is it translating, which is not surprising given the amount it’s rising across the industry.  And we’re going to come out the other side with more balanced ROI-driven investment portfolio of how we spend these things. And we’re optimistic about how it’s going to work out very well for us.

Salesforce (NYSE: CRM)

Leading AI companies are all Salesforce customers, in particular, Slack customers; Slack was half of Salesforce’s $1 million-plus wins in 2026 Q1 (FY2027 Q1), up 80% year-on-year; Slack is AI startup Anthropic’s core operating system; Slackbot is also a MCP (model context protocol) client; Slack MCP has seen 1 million users in 6 weeks; Slack’s agentic work units (AWUs) was up 350% sequentially in 2026 Q1 (FY2027 Q1); management thinks that in 2 years, there will be more agents using Slack than people; management thinks agents need the context and data that resides in Slack; internal usage of Slackbot by Salesforce has led to 3.8 million hours of annualised productivity gains; Anthropic is one of the biggest users of Salesforce’s Sales Cloud; Slackbot has increased the productivity of Salesforce by around 3%; management sees Slack as the place where humans and agents work together; management sees the work graph of enterprises living in Slack, which is already one of the richest work contexts, becoming even richer over time; 3 million custom apps were built by the community on Slack in 2026 Q1 (FY2027 Q1), up 8x sequentially, and 250,000 of the custom apps were 3rd-party AI agents, which doubled sequentially; management sees Slack on a fast track towards being a $10 billion cloud

OpenAI, Anthropic, Google, companies building the future of AI, all of them Salesforce customers, all of them Slack customers, building these incredible new capabilities with Agentforce…

…Slack, which every AI company in the Bay Area here is using to run their business, including OpenAI and Anthropic, transforming our customers into agentic enterprise. Slack was nearly half of our 1 million-plus wins this quarter, up 80% year-over-year…

…Anthropic calls Slack its core operating system, and that’s what Slack is becoming for every enterprise. All of our apps are Slack first. So now a service agent can summarize a case, update the record, escalate to a human right in Slack. And Slackbot is also an MCP client, so you can tell it to create a purchase order in NetSuite or update a project in Jira, and it happens, no switching tools. We’ve seen 1 million users of Slack MCP in the first 6 weeks, and Slack AWUs grew nearly 350% quarter-over-quarter.

In 2 years, there’ll be more agents using Slack than people. Every one of those agents needs the context and the data and the insights directly from Slack. Every workflow needs the data. Every action needs the integration and every customer needs to see what’s happening across the entire business. We have the largest collection of trusted CRM context ever assembled between Data 360, Informatica, MuleSoft, Tableau manage and deliver all that context so that any agent can reason, act, and deliver real outcomes…

…Slackbot, which is embedded directly into the flow of work, is now our fastest adopted AI tool in Salesforce’s history, driving 3.8 million hours of annualized productivity gains for our employees…

…Anthropic is one of our biggest users of CRM of Sales Cloud…

…Slackbot is our personal assistant. It has increased the productivity of the whole company around 3% more or less…

…When we say agents and humans work together, you experience it in Slack. When you’re in a channel and suddenly in a lot of these — especially I see it now in my engineering channels, like half the time, somebody puts a question or a request on a Slack channel and the agent is listening and answering it, developers do a PR request in Slack. And then suddenly, the agent is picking up and trying to do it. They want status reports. So I think Slack is where people can really understand the manifestation and they’re all asking questions as a human and Slackbot is even a better way of articulating that in a packaged way…

…Because that work graph that will become one of the richest work context in the enterprise is getting richer and richer. So we build — I mean, the community built 3 million custom apps on Slack in Q1. That’s 8x quarter-on-quarter. I mean there is a huge boom. Out of those custom apps, there were 250,000 that were AI agents that were built, third-party AI agents, and that grew more than doubled in quarter-on-quarter, grew eightfold year-on-year…

…I’m not giving guidance by what I’m saying, but sales is a $10 billion cloud already. Service is a $10 billion cloud already. Data is already a $10 billion cloud. I think when we see the growth rate that’s happening inside Slack, you saw the ACV was incredible in the first quarter. This is going to be fast track from something we bought with less than $1 billion that I’m sure we’ll be talking in short order about Slack being a $10 billion cloud as well.

Agentforce ARR reached $1 billion in 2026 Q1 (FY2027 Q1) (was $800 million in 2025 Q4, up 169% year-on-year); Agentforce and Data 360 reached nearly $3.4 billion in ARR (annual recurring revenue) in 2026 Q1 (FY2027 Q1) (was $2.9 billion in 2025 Q4, up 200% year-on-year); 50% of Agentforce and Data 360  bookings in 2026 Q1 (FY2027 Q1) were from expansions by existing customers; management recently announced Agentforce Coworker, where every Salesforce application now comes with a built-in autonomous agent; bookings for A1E and A4X, Salesforce’s premium SKUs that include agentic capabilities, was up 60% year-on-year in 2026 Q1 (FY2027 Q1); top 10 customers by AWUs (agentic work units) in 2026 Q1 (FY2027 Q1) increased their total Salesforce spend by 1.5x in the last 12 months; Agentforce allows every user of Salesforce to create agents

We’re seeing incredible demand for Agentforce with ARR now greater than $1 billion. And combined with Data 360 and Informatica Cloud, we’ve delivered $3.4 billion in AI and Data ARR. 50% of Agentforce and Data 360 bookings were from existing customers expanding their commitment.

…Very excited about our new Agentforce Coworker, which we announced last week. If you haven’t heard about that, every single one of our Salesforce applications now comes with a built-in autonomous agent. No complex configuration. You just turn it on. It becomes your coworker, finding answers, taking action, getting work done fast. To give you an idea of the impact that Coworker will have, people search for information inside Salesforce 1 billion times a month. Coworker turns search into answers and answers into action…

…Agentforce ARR surpassed the $1 billion mark this quarter. Our largest applications, sales and service saw year-over-year seat growth with humans and agents both expanding on the platform. Bookings for A1E and A4X, our premium SKUs anchored in sales and service, including the value from our agentic capabilities, grew nearly 60% year-over-year. As customers adopt Agentforce, they expand across our platform. On average, our top 10 customers by Q1 AWU usage have increased their total Salesforce spend by 1.5x in the last year…

…Those of you who are Salesforce users, the millions of people who use Salesforce every day, the search bar is a critical part of how the application operates. Now Agentforce is that search bar. So you can not only search and aggregate and get insights into information throughout every single app we have, but also create agents, and those agents can appear in Slack and Microsoft Teams and other applications, even in an app that’s going to run directly on your phone called Salesforce Coworker.

Salesforce has processed 28.6 trillion tokens to-date in 2026 Q1 (FY2027 Q1), up 152% sequentially (was 19 trillion to-date in 2025 Q4); Salesforce has delivered 3.8 billion AWUs (agentic work units) to-date, up 111% sequentially (was 2.4 billion in 2025 Q4)

To date, we processed 28.6 trillion tokens, up 152% quarter-over-quarter and converted them into 3.8 billion, as I mentioned already, agentic work units for our customers, up 11% — sorry, up 111% quarter-over-quarter. 

Salesforce acquired Qualified in 2026 Q1 (FY2027 Q1); Salesforce has integrated Qualified’s sales development representative (SDR) agent, Piper, into Salesforce; more than 700 customers are already using Piper; Piper is deployed on Salesforce’s website and is engaging with 50% of the website’s traffic, delivering 45% more pipeline than traditional web agents

In Q1, we completed the acquisition of the Qualified and integrated Piper, their SDR agent, into Salesforce. Brought all those great Salesforce alumni back home. More than 700 customers are already using Piper. It’s an incredible success, and we deployed Piper on salesforce.com, as I mentioned. So you’re going to be able to use it firsthand. I think that’s so great. It’s engaging 50% of our traffic and qualifying thousands of leads and delivering 45% more pipeline than traditional web agents.

Salesforce’s management recently announced Headless 360, which makes all of Salesforce accessible through MCP (model context protocol) clients, APIs, and CLA (command-line agent) prompts; since Headless 360’s launch in April 2026, Salesforce has already processed 4.5 million MCP calls and 1 trillion API calls; management thinks Headless 360 expands Salesforce’s addressable market into previously unmonetised areas; management is excited about Headless 360 in 2 areas, namely, (1) Headless 360 making it easier to implement Salesforce with coding agents, and (2) customers getting more value out of Salesforce through Headless 360; management is not seeing customers build in-house applications with Headless 360 to replace Salesforce; Slackbot is an example of a Headless 360 experience; the Headless MCP server for Slack has done 50 million tool calls; Headless is a way for agents to connect to Salesforce with APIs, because agents require slightly different types of APIs than what human developers used when connecting with Salesforce in the past

This quarter, we also announced Headless 360. Again, making all of Salesforce accessible through our MCP clients, APIs, CLA prompts. Headless 360 bringing together the human agents and headless platforms so you can use Salesforce with any coding agent across any surface. It’s going to speed implementations, drive consumption, more actions, more workflow, more data, more intelligence, all compounding across Salesforce. We’re meeting our customers where they are. Since launch in April, we’ve already processed 4.5 million MCP calls into our platform. Q1 alone, we processed nearly 1 trillion API calls, incredible…

…Looking ahead, the Headless 360 strategy that Marc walked through expands our addressable market into surfaces we’ve never previously monetized…

…I think what’s so exciting about Headless is 2 things. One, it’s having a real impact on making it easier to implement with Salesforce. So building out with Salesforce has now become easier than ever because we’ve seen these coding agents, Claude and Codex from OpenAI. As you use these things, what you realize is you need to be able to connect the underlying APIs, which you do through this layer that’s called MCP. And if you can connect those into the coding agents, it makes it faster than ever to implement and deploy Salesforce. And I think we’re seeing that show up in the numbers. Just this quarter alone, Agentforce customers in production grew by 50%. So I think we’re starting to see a little bit of that impact as not just our customers, but also our global SIs across the entire platform, absolutely implementing Data 360, implementing Agentforce, implementing a service. All of this — life sciences, all of this now becomes really just a conversation. So that’s one end.

But the other end is really what we heard from Miguel, which is this is really changing how people get value and consume Salesforce. In my experience, we’re not seeing people take this capability and the coding agents, for example, and try to build all of this stuff themselves. What they want to do is they want to take this capability and they want to use Salesforce in different ways and get more value out of it. So rather than logging into this discrete application and this application and this application to get an answer to one question that might span multiple applications or multiple kind of sources of information, you can now just take these MCP servers and plug them into any tool that you want…

…If you’re a Slack customer, you can get to it right with Slackbot. That’s really a Headless experience as well…

…We announced the Headless MCP server for Slack and Slack has done 30 million — 50 million tool calls…

…When you’re a builder, when you’re out there building something, and this is especially true today because there’s now an ocean of builders that have been created as a result of this coding agent boom. When you go to build something for your business, you, at some point, are likely going to want to connect to Salesforce that is what we see. And it doesn’t matter what platform you’re doing it on. You can be building something on a competitive platform to Salesforce or on Google or AWS or one of our partners. But at some point, you’re going to want to connect into Salesforce. And that’s why those APIs have always been hugely, hugely used. But when you are building with an agent, you need a slightly different type of API. That’s what we call MCP. And so by really putting those MCP servers out and saying, yes, this is how we want people to build.

Informatica has been a successful acquisition, performing the heavy lifting and data management that customers need to move agentic workloads from pilot to production; Informatica has helped drive an acceleration in revenue growth at Salesforce; Informatica’s bookings growth has accelerated significantly since being acquired by Salesforce

Informatica has an amazing acquisition. It performed incredibly well this quarter. It’s doing the heavy lifting and data management that every customer needs to move from pilot to production…

…And now with Informatica as part of Data 360, we’re already unlocking synergies with revenue growth accelerating since the acquisition. This is the flywheel we laid out at our Investor Day, and it’s working. Those signals show up in the headline numbers…

…Informatica was a business that was growing single digit, both on bookings and revenue. In just 2 quarters, we have significantly reaccelerated that the bookings of the chart beyond anybody’s expectation because data is king.

Salesforce deployed Agentforce on its support website 15 months ago and it has already handled 4 million inquiries autonomously; Agentforce now handles 2x what human agents are handling on Salesforce’s support website; Agentforce Sales worked 220,000 leads for Salesforce autonomously in 2026 Q1 (FY2027 Q1), generating a $42 million pipeline; Agentforce Coworker is able to quickly answer questions that would have taken an hour to do so in the past

Since we deployed Agentforce on help.salesforce.com and on 1-800-NO-SOFTWARE, well, only 15 months ago, it’s autonomously handled now 4 million inquiries. It’s now double what human agents are handling…

…Over 25 years, Salesforce has generated tens of millions of leads. We never called back. In Q1 alone, Agentforce sales worked 220,000 leads autonomously, generating $42 million in pipeline, awesome…

…Agentforce Coworker was able to pull together and navigate our complex sales and ERP data to answer questions that just yesterday would have been 60 minutes of swivel chairing between screens and systems. It was pretty cool to see that.

Wine company Vivino is using Agentforce to support 74 million users with just 37 reps; Agentfore has helped Vivino reduce resolution time of customer queries by 70%; cyber security company McAfee has replaced ServiceNow with Salesforce’s Agentforce IT Service; Florida Prepaid is using Agentforce to autonomously handle 75% of business hour calls, and 100% of after-hour calls; cyber security company Fortinet is using Agentforce Sales for predictive lead scoring; Agibank built a sales development representative (SDR) agent with Agentforce Sales

Vivino, the world’s largest wine company supporting 74 million users with only 37 reps, kind of hard to believe, but it’s possible because its agent, Vivina, autonomously handles order status, lookups, account questions more autonomously slashing resolution time by 70%. McAfee has selected our new Agentforce ITSM product or what we call Agentforce IT Service to replace ServiceNow. They are using it for everything, ticket deflection, hardware provisioning, incident management. Florida Prepaid, a college savings plan provider with more than 200,000 accounts is using Agentforce voice to autonomously handle 75% of business hour calls and 100% of after-hour calls…

…Cybersecurity leader, Fortinet using Agentforce sales to power predictive lead scoring. Financial leader, AgiBank now built an SDR agent that instantly qualifies leads on WhatsApp.

Indeed is using Headless 360 to build and deploy Agentforce agents directly from Cursor; Just Eat is using Headless 360 to bring agents into WhatsApp for engaging 350,000 partners across 15 countries; Adecco is excited that agents they are building outside of Agentforce can now leverage Salesforce because of Headless 360; Anthropic’s usage of Slack through 2026 Q1 (FY2027 Q1) has grown 5x partly because they are using Sales Cloud via Headless 360; the presence of Headless 360 has made Sales Cloud even more strategic for Anthropic

With Headless 360, Indeed is building and deploying Agentforce agents right from Cursor and Just Eat Takeaway, one of the leading online food delivery platforms in Europe, we just had them speak to our entire management team with such an amazing story, is using Headless 360 already to bring agents into WhatsApp and other channels, engaging with 350,000 partners across 15 countries…

…Adecco, great customer across the board. They use pretty much every cloud. They went into Data Cloud and Agentforce last year. They did a big commitment in Q1, at the beginning of Q1. They are basically design and AELA, wall-to-wall. They have amazing recruiter agents going there, millions of transactions. They’re moving into voice. When we announced Headless, they called us and they are like, “Wait a minute, this is — let me try to understand what you’re doing.” So now because they are also using other platforms to develop other agents. So they have agents with some of the AI labs that they’re also trying to access our data. Are you saying that now these agents that we are building outside Agentforce can also leverage Salesforce? And we said, exactly, we did it for that. So now there’s going to be a lot of new agents that are going to be accessing our platform…

…Anthropic is one of our biggest users of CRM of Sales Cloud. And obviously, Slack, their usage through Q1 has exploded fivefold because now they are using Sales Cloud from a Headless perspective, and they are approaching it from Coworker, from other applications from Slack, they’re hitting Sales Cloud. So Sales Cloud has become more prominent and more strategic for them than ever because of Headless.

PenFed Credit Union handles 500 transactions every second, and 160 million member transactions annually, and wanted to deliver hyper-personalisation for customers; PenFed Credit Union chose Salesforce to enable the hyper-personalisation and now has 76 agents across various functions; PenFed Credit Union chose Salesforce because it has the products, engineers, and reputation that PenFed Credit Union was looking for in a vendor; PenFed Credit Union built Agent Wingman with Salesforce; in 2026 (FY2027), Agent Wingman will help PenFed Credit Union (1) save $1.6 million, (2) lower call handle time by 10%, (3) lower after-call work time by 50%, and (4) lower held calls by 40%; PenFed Credit Union has agents listening to a phone call with members for transcription; PenFed Credit Union only developed its agentic vision about 2 years ago

[PenFed Credit Union CEO] When we’re competing against 8,000 other firms, we got to deliver hyper-personalization and every transaction, we do about 500 transactions a second, 160 million member transactions a year. They have to be right anywhere in the world real time. So we built our entire platform over the last few years. We went from about 400 platforms down to literally 12 strategic partners. Our call center, our mobile, our web, and our branches all run on Salesforce. Every additional partner or tech siloed capability is a tax on innovation, it’s a tax on speed, and it’s a tax on security. So by building it around Salesforce, I really think it’s taking me 25 years to realize Jim Collins’ Flywheel Effect, we have 76 agents now running across operations, mortgages, IT, HR. All of our areas are adopting it to make our employees be more productive. We like to say they’re bionic employees now. We’re not losing employees. We’re able to add more volume at scale, industrialized scale with the same number of people, and we’re very proud of that…

…How is the decision really made? First of all, does the firm, in this case, Salesforce have the product and service that we need? Second, do you have the engineers, the architects, the professionals to work with my team in order to bring that vision to reality? And then lastly, even if another firm had those first 2, who is the firm standing behind it that can be there through good times and bad times that’s going to stand behind that product or service. When you line up all 3, that’s where a good trusted partnership exists. That’s why we went with Salesforce. So we work with your team literally hand in hand. We said we want to streamline processes. We want to take out latency in the code. We want to do X, Y or Z. Your team was there in the trenches at every level, engineers, architects, building out the vision. But then it’s not just pie in the sky on the white [ sheet ], it’s implementable. We have 76 agents running side by side with our employees. 

A good example is in our call centers. We have Agent Wingman. I’m an aviator, so I think they named it because I like Wingman. Agent Wingman is going to save me nearly $1.6 million this year, has decreased our call handle time 10% this year, 50% reduction in after-call work time and 40% reduction in held calls. So better experience for the member…

…I want my employees to do the knowledge work, building trust in the relationship, not entering what just happened on the phone call. We have agents that listen to the phone call, transcribe it. The human is still in the loop. They approve what was just talked about, but then it’s 360, if the transaction occurred in the branch, web, mobile. So the next person that deals with that consumer, that member, they know exactly the relationship. They know what we might want to sell them next or what they need next for their daughter, their graduation…

…We had the vision when we saw what was possible 2 years ago. You can build it quickly. The most important thing is having the right partner and not to have too many partners. Too many partners slow things down.

UCLA Health has been working with Salesforce for some time; UCLA Health recently consolidated into a single instance of Salesforce’s Health Cloud; UCLA Health recently launched its first experiment with Agentforce, which is a customer-facing virtual concierge; UCLA Health was very cautious about launching a customer-facing virtual concierge

[UCLA Health executive] We’ve been working with Salesforce for quite a few years. But most recently, we’ve consolidated into one single instance of Health Cloud, and we’ve built on top of that with Marketing Cloud, Data 360, and most recently launched our first experiment with Agentforce, and that’s a customer-facing chatbot that just — it’s — right now, it’s only scraping our website to act as a little bit of a virtual concierge to direct patients to where they need to go. It’s helping with find a provider. It’s helping with general inquiries. It’s helping with clinical trials…

…I would say it took a while for us to sort of dip our toe in the water in the customer-facing space. We’re doing a lot on the back end when it comes to research, but this really has an impact on our operations. And we took a lot of precautions. This particular product really helped us from a testing perspective. There were a lot of protocols in place that allowed us to validate every step that we were taking. And that offered a lot of certainty for senior leadership to kind of sign off on the first experiment that we took here.

The use of AI coding tools by Salesforce employees has doubled the amount of features and codes shipped in 2026 Q1 (FY2027 Q1) compared to a year ago; Salesforce’s engineering team has been kept at 15,000 for the past 2 years because of the higher efficiency of the engineers through the use of AI coding tools

In Q1, AI coding tools enabled us to double the amount of features and codes shipped year-over-year, while simultaneously reducing incidents and defects…

…Srini is here at the table. He’s got what about 15,000 engineers, and you’ve had the 15,000 engineers for about 2 years, it’s been mostly flat, right? And I would say that the reason it’s been mostly flat is because we have been using AI to create more efficiency for our engineers. And especially this year, now with these new coding agents, we’re seeing even more dramatic capability.

The biggest way for Salesforce to monetise AI is by selling Flex Credits

The biggest way that we have to monetize AI is with customer-facing use cases by selling Flex Credits, by putting fuel in the tank 6 of the top 10 deals, 6 of the top 10 deals were AELAs, unlimited enterprise license agreement, where we threw in a bunch of Flex Credits and customers are deploying use case after use case, channel after channel.

Salesforce has been able to protect its margins despite investing in AI because it’s not hiring more engineers as a result of higher productivity; Salesforce’s headcount is growing only because of an expansion of the sales team, and that is because AI agents cannot actually sell; Salesforce’s margins are protected despite the company spending a lot with OpenAI and Anthropic

Srini is here at the table. He’s got what about 15,000 engineers, and you’ve had the 15,000 engineers for about 2 years, it’s been mostly flat, right? And I would say that the reason it’s been mostly flat is because we have been using AI to create more efficiency for our engineers. And especially this year, now with these new coding agents, we’re seeing even more dramatic capability. So that’s a key part of our margin story is that we’re not hiring more engineers. We’re not hiring more GA. We’re mostly expanding only in one area.

You can see head count has grown, but it’s mostly growing in Miguel’s area in sales because I think we all realize the one thing that we’re doing here with you selling and communicating that agents are not exactly doing that. They can qualify, okay? They can provide service. But in sales, we still scale because there are so many different parts of the market that we have to get to. So that will be a critical part of expanding our company, but at the same time, expanding our margins…

…It’s not that we’re not spending a lot with OpenAI. We are. We’re using their platform. We’re using Codex, their coding tool. We’re using Anthropic. We’re using their platform and their coding tool Cowork. We’re using both of these platforms.

Sea Ltd (NYSE: SE)

Sea’s management has used AI in Shopee’s search and recommendation systems to improve product discovery; Shopee has AI content tools for sellers to create better product listings, which has helped the purchase conversion rate improve by 14% year-on-year in 2026 Q1; AI-powered advertising personalisation and targeting contributed to Sea’s 80% advertising revenue growth in 2026 Q1; management is exploring an AI shopping assistant for buyers that can deliver personalised recommendations and cost savings; management is building an AI agent for sellers that can be a business advisor; the AI shopping assistant and AI agent for sellers are both in the early stages

We have taken a practical resource-oriented approach, embedding AI into our operations to drive better outcomes for our users and greater efficiency across our platform. This is already making a meaningful impact. AI-powered enhancements to our search and recommendation algorithms have led to better product discovery. Our AI-generated content tools are helping sellers create more compelling product listing. These efforts supported a 14% improvement in purchase conversion rate year-on-year in the first quarter. And AI-driven personalization and targeting helped to contribute to the strong year-on-year ad revenue growth we saw this quarter…

…For buyers, we are testing an AI shopping assistant that leverages purchase history and preferences to deliver personalized recommendations and optimize savings.  For sellers, we are building an AI agent that acts as a virtual business adviser, providing diagnostic and actionable insights on shop performance. Both are in early stages with plans to roll them out more widely over time.

Around 80% of Sea’s customer queries are now handled by its AI chatbot; AI has reduced Sea’s customer service cost per contact by 30% year-on-year in 2026 Q1 while maintaining satisfaction

Around 80% of customer queries are now handled by our AI chatbot. AI usage helped reduce customer service cost per contact by around 30% year-on-year, while maintaining high satisfaction rate.

Tencent (OTC: TCEHY)

Tencent has made significant progress in its Hunyuan large language model in the last 6 months; Tencent has overhauled its foundation model team and system and processes for pretraining and reinforced learning; management has moved away from from chasing public model benchmarks that can be gamed and has chosen to evaluate Tencent’s models with the latest exams, human tests, product feedback and in-house tasks; management launched Hunyuan 3 Preview in April; Hunyuan 3 Preview was designed to deliver comprehensive intelligence with cost efficiency; Tencent reduced Hunyuan 3 Preview’s inference costs significantly by designing inference together with the model; Hunyuan 3 Preview is already deployed across 131 of Tencent’s products, including Yuanbao, QQ, and WorkBuddy; Hunyuan 3 Preview has been ranked 1st on OpenRouter by token usage since April 28, even after its free period ended on May 8; the Hunyuan team is already working on a larger parameter model; Hunyuan 3 Preview is a smaller model, but is still very capable; Hunyuan 3 Preview is significantly better than Hunyuan 2 for agentic work; Hunyuan 3 Preview’s total token usage is at least 10x compared to earlier generations; Hunyaun 3 is currently not fully integrated into Weixin because it depends on Weixin’s own evaluation on what’s the best model for users; the adoption of Hunyuan 3 Preview in actual use cases has been much better than management expected 

Over the last 6 months, we have made significant progress on our Hunyuan large language model..

…We started the initiative by completely overhauling our foundation model team, centering around newly added elite AI researchers and engineers with deep expertise in large language models. Our new team is young, energetic and cohesive, enabling us to make progress quickly in this highly dynamic AI era. 

In February, we reengineered the system and process for pretraining and reinforced learning from the ground up. We rearchitected the infrastructure to support robustness, scalability and efficiency across pretraining, data and reinforcement learning. On data, we expanded our data set significantly and strengthened our data collection, cleansing and synthesis capabilities with a focus on data quality. On training, we upgraded the process for pretraining and supervised fine-tuning, and we scaled up reinforcement learning. And for evaluation, we’re moving away from chasing public benchmarks that can be gamed. Instead, we evaluate our model through the latest exams, human tests, product feedback and in-house tasks to see how the model actually performs in the real world.

In April, we launched Hunyuan 3 Preview. When we set out to build this model, the purpose was to build a cost-efficient and solid model for diverse applications and derisk scaling toward larger models. The core design principles behind Hunyuan 3 Preview was to deliver comprehensive intelligence and cost efficiency, optimizing it for real-world deployment. We moved beyond narrow expertise and towards comprehensive intelligence such as integrating reasoning, long context understanding, instruction follow, dialogue, coding and tool-use capabilities. And by codesigning inference with model, we’re able to reduce costs significantly so that the intelligence is economical enough to be used at scale. Hunyuan 3 Preview has delivered on these expectations.

The model has already become a leading reasoning model in China and has proven effective in real-world software engineering and other productivity agent tasks. Internally, the model has been deployed across 131 widely used internal products, including Yuanbao, QQ and WorkBuddy, providing valuable feedback and iterative improvement vehicle design process. And externally, Hunyuan 3 Preview has been well received by users and developers in real applications. It has ranked first among all models available on OpenRouter by token usage since April 28 and continued its lead even after its free period ended on May 8…

…Our Hunyuan team is already working on a larger parameter model, leveraging our infrastructure and learnings from Hunyuan 3 by aggregating bigger and better data sets and scaling more powerful reinforcement learning, we can strengthen the model’s contextual understanding, enhance its agent capabilities in areas, including coding and increase the model’s general intelligence. Through codesigning and collaborating with other Tencent product teams, we are optimizing data set selection and focusing reinforcement learning for high-value use cases…

…We have given a pretty comprehensive overview of Hunyuan 3. And as you can see from the prepared remarks, it’s more intelligent and it’s actually very strong in terms of reasoning despite being a smaller model. And at the same time, it has significant improvement vis-a-vis Hunyuan 2 on agent capabilities…

…The total token usage is actually at least 10x compared to Hunyuan, so that’s the clear indication that Hunyuan 3 is actually well designed…

…In terms of the integration into the Weixin workflow, I think it will be a step-by-step process. And Weixin itself actually sort of have been always using some part of their products, Hunyuan 2 and they upgraded already to Henyuan 3. And in some cases, they use different models and they evaluate different models and evaluate what’s the best model to use for their users, right? So as Henyuan 3 continue to be getting better and better, then they will be adopting more…

…if you look at how this is received in the actual use cases, it’s actually better than our expectation by quite a bit.

Tencent’s management thinks agentic AI is a breakthrough use case for AI; management thinks agentic AI first delivered value in coding through enhanced productivity and is now shifting to more workloads and occupations; management thinks Tencent’s apps, such as Weixin, Yuanbao, and more, are great avenues for users to control AI agents; in the future, management will enable AI agents to access Tencent’s Mini Programs as AI skills; management sees Tencent having a lead in agentic AI deployment through the leading DAU (daily active users) of WorkBuddy; Tencent’s agentic products, CodeBuddy and WorkBuddy, are still early in their lifecycle but currently have strong organic growth and high retention rates; the high usage of Tencent’s agentic products is a virtuous feedback loop for the company, as more usage leads to insights for product development, which leads to more agentic usage, and as agentic usage grows, token usage in Tencent Cloud also grows; management thinks the breakthrough of agentic AI as a use case is a very recent phenomenon

It has become increasingly evident that agentic AI represents a breakthrough use case after AI chatbots have become popular. Agents are more valuable in uplifting productivity from initial use cases supporting programmers in creating code, such as with our product CodeBuddy to now catering to a wider range of workloads and occupations such as with Claws and WorkBuddy. These breakthroughs were made possible by more powerful models and by the hardness infrastructure that allows models to utilize tools and act as interfaces that enable users to manage agents effectively.

Our platform inherently has many benefits of hosting AI agents as users can control AI agents through our communications and browsing interfaces such as Weixin, WeCom, QQ, Yuanbao and QQ Browser in addition to third-party applications…

…And in the future, AI agents will be able to access our Mini Programs ecosystem using Mini Programs codes as AI skills.

Tencent has established an early lead in agentic AI deployment evidenced by the leading DAU of our product, WorkBuddy. While early in adoption cycle, CodeBuddy and WorkBuddy are already achieving strong organic growth and high retention rates among active users and paying users. The high time spent and high-frequency interaction with AI agents among early adopters act as a virtuous feedback loop to Tencent, enable us to identify and provide complementary software and services, which in turn drives increased AI agent usage among a broader enterprise and prosumer user base. As users utilize more AI agents for more complex tasks, paying user conversion increases, resulting in rapid growth in token usage on Tencent Cloud in recent weeks…

…The upturn in sort of productivity AI is really something that’s happened not in the last few quarters or even last few months, but last few weeks. And I think that’s true globally actually, that really, it’s since late in or since the end of the first quarter that the Agentic AI has broken through in terms of its ability to create code, in terms of its ability to make people more productive.

Tencent’s management believes Tencent Video has competitive advantages in creating animated series, partly because of the use of generative AI for storyboarding and producing animation

We believe Tencent Video possesses competitive advantages in creating animated series, including our ability to cross over IP from China literature and our games into animated IP and our use of technology tools such as Unreal Engine and generative AI for storyboarding and producing the animated content. Tencent Music subscription revenue increased 7% year-on-year, driven by growth in ARPU and subscribers.

Tencent’s management has improved the content recommendation model for video accounts, which has led to a 20% year-on-year increase in total time spent on video accounts; management has upgraded the developer toolkit architecture for Mini Programs to enable users to better leverage AI plug-ins; Weixin Search’s query volume was up 25% year-on-year in 2026 Q1, driven by foundation model powered ranking and broadening AI search coverage to include image-based queries

We scaled up the number of parameters and enhanced the algorithm for video accounts content recommendation model, enabling deeper understanding of users’ interest to recommend more personalized and relevant content and total time spent on video accounts increased over 20% year-on-year. For Mini Programs, we’ve upgraded the developer toolkit architecture so users can better leverage AI plug-ins, including CodeBuddy to create and debug Mini Programs…

…Total query volume on Weixin search increased over 25% year-on-year, benefiting from foundation model powered ranking and broadening AI search coverage to include image-based queries.

Tencent’s management sees AI being really helpful for game production in areas such as accelerating 3D asset production and animation, improving the player experience, and delivering better graphics; the use of AI in game production can be directly revenue-generating, and management has seen this happen; management sees Tencent as a global leader in utilising generative AI to improve game production; management’s objective with generative AI in the games business is to speed up content creation and generate incremental revenue; management is not intentionally using AI in the games business to expand margins, even though operating leverage should happen in the games business if AI is applied correctly to boost revenue

AI provides increasingly helpful tools, facilitating our game developers to deliver more content and enhanced experiences. Currently, AI for games is most beneficial in areas, including accelerating 3D asset production and animation, enriching player experiences with intelligent in-game guides and delivering more realistic graphics via AI rendering techniques…

…Generative AI enables us to produce more content faster. And that content is, in some cases, to enhance the overall player experience. But in some cases, it results in direct monetization. For example, if the content is a virtual outfit. And so that’s what we are doing, and that’s what we are seeing. And we think that we’re a China leader and to some extent, even more so a global leader in terms of deploying that capability and achieving that benefit. And the objective at this point is really faster content creation and incremental revenue generation. We’re not prioritizing margin expansion per se. It’s more that as we deliver the revenue uplift that we’re seeing and if we can keep headcount fairly stable, then I suppose mathematically, that combination would tend to result in higher margins over time, but that’s sort of a happy output rather than the intention of the process.

Tencent’s AI Market Plus automated campaign management solution, powered 30% of total advertising spend; management has upgraded Tencent’s runtime advertising recommendation models with a unified transformer-based architecture; Tencent’s video accounts ad impressions grew rapidly year-on-year in 2026 Q1

Our automated campaign management solution, AI Marketing Plus powered around 30% of total marketing services spending from advertisers with us in the quarter. We upgraded our runtime advertising recommendation models with a unified transformer-based architecture. This upgrade provides deeper understanding of user context and the intent while balancing model complexity with system efficiency. By inventory, video accounts ad impressions grew rapidly year-on-year, supported by increased total time spent video views and ad load. We released more inventory of rewarded ads, which deliver high click-throughs for advertisers.

Within the Fintech and Business Services segment, Business Services revenue grew 20% year-on-year in 2026 Q1, driven by higher demand and a better pricing environment for cloud services; Tencent Cloud benefited from AI-related demand across GPUs, CPUs, and storage; management had upgraded Tencent Cloud’s AI agentic solutions, which led to rapid usage growth and token monetisation; Tencent Cloud’s international business increased revenue by 40% year-on-year in 2026 Q1; Tencent Cloud finally has sufficient GPUs to serve all the external demand it’s seeing; previously, management had prioritised Tencent’s internal AI use cases for its AI compute but newer AI compute capacity will be focused on meeting external demand for Tencent Cloud

Turning to Business Services. Revenue in the first quarter grew 20% year-on-year, driven by increased demand and better pricing environment for our cloud services alongside rising technology service fees generated from mini shops e-commerce. For Tencent Cloud, AI-related demand contributed to increased revenue year-on-year across GPU, CPU and storage. We upgraded Tencent Cloud’s AI agent solutions with proprietary security infrastructure, skill hubs and interfaces, contributing to rapidly increasing usage and initial token monetization. Tencent Cloud’s international business grew its revenue over 40% year-on-year as we expanded our global footprint and captured demand for our Platform-as-a-Service solutions, including media processing services and TDSQL cloud database…

…For Tencent Cloud, where until now, we actually haven’t had sufficient GPUs to begin to service the external demand, the KPIs will be more revenue and market share related…

…We’ve already made the choice and paid the price in that we have prioritized a multiplicity of internal services ahead of Tencent Cloud…

…And the reason why we have been able to support all of these at once is because we have not been active in leasing out GPU capacity in Tencent Cloud. Now looking through the rest of this year, as the supply of China design GPUs progressively ramps up, then we’ll be remedying that situation, and we will be making more capacity available in Tencent Cloud and consequently driving up Tencent Cloud’s rate of expansion. But that’s where the trade-off has been made that we have been consciously late to monetize the AI opportunity through Tencent Cloud because we’ve been simultaneously supporting a number of AI initiatives internally.

Tencent’s operating capex in 2026 Q1 was up 18% year-on-year and up 84% sequentially because of higher server investments; non-operating capex was down 36% year-on-year (was RMB 1.1 billion in 2025 Q1); free cash flow was up 20% year-on-year, and up 67% sequentially

Operating CapEx was RMB 31.2 billion, up 18% year-on-year and 84% quarter-on-quarter as we accelerated investment in server infrastructure. Nonoperating CapEx was RMB 0.7 billion. Free cash flow was RMB 56.7 billion, up 20% year-on-year, driven by growth in games, gross receipts and advertising billings, partly offset by higher server infrastructure and compute spending. On a Q-on-Q basis, free cash flow was up by 67%, reflecting seasonally higher game gross receipts and the timing of certain seasonal accounts payable settlements, partly offset by higher server infrastructure and compute spending.

Tencent’s management thinks it’s still too early to determine the impacts that agentic AI can have on the e-commerce industry, but they don’t see agentic AI as a risk to Tencent’s advertising business

[Question] With agents increasingly potentially replacing the traditional click-throughs on the web pages and also the apps, could management share your view on the future advertising pricing and also the resulting impact on advertiser budget?

[Answer] It’s certainly more of an issue potentially for e-commerce companies than it is for us because users actively choose and desire to spend their time watching short videos or listening to music or consuming content or chatting with their friends versus generally speaking, when users spend time on e-commerce, it’s because they’re trying to find the lowest price. It’s not because they necessarily enjoy that process. So to the extent that AI agents play a bigger role in the future in facilitating price comparison, then it’s possible that users will spend less time on e-commerce sites and be less exposed to ads than they are today, while the AI agents can scan infinite listings and therefore, not influenced by ads the way that human beings with a finite attention span are influenced. All of that said, there’s been many prior iterations of price comparison services, including search engines and the big e-commerce companies are generally thrived despite the existence of those price comparison services. So I think it’s premature for us to sort of have a definitive view at this point on how it will affect our friends in the e-commerce industry. But we don’t see it as a primary risk for Tencent.

Tencent’s management continues to see Tencent increasing capex substantially in 2026, especially in 2026 H2, to meet AI-related demand; Tencent’s AI-related capex in 2026 will be focused on AI chips designed by Chinese companies; the KPIs management is looking at to determine the ROI (return on investment) of AI-related capex includes (1) revenue and profit for the advertising and games businesses, (2) intelligence, usage, and token consumption for the new AI products, and (3) revenue and market share for Tencent Cloud; Tencent Cloud finally has sufficient GPUs to serve all the external demand it’s seeing; in management’s eyes, the ROIs on AI-related capex have both near-term and long-term components, with advertising being a near-term example and Hunyuan being a long-term example; previously, management had prioritised Tencent’s internal AI use cases for its AI compute but newer AI compute capacity will be focused on meeting external demand for Tencent Cloud

We are seeing increased demand, both from internal products as well as from external users of our model for our AI-related services. And we had previously guided that we’ll be increasing CapEx this year versus last year, and we’re now more affirmative, more confident in that guidance. And we and you should expect a substantial increase in CapEx, especially in the second half of this year as more China designed ASICs become available to us month by month through the year…

…At a high level, for our existing activities such as advertising and games, the KPIs would be more revenue and profit related. For our new AI products, the KPIs would be more capabilities, how intelligent is our foundation model and usage, how much token consumption is happening on world body related. And then for Tencent Cloud, where until now, we actually haven’t had sufficient GPUs to begin to service the external demand, the KPIs will be more revenue and market share related…

…AI includes a range of sort of shorter cycle investments as well as longer cycle investments. And so if we buy GPUs and we deploy them into our ad tech, then that’s a relatively short-cycle investment. The GPUs yield better targeting, higher click-through rates and higher revenue and profit on a pretty accelerated basis. On the other hand, when we deploy GPUs into our Hunyuan foundation model, that’s something which we view as important for our franchise and where we’re taking a longer-term view…

…We’ve already made the choice and paid the price in that we have prioritized a multiplicity of internal services ahead of Tencent Cloud…

…And the reason why we have been able to support all of these at once is because we have not been active in leasing out GPU capacity in Tencent Cloud. Now looking through the rest of this year, as the supply of China design GPUs progressively ramps up, then we’ll be remedying that situation, and we will be making more capacity available in Tencent Cloud and consequently driving up Tencent Cloud’s rate of expansion. But that’s where the trade-off has been made that we have been consciously late to monetize the AI opportunity through Tencent Cloud because we’ve been simultaneously supporting a number of AI initiatives internally.

Tencent’s management thinks society is still at a very early stage in terms of AI diffusion; management thinks many new kinds of products will appear, beyond agentic AI; management believes that it’s much more important to find high-value use cases in AI as compared to focusing on gathering users because AI is expensive to produce for each user, unlike the internet which supports infinite scaling of users; management thinks building a subscription model for consumer AI in China is very difficult compared to the USA because the USA’s living standards are high and its population has a habit of paying high prices for subscriptions; management thinks the consumer AI market in China will not be a winner-takes-all market; management thinks it’s still early days for monetisation of AI in e-commerce and advertising even in the USA

In terms of how we think about the different products, we felt this is actually sort of a very early stage in terms of AI diffusion, right? And we would see many different products coming up going forward. Initially, it was chatbot and everybody felt chatbot is actually the king of the product. And then suddenly, you have a coding that came up and this becomes sort of even more eye-catching and less significant use case because it’s very high value, right? And now we are seeing sort of agentic capability proliferating right? And I think that would actually allow AI to be diffused to different industries, and you have many different agents coming up, which can help you to do work, right? And there’s going to be new products coming up. So I think that would continue to propagate.

And I think to some extent, right, you actually have to — in the AI world, you actually have to find a high-value use case as opposed to sort of just purely focused on DAU because the difference between the AI revolution and Internet is that this is about intelligence and intelligence manifest its value in sort of how much people are willing to pay for it. And at the same time, the intelligence is not free, right? In the Internet world, you basically sort of have mostly existing information. And then you also create some new information and content, but then that’s a fixed cost and then sort of the variable cost for delivering is actually very small, right? You only have to pay for bandwidth. and the compute sits on people’s devices, right? And as a result, you can almost like go for infinite scaling. But in this case, right, every single delivery of a DAU actually cost you quite a bit. And as a result, you can’t just apply the same logic as Internet and apply it to AI. And I would say the ability to find high-value use cases is going to be as important, if not more important than just sort of blindly get a lot of use DAU and user time…

…In terms of the 2C monetization, I would say it’s actually not easy, right? If you look at global standard in the Western market when the paid service is actually very well penetrated and the living standard is actually very high. So the subscription price in the Western market is multiple times of what the equivalent service in China is like, be it music service or be it video service. The paying penetration is probably in the single digit, right? And — and when you sort of applied it to China, I think the subscription model is not going to be that big for the China market…

…I think the more important implication is that when you have to have payment to support a service, then most likely the service is not going to be a winner take-all business. It would basically sort of be supporting multiple players who would have a share of the market and each one of them would sort of have some kind of users and some share of subscriptions…

…When we look at e-commerce or advertising as a way to monetize, I think it’s also very early for even the U.S. players where the eCPM is actually much higher, right? The leading player has not been able to roll out very robust advertising model.

Tencent’s management sees Tencent as having many more flagship internal use cases for AI as compared to the hyperscalers in the USA

And so I think most big tech hyperscale companies with cloud businesses have one flagship internal use case where they’re allocating a large number of GPUs. We have multiple flagships. We have the foundation model. We have agentic developments within Weixin. We have — support. We have the AI deployment for advertising for games, now also for the WorkBuddy and CodeBuddy use cases. 

Tencent’s management thinks policy restrictions from the USA and limited manufacturing capacity in China are the reasons why there was a supply shortage of GPUs in China; the GPU supply shortage in China is now easing because there’s more capacity from China fabs and other foreign fabs to manufacturing China-designed AI chips; management does not see any supply shortage in China for CPUs and other networking chips; management is seeing that the suppliers of CPUs and networking chips are not raising prices indiscriminately over the short-term; management is seeing that the suppliers of CPUs and networking chips are negotiating long-term contracts with customers, and they are looking for a variety of customers 

The reason why there’s been a GPU bottleneck that’s been much more pronounced in China than elsewhere is a combination of policy restrictions on certain foreign design GPUs being brought into China and then the China design GPUs facing limited fab capacity within China. And as a result, the country has really been short of GPU or ASIC capacity. And that’s now being addressed because the China designed ASICs are seeing more supply from fabs within China as well as more supply from fabs in neighboring countries.

But by contrast, we haven’t faced those sort of artificial additional constraints CPU or networking chips. We’ve been a big buyer of CPU and networking chips for many years before GPUs became such a big presence in data centers. We have very long-term relationships with the companies that supply the CPUs and supply the networking chips. And on their side, while one might think that these suppliers would be sitting back and just selling at the highest possible price into the spot market, that’s not actually the reality. The smart suppliers are taking very conscious 3- to 5-year forward views and negotiating long-term agreements in order to give them certainty of their revenue outlook over the next 3 to 5 years. And when they’re deciding with whom to sign those long-term agreements, they’re looking to work with a number of partners, not just a single partner, and they’re looking to work with partners who have been there for many years already and will be there for many years to come and ideally with partners whose demand they believe will grow substantially over time. And happily, we fulfill all of those criteria. We’ve been a big customer for the Intel and AMD and so forth for many years. We’ve been progressively growing our volume with them for many years, and they believe it will continue to progressively grow our volume for many years to come.

Veeva Systems (NASDAQ: VEEV)

Veeva’s management sees the company changing from an industry-specific application provider to an industry-specific application and agent provider; management wants Veeva to support both human users and agentic users; management is seeing pharmas leaning into a new technical architecture called MAAP (models, agents, and applications); management sees pharmas wanting to see AI in Veeva’s applications; management is thinking of building very specific agents that would go the last-mile and automate standardised actions for pharmas, and management thinks Veeva can lead in this area

Veeva is moving from an industry-specific application company to an industry-specific application and agent company. In our first chapter, we became the leader in applications. In this next chapter, we intend to also lead in industry-specific agents. This includes agents that support human users, as well as agentic labor, which represents an entirely new market and type of application user…

…[Question] As we think about pharma appetite for AI applications more broadly, I’m curious what areas you think they lean into first

[Answer] It’s not that they’re thinking mainly about transition from applications into AI applications. What they’re really leaning into is this new technical architecture, we call it the MAAP architecture of Models, Agents and Applications. So the applications that they get from Veeva, they’re looking for them to be more efficient, to have AI in there and help the users. What they really want to get to be is an agentic biopharma so that agents can do a lot of the work. And so the humans can do the more higher value work…

…Let’s just say there’s 100 million documents collected from clinical research sites around the world every year having to do with clinical trials, they have to be checked for quality and they have to be sorted into the right places. That’s work that agents can do, it’s difficult, specific work, but we can make agents that are very specific on that. Agents that take in a bunch of free text via e-mail or other channels and have to sort it out to see, is this a product complaint? If so, how to handle that? And categorize that? Or no, this is an adverse event. This is the issue with a medicine-making somebody potentially ill, okay? Well, what is that illness? Is that a headache or a throbbing headache? How serious is that? Is that involved in the clinical trial? What drug is that involved with? We will make agents to do that and do those very standard things. And this is an area where I’m enthused because Veeva can lead. 

This is where — just like for cloud applications, you got the very specific industry-specific cloud applications could add tremendous value if you went to the last mile and solve the thing. In industry-specific agents, agentic labor, we may be able to go the last mile and make specific agents that just do the thing for life sciences because we’ll go to that last mile and make it work, we may make agents that are better safety case processors and more reliable than humans.

That’s a heck of a lot of work, but we have a structural advantage to do that because we’re deep in life sciences, we have a consulting in life sciences, and we have the applications that those agents can use, it’s the same reason why Claude is getting very good at Claude Code because they have the agent, the coding agent and they have the model, and they have 2 layers. We don’t have a model we use, but we have applications and the agents. So that is a structural advantage.

Veeva’s agentic products will have different pricing depending on the type of agent

Pricing and packaging also vary by agent. Some agents are charged by usage, while others are part of a fixed-price subscription license.

Veeva recently acquired Ostro, which provides conversational AI for brands to provide patients and doctors with immediate, compliant answers; management believes Ostro can be a significant revenue driver for Veeva; Ostro had no material impact on Veeva’s financial results in 2026 Q1 (FY2027 Q1), but accounted for 25% of headcount growth; the buyer of Ostro’s product is the biopharma company, but the user is a healthcare professional or patient; Ostro is a brand engagement platform; management thinks it’s really hard to do what Ostro is doing; management thinks Ostro will be a really significant acquisition for Veeva; management has organised Ostro smartly so it can retain the speed of a startup

In March, we acquired Ostro, the leader in conversational AI for brands to provide patients and doctors with immediate, compliant answers through an easy-to-use chat experience. Ostro operates as a startup within Veeva and is now an important part of our Commercial Cloud. Things are going well, revenue and pipeline are growing as anticipated, and we have an ambitious product roadmap. We believe Ostro can be a significant revenue driver for Veeva and transformative for the industry, fundamentally changing how patients and doctors get information…

…We also acquired Ostro in the quarter, which had an immaterial impact on Q1 financial results and accounted for about 25% of net headcount growth…

…The buyer of Ostro is the biopharma company, the user of Ostro is the health care professional or the patient. So it’s a brand engagement platform for biopharma companies to help HCPs and patients ask questions and get answers instantaneously and do that in a compliant way. That’s very, very hard to do. It’s hard to do that at scale. It’s hard to do it in a compliant way, and that’s exactly what Ostro does…

…It’s going to play a bigger and bigger role in Commercial Cloud over time, and we see it as a really significant acquisition and a potential long-term growth opportunity for us…

…In an operating model for Veeva, we have a notion of the start-up models in the core models. And in the core models, we’re organized functionally like the central sales team, engineering team, things like that. In the startup model, it’s all fully contained under CEO, and we use that either when the market is very different or when the product really needs to evolve. So Ostro is in the start-up model. Everybody who works on Ostro is fully reporting to the CEO of Ostro. There’s guidance and help from other functional areas of Veeva, but it’s — and they’re certainly inroads like, okay, Ostro doesn’t have to use their own master subscription agreement anymore and all that type of stuff. So it operates as a start-up, they can retain its speed, but it has a really smooth ramp up.

Veeva’s management will soon release standard agents and the ability to build custom agents for all Vault applications; management will soon release Veeva Falcon, an agentic platform and for clinical, regulatory, and safety; Veeva Falcon is on track to be released in November 2026; Veeva Falcon will be the first agentic solution for the industry; management recently talked about Veeva Falcon to Veeva’s customer base, and it was very well received; management envisions Veeva Falcon to be replacing jobs that humans used to do; the presence of Falcon means Veeva’s applications need to be headless; agents within Vault applications are meant for human users and to improve the productivity of human users; Veeva Falcon is not a platform for pharmas to build custom agents; the platform for pharmas to build custom agents would be Vault AI or other 3rd-party agentic platforms; nobody is asking for the kind of solution Veeva Falcon presents, but management believes it’s the way to go; management is very positive on Veeva Falcon; Veeva Falcon will be tackling the simplest and highest volume labour, specifically the processing of documentation related to clinical trials, and processing safety cases; management’s still unsure how Falcon will be priced, but they’re toying with the idea of pricing Falcon on a per document or per case basis; management sees Veeva Falcon as being completely accretive to Veeva; management expects small biopharmas to be among the first customers of Veeva Falcon because the small biopharmas are running all their processes on Veeva; Veeva Falcon reports directly to Veeva’s CEO; the kind of labour Veeva Falcon is designed to replace does not involve CROs (contract research organisations), and Veeva Falcon could in fact even benefit CROs

In August, our standard agents and the ability to develop custom agents will be generally available across all Vault applications. 

We also announced Veeva Falcon, our agentic platform and standard agents that provide agentic labor for clinical, regulatory, and safety. Many of the processes in these areas are ripe for automation. We are on track with our plan to release Falcon for early adopters in November. Delivering agentic labor in this area will be a first for the industry and the quality and control requirements will be significant. Falcon is a disruptive technology trying to solve a very hard and valuable industry-specific problem. It’s an outstanding fit for Veeva…

…Veeva Summits bring the industry together and are key to driving customer success and product excellence. It was a milestone event as we talked about Falcon to a broad audience for the first time. Falcon was very well received, and customers are excited about the potential to lower costs and increase speed in drug development…

…Falcon specifically is at the agent layer and that’s agentic labor. So fully replacing parts — jobs that people used to do. People who used to do these jobs using our applications, now will deliver the agentic labor to do that. So it’s a big new area for Veeva. It’s something we haven’t done before, and that’s why it’s disruptive. Those agents have to become users of our applications, which means our applications have to become very good in operating at a headless manner. Now at the same time, we have agents inside of the Vault applications. So that’s Vault AI inside of the applications. That’s where when people are actually using the application because there’s definitely things that people still need to do in our applications, that’s where the AI agents can help them do it more efficiently, much like you might use ChatGPT or Gemini at your work, okay, that helps you do it more efficiently…

…For Falcon, the actual effort there is taking the path less traveled. So that’s a platform for us to build and operate standard agents to actually solve the problem for the industry. So it’s not really a platform for customers to develop their custom agents. For custom agents that live inside of our applications, of course, they can use Vault AI for that. For custom agents that are outside the applications, there are many agent building tools, and they will dip into the Veeva applications operating in a headless manner…

…In 2012 for the first time we laid out our first visions for Development Cloud. 2014, they got sharper; in 2016 that really became apparent what we were doing. We’re trying to simplify and standardize and integrate the tech of the development area of life sciences. That’s not anything that anybody asked us for, right? That’s the vision that we have, and that’s not anything that anybody has tried to do before. Falcon is the same thing. It’s the same magnitude of disruptive innovation. It’s not giving tooling to people to design agents. This is to designing and operating the standard agents for the industry rather than the industry having to hire humans for those specific jobs…

…I think Falcon is just going to deliver value. It’s going to be great revenue for Veeva, but it’s going to deliver value far above and beyond that for the industry, and that’s going to allow the industry to grow. It’s a disruptive thing. It’s not an incremental thing or a tool…

…[Question] How are you deciding which labor roles to address or to attack with Falcon agents? 

[Answer] I think the most right there ones are actually the simplest and the highest volume. And actually, when you look inside of life sciences, those are the areas where they have a tendency, some of the companies to do some outsourcing today already. So that makes it also — they’re used to outsourcing. Of course, they would outsource that to humans. In Falcon, the first ones we’re looking at are processing of documentation involved with clinical trials, specifically the stuff that comes from clinical sites, the millions and millions, hundreds of million, tens of millions of documents that come from research sites. They need to be collected, inspected for quality, categorized, the metadata pulled out of them, filed in the TMF the right way. So that’s one, the intake and control of documents. Another one is the safety cases, the safety cases that come in, the triage and the categorization and the collection of the safety cases. So those are the 2 main ones, we’ll also take on regulatory health authority correspondences because that’s another high-value one, and there’ll be more…

…[Question] How are you pricing Falcon?

[Answer] You can imagine most likely that Falcon will be charged by the document, most likely. We haven’t fully decided that. You can imagine that safety will be most likely charged by the case. So that’s how that is…

…[Question] On the Veeva Falcon. You’re mentioning the displacement of potential roles at these larger firms. I’m just wondering, is there anything that you would consider timing-wise from an economics perspective. So let’s say, these roles were to move in another direction? Do you think it could potentially cannibalize some of the revenue that you get from those customers?

[Answer] Definitely all accretive because this is not a market we address today. We don’t play in that market today. This is not type of labor or work that we supply. So it’s definitely going to be accretive. And these agents, they need a system of record. You can’t operate them without a system of record. So it definitely doesn’t cannibalize the systems of record…

…Veeva Basics, small biotechs. We continue to win a lot of those that are going on Veeva Basics. And by the way, those will be some of the first consumers of things like Falcon and our other AI solutions…

…Basics are smaller companies, very nimble. Also, they’re running not only our products, but they’re running our processes. So they have an absolute standard configuration of Veeva, where they’re running our processes. So we don’t have to wonder how they have configured Vault or MAAP Vault or done this Vault or with that Vault. They’re running absolute — let’s say, we have over 100 Basics customers in the clinical area, their configuration is exactly the same. How they’re using product is exactly the same. And we operate those systems in a way for the customers. So that’s — if we have our agent working on for one Basics customers, it will work for them all. With the enterprises, the larger companies, our agents have to be a little more adaptive. They have to first go through a phase of, okay, understanding how that customer is using that Vault, testing it out. Okay, I’m going to classify these documents that they’ve previously classified. Do I get the same of what they got. And if so, that’s good. If not, what happened there? Basics is just going to be smoother, very, very smooth…

…Falcon, for example, reports directly to me. This is our first step into digital labor. You can’t — you have to operate that effectively, back when we were the CRM company, way back when before we went public, Vault was this tiny little thing that reported directly to me. Falcon is like that…

…In terms of where can agentic labor play and what can agents do. The best places to do are high-volume repetitive work that actually gets outsourced. So that type of work actually it’s not so much the CROs, it’s other specialized labor providers that do that. So I think this could actually be beneficial for the CROs because that — we can do that lower volume work, which is generally done by the pharma company or a specialized outsourcer. We can do that cheaper, faster, better. That will hopefully allow pharma companies to run more trials, and that’s where the higher margin work is for the CROs.

Veeva’s management believes AI will change the commercial model for pharmas, and Veeva is well-positioned to bring the right solutions; Veeva’s Agentic Call Report in Vault CRM and Ostro help biopharmas capture compliant Commercial Evidence for the first time at scale; there are currently 10 customers live with Vault AI for PromoMats’ Quick Check Agent; management will be focused on commercial content for AI investment to solve the MLR (medical, legal, and regulatory) review bottleneck; management thinks agents on the commercial side will not be a full replacement for field salespeople

While it is early days, AI will fundamentally change the commercial model. This represents a major transformation, and we believe Veeva is well-positioned to help the industry bring the right medicines to more patients through new and better ways of working with AI. With major innovations like the Agentic Call Report in Vault CRM and Ostro’s conversational AI on brand websites, biopharmas are now able to capture compliant Commercial Evidence for the first time at scale. It’s a real breakthrough that allows companies to gain insights and take actions that were simply not possible before AI…

… I am also excited about the progress of Vault AI for PromoMats. We have 10 customers live today for Quick Check Agent, across both small and large biopharma. Commercial content will be a key area of AI investment as we look to solve the MLR content review bottleneck for the industry…

…In commercial, that won’t be — agentic labor there will not be — you’re not going — you’re going to have helper agents that help the field teams do things, but I don’t think you’ll have — you will — you’re not going to replace a field person. That’s about managing relationships, things like that. There may be some things in commercial for example, there’s a medical legal regulatory process that is burdensome and expensive and occupies many parts of people’s time in Life Sciences. I think that can largely be automated, 70% or more with the right agents over time. But the actual field person, I think, it’s going to augment them. 

Veeva’s management expects immaterial AI revenue and margin-impact in 2026 (FY2027)

For this year, our overall expectation had been for AI to be fairly immaterial outside of Ostro. And we’re really focused on getting AI live in all of our customer areas, getting the product excellence, getting to customer success. It starts with that deep value creation for customers. So on the margin side, you also don’t see a material impact, Craig. And in Vault AI, where its usage based on tokens. I think we have a pretty good understanding of what that dynamic looks like, and it’s factored into our guidance. But I don’t expect there to be a material impact on margins driven by AI this year.

Veeva is using AI throughout the company, including general-purpose tools and specific tools; Veeva is using Claude Code from Anthropic and finding great efficiency, which has led to Veeva needing to hire less; management thinks the productivity from AI tools, and the need to hire less, outweighs the cost of tokens

We use AI throughout the company, we’ve got general-purpose tools and then also specific tools and major functional areas. Probably the most significant place for using it is around the product because that’s where we spend the most. And so you heard Peter mention earlier, in product engineering, we use Claude Code, and it’s come a long way. So we’re seeing great efficiency from that tool. And I think in general, that means we’ll hire a little less than we would have and accomplish more than we would have and go a little bit faster. But for us, it’s more about productivity and the combination of hiring a little less, accomplishing a little more, we think easily outweighs the token cost, and that’s all factored into our guidance.

Wix (NASDAQ: WIX)

Base44 has reached $150 million of ARR, or annualised recurring revenue (was $100 million in March 2026); Wix Harmony and Base44 can now be accessed within popular AI chatbots such as ChatGPT and Claude; management recently released Superagents inside Base44; Superagents allow users to build and deploy autonomous AI agents without coding; Superagents can run continuously in the background without any manual intervention; Base44 users can interact with their Superagents through popular messaging apps such as WhatsApp and Telegram; Base44 now has better app-design tools; Figma is now integrated with Base44; Base44 is currently incurring significant AI processing and compute costs as usage ramps, but management believes the costs are front-loaded as new Base44 users tend to consume more AI inference bandwidth during their initial build phase; Base44’s user behaviour and cohort quality look positive, with retention improving, and monetisation steadily increasing; management has been lowering inference costs in the core Wix business through optimising 3rd-party models, open source models, and building a proprietary LLM, and management expects to apply the same strategy to Base44’s AI costs; use-cases in Base44 remain wide, but management thinks specialisation will happen over time; some use-cases seen in Base44 are also applicable for business owners on Wix

Base44, which is now the leading AI-powered application creation platform in North America (per Similarweb data) with ~$150 million of ARR as of May…

…Both are now accessible within ChatGPT, Microsoft Copilot, and Anthropic’s Claude. Users can type “@Wix” or “@Base44” in these platforms, describe their idea, and a full website or application is created in conversation and managed there too, without any context switching…

…In March, we unveiled Superagents, a new experience inside Base44 that lets anyone build and deploy their own autonomous AI agent simply by describing what they want it to do. Base44 automatically builds the underlying workflows, connects the necessary tools, and deploys the agent. No coding, no configuration and no infrastructure to manage. Once deployed, Superagents run continuously in the background, responding to triggers, schedules, and real-time events, executing tasks without the need for any manual intervention. It can connect to third-party platforms and applications, remember preferences and priorities across conversations, and become more effective over time. Users can also interact with their agents directly through iMessage, WhatsApp and Telegram – wherever they are already messaging…

…Base44 now includes a fully rebuilt set of tools for shaping how an app looks and feels. Users can set colors, typography, and overall style across their entire app from one place, with any change carrying through automatically. Images, documents, and data files can be uploaded to an asset library or generated on the fly, and pulled into any app directly from the visual editor or chat…

…Design screens in Figma, paste the frame link, and Base44 builds a working app on top of it. The layout stays intact, and users go straight from design to a live app…

……Creative Subscriptions non-GAAP gross margin was 80% in Q1’26, down from 84% in Q1’25. Creative Subscriptions non-GAAP gross margin in our core Wix business was stable in the first quarter as AI costs remained minimal while we carefully controlled costs as we scale our platform, particularly Harmony… Creative Subscriptions non-GAAP gross margin was driven by accelerating contribution from Base44, which is incurring significant AI processing and compute costs as demand and usage continues to ramp. We believe these AI costs to be front-loaded as new Base44 users consume more AI inference bandwidth during their initial build phase…

…We also saw positive signs in the user behavior and cohort quality of Base44. Retention is improving as more users are choosing annual subscriptions, either through new purchases or renewals. Monetization is also steadily increasing, resulting in stable TROI even as marketing spend stepped up in the first quarter…

…We have been lowering inference cost of users by optimizing third-party AI model usage, leveraging open source models and most recently building our own LLM to power Harmony. As we apply this strategy to more of our products, particularly Base44, we believe that the large majority of these AI costs will be firmly in our control…

…About the Base44, I think we’re happy actually to say that we’re still using — we’re seeing a very wide variety of use cases. And it’s really — some of it is personal uses, some of it is solopreneurs, some of it is small businesses. And we think that there’s going to be more and more specialization that’s going to go and happen throughout the platform over time as we understand what is — where there is differentiation happen between those different use cases and where everyone can benefit from the generalized platform…

… I think there’s another opportunity that is very interesting, which we’re seeing is that some of the more small business-oriented use cases can also be relevant to applications needed by business owners that on Wix.    

Wix’s management thinks the differentiation for website builders is not in the AI models, but in the experiences built around the models, and Wix has the necessary knowhow

As powerful AI models become increasingly accessible across the industry, I believe differentiation will come not from the models themselves, but the experiences built around them. The real value lies in the capabilities layered on top of the models: the backend infrastructure, agent orchestration, tooling, integrations, and everything that comes after turning a prompt into a website or app. With our deep infrastructure, world-class distribution, product expertise and years of technological innovation and market intelligence and understanding, this is where I believe Wix is uniquely positioned to win in today’s AI world. 

Wix’s management recently built Wix’s first proprietary large language model (LLM) that’s designed to power Wix Harmony, the company’s first-of-its kind website builder blending visual editing with vibe coding; Wix’s proprietary LLM is faster and has fewer errors when building websites; having its own LLM means Wix can move faster, and operate with lower inference costs; Wix is currently experiencing only tiny benefits from using its own LLM, but management expects the company’s own LLM to drive the company’s profitability over the long run; the LLM is just the first in a broader portfolio of AI models that Wix will release; Wix can build websites with its own LLM at just 5% of the cost of 3rd-party alternatives; management thinks the AI advantage in website building belongs to the most specialised model; Wix Harmony is now in all of Wix-supported languages; Wix Harmony and Base44 can now be accessed within popular AI chatbots such as ChatGPT and Claude; AI has made creating a website easy, but the real work is done after the website is published; despite having its own LLM now, Wix still has the flexibility to use the best 3rd-party models when appropriate; Wix Harmony was rolled out to the company’s main geographies in late-January 2026; management thinks Wix’s own LLM could eventually be used for Base44, but there’s no exact time line; Wix spent only a small sum of money to train its own LLM, so ongoing training costs will also be reasonable 

We recently built our first proprietary LLM, purposefully designed to power Wix Harmony – a significant milestone in our innovation journey and a project I am personally very proud of. Thorough A/B testing is showing that our Wix-built model is faster while resulting in fewer errors and significantly better results when applied to building Wix Harmony websites. Having our own model means that we can accelerate the cycle of improvement, which we believe creates a continuous flywheel for our platform that general-purpose models can’t replicate with success.

Importantly, building and relying on our own LLM means significantly lower inference costs that sit completely within our control as we scale the Harmony platform. While the margin benefit is small today, we expect this model to drive profitability over the long term. We expect this to be just the first in a broader portfolio of proprietary AI models across a number of use cases as they become increasingly central to our product roadmap…

…Big LLMs optimize for broad scopes and with limited feedback; we’re optimizing for one thing, every day, with millions of real users building real websites. This gives us full control over our roadmap, reduces dependency on external vendors, and significantly accelerates our iteration cycle. The result is a model that’s faster and more accurate, and we will be able to create beautiful websites that are optimized specifically for our users’ needs at approximately 5% of the cost of third-party alternatives. We believe that the AI advantage won’t go to the biggest model; instead it will go to the most specialized one…

…Wix Harmony is now available in all Wix supported languages…

…Both are now accessible within ChatGPT, Microsoft Copilot, and Anthropic’s Claude. Users can type “@Wix” or “@Base44” in these platforms, describe their idea, and a full website or application is created in conversation and managed there too, without any context switching…

…AI has made building online simple and anyone can generate a simple good-looking website in minutes. But that’s as far as it goes. The real complexity begins the moment you hit publish. How does it drive engagement? How do you host it, get found on search engines, run your storefront, secure your customers’ data and actually operate a business day-to-day. These are the hard problems, and we’ve been solving them for 20 years through continuous product innovation and user feedback…

…Still, we also have the flexibility to continue to leverage the best third-party models for the right use cases. So we are never constrained…

…Harmony, which was rolled out in late January across our main geographic markets…

…Where can we expect to have the same thing on Base44. The answer is that I don’t have an exact time line. Obviously, it’s a bigger or more complex undertaking than the Harmony one just because it is much more generalized the — the Harmony use case. That being said, it is something we believe and our top engineers are the ones who are dealing with it…

…In terms of the spend on the Harmony LLM and again, we’re not breaking out the exact number, but it’s quite small, okay? These are not like massive research costs and GPU investments that you can consider when you think about big frontier models. This is something that we managed to do at a very reasonable cost, which also means that for us to continue training it and improving it, should not be something that puts any real weight on our expenses.

Wix websites are now optimised with agentic AI

Wix collaborated with Microsoft to enable users to connect their sites to NLWeb directly from their Wix Dashboards, making Wix sites agentic-optimized. Now available through the Wix SEO & GEO Dashboard, the integration allows structured, continuously updated site data to be queried by AI systems using the ASK protocol, delivering accurate, context-aware answers in real time. 

Wix has ramped up the use of AI in its customer-care organisation for the last 3-plus years, and this has led to a 40% decrease in headcount since 2022, while maintaining or improving service; management is shifting Wix’s R&D (research & development) to be more aligned with Base44’s

We have ramped the integration of AI over the past 3-plus years. This has allowed us to optimize headcount, which has decreased by more than 40% since 2022, while maintaining or even improving in some areas, our services to users…

…We are working to shift our Wix R&D structure to align more closely with that of Base 44, which has been a leader in leveraging AI to drive productivity since day one. We are learning from them and working to implement those same operating principles at Wix. As we execute on this strategy with good line of sight, we expect faster output will more than balance out the cost of AI usage across our organization.

Wix’s Partners are using other AI platforms as well as Wix; the Partners are generally happy with Wix Harmony, but are also pointing out specific areas for improvement; a decent amount of Wix’s Partners are also using Base44

I also think in terms of what they’re using, they are using some AI platforms. By the way, some of them are using Harmony and are very happy with it on one end. And also they’re pointing out to us specific holes, if you may, or missing capabilities that are obviously there because we build Harmony for self-creators and not in the view of partners, but it gives us great visibility into what kind of innovation, what do we need to do next on the partner side in order to make them more successful and happier…

…I’m not going to share percentages, but I can say that we are seeing like there is a decent amount of partners’ usage on Base44. So it’s not marginal.

Wix’s management has no current plans to change the pricing strategy for the core Wix product

I think on Wix at this stage, we think the current structure is the right one. Obviously, if at some point, we introduce something which is very intense on token consumption, then we’ll have to charge for that as well. But at least for now, that’s not the case.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Alphabet (parent of Google), Amazon (parent of Amazon Web Services), Meta Platforms, Microsoft, MongoDB, Nu Holdings, Okta, Salesforce, Sea, Tencent, Veeva Systems, and Wix. Holdings are subject to change at any time.

Last week, I published The Latest Thoughts From American Technology Companies On AI (2026 Q1). In it, I shared commentary in earnings conference calls for the first quarter of 2026, from the leaders of US-listed technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

A few more technology companies I’m watching hosted earnings conference calls for 2026’s first quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here
• 2024 Q4 – here, here, and here
• 2025 Q1 – here and here
• 2025 Q2 – here and here
• 2025 Q3 – here, here, and here
• 2025 Q4 – here, here, here, and here

With that, here are the latest commentary, in no particular order:

Airbnb (NASDAQ: ABNB)

AI is now writing nearly 60% of the code Airbnb’s engineers produce, 2x higher than the industry average; AI code-writing is helping Airbnb ship more features faster and deliver better experiences for guests and hosts; management thinks AI makes companies move faster; management thinks AI requires a company’s employees to be hands-on; management is seeing many of the company’s design managers and engineering managers return to coding with the help of Claude Code

Nearly 60% of the code our engineers produce is now written by AI, which we estimate is about twice the industry average. That means our teams are shipping more features and iterating more quickly. But it’s not just about speed, it’s about delivering a better experience for our guests and hosts…

…AI, I think we should think of as an accelerant to everything. And we can think of it as a disruptive technology. I actually think of it more as an accelerating technology. I think the #1 characteristic of AI is speed. It just speeds every single thing up.

I also think it makes — it requires everyone to be more hands-on and requires everyone to be more nimble and more adaptive to change. I think one of the benefits of the way Airbnb is run is that — and I think there was a term that was coined. Paul Graham, Founder Mode based on a talk I gave, but it’s really this notion that leaders should be hands on. I do not think there’s going to be as much of a role for pure people managers. Said differently, 30,000 feet hands-off managers. I think everyone is going to have to be much more hands-on, much more in the details of the company and all the data. I think now data inside a company is completely democratized. You don’t need to inquire with the data scientists to get data, we all have self-serve dashboards.

I’m seeing like many of our design managers and engineering managers going back to coding or using Claude Code.

Airbnb’s AI assistant now solves more than 40% of issues that guests face, up from 33% in 2025 Q4 and at a significantly faster pace; Airbnb’s AI assistant has helped to reduce cost per booking by 10% year-on-year in 2026 Q1; it’s really difficult to use AI for customer service; management believes that Airbnb’s 40% rate of using AI to solve customer issues is industry-leading

When guests contact us through our AI assistant, over 40% of issues are now resolved without a human agent. And this is up from about 1/3 in Q4 with significantly faster resolution time. We’ve seen the cost per booking decrease about 10% year-over-year in Q1, and we expect to see more of this as we improve AI customer support this year…

…We want to focus on the hardest problem in AI, which we thought was customer service. The reason why is the stakes are high, you have — you cannot hallucinate, you have to answer things very, very quickly because they are calling and they have problems. You have to be multilingual, often in the same conversation because sometimes guests and hosts don’t speak the same language. You have to adjudicate very difficult things. You have to escalate to human accurately, especially if it’s timely or there’s a trust and safety incident. And you have to deal with personally identifiable information that means that you have to be able to protect people’s data, you have to be able to read and train based on nearly 100 policies, tens of thousands of evolving conversations and look at like millions of data points of how a prior case was adjudicated to be able to answer correctly…

…Over 40% of people connect with our AI assistant self-solve. And I believe it’s, by far, the best AI self-solve in all of travel. I’m pretty confident of that.

Airbnb’s management thinks the ultimate search experience in Airbnb in the AI paradigm will be deep personalisation; Airbnb knows details about its users, which makes deep personalisation possible; management thinks this is similar to what all e-commerce sites will look like eventually; management’s AI strategy for search starts at the bottom of the funnel, unlike competitors; Airbnb now has AI summaries in its listings page; Airbnb is using AI for matching; management is currently testing AI search in Airbnb 

I think the ultimate, like, paradigm is not this tab versus co-mingle inventory. I believe that’s a pre-AI paradigm. I think post an AI paradigm that we’re moving towards and this relates in a second to AI search is deep personalization, understanding every user, every member. And I just want to remind everyone listening that 100% of people who booked have an account, and they have to have a verified ID. You cannot book as a guest. You have to have account, you have to be a member of the community. Therefore, we know something about you. We can infer a lot, not only about what you’re clicking on the site, but all of your past booking activities…

…We have hundreds of millions of reviews on Airbnb. And one of the things our guests told us is when they get to an Airbnb, it’s great when they see like 100 reviews, it’s awesome, but they don’t have time to read all 100 reviews. So we now have AI summaries. And AI summaries are really great. We have filters, we have AI summaries. We’re now using AI for matching. AI is really helping our search ranking and our relevance…

…Finally, it’s top of funnel, which you would call AI search. This is top of funnel. And this is what we’re currently testing.

Airbnb’s management thinks that a company needs to be really good at technology, data, and infrastructure in order to be good at AI; management has been cleaning up Airbnb’s data for the last few years to prepare for AI

When you break AI under the hood, you realize that you need — in order to be good at AI, you need to be really good at technology, foundational. You need to be really good data and infrastructure. So what we have been doing over the last few years is really getting our data warehouse really, really clean because your AI is only as good as your data.

Airbnb has an AI-native executive running its technology stack, which management believes is the only example of its kind within the travel industry

I mentioned in our last earnings call, we hired Ahmad, our CTO, who was the leader of the Meta LLaMa model. So we are probably one of the only technology companies in the world certainly only in travel that has an AI-native person running as the entire technology stack.

Airbnb’s management is currently experimenting with the best ways to implement AI in the business; management thinks nobody has figured out AI for travel e-commerce yet, even though ChatGPT traffic converts on Airbnb at a higher rate than Google traffic, for 5 reasons, namely (1) travel e-commerce is photo-forward, whereas AI chatbots are text-based, (2) chatbots do not allow users to directly manipulate search results, (3) chatbots do not allow easy comparison between a wide variety of options, (4) chatbots are not multiplayer, and (5) chatbots are not map-native; management thinks AI is a risk to Airbnb, but it’s also an opportunity; management foresees a lot of AI-focused innovation from Airbnb in 2027

We are essentially piloting a variety of different ways to use AI, whether it’s in the search box, whether it’s once you search, interrupting on the search, it’s the filter panel, once you book a trip. So we’re trying a lot of different things. We’re really in the exploration, research development mode…

…I don’t think anyone figured out AI for travel or e-commerce yet. Let me use an example, ChatGPT. Last year, ChatGPT announced the creation or of third-party apps. And then this past March, they shut that project down. And one of the things we noticed is that while ChatGPT is — traffic converts higher than Google traffic when it’s sent to Airbnb, we think the design of a chatbot fundamentally as its currently constructed today does not work for travel e-commerce. There’s essentially four problems.

The first problem with the chatbot is there’s too much text. Chatbot are LLMs, large language models. They’re language. And most of e-commerce is not language forward, it’s photo forward. That’s the first problem. The second is there’s no direct manipulation. You can’t touch anything. You have to type everything. And that’s great for a conversation. But if you want to like move the price slider, that’s much easier than type, well show me X, Y and Z. The third problem is comparison. You go to Airbnb in Paris, there’s tens of thousands of homes, I think over 100,000 homes. Imagine trying to compare 100,000 homes in a chat bot, you get lost. And so it wants to show you just three options. You want to see more than three and pretty soon you get confused in a thread. And the fourth problem is that almost all bookings of Airbnb have multiple guests, what we call multiplayer. Chat bots are primarily single player. This doesn’t account for the fact that 85% of people booking Airbnb send a message, 100% have an account. And also chat bots are not map-native…

…AI is a risk to us and everyone. If it’s a risk to us, it’s a risk to everyone. So risk to everyone is an opportunity for us…

…I believe that over the next year, you can see a lot of innovation around AI search, AI-native interfaces.

Airbnb’s management buckets its alternative accommodations supply into 2 buckets, namely, the API (application programming interface) bucket, and the primary homes and vacation homes bucket; for the API bucket, management thinks AI enables Airbnb to build more tools to serve hosts; management thinks Airbnb has been lagging behind 3rd parties in building great tools for the API bucket; hosts within the API bucket have sounded out to Airbnb that they need better tools to manage their businesses, and Airbnb has struggled in the past for resources to build these tools, but now the company has a productivity-boost from AI in software development and so are able start building the tools; for the primary homes and vacation homes bucket, management thinks AI can make it much easier for primary homes hosts to list their properties

You can think about our core accommodations business of homes as a few different categories. So you have essentially hosts that connect via an API. You might call that host API partners. These are primarily property managers. That’s one category. Then we have primary homes, homes that people live in primarily, so typically more than 180 days a year. Then you have vacation homes, then you have things like private rooms. So you have to think about each. And I would break them into two, the API and the primary homes or vacation homes. These are two buckets.

I think with the host API partners, I think it’s more about AI enabling us to build more tools. I think we’ve been a little bit lagging behind third parties and building great tools for host API partners. And as a segment, the host API hosts are growing really, really fast, and we see a really big opportunity to better serve them. One of the things we found is that the more properties you manage at Airbnb, the lower your rating is. And so said differently, our customers have higher satisfaction with individual hosts over property managers. Now on the one hand, that’s encouraging because that inventory is more unique and exclusive to Airbnb. Other hand, we see that as opportunity. And one of the things those API partners say is, well, we want to be better host, but we need better tools. So AI is a like — maybe here’s an analogy. In the old world, you might need a team of 20 engineers. In a new world, an engineer can spin up 10 agents. And those agents can work 24/7. I mean I’m kind of exaggerating a little bit. You have to be there to prompt them and the amount of work they can do without supervision isn’t overnight, typically for most tasks, but you can see a huge amount of leverage. So the fact that we’re adopting AI tools is a way for us to get a lot more leverage around the software for most API partners…

…Originally, we didn’t have the resources to do all of the host API work we want to do. And now with AI, we’re reevaluating how much productivity we have, and we’re able to accelerate the development of this work…

…AI, especially though, can help the sourcing discovery in the listing of primary homes. So without, again, giving away some of the things we’ll show in 20 — May 20, we do find that AI can make it much easier to list your property. So right now, you have to type everything in, you type in your address, you type in your title, you have to type in your listed description. Eventually, I imagine a world where you can just say like, list my place, you put in your address, it can scrape information on the Internet. You can take photos. It can even write your description based on computer visioning of the photo. So it’s very, very difficult for a regular person to list a property.

Airbnb’s management thinks that AI agents still cannot work for long hours in an unsupervised manner

So AI is a like — maybe here’s an analogy. In the old world, you might need a team of 20 engineers. In a new world, an engineer can spin up 10 agents. And those agents can work 24/7. I mean I’m kind of exaggerating a little bit. You have to be there to prompt them and the amount of work they can do without supervision isn’t overnight, typically for most tasks.

Arista Networks (NYSE: ANET)

Arista Networks’ management sees AI workflow patterns as being different from typical cloud computing workflows; AI workflows have 2 main categories, namely, long-lived massive flows, and short-lived, unpredictable flows; the difference between AI workflows and typical cloud workflows mean the performance of a flow is important

Unlike typical workloads, AI workflow patterns can be long-lived elephant flows or short-lived and simply not predictable. This implies careful attention to performance where a flow can cause burstiness for a long duration of milliseconds. The intensity of a flow can determine the line weight throughput, the shifting traffic patterns to massive flows synchronized to all-in-all or all-reduce or burst with collective communication are all important for AI training and inference applications.

In the scale-up AI networking use case, Arista Networks’ management sees ESUN (Ethernet for Scale-Up Networking) paving the way for Ethernet technologies to increase and decrease computing power flexibly to match workload demands; Arista Networks will be entering the scale-up networking business in 2027; Arista Networks will be working with its customers to build AI racks with rapid interconnects for CPC (co-packaged copper) and CPO (co-packaged optics); management has no doubt that Arista Networks will have a number of scale-up use cases in 2027 and most of them will start with 1.6 terabit switches; the scale-up use cases in 2027 include 5-7 rack opportunities that Arista Networks is actively designing with customers; today’s scale-up AI networking products are mostly from NVIDIA’s NVLink and PCIe; CPOs are very much still science experiments in the eyes of Arista Networks’ management; management thinks scale-up racks would not be possible with XPO 

In scale-up mode, we have familiar technologies such as NVLink and PCIe that have enabled vertical scaling of single compute nodes or racks. The advent of ESUN, Ethernet for Scale-Up Networking, specifications allows for increasing or decreasing computing power in a flexible manner with Ethernet to automatically adapt to workload demands. Scale-Up will be a new entry for Arista in 2027 and beyond, where we will be working closely with our customers to build AI racks with very fast interconnects for co-packaged copper, CPC, or open co-packaged optics, CPO, as well as supporting collectives and memory acceleration…

…there is no doubt in our minds that we will have a number of racks and number of scale-up use cases in 2027. Maybe some of them will be in early trials, but majority of them are looking at really starting with 1.6T, and 1.6T chips will really happen in 2027. There may be a few, a handful of them that tried some experimental stuff at 800 gig. But we continue to see at least 5 to 7 rack opportunities. Some of them are multiple racks with the same customer. We’re actively designing with them. There’s a huge amount of liquid cooling designs with very dense cabling options, acceleration of collectives and memory, features we have to work on for low latency. So I definitely feel we’re in active engineering phase with Ken and Hugh’s teams this year. But unlike the ODMs, I think we’re held to a higher bar, and we have to just make sure that this thing is production worthy and specification adhering to ESUN. So I would say today’s scale-up is mostly limited to NVLink from NVIDIA and maybe some PCIe switching. But majority of the Ethernet scale-up will only really happen in ’27 and ’28…

…While the industry has been talking a lot about co-packaged optics, these are still science experiments, and they’re very proprietary with individual vendors doing their own thing…

…We embrace open CPO a few years from now, but we think XPO has a 10-year run, especially at 1.6T and 3.2T where you need liquid cooling and you need that kind of capacity. So all the scale-up racks we’re talking about wouldn’t be possible without XPO or CPC or any one of those technologies.

In the scale-out AI networking use case, Arista Networks already has more than 100 cumulative customers to-date in 800 gigabit Ethernet deployments; management expects to see 1.6 terabit Ethernet solutions in 2027 at production scale

Scale-out or horizontal scaling involves adding more machines to a leaf-spine fabric, moving workloads across multiple servers or nodes or even connecting other elements like storage or CPUs. As you scale up or out with massive data sets, bottlenecks can be resolved with collective and protocol acceleration at L2, L3, cluster load balancing, all at wire rate. The system must deliver consistent performance without degradation as more nodes participate. Arista is a shining example here with greater than 100 cumulative customers to date in 800 gigabit Ethernet deployments, and we expect the addition of 1.6 terabit in 2027 at production scale.

In the scale-across AI networking use case, Arista Networks’ management thinks the company’s 7800 R3 and R4 series of products, which provides sophisticated traffic engineering, deep routing, encryption properties, and integrated optics atop its EOS (Extensible Operating System) stack, are a great solution; management sees the 7800 series as the premier scale-across product; scale-across AI networking was only a small part of Arista Networks’ business in 2025, but will contribute at least 1/3 of the company’s $3.5 billion in AI networking revenue in 2026; the presence of Alphabet’s TPUs and AMD’s GPUs has created a huge opportunity for Arista Networks in scale-across AI networking; management thinks scale-across is the most significant and differentiated opportunity in AI networking for Arista Networks

Scale across — drives across the cloud and AI as the AI accelerators in a location may need to be distributed to achieve the appropriate bandwidth capacity with the optimal power. As workloads become more complex and more distributed, the bi-sectional bandwidth must scale smoothly to avoid bottlenecks and preserve performance. This demands sophisticated traffic engineering, deep routing, encryption properties, and integrated optics based on Arista EOS stack, and using Arista’s flagship 7800R3 or R4 series. The 7800 has established itself in this category as the premier scale across choice…

…I think last year, on scale-across, we were just beginning. So I think they were small numbers. And majority of the numbers were really scale-out. That’s sort of our heritage and that’s where we excel. If I were to anticipate how it would be this year, again, scale-up is virtually 0 and nonexistent because it really only comes to play after the ESUN spec. So consider that more a’27, ’28 kind of number. So I think the number will be really shared between scale-across and scale-out. I don’t know if I can say it’s 50-50 or 70-30 or 60-40, but scale-across will definitely contribute at least 1/3 of our AI number…

…In general, we are seeing diverse accelerators. Last time I spoke about the AMD accelerators. This time, I will definitely give a nod to the TPUs because in particularly scale across use cases, we’re seeing multitenants connecting to different AI accelerators, including TPUs as well. So I think the diversity of accelerators is creating tremendous multiaccelerator opportunity and multiprotocol features that we can provide for them in our network…

…Scale-across is by far the most significant and differentiated opportunity that really highlights Arista’s prowess in both platforms and software.

Arista Networks’ management thinks the company’s Etherlink portfolio handles both massive synchronous flows for AI training, and low latency flows for real-time inference

Arista’s Etherlink portfolio addresses both the synchronous flows for massive training and the low latency for concurrent swarms of real-time inference in this era of trillions of tokens, terabits of performance, and terawatts of power.

Of Arista Networks’ 4 major AI customers that are deploying AI with Ethernet, 3 had deployed 100,000 GPUs each with Ethernet as of 2025 Q4; the last remaining customer has migrated from Infiniband to Ethernet at production scale; since 2024, Arista Networks has expanded to many more customers beyond the 4 major ones

In 2024, you may recall, we discussed 4 Ethernet-based AI training deployments. And of course, since then, we’ve expanded and exploded to countless others. This fourth customer from the group has officially moved from InfiniBand to Ethernet at production scale over the last 2 years.

Arista Networks’ management thinks the high-speed Ethernet AI leaf-spine architecture, with flexible air or liquid cooling, can overcome the constraints of power and space for AI workloads; management thinks the architecture can help build a low latency distributed AI supercomputer fabric globally

The high-speed Ethernet AI leaf-spine with flexible air or liquid cooled infrastructure overcomes the physical constraints of power and space for AI workloads. It results in a low latency distributed AI supercomputer fabric across global regions.

Arista Networks’ management recently introduced its extended pluggable optics, the XPO form factor; management thinks the company’s networking progress has been important for high-speed optics transmission; the XPO form factor is now endorsed by more than 100 vendors and delivers a record-breaking 12.8 terabits of throughput per pluggable module, and unprecedented rack density, among other traits; management thinks XPO will have a 10-year run; management thinks scale-up racks would not be possible with XPO; management thinks XPO is a very important innovation for the industry; management sees XO unlocking a standard multivendor way to obtain 4x the network density in liquid cooling, which is critical for AI use cases; management thinks XPO and OSFP (Octal Small Form-factor Pluggable) are partnering technologies, where XPO is more suitable for higher data speeds; management thinks XPO will be more suitable for scale-out and scale-across workloads compared to scale-up

What is clear to me and us is our networking progress with data, control and management, and multiplanar orchestration is not only central to our AI switching performance, but also important for high-speed optics transmission. At the recent Optical Fiber Conference, Arista unveiled its extended pluggable optics, XPO form factor, designed specifically for optics innovations at high speed. Now endorsed by greater than 100 vendors, salient features include record-breaking throughput, delivering 12.8 terabits per pluggable module, unprecedented rack density achieving 204.8 terabits per OCP rack unit, integrated cold plate capable of cooling up to 400 watts power per module, and the universality and flexibility across a range of pluggable optics, copper as well as linear halftime or retimed interfaces…

…We embrace open CPO a few years from now, but we think XPO has a 10-year run, especially at 1.6T and 3.2T where you need liquid cooling and you need that kind of capacity. So all the scale-up racks we’re talking about wouldn’t be possible without XPO or CPC or any one of those technologies…

…99% of the optical market today that we connect to is all pluggable optics. So this is a very crucial invention and innovation, not just for Arista, but the industry at large…

…What XPO unlocks is a standard, interoperable multivendor way to get to 4x the network density in liquid cooling, which is absolutely critical for these AI use cases. Without that, you’ve got this huge bottleneck at the front panel, the amount of extra rack space is required to get through OSFPs. It’s — so we’re really enabling the future growth of our industry this way, which we benefit and others benefit as well…

…You should look at XPO as a partner to OSFP. So at 400 gig and 800 gig you’ll be fine with OSFP. And as we go to higher speeds in ’27, ’28 or even beyond, OSFP will run out of steam, and this will be the new connector of choice. So the migration to higher speeds equals the migration to XPO, particularly for scale-out and scale-across. Within a rack and scale-up, there’s still a number of choices. I think within short distances of 2 to 3 meters, you’re still going to see a lot of co-packaged copper and I think XPO in terms of density will be another alternative. But I don’t rule out open CPO as well over there. They’re really looking to maximize the density in a minimum amount of space. So I think XPO will be particularly prevalent in scale-out and scale-across and will be one of the choices in scale-up.

Arista Networks recently won a neocloud as a customer for AI networking; the neocloud’s initial white box architecture could not handle massive scale-out requirements; Arista Networks was selected by the neocloud for its scale-out architecture, which could connect with AMD XPUs; the neocloud is also using AVD (Arista’s Validated Design framework) to automate networking provisioning and thus lower the total cost of ownership; Arista Networks’ management is seeing tremendous opportunity with neocloud and sovereign cloud customers; management thinks the neoclouds are a very important sector for AI networking because they do not have the resources to tackle networking, and so will rely on vendors such as Arista Networks

Our first highlighted win is a neocloud AI network. The customer was constrained by an incumbent white box architecture that simply could not keep pace with the massive scale-out requirements of AI. Arista was selected as a commercially proven and reliable scale-out architecture with unmatched stability of EOS and the ability to connect AMD MI Series XPUs. Arista’s AI leaf and spine Etherlink products were deployed at 800 gigabits to provide the incredible performance modern AI networks require. The AI fabric was tuned using Arista’s cluster load balancing to scale out to thousands of XPUs minimizing hotspots and congestion. On the software side, the customer leveraged AVD, Arista’s Validated Design framework, to automate network provisioning, which both reduces the total cost of ownership, but also provides an easy path to reliable network deployment at scale, where without AVD automation, a small mistake can cause precious days of debugging time. This was a strategic neocloud win with large potential for upside growth in an area where we are seeing enormous opportunity and velocity in both neocloud and sovereign cloud customers…

…It’s easy to talk about the titans because the numbers are so ginormous, right? But the neoclouds are a very important sector because they don’t always have the staff to do everything they want to do, and they really lean on Arista’s design expertise, EOS expertise, network design configurations we can provide them, a family of 22 products we have in AI. 

Arista Networks’ management is seeing industry-wide supply shortages across the silicon board, which has led to higher supply costs and thus gross margin pressure; demand for Arista Networks’ networking products is outstripping supply; management hopes the supply shortages will ease in 1-2 years; despite the supply chain challenges, management has raised guidance for Arista Networks’ AI networking revenue for 2026 to $3.5 billion (previous guidance was for $3.26 billion); Arista Networks’ purchase commitments at the end of 2026 Q1 was $8.9 billion, up 31% sequentially; the sequential increase in purchase commitments was for chips related to new products and AI deployments; management is willing to hurt Arista Networks’ gross margin in order to meet demand for AI networking; management is seeing shortage of power in data center sites; management has chosen not to raise prices, which explains the gross margin pressure; Arista Networks’ purchase commitments extend to multiple years because the lead times for chips are that long

Our demand is actually the best I’ve ever seen in my Arista tenure. The supply, however, is a slightly different and opposite tale. We are experiencing industry-wide shortages across the board, be it wafers, silicon chips, CPUs, optics, and of course, memory that I referred to last quarter, coupled with elevated costs to procure these. Clearly, our demand is outstripping our supply this year. While we hope the supply chain will ease in the next year or 2, the Arista operations team has been diligently engaging with our vendors in strengthening supply agreements and engaging in multiyear purchase commitments. We anticipate gross margin pressure due to mix and trade-offs we are making to pay more to assure supply continuity to our customers. Nevertheless, it gives us confidence to increase our forecasted growth slightly to 27.7%, aiming now for $11.5 billion for 2026. We also increased our AI target now to $3.5 billion this year, thereby more than doubling our AI sales annually…

…Our purchase commitments at the end of the quarter were $8.9 billion, up from $6.8 billion at the end of Q4. As mentioned in prior quarters, this expected activity mostly represents purchases for chips related to new products and AI deployments…

…We see multiyear demand, and we are going to do everything, including hurt our gross margins to supply to that demand this year and next year because we believe that we certainly don’t want to keep GPUs idle and AI infrastructures underutilized because Arista didn’t supply the network…

…The other thing we’re seeing with a lot of these use cases is the lack of power in sites, and the ability and demand to distribute and get a more multitenant scale-across is very high in these 2 use cases…

….One thing to clarify also on gross margins. So we view this as a partnership with our customers. So while we would consider and have raised prices a little bit, unlike our competitors, we haven’t done 2 price increases. We haven’t done major price increases. And the price increases really come into play once our backlog starts to reduce, right? So you won’t see the impact of that. So our gross margins are a strong factor of cost going up and are still eating a lot of the costs and giving our customers the benefit and promise of the pricing we said we would give to them…

…I would just say our purchase commitments are multiyears because we’re having to deal with forecasts that are out multiple years so that we get them in time because the lead time of these chips is so long. So I think that’s the biggest hole, lead times.

Arista Networks continues to have a great relationship with its 2 largest customers, Microsoft and Meta Platforms, in both cloud and AI; management sees the potential for 1-2 new large customers for Arista Networks that use all 3 AI networking use cases – scale-up, scale-out, and scale-across

Microsoft and Meta, they’re our all-time favorites. They’ve been our 10% and greater customers for over a decade. And the partnership could never be stronger, and it continues to get better both in cloud and in AI. In terms of the new entrants, we still expect at least 1, maybe 2 — and maybe I should caveat this by saying, certainly, in demand, we see 1 or 2. We shall see, Todd, how we do on shipments to see if we can achieve the greater than 10%. The 2 of them have very interesting characteristics. They exhibit what I would call the 3 use cases I just alluded to, scale-up, scale-out and scale-across where we really have a fabric notion of creating — so far, we’ve been working with them a lot on the front end, and now we get to complement that on the back end, definitely for scale-out and scale-across and maybe even a little bit of scale-up in some of these use cases.

The biggest use case that Arista Networks’ management sees right now in agentic AI is training, but it will move to distributed inference; management thinks agentic AI will be moving into plenty of enterprise use cases; agentic AI has caused Arista Networks to see a lot more back-end activity now because the hyperscalers have to deal with billions of parameters and tokens, to the extent that the hyperscalers are ignoring the front end refresh; the rise of agentic AI has changed management’s view on the ratio of front-end deployments to back-end deployments from 2:1 to 1:1 or even less; Arista Networks has the same set of products in the same common operating system  across the front-end and back-end, which management sees as lowering costs for customers; Arista Networks is the only vendor that has the same set of products in the same common operating system across the front-end and back-end

The biggest killer application we see in agentic AI right now is still training. And indeed, it’s going to move to more distributed inference. And we’d also like to see agentic AI move into a lot of enterprise use cases, all of which we’re seeing, by the way, but I would say large, medium, small. The largest killer agentic AI application is training, the medium is enterprise and the smallest — medium is inference, and the small is obviously enterprise. The — in terms of back end versus front end, we are now seeing way more back-end activity, particularly with our large AI titans and cloud titans because there is just so much scale they need to prepare for the billions of parameters and tokens, and this is where a lot of — so much so that I think the front end, they might come back and refresh, but they’re almost ignoring right now in favor of the back end…

…By virtue of the back-end deployments, I don’t know if we any more see a 2:1 to the front end, but we at least see a 1:1. And the 1:1 can be wide area, CPU, and storage. Those are probably the 3 common use cases. Not all the customers are up and lifting everything and doing all 3, although we’ve had cases where some of them did an upgrade at the front end before they went into the back end. But usually, they will have to come back to that because the minute you put that kind of performance pressure and scale on the back end, you almost have to do something in the front end. But at the moment, I would say it’s more one-to-one…

…The other thing I have to mention here is just how good it feels to be — have the same set of products in the same common operating system management suite and operating model across the front end and back end. This lowers cost for the customer, simplifies their design process to get that leverage, and we’re one of the few vendors who can do that…

…I think only.

When it comes to greenfield deployments of AI data centers, Arista Networks’ management has observed that customers think of both scale-out and scale-across solutions concurrently; Arista Networks has strong market share in both scale-out and scale-across in greenfield deployments; when it comes to brownfield deployments of AI data centers, Arista Networks now has the opportunity to offer scale-across solutions; the lack of power supply has resulted in data center operators having to distribute the centers, which gives Arista Networks the opportunity to participate in the build out

[Question] You said most of the cloud revenue near-term is going to be scale-out and scale-across as we wait for scale-up to ramp. How are you thinking about your market share when it comes to scale-out versus scale-across in the early days of scale-across? What are you seeing in terms of market share? And are you seeing customer decisions being led in scale-across by sort of the incumbent in scale-out? Or is it a different decision altogether in terms of how they’re designing vendors for scale-across?

[Answer] If it’s greenfield deployment, then they tend to think of it together because they’re not only building the sites, but they’re thinking of the interconnect across them. And therefore, market share is generally strong in both. In some cases, where Arista has not been a historical participant within the data center, we now have an opportunity to offer the scale-across multitenant even in a nongreenfield situation and let’s say, in a brownfield, where now they’ve got disparate data centers or AI clusters that we now have to bring in. And so once again, I think Arista is really fitting example to be in scale-across for both those use cases, but has the additional opportunity in a brand-new data center to be in all use cases, if that makes sense. So it’s giving us a chance to participate with different types of accelerators and different types of models because people aren’t getting the power and they’re having to distribute the data centers. And as a result of distribution, you need more traffic engineering, routing, multitenancy. So I would say scale-across is the common denominator in all our use cases and scale-up and scale-out maybe nice options in brand-new greenfields.

Arista Networks’ management currently sees AI training workloads dominating, but they also see an inference paradigm coming, where CPUs will become more important than GPUs; management is seeing customers wanting to deploy small-ish clusters, in the thousands of GPUs, for inference

While today we are in a training fever, that a more distributed AI — generative AI paradigm with inferences, which means you don’t always need the GPU. You’re going to have high-end CPUs and you’re going to have a smaller set of parameters and tokens to manage, and you’re going to have specific agentic AI use cases and applications. We’re seeing very, very early trials and stages. Nothing super big yet. But we are seeing — I mean, they’re not in the hundreds of thousands of GPUs like you see on the AI titans. But we are frequently seeing our customers in certain high-tech sectors want to deploy clusters that are 1,000 — few thousand, definitely not 10,000, but in hundreds of thousands. And they tend to be exactly, as you said, not training, but more inference based — more agentic AI edge inference based as well. So I think we’ll see more of that. This is the calm before the storm, if you will. And as we — as the AI gets more distributed, I think it doesn’t need GPUs alone, it’s going to need more high-performance compute.

Cloudflare (NYSE: NET)

A rapidly-growing technology company in the Asia Pacific region is experiencing explosive growth, driven by AI coding, and expanded its relationship with Cloudflare; the technology company chose Cloudflare over a hyperscaler

A rapidly growing technology company in APAC expanded their relationship with Cloudflare, signing a two-year $8.7 million contract for application services and our Workers developer platform. Driven by the boom in AI-powered live coding, this company has seen explosive growth, and Cloudflare has become core to their infrastructure, intelligently routing billions of daily requests across the globe. This customer chose Cloudflare over a competitive bid from a hyperscaler due to the strength of our unified platform and our seamless low-latency security. 

A Fortune 100 technology company expanded its relationship with Cloudflare after facing an urgent need to handle massive user-initiated agentic traffic; the technology company was up and running with Cloudflare within a week

A Fortune 100 technology company expanded their relationship with Cloudflare, signing a two-year $8 million contract for our privacy proxy solution, the fifth privacy engagement with this customer, solidifying Cloudflare as their go-to privacy partner. They approached us with an urgent need to handle massive scale with precise geolocation accuracy for user-initiated agentic traffic. We delivered a fully operational solution within one week, demonstrating the speed, trust and engineering depth that continues to set us apart.

A leading AI company expanded its relationship with Cloudflare despite having a strong build-over–buy mentality; the AI company is a massive target for cyberattacks and needed a strong security layer to protect its infrastructure; the AI company is already testing Cloudflare’s AI gateway for AI workloads

A leading AI company expanded their relationship with Cloudflare, signing a one-year $4.1 million contract for application services. As one of the most visible targets for cyberattacks globally, this customer needed a security layer to protect their massive infrastructure build-out. Despite a strong build-over-buy mentality, they chose Cloudflare, trusting a battle-tested network that has proven its resilience against the largest attacks. This is a customer that moves fast and pushes boundaries, and they’re already testing our AI gateway for their AI workloads.

A leading AI company expanded its relationship with Cloudflare with a contract for Argo Smart Routing just one quarter after inking a Workers Developer Platform deal; the AI company used Cloudflare to lower its average global latency by 30%; the hyperscalers could not match Cloudflare’s speed

Another leading AI company expanded their relationship with Cloudflare, signing a 10-month $2 million contract for Argo Smart Routing coming just one quarter after signing a Workers developer platform deal. This customer wants to be the fastest and most reliable AI provider in the market, and Cloudflare is delivering. After deploying Argo, they immediately reduced their average global latency by 30%. In the AI space, that kind of speed is a real advantage that our hyperscaler competitors simply can’t match.

Cloudflare’s management is seeing agentic AI reshape how companies are structured, operate, and create value; Cloudflare itself is the first and most demanding customer of its own AI tools; prior to November 2025, management was cautious about deploying AI internally because management was unclear about the ROI from AI investments; from November 2025 onwards, Cloudflare started experiencing massive gains in productivity from the use of AI; in 2026 Q1, Cloudflare’s usage of AI increased by 600%; nearly all of Cloudflare’s R&D team are using AI coding tools powered by the company’s Workers Developer Platform; 100% of the code submitted by Cloudflare’s R&D team for production are now reviewed by autonomous AI agents; management thinks there will soon be a huge uptick in reliability in software development across the technology industry because AI can now be used to check code; in 2026 Q1, Cloudflare experienced an unprecedented increase in new code generated, solved bugs, and burn-down of technical backlog; employees across Cloudflare are running thousands of AI sessions daily, and these workflows rely on dozens of MCP (Model Context Protocol) servers; Cloudflare has built an agentic harness called Cloudflare OS for teams to get started quickly with agentic AI; Cloudflare’s newfound productivity from AI use has led management to reduce headcount by 20%, but growth is expected in the company’s sales team; Cloudflare has been able to keep the costs of internal AI deployment manageable by running the models on its own infrastructure when appropriate, instead of the model providers’ infrastructure; Cloudflare has been able to achieve significantly higher utilisation of GPU resources than hyperscalers and AI labs; Cloudflare’s AI Gateway enables it to route workloads to the right models, thereby achieving cost-efficency 

In nearly every customer conversation, it’s clear. The emergence of generative and agentic AI is not just redefining the economics of the Internet and software companies, they’re redefining the business models of all companies, fundamentally reshaping how organizations are structured, operate and create value.

At Cloudflare, we don’t just build and sell AI tools and platforms. We are our own most demanding customer. AI and agents are no longer pilot projects at Cloudflare. They are now core parts of our workforce. It’s been an interesting journey. We’ve been selling picks and shovels in the AI gold rush for the last four years, but we ourselves were cautious users wanting to ensure there was real ROI before making significant investment. We avoided a lot of the performative AI some companies engaged in. Internally, the tipping point was last November. At that point, across our teams, we began to see massive productivity gains, team members that were 2x, 10x, even 100x more productive than they had been before. It was like going from a manual to an electric screwdriver. Cloudflare’s usage of AI has increased by more than 600% in the last three months alone. For team members in R&D, 97% use AI coding tools powered by the same Workers Developer Platform we ship to our customers and 100% of their contributions to our production code bases are now reviewed by autonomous AI agents.

I think across the industry, you’re about to see a massive uptick in reliability as every code or configuration change can now have a tireless and uncorrelated set of eyes trained on every incident from the last 10 years, checking to avoid problems. At the same time, the impact on developer velocity is clear. We’ve never seen a quarter-to-quarter increase in new code generated, bugs squashed and technical backlog burn down like we did last quarter…

…Employees across Cloudflare from HR to marketing run thousands of AI sessions each day to get their work done. Those agentic workflows rely on dozens of MCP servers to reach data in systems of record and use hundreds of centrally managed skill files as well as many more that have been created and shared within individual teams. The harness that we’ve built, which we call Cloudflare OS allows teams across the company to quickly get up and running…

…By fully embracing an agentic AI-first organizational structure and operating model as Cloudflare’s revenue scales, our efficiency and productivity will scale even faster. Unfortunately, this decision means parting ways with colleagues who have helped build the strong foundation Cloudflare stands on today, resulting in a reduction of the size of our team by approximately 20%. These reductions are across all functions and geographies and reflect how broadly AI is accelerating our operational velocity. Importantly, however, we continue to expect growth in the net capacity of our quota-carrying sales force to accelerate in 2026 with today’s actions compounding productivity to fuel our growth…

…[Question] How do you think about balancing R&D agentic coding adoption with the cost?

[Answer] We have seen as usage has gone up 600% in the last quarter, we have seen costs go up. But I don’t think it’s gone up nearly as much as some others. And that’s driven by a number of things… more importantly, though, is a lot of times, we’re able to run those models instead of on their infrastructure, on our own infrastructure. And so we have a fleet of GPUs, and we have all of the tools with Cloudflare Workers and Workers AI to be able to build and use those tools themselves. And so most of the use of various AI coding tools isn’t even leaving our network. It’s running on our infrastructure because we’re very good at routing to wherever there’s capacity, we’re able to get a lot out of that. And so I think that’s one of the reasons why we see significantly higher utilization across our GPU resources than some of — than any of the hyperscalers and then — than any even of the AI labs are able to drive. 

And then when we’ve built what we call Cloudflare OS, we’ve paired that with our AI Gateway product. And that AI Gateway product allows you to route different requests based on what’s the right model for the right task. And so that means that if we have a task which we can evaluate as being relatively simple, then we can route that to a model that might be running on our own infrastructure and be able to be delivered at essentially no marginal cost to us. Whereas if we have something that is more important, we might send that off to one of the frontier models and pay more for that…

…Across most of the hyperscalers, you’re seeing utilization rates of their GPUs that are in the single digits, whereas we’re slowly getting our GPU utilization to approach what our CPU utilization is, which is up in the 70% to 80% range.

Cloudflare’s management thinks AI is the biggest tailwind for the company’s network and Workers Developer Platform in its history; management thinks Cloudflare got lucky by already having the right set of tools for agentic AI 

AI is driving a fundamental replatforming of the Internet as well as a paradigm shift in how software is created and consumed, and it’s shaping up to be the biggest tailwind for both our network and our Workers Developer Platform that we’ve ever seen in Cloudflare’s history…

…In our workers platform, we have built a platform that allows you to build agents that are just significantly more efficient than anyone has before. And so across all of the parts of our business because even in the Zero Trust and SASE space, it turns out that having more fine-grained controls about data is exactly what you need if you have kind of these somewhat new agents running around doing things, you want to make sure that they only have access to the things they should. It’s — I wish I could say that we saw all this years ago and built Cloudflare for it. But I think that the reality is that we happen to have built exactly the right set of tools for this moment.

Cloudflare’s management is seeing hundreds of billions of agentic requests monthly, and the requests are growing

So today, literally, we’re seeing hundreds of billions of agentic requests per month, and that number is growing exponentially.

Cloudflare’s management thinks the predominant business model of the internet will be changing dramatically over the next 5 years because of AI, but the end-state is still an open question; management thinks Cloudflare could help define the new business model(s) for the internet; management thinks micro transactions for agentic traffic to websites will be one of the new business models, because agentic internet traffic could surpass human internet traffic in 2027; management thinks that nobody currently has the appropriate infrastructure to handle the potential volume of agentic micro transactions; because of unwanted agentic traffic on advertising-supported media websites, Cloudflare has gone from low penetration in the space to dominating it; media companies have been able to sign better deals with AI companies because of the tools Cloudflare has built; management is focused on making substantial progress with the internet’s new business models, but they are unsure when these will become meaningful 

The business model of the Internet, which has historically been advertising and subscriptions, is about to change dramatically over the next five years. And exactly what it changes to I think it’s still an open question. And I think it might not be one thing. I think it might be several things. Because of how much of the Internet sits behind Cloudflare, we have a seat at the table of defining that…

…Some part of this is going to be some kind of micro transactions for any request that agents are making to website. It might be fractions of fractions of pennies. But if you think about the — I don’t know, about 500 billion requests that pass through Cloudflare in any given second, that some percentage of those we think that there’s going to be some ability to have some micro payment that is made for that because somebody has to pay for the infrastructure. And if you look at the growth in agentic traffic, if you look at the growth in sort of non-human traffic on the Internet, somewhere in 2027, we think it’s going to surpass human traffic, and it’s not going to slow down. And so we’ve got to figure out something else to build it…

…The challenge is like nobody can handle the volumes right now. And so we’re looking around to partner with people. We’re looking around for everything. But right now, the sort of transaction volumes that people are excited about like one million transactions per second, we need something that’s significantly larger than that…

…If you’re an ad-supported business, then your content being crawled is actually a threat. So I think we’re trying to provide tools on both sides of that. The side that you focused on is the folks that want to block it, the ad-supported folks that are out there. And I would say that the first milestone that we’ve seen is that we went from being relatively low in terms of our penetration in the media space to today dominating that space. And so I think that’s the first sign. And what I hear from media company execs is they are signing better deals with AI companies because we’ve given them the tools to be able to control who has their content…

…I don’t know exactly when that will come. But I do — I will say that when we listed what our top six priorities were for 2026, one of the six was making sure that we make real progress and see the first revenue that we can then pass back to that long tail of the Internet in order to help make sure that we continue to create a healthy ecosystem for content creators. And I’m pretty confident we’ll make that goal.

Cloudflare’s management thinks the company’s business is very different from that of the hyperscalers when it comes to providing AI compute infrastructure

The hyperscalers business is to buy a server and then to lease that server back ideally for 5x or more of what they paid for it. And so if they don’t have servers to lease, then they can’t grow their revenue. And so their CapEx has to invest ahead of whatever that demand is that’s out there. We focus on very different things. So the thing to watch for us is when you see us publish a blog post about how we figured out how to get more utilization across our fleet of GPUs or how to get more models loaded quickly across GPUs. That’s real IP that we’re inventing internally and the metaphor to think about is once upon a time when I was in college, I remember a new thing called the web was starting, and so we needed to have a web server. And so we literally — from Gateway, I remember ordering a box that came with cow prints on the outside of it. We bought a gateway server and we plugged it in because there was no idea of virtualization. And then VMware came along and then after that, you had Docker and containers and that was sort of the journey that everyone went on. We’re still at the stage with GPUs of buying the physical server and needing to use that for most of the industry.

Cloudflare has a recent product called Dynamic Workers which allow a company to stand up an AI workflow rapidly; a large AI studio went from zero Dynamic Workers to 1 million in 15 days

We launched something called Dynamic Workers, which allows you to very, very quickly stand up something which is significantly more efficient than a container. Containers are too slow and too heavy to actually be able to respond to these incredibly fast agentic workloads. And so what AI studios are doing is they’re looking at this and they’re seeing the opportunity. And so to give you a sense of — with the — I’m naming them, one of the large AI studios in just the last 15 days went from essentially zero Dynamic Workers to over one million Dynamic Workers running across the platform.

Cloudflare’s management thinks agentic AI will provide tailwinds for its legacy businesses

Every time an agent does something, like if you think about it, you just — if you type something into ChatGPT or any of the things, like to search — the number of sites that get searched, the amount of traffic that gets generated, if I’m looking for a digital camera as a human, I might visit five websites if I really care about it. My agent is going to visit 5,000. And so that’s going to just drive significantly more usage, which is the biggest driver of kind of our Act 1 revenue…

…For Act 2, again, as we talked about already, I think being able to very narrowly define what data an agent has access to and what data they don’t. We’re just seeing more and more of that usage, especially in the self-service category, which there really isn’t another sort of SASE, Zero Trust, self-serve competitor out there with any sort of scale. And so that’s with things like OpenClaw driving a lot of usage there. And what we found time and time again is as hobbyists or individuals adopt technology, they inevitably start to bring that technology more and more to work. And that’s what we’re seeing as we win more of the enterprise accounts across Act 2. 

Coupang (NYSE: CPNG)

Automation and AI is improving Coupang’s service levels and lowering its cost to serve; management expects automation and AI to help Coupang improve its customer experience and margins in the years ahead

Automation and AI across our services, including our Fulfillment and Logistics network, continue to improve service levels and lower cost to serve in parallel, and we expect them to be meaningful contributors to both the customer experience and margin expansion in the years ahead.

Datadog (NASDAQ: DDOG)

Datadog engineers are equipped with the latest AI coding tools and they are building rapidly; management sees the company’s AI initiatives as being split into 2 buckets, namely (1) AI for Datadog, and (2) Datadog for AI; AI for Datadog is about making Datadog’s platform better with AI products and capabilities while Datadog for AI is about Datadog’s end-to-end observability and security capabilities across the AI stack; in AI for Datadog, the company launched MCP (model context protocol) Server for general availability recently and it allows developers to debug applications directly in their AI coding agents; in AI for Datadog, the company launched Bits AI Security Agent recently and it reduces investigations from hours to as little as 30 seconds; in AI for Datadog, the company launched Bits Assistant in preview recently and it allows users to search and act across Datadog with natural language; in Datadog for AI, the company recently launched GPU Monitoring for users to understand their GPU fleets’ performance and drive higher GPU ROI (return on investment)

Our engineers enabled with the latest AI coding tools are building rapidly to help our customers confidently and securely deploy their applications…

…As a reminder, we’re talking about our AI efforts in 2 buckets: AI for Datadog and Datadog for AI. 

So first, AI for Datadog. These are AI products and capabilities that make the Datadog platform better and more useful for our customers. In March, we launched our MCP Server for general availability. With MCP Server, developers access live production data to debug their applications directly in their AI coding agent or IDE. We delivered Bits AI Security Agent, which autonomously triages Datadog Cloud SIEM signals, conduct in-depth investigations of potential threats and delivers actionable recommendations. We’ve seen Bits AI Security Agent reduce investigations that could take hours to as little as 30 seconds. We also shipped Bits Assistant now in preview, which helps customers search and act across Datadog using natural language prompts.

Moving on to Datadog for AI. This includes Datadog capabilities that deliver end-to-end observability and security across the AI stack. We launched GPU Monitoring, enabling teams to understand GPU fleet utilization, workload efficiency, thermal and power behavior and interconnect performance. This drives higher GPU ROI and operational reliability.

Datadog now has 6,500 customers sending data for their AI integrations (was 5,500 in 2025 Q4); these 6,500 customers are only 20% of Datadog’s total customer count, but represent 80% of the company’s ARR; customers’ usage of AI within Datadog is growing rapidly; Bits AI SRE agent investigations have increased by more than 100% from December 2025 to March 2026; the number of LLM spans customers are sending to Datadog is up 3x sequentially in 2026 Q1; the number of Datadog MCP Server tool calls is up 4x sequentially in 2026 Q1; the number of Bits Assistant messages is up 12x sequentially in 2026 Q1; some of the growing AI-related volume that Datadog is processing is because of enterprises’ adoption of AI coding tools; management is seeing an inflection point in AI consumption from customers, driven by a real move towards production-level AI workloads from both AI native and non-AI companies; management is seeing a massive increase in agent usage

We now have over 6,500 customers sending data for one or more of our AI integrations. Though this is only 20% of total customers, they represent about 80% of our ARR. And our customers’ usage of AI within Datadog platform continues to grow rapidly. Bits AI SRE agent investigations have more than doubled from December to March. The number of spans sent to our LLM observability product nearly tripled quarter-over-quarter. The number of Datadog MCP server tool calls quadrupled quarter-over-quarter and the number of Bits Assistant messages increased by a factor of 12 in that period…

…[Question] Is there any way to conceptualize the growth in the sheer raw volume of code that’s being produced in the world today due to adoption of code generators such as Claude Code and Codex and Cursor because they seem to be developing the capability to take on full projects?

[Answer] We definitely think and see that there’s many more applications being created. There’s going to be way more complexity in production. We see some of that happening already today. Some of those new applications are getting into production. They’re finding users. We see some signs of that at every layer of our platform. We quoted a few stats on the increasing data volumes we see in our AI products. That’s definitely a reflection of that. So we see an inflection point there in consumption from customers. We see a move to production that is very real, and we see that across both AI native and non-AI companies…

…We see both a stratospheric increase of agent usage. So we have a ton of usage on our MCP Server. We see customers trying to automate a lot with their own agents, using our agents, using a combination of those.

Example of a 7-figure and 8-figure land deals with the AI research divisions of 2 of the world’s largest technology companies (likely to be 2 of Meta Platforms, Microsoft, and Alphabet, with a likelier pairing of Meta and Microsoft because the deals included GPU monitoring for training workloads, and Alphabet trains on TPUs); the 2 technology companies are training advanced AI models and are relying on Datadog to reduce engineering friction and increase training velocity; the 2 technology companies will be using GPU Monitoring on large parallel GPU grids; the hyperscalers are the companies that make the most sense to pursue observability tools themselves, but they still choose Datadog to be efficient with their own resources; the hyperscalers are using Datadog for both traditional observability and GPU monitoring; it’s still early days for the hyperscalers in terms of their usage of Datadog, but Datadog’s management is optimistic that the 2 hyperscalers can be an example for other AI model builders in the future

We landed 2 large deals, a 7-figure and an 8-figure annualized deals with the AI research divisions at 2 of the world’s largest technology companies. These organizations are building and training the most advanced AI models in the world. It is critical for them to reduce engineering friction and increase training velocity, but fragmented internal and open source tooling made it harder to identify and solve issues and reduce engineering and research productivity. By using Datadog, both companies are accelerating their pace of innovation on their hyperscale AI training workloads. And this includes optimizing their workflows using GPU Monitoring on large parallel GPU grids…

…The thing that’s also interesting, in particular this quarter is that we also landed some large parts of hyperscalers. And hyperscalers typically have a culture of building everything themselves, and they certainly have the balance sheet and the human capital to support some of that build-out. Like if there was ever a set of companies for whom it makes sense to do it themselves, that would be those companies. And yet, we see that they have the same issues. When it comes to going as fast as they can and being as efficient as they can with their resources, like they come to us to replace some of the things that we were using before…

…[Question] About the hyperscalers because I thought that was particularly interesting. And the reason why is I don’t think you called them out previously before, and they are so prevalent in the modern tech stack. To your point, they could do this themselves. So I guess how are they using Datadog? Is it for more kind of traditional observability? Or is it for these newer areas like GPU monitoring that Datadog has performed so well of late?

[Answer] It’s both actually. When you look in general at the large AI customers, they use Datadog the way other companies are largely with a fairly broad set of our products to cover the full surface of observability. What’s new is we now have a product for GPU monitoring. It’s a very new product. And we see the hyperscalers that are coming to us for training workloads in particular, being very interested in that. So again, it’s too early in the product life cycle and the customer life cycle for these specific customers to call definitive victory there, but we see that as a very encouraging sign of where the market might go in the future because we think this might be a bellwether of what the next 10, 100, 500 companies that are going to start training workloads are going to want to do. We have some signs that go beyond the customers we signed this quarter that point that way too.

Datadog’s management continues to believe that digital transformation, cloud migration, and AI adoption are long-term growth drivers of Datadog’s business; management is seeing democratisation of AI training and a growing variety of AI accelerators being used (in management’s words, “the heterogeneity of silicon”), and management thinks both trends are positive for Datadog; the heterogeneity of silicon currently applies to only a very small handful of companies, but management sees a growing opportunity; management was historically more optimistic for AI inference as a growth market for Datadog, but they are increasingly seeing AI training as also a growth market for the company too, driven by growing adoption by the hyperscalers; management is agnostic about the source of usage on Datadog, whether it’s humans or agents; AI training is becoming a growth market for Datadog because it has changed from something artisanal to something in production-mode that has scaled by orders of magnitude and that needs to be incredibly reliable; management is investing heavily into security for AI agents; management thinks there’s a chance a good portion of the market leans towards on-premise observability products

There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. But we now have an additional secular growth driver with AI as we help our customers deliver more value with this transformative new technology. Now more than ever, we feel ideally positioned to help customers of every size and every industry as well as all types of users, whether humans or AI agents, so they can transform, innovate and drive value through AI and cloud adoption…

…The broader market that’s interesting here is training, the training used to be something only 2 or 3 companies were doing or maybe 4 or 5 at a large scale. And it looks like training actually might democratize quite a bit more, and many companies will train models on a regular basis. So it becomes more of a viable category for service providers like us basically. I think the heterogeneity of the silicon is definitely a trend that plays in our favor there. The more heterogeneous, the more you need someone else to make sense of everything for you and tie it all together and also tie it all with the non-GPU aspects and the rest of the infrastructure and the applications and the users and the developers like basically everything we do for living…

…When you think of who is actually — who actually has heterogeneous environments today, that is still a very small number of companies, Google, barely just started selling their TPUs to the outside. So I think it’s still a small number of companies that are there, but we see a growing opportunity there.

Interestingly, last year, when we reported earnings, we said we’re mostly interested in inference workloads and training is not really a market for us yet. Now we actually see training becoming a market. We started landing customers that are actually hyperscalers that have a whole host of homegrown technologies and that are using us specifically in their super intelligence labs to help monitor their workloads, accelerate the training runs, monitor the GPUs also. So we see that as a point of validation that there’s going to be a great market for us…

…We don’t care whether most of the usage is humans, most of the usage is agents. Our business model lends itself to it pretty well, like we’re usage-based, and it doesn’t really matter where the usage is coming from, from that perspective…

…Training was very new a couple of years ago. It was something that was only done by very few companies, and it was, in a way, very artisanal. Like, it was not a production workload. It was something that researchers were building and that was very one-off and homegrown in ways. And now it’s turning into production. It’s turning into something that many more companies are doing. It’s scaling by orders of magnitude. And it’s becoming something that has to be on all the time, reliable and every minute you lose is — or rather every failure you have in your training runs is a week you give away to the competition. And so as a result, it becomes way more interesting as a market for us. And we see some signs of that. Again, we didn’t have a lot of it. We didn’t see a lot of it last year. Now all of a sudden, we’re starting to see quite a bit of activity there and demand…

…On the security of agents, we interface with that in 2 ways. So first, there’s the agents we build ourselves because we are building a lot of automation inside of our product for our customers and agents that automatically identify but also resolve issues without you having to do anything. And there, a lot of it has to do with understanding what permissions to apply, what kind of guardrails to apply, what kind of — how to interface with the humans and how to make that trustworthy and visible in the right way. And so that’s pretty much the whole product surface is to [indiscernible] data. The automation itself actually kind of works already. So you should expect to hear more about that at our conference. This is definitely one big area of investment for us…

…There was a question earlier on data residency and living in customers’ environments. We definitely see a great opportunity there. There is a chance that a good portion of the market leans this way in the future. Today, it’s not the largest part of the market, but we definitely see a potential for that. So we’re investing heavily in that sort of our product.

Datadog experienced adoption growth in AI native customers in 2026 Q1 that significantly outpaced non-AI customers; the AI native cohort continues to diversify and grow; 22 customers in the AI native cohort now spend more than $1 million annually, with 5 spending more than $10 million annually

Our AI native customer growth continues to significantly outpace the rest of the business. This group continues to diversify and grow, including 22 customers spending more than $1 million annually and 5 spending more than $10 million annually. This group includes the leading companies in foundational models, code-gen tools and vertical-specific AI solutions.

MercadoLibre (NASDAQ: MELI)

MercadoLibre’s management rolled out the company’s 1st AI-powered search experience in the marketplace business in 2026 Q1; the new search experience, which involved LLMs (large language models), has led to uplifts in conversion and click-through rates for sponsored listings in Brazil and Mexico; daily active users of MercadoLibre’s Seller Assistant grew 40% month-on-month in March 2026; an AI assistant has increased the productivity of MercadoLibre’s fulfillment network; the new search experience is able to better understand users’ intent

We rolled out our first AI-powered search experience in our marketplace in Q1’26, shifting the architecture away from keywords and rebuilding it around LLMs. In Brazil and Mexico, the improvement in product relevance led to uplifts in conversion and click-through-rate for sponsored listings, both of which represent incremental revenue. These are early results, which we believe have the potential to transform how our customers search and discover products on our platform. Engagement with our Seller Assistant is strengthening, with daily active users growing more than 40% MoM in March. In shipping, an AI-powered assistant that provides reps with real-time process information and performance challenges has increased productivity across our fulfillment network…

…I think it’s worth highlighting the fact that we deployed LLMs in search in commerce for the first time this quarter. And basically, that is live in Brazil, Mexico and Argentina. So now we are using this technology to better understand users’ intent, combining both knowledge on the user behind the query and better interpretation of the query itself.

MercadoLibre’s AI Assistant in MercadoPago is now automatically alert users about negative balances and also identifying opportunities for users to earn higher yields on their savings; AI tools are helping MercadoLibre’s sales force for the Acquiring business to be more productive

In Fintech, our AI Assistant is becoming more proactive. In Brazil, it now alerts users to negative balances in accounts connected via Open Finance and identifies funds held elsewhere that could be earning a higher yield with Mercado Pago — and crucially, it can act on these opportunities instantly, moving balances between accounts within seconds. This is a meaningful step beyond a traditional assistant: it is not just surfacing information, it is helping users take action. In Acquiring, AI tools continue to drive significant improvements in sales force productivity, contributing to the strong market share gains we are seeing across the region. 

Through AI, MercadoLibre’s productivity KPIs were up 56%-80% year-on-year in 2026 Q1 even though headcount was up by just 8%; senior engineers now spend  time building code instead of reviewing code; MercadoLibre is rolling out Claude CoWork to its 31,000 employees

Headcount grew 8% YoY in Q1’26 – a carryover effect of 2025 hiring – but productivity KPIs are growing 7-10x faster. Many of our most senior engineers that were previously spending most of their time reviewing code are now also building code because of the productivity gains enabled by AI tools. Rollbacks – code that is returned to its developer due to errors – are materially lower YoY. More broadly, we have rolled out Claude Cowork to 31,000 employees, making Mercado Libre one of the earliest, large-scale enterprise adopters globally. 

Shopify (NASDAQ: SHOP)

Shopify’s management had bet early on AI and now AI is embedded in everything the company does; Shopify shipped 300 new products and features in 2025 while keeping headcount flat; Shopify has an AI coding partner built right into Slack

In 2026, AI is now Shopify’s native language. We bet early on AI and forced its adoption. It’s embedded in everything we do, the products we build, the channels we power, the way every single person on the team operates. AI has become an exoskeleton for everyone at Shopify, giving them a virtual team of agents and that makes room for rapid experimentation. It allows them to pursue multiple ideas at the same time and then double down on the winners…

…We shipped over 300 new products and features last year alone. We kept our flat head count, which we’re very proud of. And that’s only possible because something has changed fundamentally. And I know Tobi has been talking a bit about river, which is a perfect example of it, but it’s this AI coding partner built right into Slack for the entire team where they can pull into any threat, any conversation and do, frankly, a remarkable amount of the engineering work. And we built it because we needed it, and now it’s deeply embedded in how we operate.

Shopify’s management believes that entrepreneurs will benefit deeply from AI because AI-powered shopping democratises discovery, and this in turn benefits Shopify; each time the world gets more complex, Shopify becomes more valuable for merchants because the company absorbs the complexity into its systems; management sees 3 reasons why Shopify is in a very strong position in the AI age, namely, the company’s (1) data on millions of merchants, hundreds of millions of buyers and billions of products, that enables it to build products informed by the insights developed from the data, such as Sidekick mentioned, (2) demand conversion flywheel, and (3) ability to absorb complexity for merchants; Shopify’s structural advantage is that it gives merchants everything they need, and the company is shipping products even faster now through AI

No group benefits more from AI than entrepreneurs. The logic is simple. AI is making entrepreneurship dramatically more accessible and in fact accelerated. That means we’re going to see more entrepreneurs, and they’re going to scale more easily. AI-powered shopping democratizes discovery. Reach is not just influenced by budget anymore, it is influenced by relevance, which benefits both merchant and buyer. And the right products find the right shopper at the right moment. And this is enormous potential for new and scaling merchants. And because we win when they win, it also has enormous potential for Shopify…

…Every single time the world gets more complex, Shopify gets more valuable. We absorb more of that complexity into our systems and become more valuable to merchants. So when we look at this new era of commerce that we’re in, there are really 3 core principles that explain why Shopify is in such a strong position…

…The first principle, Shopify has a huge advantage that is about to compound. We have 20 years of commerce data. We have data on purchase intent across millions of merchants, hundreds of millions of buyers and billions of products. And in a world where real-time information is now table stakes, the edge is the insight beneath it. And that requires depth, not just access, but experience. We’ve seen merchants start, stall, pivot and scale millions of times across every category and geography. It allows us to build on the real behavior of commerce and to keep shipping products grounded in insights only we have, deep experience applied at speed. That is very hard to replicate and it compounds…

…The second principle, which is the demand conversion flywheel. It should be getting more obvious that every quarter that Shopify is no longer just the platform to convert demand, we are becoming the platform to create it too. And that end-to-end position is a major advantage for merchants…

…The third principle I’ll leave you with is what I call invisible complexity. Here’s the thing. The hardest parts of commerce are the parts that nobody sees, and this is where Shopify thrives…

…That’s the structural advantage of Shopify. We give you everything you need by operating across the entire commerce stack. It’s not the power of any one element of the platform. It’s how they all work together to help merchants accelerate their success. It’s the knowledge and expertise readily available through Sidekick. It’s the speed, context and simplified complexity behind checkout. It’s the ability to sell across every channel, every surface and every geography from day 1. Internally, we are making every function faster, sharper and more productive, and output per employee is improving through deliberate AI usage. The result is that we are building more, shipping more and serving more merchants.

Sidekick is Shopify’s intelligent assistant for merchants that is trained on the company’s knowledge base; the number of weekly active shops using Sidekick grew 385% year-on-year in 2026 Q1; 12,000 custom apps were created with Sidekick in 2026 Q1, up 200% sequentially; half of all Shopify Flows (Shopify’s workflow builder) generated in 2026 Q1 were built with Sidekick; theme edits with Sidekick was in the multimillions in 2026 Q1, up 1,000%; Sidekick has a smart suggestions feature called Pulse; Pulse recently suggested to an accessory brand to create a social proof page and when the accessory brand agreed, the page was created in minutes at no incremental cost to the accessory brand; in the past, the accessory brand would have required a team and several weeks to build the page; merchants that use Sidekick become power users very quickly; Sidekick is used internally at Shopify; management sees Sidekick as a complement to Shopify’s App Store, not a replacement; Sidekick is enabling merchants to build individualised apps rapidly, and thus, move much faster

Sidekick is the perfect example of this. As a reminder, this is our intelligent assistant, which is trained on our knowledge base, paired with completely personalized intel, it has about each merchant’s particular business…

…The number of weekly active shops using Sidekick in Q1 was up 4x year-over-year. We saw over 12,000 custom apps created in Q1 alone using Sidekick. And nearly half of all Shopify flows generated in Q1 were built with Sidekick. And theme edits just from last quarter are in the multimillions, growing over 1,000% in a single quarter. And theme edits just from last quarter are in the multimillions, growing over 1,000% in a single quarter…

…And then there’s Pulse. Sidekick’s smart suggestions feature, which proactively delivers personalized recommendations for merchants using market trends and data from their store, which Sidekick then executes on the merchant’s behalf. And I’ll give you a great example that I just saw the other day. It was an accessory brand, and Pulse noticed that this brand was getting attention in the right places. Its products were being endorsed by fashion publications and showing up on celebrities’ Instagram profiles. So it proactively suggested that the merchant create a social proof page on their website to build trust and validation. And once the merchant agreed, Sidekick created that page on the merchant’s behalf, and it was already all within minutes. Now just a few months ago, that process multiple specialists, marketing, UX design, copywriting and often an incremental cost to the merchant and likely several weeks from start to finish. And now it is happening autonomously in minutes at 0 incremental costs to the merchant. And that is just one of the smart recommendations being served up to that merchant as part of their daily operations…

…Weekly active shops are up 385% using Sidekick. We saw 12,000 custom apps built in Q1, which is up like over 200% quarter-over-quarter…

…Merchants that are just starting to play with it really become power users very, very quickly…

…The impact that we’re seeing not only in terms of how our merchants are using Sidekick, but how we’re using it internally has been super impactful…

…Some of them have actually discovered this incredible tooling, they’re building for their own business and then put in the App Store as well. But in terms of what Sidekick is doing, like Sidekick actually, we see as a real supplement to the App Store, not a replacement…

…The applications that are being built by Sidekick are really very specific nuanced feature sets for particular merchant businesses. And so for most of them, it really is just for the individual merchant. We see them — we see those — the opportunity for the app developers just to continue. That being said, though, what is happening that is super interesting is that now merchants who may have had to spend weeks or even months building a feature either internally or hiring an agency to do so, they’re able to do so much more work themselves using Sidekick, and that means they’re able to go much faster.

Shopify’s management thinks that emerging AI channels for shopping, such as ChatGPT, Microsoft Copilot, and Google and Meta’s AI services, will be a tailwind for e-commerce; Shopify is the only platform enabling discovery and selling inside ChatGPT, Copilot, and Google from a single system of record; AI-driven traffic to Shopify stores is up 8x year-on-year in 2026 Q1; orders from AI-powered searches are up 13x year-on-year in 2026 Q1; new buyer orders from AI-channels are happening at 2x the rate of other channels; Shopify’s Catalog feature provides the necessary information on 1 billion products for AI agents to surface the most relevant products in seconds; traffic from Catalog-powered AI searches converts 2x more traffic than general AI searches; usage of Shopify’s Sign In With Shop user verification tool is up 3x year-on-year in 2026 Q1; Sign In With Shop is important for agentic commerce because it enables agents to know who they are buying for; agents are not bypassing Shopify; Shopify is the storefront within ChatGPT’s recent move to having in-app browsers for checkouts; Shopify recently introduced an agentic plan that allowed brands to sell in AI channels through Shopify Catalog with no Shopify stores required; non-Shopify merchants are realising that Shopify Catalog is enabling their products to surface on agentic surfaces much better than web-scraping, and it is leading these merchants to join the Shopify ecosystem; OpenAI and Microsoft are already using Catalog

We believe that new and emerging AI channels, places like ChatGPT, Microsoft Copilot, Google AI Services and Meta will be a tailwind to driving e-commerce growth and penetration over time…

…We are the only platform that enables discovery and selling inside ChatGPT, Copilot and Google, all from one single system of record. And the early signals on AI channels are really compelling. And in the first quarter, AI-driven traffic to Shopify stores has grown 8x year-over-year, while orders from AI-powered searches have increased nearly 13x. And within this, new buyer orders are occurring at nearly twice the rate of other channels…

…Let’s talk about Shopify’s catalog because this really, really matters. To date, we’ve structured more than 1 billion products with clean attributes, real-time pricing and accurate inventory so AI agents can surface the most relevant products in seconds, and the results speak for themselves. Traffic from catalog-powered AI searches converts 2x more than traffic from general AI searches where the agent is working from scraped or often outdated information from across the web…

…Sign in with Shop is our user verification tool, which recognizes buyers across devices, stores and surfaces with no sign-in friction. And usage is growing steadily. We are up 3x year-over-year, and it is now enabled across nearly our entire merchant storefront base. In an agentic world, this really matters. Agents need to know who they are buying for and we are ready…

…Agents do not bypass Shopify, just the opposite. In fact, they write right into Shopify. I mean, I think you saw in sort of recent headlines that merchant storefronts really matter. You saw ChatGPT move to in-app browsers for their checkouts. So it’s literally the Shopify storefront within the chat. And again, when a buyer is shopping in ChatGPT, they’re browsing Shopify’s incredible catalog. So the momentum on agentic has been amazing…

…In terms of some of the stuff we’re doing with the agentic plan, for example, again, that rolled out early March. That means that any brand on any platform can now sell across AI channels via Shopify Catalog and no Shopify stores required…

…The big thing, though, with catalog is that I think a lot of non-Shopify merchants are seeing that catalog is actually doing a much better job of organizing and syndicating their products across every agentic surface versus sort of the old scraping thing that was happening prior to catalog. So it’s doing 2 things. One, it is unequivocally getting Shopify connected with a lot more non-Shopify merchants per se and beginning those conversations, which, again, may lead to them joining the agentic plan or ultimately may lead them to come into Shopify for their entire migration, which obviously is our plan and our hope. But even if they just want to be part of catalog and just be part of the agentic plan on its own, that already is a massive lift to them relative to everything else…

…OpenAI and Microsoft are already using the Catalog power discovery.

Shopify co-developed the open Universal Commerce Protocol (UCP) with Google; UCP enables the full commerce journey from product discovery to post-purchase support; management built UCP because they believe that agentic commerce should be based on open standards; management has created the UCP Tech Council, which recently saw Amazon, Meta, Microsoft, Salesforce, and Stripe become members

You might have seen with the latest news on the Universal Commerce Protocol, or UCP, which we co-developed with Google. UCP is an open protocol that makes Agentic commerce work at scale. It enables the full commerce journey, product discovery, checkout, payment, post purchase across any platform with any payment processor.

We co-developed UCP because we believe the future of commerce runs in open standards, not closed systems. And then we created the UCP Tech Council, the technical body that steers the protocol’s direction to ensure it evolves to meet the needs of businesses, platforms, developers and consumers. We are now seeing the biggest and most innovative companies across essentially the entire industry coming together around UCP to help push Agentic commerce forward. And last month, Amazon, Meta, Microsoft, Salesforce and Stripe all joined the council, committing their expertise in Internet scale transaction processing to build one universal protocol for commerce.

Gross margin for Subscription Solutions was similar to a year ago, as economies of scale and efficiencies in support were partially offset by increased LLM costs from growing usage of Shopify’s AI products; management expects pressure on the gross margin from usage of Shopify’s AI products to continue

Gross profit for Subscription Solutions grew 21%, with gross margin coming in at 80%, in line with Q1 2025. Economies of scale and efficiencies in support were partially offset by increased LLM costs, driven by growing merchant usage of our AI products, most notably Sidekick. We expect this dynamic to continue.

AI is writing about 50% of Shopify’s code today; there are more app developers building for Shopify’s ecosystem than ever before, and Shopify is using AI to speed up the app approval process

AI right now writes well over 50% of our code today, and that number is going up significantly, not down…

…You’re seeing more app developers build for Shopify’s ecosystem than ever before. In fact, we’ve now put the app approval process on rails using incredible AI testing so that we can get more apps into the app store faster.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Alphabet, Amazon, Coupang, Datadog, MercadoLibre, Meta Platforms, Microsoft, Salesforce, and Shopify. Holdings are subject to change at any time.

The way I see it, artificial intelligence (or AI), really leapt into the zeitgeist in late-2022 or early-2023 with the public introduction of DALL-E2 and ChatGPT. Since then, developments in AI have progressed at a breathtaking pace.

We’re thick in the action of the latest earnings season for the US stock market – for the first quarter of 2026 – and I thought it would be useful to collate some of the interesting commentary I’ve come across in earnings conference calls, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. This is an ongoing series. For the older commentary:

• 2023 Q1 – here and here
• 2023 Q2 – here and here
• 2023 Q3 – here and here
• 2023 Q4 – here and here
• 2024 Q1 – here and here
• 2024 Q2 – here and here
• 2024 Q3 – here and here
• 2024 Q4 – here, here, and here
• 2025 Q1 – here and here
• 2025 Q2 – here and here
• 2025 Q3 – here, here, and here
• 2025 Q4 – here, here, here, and here

With that, here are the latest commentary, in no particular order:

Alphabet (NASDAQ: GOOG)

Gemini Enterprise has 40% sequential growth in paid monthly active users in 2026 Q1; Gemini 3.1 Pro is pushing the frontier in reasoning, multimodal understanding, and cost; there are now a wide variety of models in the Gemini 3.1 family to meet different developer needs; Gemini 3.1 Flash Live is powering conversational features in search and the Gemini app, and speech-to-text is now available in 70 languages; Gemini 3.1 Pro has delivered a big upgrade to Alphabet’s Deep Research product; the Lyria 3 model has generated over 150 million songs since its launch in the Gemini app; Nano Banana 2 has generated 1 billion images in half the time of Nano Banana 1; management recently launched Gemma 4, Alphabet’s best open model to date, and it has been downloaded more than 50 million times in a few weeks; Nano Banana 2 was recently integrated into the Gemini app to enable personalised image creation; Gemini is now integrated with Google Maps, so users can converse with Google Maps via chat

Gemini Enterprise is seeing tremendous momentum with 40% growth quarter-over-quarter in paid monthly active users…

…Gemini 3.1 Pro continues to push the frontier in reasoning, multimodal understanding and cost. We have quickly expanded the Gemini 3.1 series of models to offer more choices for developers, including our cost-efficient Flash models. 3.1 Flash Live, our latest audio model, has improved precision and reasoning, making voice interactions more natural and intuitive. It’s now powering conversational features in search and the Gemini app. Speech-to-text is now available in 70 languages. And with 3.1 Pro, our Deep Research agent got a big upgrade, including MCP support and native visualizations.

Our generative media models are incredibly popular. Lyria 3 has generated over 150 million songs since launching on the Gemini app. Nano Banana 2 reached 1 billion images in nearly half the time of Nano Banana 1. And Veo 3.1 Lite is our most cost-efficient video model to date.

On top of this, we launched Gemma 4, our most intelligent open model. It’s been downloaded over 50 million times in just a few weeks. In fact, our open models have now been downloaded over 500 million times…

…This month, we integrated Nano Banana 2 to make personalized image creation possible in the Gemini app. Maps recently got its most significant upgrade in over a decade with Gemini. Users can now have a conversation with Maps and get more personalized suggestions and intuitive directions.

Alphabet’s management thinks Google Cloud has the widest variety of compute options with Alphabet’s custom TPUs and Axion CPUs, and NVIDIA GPUs; Google Cloud will be among the first cloud providers to offer NVIDIA’s Vera Rubin NVL72 systems; Alphabet recently introduced the 8th generation of TPUs that has a training variety and an inference variety; TPU 8t, the training variety, offers 3x the processing power and 2x the performance of the previous generation; TPU 8i, the inference variety, has 80% better performance per dollar in inference compared to the previous generation; Alphabet’s TPUs are powering the company’s AI research in both training and tooling; management will begin to deliver TPUs to select customers in their own data centers to expand the TPU opportunity; management expects to recognise most of the revenue of external TPU shipments in 2027; management does think about the ROIC of external TPU shipments compared to internal deployment

Our custom TPUs, Axion CPUs and the latest NVIDIA GPUs continue to form the industry’s widest variety of compute options. NVIDIA GPUs are a core part of our AI accelerator portfolio and will be among the first to offer NVIDIA Vera Rubin NVL72 in addition to the Blackwell and Hopper-based instances already available.

At Cloud Next, we introduced our 8-generation TPUs, individually specialized for training and serving and able to take on the most demanding agentic workloads. TPU 8t provides high-performance model training with 3x the processing power of Ironwood and 2x the performance. TPU 8i delivers cost-effective, low-latency inference with 80% better performance per dollar than the prior generation. This exceptional infrastructure powers our world-class AI research that includes models and tooling, which continue to progress really well.

Our TPUs continue our leadership in performance, cost and power efficiency for customers like Thinking Machines Lab, Hudson River Trading and Boston Dynamics. As TPU demand grows from AI labs, capital markets firms and high-performance computing applications, we’ll begin to deliver TPUs to a select group of customers in their own data centers in the hardware configuration to expand our addressable market opportunity…

…We expect to begin recognizing a small percent of the revenues from these agreements later this year with the vast majority of revenues to be realized in 2027. It is important to keep in mind that revenues from TPU hardware sales will fluctuate from quarter-to-quarter, depending on when TPUs are shipped to customers…

…On the second question around TPUs, obviously, I would — we do think about it as what are we doing through Google Cloud to help our customers? And that’s the framework with which we think about it. In that context, there are situations where it makes sense. For example, you take customers like capital markets where they are running this highly performant AI workloads. They wanted TPUs in their data centers. So there are — and those trends are true across a diverse set of industries and in certain cases, frontier AI labs, too. And so we are opportunistic about it. But I do think we step back and think about it overall as the opportunity for Google Cloud. A lot of it is providing infrastructure through cloud. At times, it is direct sales of TPU hardwares to a select group of customers. But again, we do take ROIC approach. And some of it helps us get more economies of scale, scale in our overall compute environment as well. And so helps us invest in the cutting edge, which we need to do in the next generation as well.

Alphabet is using Antigravity, the company’s 1st-party agentic coding solution, to manage fully autonomous digital task forces

With Antigravity, we are shifting to truly agentic workflows. Our engineers are now orchestrating fully autonomous digital task forces and building at a faster velocity. Much more to come here. 

Google Search queries are at an all-time high, driven by AI; AI Overviews is driving overall search growth; AI Mode is seeing strong growth in both users and usage globally; management recently shipped agentic experiences in Google Search, such as restaurant booking, to new countries; management recently shipped the multi-modal capability, Search Live (where users can have voice conversations AI while sharing their phone’s camera feed to study surroundings), globally; search latency has been reduced by 35% in the past 5 years despite the new AI features introduced in Google Search; management has reduced the cost of responses by AI Overviews and AI Mode by 30% since they were upgraded to Gemini 3

I continues to drive search usage and queries are at an all-time high. We continue to invest in improvements to AI Overviews, which are driving overall search growth and we are also seeing strong growth in both users and usage of AI Mode globally…

…We also shipped agentic experiences like restaurant booking to new countries and new multimodal capabilities like Search Live globally…

…Even as we have brought new AI features into our results page, we have reduced search latency by more than 35% over the past 5 years. And since upgrading AI Overviews and AI Mode to Gemini 3, we have reduced the cost of core AI responses by more than 30%, thanks to continued hardware and engineering breakthroughs.

Alphabet’s management thinks a key point of Google Cloud’s differentiation is its 1st-party solutions across the enterprise AI stack; Google Cloud’s enterprise AI solutions became Google Cloud’s primary growth driver for the first time in 2026 Q1; revenue from products built on Alphabet’s GenAI models was up 800% year-on-year in 2026 Q1; new customer acquisition doubled in 2026 Q1 from a year ago; the number of $100 million to $1 billion deals doubled year-on-year in 2026 Q1; Google Cloud customers outpaced initial commitments by 45% in 2026 Q1, accelerating from 2025 Q4; Google Cloud recently introduced new capabilities across its vertical AI stack, including a new Gemini Enterprise AI Platform that helps users build and manage agents; Gemini Enterprise paid monthly active users was up 40% sequentially in 2026 Q1; the partner ecosystem for Gemini Enterprise had 9x year-on-year growth in 2026 Q1 in seats sold by partners and number of partners using Gemini Enterprise internally; 330 Google Cloud customers processed over 1 trillion tokens each over the last 12 months, with 35 processing over 10 trillion tokens each

Google Cloud is differentiated because we are the only provider to offer first-party solutions across the entire enterprise AI stack…

…Our enterprise AI solutions have become our primary growth driver for cloud for the first time. In Q1, revenue from products built on our GenAI models grew nearly 800% year-over-year. We are winning new customers faster with new customer acquisition doubling compared to the same period last year. We are seeing strong deal momentum, doubling the number of $100 million to $1 billion deals year-on-year and signing multiple $1 billion-plus deals…

…Customers outpaced their initial commitments by 45%, accelerating over last quarter.

At Cloud Next last week, we introduced hundreds of new capabilities across our vertically optimized AI stack that are designed to work together for our enterprise customers. We introduced a new Gemini Enterprise Agent Platform that empowers users to build, orchestrate, govern and optimize agents with the controls that enterprise customers need. Along with new capabilities in Gemini Enterprise app like Projects, Canvas, Long-Running agents and Skills, every employee can build agents.

In Q1, Gemini Enterprise paid monthly active users grew 40% quarter-over-quarter. That includes major global brands like Bosch, Citi Wealth, Merck and Mars Inc. Our partner ecosystem plays an increasingly critical role in driving Gemini Enterprise adoption. We saw 9x year-over-year growth, both in seats sold with partners and in the number of partners adopting it for internal use…

…Over the past 12 months, 330 Google Cloud customers each processed over 1 trillion tokens. 35 reached the 10 trillion token milestone.

Gemini is applied in Youtube for better matching and discovery between brands and creators; Gemini now powers YouTube Creator Partnerships; management has made it easier for advertisers to buy premium advertising space on Youtube; Supergoop! partnered with a YouTube creator for a Shorts and CTV campaign and it led to a 93% lift for a product and a 55% overall brand lift.

We are applying Gemini to drive better matching and discovery between brands and creators of all sizes. And Gemini now powers YouTube Creator Partnerships, a centralized platform integrated directly into YouTube Studio for creators and Google Ads for advertisers. 

We’ve also made it easier to buy premium ad space in top-tier podcast shows by curating the most watched podcasts into popular genres. For example, Supergoop! partnered with YouTube creator, Liza Koshy on a multi-format shorts and long-form CTV campaign, resulting in a 93% lift for their Glowscreen product and a 55% overall brand lift.

Waymo has so far launched in 6 new cities in 2026 and is currently in 11 major US cities; Waymo is now providing 500,000 rides per week (was 400,000 in 2025 Q4)

Waymo is on a great trajectory. It launched in Nashville a few weeks ago, that makes 6 new cities so far in 2026 and operations in 11 major U.S. cities in total. Waymo also surpassed 500,000 fully autonomous rides per week, doubling in less than a year.

Alphabet’s management is accelerating the deployment of Gemini across the company’s entire advertising infrastructure; the deployment of Gemini has led to new performance breakthroughs in advertising quality, advertiser tools, and new AI user experiences; Alphabet is making significant strides in improving relevance even when there isn’t a direct user query; advertising in Discover is getting better aligned with unique user interests; promoted pins in Maps are deeply relevant to user surroundings, location of interest, history and intent; Alphabet’s advertising relevance has increased by nearly 10%; Gemini is now powering Smart Bidding to more accurately match user intent to an advertiser’s product; management launched AI Max to help advertisers adapt to a new conversational way of searching by consumers; AI Max was moved out of beta earlier in April 2026; Hilton EMA used AI Max to capture 33% more clicks at 20% of the spend, and to increase average booking value by 55%; Etsy used AI Max to increase search volume by 10% with 15% of queries being net new; more than 30% of customer search spend now uses AI Max or Performance Max, and advertisers using the tools enjoy more conversions for the same spend; management is reinventing advertising formats for AI-native experiences; direct offers in AI mode are resonating with users; management is testing a new advertising format in AI Mode that displays retailers who sell recommended products in the AI Mode’s answer to a query; management launched Universal Commerce Protocol (UCP) in January 2026; UCP has new members consisting of major technology companies; brands such as Sephora, Macy’s and Ulta Beauty have already rolled out UCP; Ulta Beauty recently launched agentic commerce experiences in AI Mode and the Gemini App; management has received great feedback on UCP and they think UCP will power a new checkout experience in AI Mode, Search, and the Gemini app

We are accelerating the deployment of Gemini across our entire ads infrastructure to help businesses reach more customers in more places than ever before. This is driving significant improvements across all areas of marketing and continues to fuel new performance breakthroughs across 3 areas critical for our customers’ success, ads quality, advertiser tools and new AI user experiences.

First, ads quality. AI is boosting our ability to deeply understand user intent for a given search query and to find the most relevant ad. Even when we don’t have a direct user query, we’re making significant strides in improving relevance. In Discover, new AI models and classifiers are driving higher relevance by better aligning ads with unique user interests. In Maps, we’re using Gemini to ensure promoted pins are deeply relevant to user surroundings, location of interest, history and intent. This work is improving ads relevance by nearly 10%, leading to significant increase in user engagement. We’re pairing this strengthened prediction-driven relevance with bottom-of-funnel precision. Over the past year, we’ve made over 20 improvements to search and shopping bid strategies. Smart Bidding now uses Gemini to match user intent to an advertiser’s product and services more accurately and further drive performance. This level of granularity was previously impossible to achieve at scale.

Second, on advertiser tools, where Gemini helps advertisers drive more efficient and effective campaigns. People no longer search in fragments. They search conversationally and share more context. We launched AI Max to help advertisers adapt to this new way of searching. And earlier this month, it moved out of beta with improved performance quality across targeting and creative capabilities. Take Hilton EMA, they captured 1/3 more clicks for 1/5 of the spend while simultaneously increasing the average booking value by 55%. And Etsy saw a 10% search volume uplift with 15% of those queries being net new to their business. We see significant opportunity as advertisers continue to make good progress on AI readiness and the adoption of AI tools. For instance, more than 30% of our customer search spend now uses AI-enabled campaigns, AI Max or Performance Max. And these advertisers are seeing more conversion for the same spend.

Third, how we monetize new AI user experiences in search? We aren’t just bringing existing ad formats into AI experiences. We are reinventing ads for this new era. Direct offers in AI Mode are resonating with users and continue to receive positive customer feedback. Gap, L’Oreal and Chewy are just some of the latest partners who have now signed up to test this Google Ads pilot.

We’re also exploring new formats for retailers. AI Mode already surfaces organic product recommendations based on the user’s query and we’re now testing a new ad format that displays retailers who sell those recommended products. In addition, the retail industry is rapidly coalescing around the open source Universal Commerce Protocol, or UCP, we launched in January in partnership with the ecosystem. Last week, we welcomed Amazon, Meta, Microsoft, Salesforce and Stripe as new members to the UCP Tech Council. They joined founding members, Shopify, Etsy, Target, Wayfair and Google to further accelerate the transition towards an agentic future. Partners like Sephora and Macy’s have joined companies like Ulta Beauty, who are already rolling out UCP and can now redefine consumer journeys from discovery to checkout. Ulta Beauty just last week launched agentic commerce within AI Mode and Search and the Gemini app. Shoppers can now review product recommendations, compare options and complete streamlined checkout for eligible purchases directly within AI Mode and Gemini…

…We’ve received tremendous feedback so far from hundreds of top tech companies, payments partners, retailers, really interested in integrating. And it will help power a new checkout experience in AI Mode, in Search and the Gemini app and allowing shoppers to actually check out from select merchants, right as they’re researching on Google and going through this journey.

Google Cloud had 63% revenue growth in 2026 Q1 (was 48% in 2025 Q4) driven by growth in GCP; GCP grew at a much higher rate than Google Cloud’s overall growth; Google Cloud’s growth was driven by AI solutions and AI infrastructure; Google Cloud operating margin was 32.9% (was 30.1% in 2025 Q4 and was 17.8% in 2025 Q1); Google Cloud backlog grew nearly 100% sequentially to $462 billion in 2025 Q4 (was $240 billion in 2025 Q4); most of Google Cloud’s backlog are GCP contracts, and just over 50% of the backlog is expected to be recognised as revenue in the next 2 years; Google Cloud’s impressive margin improvement was driven by leverage from revenue growth, and management’s insistence on running an efficient organisation

 Cloud revenues accelerated across all key areas and were up 63% to $20 billion. Revenue growth was driven by strong performance in GCP, which continued to grow at a rate that was much higher than cloud’s overall revenue growth rate. The largest contributor to cloud’s growth this quarter was AI solutions, driven by strong demand for industry-leading models, including Gemini 3. In addition, we had strong growth in AI infrastructure due to continued deployment of TPUs and GPUs and core GCP continues to be a sizable contributor driven by demand for infrastructure and other services such as cybersecurity and data analytics. Workspace again delivered strong double-digit revenue growth, driven by an increase in the number of seats and the average revenue per seat. Cloud operating income was $6.6 billion, tripling year-over-year and operating margin increased from 17.8% in the first quarter of last year to 32.9%.

Google Cloud’s backlog nearly doubled sequentially, reaching $462 billion at the end of the first quarter. The increase was driven by strong demand for enterprise AI offerings and the inclusion of TPU hardware sales that Sundar referenced earlier. The majority of the backlog is related to typical GCP contracts and we expect to recognize just over 50% of the backlog as revenue over the next 24 months…

…[Question] There’s a thesis out there that AI revenues are a lower margin in general but we are seeing margins improve. So more insights on just the cloud business and what’s driving that margin expansion.

[Answer] There are pushes and pulls across the business, including within cloud specifically. And I would start with the top line. When we see this robust strong revenue growth, both in Cloud and Google Services, it does provide leverage all the way down to the bottom line within the income statement. And you know we’ve been working hard to ensure we have — we’re running a productive and efficient organization. And it’s not just how we operate the business but even in areas such as our technical infrastructure, where we are investing the significant CapEx investments in our data centers and servers, we are looking at how we drive scientific process innovation within that organization. And that is reflected both in Cloud and Google Services as we allocate costs based on based on consumption. In the past, I did talk about the depreciation associated with these investments that is hitting both Google Cloud and Google Services. Google Cloud expanded margin quite significantly from a year ago, as you’ve seen in our numbers that we’ve just previewed. And a lot of it, again, is the top line growth that Google Cloud is providing or producing as well as an incredibly efficient way of running the business.

Alphabet’s management has raised capex guidance for 2026 to $180 billion to $190 billion (was previously $175 billion to $185 billion; 2025’s capex was $91.4 billion, which was itself up 65% from $55.4 billion in 2024, and 2024’s capex was up 69% from 2023); management is seeing unprecedented demand for AI compute; Alphabet’s investments in AI compute are delivering strong growth; management expects 2027’s capex to be much higher than 2026’s; management is investing in capex based on tangible demand signals and a ROIC framework; Google Cloud remains constrained by supply and would have grown faster in 2026 Q1 if supply was higher

…We will begin to deliver TPU hardware to a select group of customers in their own data centers. We expect to begin recognizing a small percent of the revenues from these agreements later this year with the vast majority of revenues to be realized in 2027. It is important to keep in mind that revenues from TPU hardware sales will fluctuate from quarter-to-quarter, depending on when TPUs are shipped to customers…

…Wiz will be reported in the Google Cloud segment. And second, we expect a low single-digit percentage point headwind to cloud’s operating margin for the remainder of 2026 related to the acquisition…

…We are updating our full year 2026 CapEx guidance range to $180 billion to $190 billion, up from our previous estimate of $175 billion to $185 billion to now include investment related to the acquisition of Intersect, which closed in March.

We are seeing unprecedented internal and external demand for AI compute resources. The investments we are making in AI is delivering strong growth as evidenced by the record revenue and backlog growth in Google Cloud and strong performance in Google Services. Looking ahead, these strong results reinforce our conviction to invest the capital required to continue to capture the AI opportunity. As a result, we expect our 2027 CapEx to significantly increase compared to 2026. In terms of expenses, as we’ve discussed previously, the significant increase in our investment in technical infrastructure will continue to put pressure on the P&L in the form of higher depreciation expense and related data center operations costs such as energy. We also expect to continue hiring in key investment areas such as AI and cloud and are investing in marketing to support our AI products…

…You’ve seen us over the past several years increase CapEx every year. And we have done it very thoughtfully to meet the demand that we are seeing, both from external customers as well as demands across the organization. And you’re seeing the proof point, the ROIC on that in terms of just the growth rate we’re seeing, whether it’s growth rate within search or certainly the cloud business and the opportunity we have within the cloud backlog…

…I do think looking ahead, our ability to invest in this moment and stay at the frontier, I think puts us in a strong position. And I think we are doing it based on tangible demand signals we are seeing. And it’s not just on the revenue side but I’m talking from a ROIC framework and that’s what is helping us navigate this moment responsibly…

…We are compute constraint in the near term. And as an example, our cloud revenue would have been higher if we were able to meet the demand.

Amazon (NASDAQ: AMZN)

AWS grew 28% year-on-year in 2026 Q1 (was 24% in 2025 Q4) and is now growing at its fastest pace in 15 quarters; AWS’s run rate has reached $150 billion (was $142 billion in 2025 Q4); the last time AWS grew at a similar rate, it was half its current size; AI’s growth is unprecedented; the 1st 3 years of AWS’s AI revenue run rate was $15 billion, 260x larger than AWS’s run rate in its 1st 3 years; management thinks customers are choosing AWS for AI for 4 reasons, namely, (1) AWS’s broader capabilities, (2) customers want their AI inference to be at where their other applications and data reside, and this happens to be in AWS, (3) customers want to consume non-AI services as they grow their AI usage, and AWS has a broad set of offerings, and (4) AWS has the strongest security and operational performance; AWS has won many new enterprise customers since 2025 Q4’s earnings call, including OpenAI, Anthropic, Meta Platforms, and NVIDIA; AWS continues to see strong growth in non-AI workloads as enterprises focus on cloud migrations; management is seeing customers who want to benefit from AI accelerate their migration to the cloud; management is seeing a strong correlation in customers’ AI spend and core growth in AWS; AWS’s AI revenue is growing triple digits year-on-year; AWS operating income in 2026 Q1 was $14.2 billion, reflecting 37.7% operating margin (was 35.0% in 2025 Q4 and 39.5% in 2025 Q1); AWS’s backlog is $364 billion in 2026 Q1 with significant sequential growth (was $244 billion in 2025 Q4), and the backlog has reasonable breadth and does not include a recent $100 billion deal with Anthropic

AWS growth continued to accelerate, up 28% year-over-year, the fastest growth rate in 15 quarters, up $2 billion quarter-over-quarter, the largest Q4 to Q1 AWS revenue increase ever. AWS is now a $150 billion annualized revenue run rate business. It’s very unusual for a business to grow this fast on a base this large. And the last time we saw growth at this clip, AWS was roughly half the size. We’ve never seen a technology grow as rapidly as AI…

…3 years after AWS launched, it had a $58 million revenue run rate. In the first 3 years of this AI wave, AWS’ AI revenue run rate is over $15 billion, nearly 260x larger.

There are several reasons customers are choosing AWS for AI. First, we’ve built broader capabilities than others…

…Second and another reason customers continue choosing AWS is that as they expand their use of AI, they want their inference to reside near their other applications and data and much more of it resides in AWS than any place else. Third, as customers expand their AI usage, they also want to consume additional non-AI services, and they’re choosing AWS because we’ve built the broadest and most capable core offerings by a wide margin. We offer thousands of features across compute, storage, databases, analytics, security and more, and Gartner consistently recognizes AWS’ leadership across their major cloud evaluation areas. Fourth, AWS is the strongest security and operational performance of any AI and infrastructure provider and start-ups, enterprises and governments continue to choose AWS as the foundation for their most critical workloads…

…Since last quarter’s call, we’ve announced new agreements with OpenAI, Anthropic, Meta, NVIDIA, Uber, U.S. Bank, Fox, Southwest Airlines, U.S. Army, Bloomberg, Cerebras, AT&T, Nokia, Fundamental, The National Geographic Society, PGA TOUR and many more…

…Moving to our AWS segment. Revenue was $37.6 billion and growth accelerated 480 basis points to 28% year-over-year, driven by both core and AI services. We continue to see customers increase cloud migrations and scale their use of AWS core services. Customers seeking the full benefit of AI are accelerating their transition to the cloud. We also see a strong correlation between AI spend and core growth. As customers spend more on AI, we see a corresponding demand increase in core. We expect this to increase over time as customers move more AI workloads into production, strengthening demand for our core services…

…Our AI revenue is growing triple digits year-over-year…

…AWS operating income was $14.2 billion and reflects our strong growth, coupled with our focus on driving efficiencies across the business…

…The backlog for Q1 is $364 billion. That does not include the recent deal that we announced with Anthropic for over $100 billion. There’s reasonable breadth in that as well. It’s not just 1 customer or 2 customers.

AWS’s chips business, including Graviton and Trainium, grew 40% sequentially in 2026 Q1; the chips business is now at a $20 billion annual revenue rate (was $10 billion in 2025 Q4), and growing triple-digits; if AWS sold its chips as a stand-alone business, its annual revenue run rate would be $50 billion; AWS’s custom silicon business is now 1of the top 3 data center chip businesses in the world; Anthropic and OpenAI both recently signed very large multi-year commitments for Trainium; Trainium now has $225 billion in revenue commitments; Trainium 2 has 30% better price-performance than competitor GPUs and is largely sold out; Trainium 3, which only started shipping at the start of 2026, is 30%-40% more price-performant than Trainium 2 and is nearly fully subscribed; Trainium 4 is already been reserved despite being 18 months from broad availability; Amazon Bedrock runs most of its inference on Trainium; Meta Platforms has committed to using tens of millions of AWS’s Graviton CPUs; Amazon management sees massive demand for CPUs as agentic AI, post-training, and inference scales up; Graviton has 40% better price-performance than other x86 CPUs; Graviton is used by 98% of the top 1,000 AWS EC2 customers; AWS is bringing in more Trainium chips than NVIDIA GPUs, but NVIDIA remains an important partner; management expects Trainium to eventually save AWS tens of billions of dollars of capex annually and provide several hundred basis points of operating margin; management believes that people will always want choice in models and chips; management is currently not interested in selling Trainium racks to 3rd party data centers, but thinks AWS could do so in the next few years

Our chips business continues to grow rapidly and is larger than what a lot of folks thought. We saw nearly 40% quarter-over-quarter growth in Q1, and our annual revenue run rate is now over $20 billion and growing triple-digit percentages year-over-year…

…If our chips business was a stand-alone business and sold chips produced this year to AWS and other third parties as other leading chip companies do, our annual revenue run rate would be $50 billion. As best as we can tell, our custom silicon business is now one of the top 3 data center chip businesses in the world, the speed at which we’ve gotten here is extraordinary…

…We’ve recently shared very large multiyear, multi-gigawatt Trainium commitments from the 2 leading AI labs in the world in Anthropic and OpenAI as well as an increasing number of companies like Uber betting on Trainium. And we now have over $225 billion in revenue commitments for Trainium. Our Trainium2 chip has about 30% better price performance than comparable GPUs and is largely sold out. Trainium3, which just started shipping at the start of 2026 and is 30% to 40% more price performance than Trainium2 is nearly fully subscribed. And much of Trainium4, which is still about 18 months from broad availability has already been reserved. Amazon Bedrock, which is used expansively by over 125,000 customers, runs most of its inference on Trainium and almost 80% of the Fortune 100 companies are using Bedrock.

We also just announced that Meta is committed to using tens of millions of Graviton cores. Graviton is our industry-leading CPU chip, which allows Meta to run the CPU-intensive workloads behind agentic AI with the performance and efficiency they need at their scale. AI is commonly seen as a GPU story, but the rise of agentic workloads, real-time reasoning, code generation, reinforcement learning and multistep task orchestration is driving massive CPU demand as well. As AI systems shift from answering questions to taking actions and as post-training and inference scale up, the compute required pulls heavily on CPUs. That’s why Meta chose Graviton, which delivers up to 40% better price performance than any other x86 processors and now used by 98% of the top 1,000 EC2 customers…

…While the largest number of AI chips we’re bringing in are Trainium, we continue to have a deep partnership with NVIDIA. We have immense respect for them, continue to order substantial quantities. We’ll be partners for as long as I can foresee, and we’ll always have customers who want to run NVIDIA on AWS, and we will also have a very large chips business ourselves. Customers always want choice. It’s always been true and always will be true…

…At scale, we expect Trainium will save us tens of billions of dollars of CapEx each year and provide several hundred basis points of operating margin advantage versus relying on others’ chips for inference…

…But the one thing you learn over and over again with every technology, it was true in databases, it was true in analytics. It was true in models. It’s true in chips, too, by the way, is that customers want choice. There is not one tool to rule the world, and they want choice…

…On the question about Trainium and the notion of our selling racks over time, I do think that’s very much a possibility. Always, we have to balance — we have such demand right now for Trainium, and we have such demand from various companies who will consume as much as we make that we have to decide how much we’re going to allocate to the existing demand and customers and how much we’re going to save to sell as racks. And for our existing customers that we sell Trainium to, how many will be Trainium plus running on our cloud infrastructure versus just the chips themselves. But I expect over time, there’s a good chance we’re going to sell racks over the next couple of years.

Amazon’s management remain confident in the returns generated by the company’s capex; much of the capex spent in 2026 will be installed in future years; customers have already committed to substantial portions of the 2026 capex; management sees attractive margins and ROIC (return on invested capital) for the 2026 capex; AWS has to spend more short-term capex the faster it grows, since AWS needs to spend on land, power, chips etc 6-24 months in advance of monetisation; AWS’s capex often fund assets with years and decades of useful lives; AWS’s capex generate attractive cumulative free cash flow and ROIC a few years after being in service; Amazon’s free cash flow in the early years of high-growth periods for AWS is limited until the early capacity is monetized and revenue growth outpaces capex growth, and management has seen this cycle in AWS’s first big growth wave and expects similar positive outcomes from the current wave; management expects to continue making significant investments in AI; management has no change on Amazon’s 2026 capex plan (original guidance for 2026 was for $200 billion, and this is up from $128 billion in 2025, and $83 billion in 2024); management first saw the trend of rising input prices for capex in 2025 H2 and has been working with suppliers to get supply; management is seeing rising memory prices be a push-factor for companies to shift from on-premise to the cloud

We continue to be confident in the long-term CapEx investments we’re making. Of the AWS CapEx we intend to spend in 2026, much of which will be installed in future years, we have high confidence this will be monetized well as we already have customer commitments for a substantial portion of it and that it will yield compelling operating margins and ROIC…

…The faster AWS grows, the more short-term CapEx we will spend. AWS is to lay out cash for land, power, buildings, chips, servers and networking gear in advance of when we can monetize it, typically 6 to 24 months before we start billing customers depending on the component. However, these CapEx investments fund assets with many year useful lives, 30-plus years for data centers, 5 to 6 years for chips, servers and networking gear. The free cash flow and ROIC for these investments are cumulatively quite attractive a couple of years after being in service. However, in times of very high growth like now, where the CapEx growth meaningfully outpaces the revenue growth, the early years free cash flow is challenged until these initial tranches of capacity are being monetized and revenue growth outpaces CapEx growth. We’ve been through this cycle with the first big AWS growth wave and like the results. We expect to feel similarly about this next wave with much larger potential downstream revenue and free cash flow…

…We will continue to make significant investments, especially in AI, as we believe it to be a massive opportunity with the potential to drive long-term revenue and free cash flow…

…I don’t have an update on — a new update on capital. Our plan is largely the same…

…Everybody knows that the cost of these components, particularly memory has skyrocketed. And we’re just in a stage where there’s just not enough capacity for the amount of demand. We have worked very closely with our strategic partners. We saw this trend happening early in the kind of the middle of the latter part of last year, and we’ve worked with our strategic suppliers here to get a significant amount of supply. And so we’re working very closely with them. I think the team has been very scrappy. I think we’ve done a good job in making sure that we’re not capacity constrained there, but we’re watching that very closely.

One of the interesting things that we see right now with the change in price and in supply on things like memory is that it is a further impetus pushing companies who have on-premises infrastructure into the cloud. And it’s because a meaningful part, these suppliers are prioritizing their very largest customers which cloud providers are. And so we have seen a number of conversations we’ve been having with enterprises for many months where it’s just been slower in getting the transformation plan to move to the cloud accelerate rapidly just because we have a lot more supply than what others have.

SageMaker, AWS’s model-building service, reduces training time of models by up to 40%; Bedrock, AWS’s fully-managed service for companies to build upon frontier models, had 170% sequential growth in customer spend in 2026 Q1; Bedrock processed more tokens in 2026 Q1 than all prior years combined; OpenAI’s latest models are already, or will soon be, available on Bedrock; Amazon management recently added the Amazon Bedrock Managed Agents feature, which helps organizations build generative AI applications and agents at production scale;  Amazon Bedrock Managed Agents is powered by OpenAI, and OpenAI is seeing unprecedented demand for the product; Amazon management believes companies will derive the most value from AI from agents; Strands, AWS’s open source AI agents SDK (software development kit) has been downloaded more than 25 million times, with downloads up 3x sequentially in 2026 Q1; AgentCore is used to deploy an agent every 10 seconds; AWS has turnkey agentic solutions, including Kiro and Quick; Kiro, AWS’s coding agent, saw users double sequentially in 2026 Q1 and enterprise usage 10x; Quick, AWS’s AI assistant, has seen new customers grow 4x sequentially in 2026 Q1; management recently launched the Quick desktop app, which helps improve productivity of users; Amazon Bedrock now has 125,000 customers; 80% of the Fortune 100 are using Amazon Bedrock; AWS delivered 4x improvement in Trainium 2’s token throughput for Bedrock, leading to more capacity to serve customers; management thinks having OpenAI’s models on Bedrock is a big deal; Bedrock is already serving 3rd-party models from all the non-OpenAI key players; management believes that people will always want choice in models and chips; management believes that most of the work being done with models in the future will be of the stateful variety; Bedrock Managed Agents is a feature unique to AWS 

We’ve built broader capabilities than others. That includes model building with SageMaker, which reduces training time by up to 40%, high-performance inference with the leading selection of frontier models in Bedrock, which saw 170% growth in customer spend quarter-over-quarter and processed more tokens in Q1 than all prior years combined.

We’re excited to make OpenAI’s models available in Bedrock. Yesterday, we added OpenAI’s GPT-5.4 model with 5.5 coming soon. Yesterday, we also started the preview of Amazon Bedrock Managed Agents powered by OpenAI, the Stateful Runtime Environment that enables any organization to build generative AI applications and agents at production scale. We believe that modern agentic applications will be stateful, and this new technology will rapidly accelerate agentic AI adoption. OpenAI has said they’re already seeing unprecedented demand for this new product, and we’re seeing heavy customer interest as well.

Most of the value companies derive from AI will be through agents. In AWS customers can build agents with their proprietary data and Strands, which has been downloaded more than 25 million times and saw 3x more downloads quarter-over-quarter. Customers can deploy agents with enterprise scale, security and reliability with AgentCore, which is being used to deploy an agent as frequently as every 10 seconds. We also offer turnkey agents for coding, software migrations, business operations and knowledge workers in Kiro, Transform, Connect and Quick, and they continue to resonate with customers. The number of developers using Kiro more than doubled quarter-over-quarter and enterprise customer usage increased nearly 10x. Customers have used Transform to save over 1.56 million hours of manual effort when migrating and modernizing their workloads. The number of new customers using Quick has grown more than 4x quarter-over-quarter, and we just announced our Quick desktop app yesterday. It’s very compelling as it can query your e-mail, calendar, Slack, local files and several other applications you use every day to flag important communications, retrieve and summarize information, make recommendations, compose and send communications to others and create agents that highlight or automatically do work that you used to have to do yourself. You can easily keep refining your preferences and Quick’s advanced knowledge graph enables its AI agents to automatically learn from your interactions to become more personalized over time…

…Amazon Bedrock, which is used expansively by over 125,000 customers, runs most of its inference on Trainium and almost 80% of the Fortune 100 companies are using Bedrock…

…Bedrock has been a significant growth driver. In 2025, we delivered 4x improvements in Trainium2’s token throughput. And since the majority of Bedrock’s workloads run on Trainium, these efficiency gains directly translate into more capacity to serve customers…

…The fact that we’re going to have all of the OpenAI models available in Bedrock is a big deal. It’s a big deal for customers. And we have — we obviously have a very large amount of AI being done in Bedrock today on the models we have and this is Anthropic and Llama and Mistral and a host of others. But the one thing you learn over and over again with every technology, it was true in databases, it was true in analytics. It was true in models. It’s true in chips, too, by the way, is that customers want choice. There is not one tool to rule the world, and they want choice…

…Most of the model work and most of the AI has been done in these stateless models, kind of tokens in and tokens out. And while I think there will continue to be lots of work done that way, I think the future of using these models is a stateful model, a stateful API. And that’s because when you’re building agents, you’re building AI applications, you don’t want to start a new every time you interact with the model. You want to store state. You want to store identity, you want to store what the conversation or the actions have been, you want to reach out and do a little bit of compute here. You want to have the tools to be able to reach — the models reach out to the different tools to accomplish different tasks. And that only happens if you’re able to store state. And so the Bedrock Managed Agents that we collaborated with and invented with OpenAI that we just announced a preview of yesterday is also — I think that’s the future of how these agents are going to be built. It’s something that nobody else has, and I think it’s very exciting to our customers.

Amazon is able to deliver items faster while lowering its cost to serve, and management sees meaningful opportunities to further improve the fulfillment network’s productivity; Amazon’s latest generation of robotics offers a step change in efficiency; management is deploying the latest generation of robotics in both new and existing fulfillment facilities, and early results are positive

Overall unit growth of 15% continues to outpace our cost to operate the fulfillment network as outbound shipping costs grew 12% year-over-year and fulfillment expense grew 9% year-over-year, both on an FX-neutral basis. As our network efficiency improves, we’re able to deliver items faster and improve the customer experience while at the same time lowering our cost to serve. Looking ahead, we see meaningful opportunities to further enhance productivity across our global fulfillment network, all while continuing to raise the bar in delivery speed. We will keep optimizing inventory placement to shorten distance traveled, reduce touches per package and improve consolidation rates.

Alongside these efforts, we deploy robotics and automation, which have been integral to our operations for decades. Our latest generation technologies offer a step change in efficiency, which we’re deploying in both new and existing facilities. All of our U.S. large-format fulfillment center launches in 2026 will have this latest generation technology. We’re seeing early positive results with improved site safety, higher productivity and lower cost to serve.

Amazon management recently launched Health AI, a personal health agent

We launched Health AI, a 24/7 AI-powered personal health agent backed by One Medical clinicians that gives U.S. customers instant clinical guidance and takes action with their permission from booking appointments to managing prescriptions to facilitating medical treatment with a real One Medical provider.

Rufus, Amazon’s AI shopping assistant, saw monthly active users grow 115% year-on-year in 2026 Q1, and engagement increase by 400%; Rufus has improved a lot over the past year

Rufus, our agentic AI shopping assistant continues to resonate with customers. Rufus can research products, track prices and auto buy products in our store when they reach a set price. Monthly active users are up over 115% and engagement is up nearly 400% year-over-year…

…If you haven’t checked out Rufus in a while, it’s really substantially improved over the last year.

Amazon management recently launched Seller Central, an AI-powered insights-hub for sellers on Amazon; the initial response to Seller Central has been very strong

We recently introduced a new AI experience for sellers in Seller Central that dynamically generates a custom, personalized visualization of data, key insights and scenarios tailored to the sellers’ goals. It’s early, but the initial response and feedback are very strong.

Amazon’s management recently expanded Creative Agent to more countries; Creative Agent is Amazon’s agentic offering that helps advertisers plan and execute the entire advertising creative process; management recently launched sponsored products and brand prompts in Rufus; 20% of shoppers interacting with brand prompts in Rufus carry on the conversation

Our Ads team also continues to invent and deliver for advertisers with AI. For example, we expanded Creative Agent, an agentic partner that plans and executes the entire ad creative process to Canada, France, Germany, India, Italy, Spain and the U.K. And we recently introduced Sponsored Products and Brand Prompts in Rufus that help brands showcase products and customers make more informed buying decisions. It’s early, but we’re seeing nearly 20% of shoppers who interact with the Brand Prompts in Rufus continue the conversation about that brand.

Amazon’s management recently expanded early access to Alexa+ to Mexico, UK, Italy, and Spain; compared to the previous Alexa, users are completing 3x more purchases on device, streaming 25% more music, and using smart home functionality 50% more 

Alexa+ early access expanded to millions more Prime members in Mexico, the U.K., Italy and Spain. Customers are loving Alexa+, talking to Alexa twice as much and for longer durations across a wider breadth of topics, completing purchases on devices 3x more, streaming music 25% more and using smart home functionality 50% more than Alexa classic.

Amazon’s management continues to be very bullish on agentic commerce; management thinks agentic commerce will be very good for customers and Amazon in the long run; agentic commerce is currently only a small fraction of referrals from search engines; management thinks the user-experience with agentic commerce from 3rd-party agents is still poor, as pricing and product information are often wrong, and the agents don’t have personalization data and shopping history; management is working with 3rd-party agent providers to improve the experience; management continues to think that the agentic shopping assistant that will prevail will come from existing retailers that customers already have a good relationship with, and management is attempting to build Rufus to be the prevailing agentic shopping assistant; management thinks agentic commerce will be a great thing for Amazon’s advertising business because of 2 reasons, namely, (1) agentic AI will drive greater volume of advertising, and (2) agentic commerce provides multiple opportunities to surface relevant products to customers

We are very bullish on what agentic commerce will look like. I think it’s going to be very good for customers in the long term. I think it will be good for us, too…

…We’ll do a lot of work with third-party horizontal agents to try and make that customer experience better. And by the way, I do think today, it reminds me in some ways the stage we’re in of what we saw in the early days of search engines and they’re trying to refer business to e-commerce. It’s never been a giant part of the referrals to our e-commerce business. But over the years, the experience got better. And what you see with agentic commerce is it’s a small fraction of what we see with the search engine referrals, but the experience just hasn’t gotten great with these third-party horizontal agents yet. They’re not often able to get the pricing right or the product information right. They don’t have any personalization data or any shopping history. And so we do want to see that get better with third-party horizontal agents. We’re having conversations with all those folks to try and make that better and find something that works for customers and all the companies.

And then it will be interesting over time which agents customers choose to use. I happen to think that if you’re going to a particular retailer that you’d like to do business with and you like to shop from, if they have a great agentic shopping assistant, you’re going to often start there because it’s where you’re doing your shopping, it’s easier to — they have better product information. They have better information about what other customers like you are buying. You can make all sorts of changes to how your account and your shipping information is working there. And so that’s what we’re aiming to make Rufus be is we’re aiming to have it be the best shopping assistant anywhere, and I think we’re on that path…

…On the Agentic Commerce and how that impacts advertising, I actually believe that we’re going to like this for advertising. I think it’s going to be good for customers, and it’s going to be good for our business. And I think, first of all, the first thing to remember is the way that our ads team has built tools and agents themselves is making it so much easier to do advertising. If you look at small and medium-sized businesses that had to take weeks and months to do creative and to pick the right audience, all of that is just — it’s so much faster and so much easier because of our advertising agentic tools. And you no longer have to take as much time or spend as much money building the creative.

So I think there are going to be a lot more advertising — advertisers with the rise of what’s happening in AI. And then if you look at the Agentic Commerce experiences, if you look at any of these agentic experiences, they tend to be multi-turn conversations where you’re not interacting with one search and getting an answer. You tend to find that you’re asking questions, you’re narrowing questions, it’s asking you questions on what you want. And in that process of having multi turns, there are multiple opportunities to surface relevant products to customers, many of which will be organic and some of which will be sponsored. And it also gives rise to opportunities like sponsored prompts.

In the 2025 Q4 earnings call, Amazon’s management said market demand for AI compute looked like a barbell with AI labs on one end spending a lot on compute for just a handful of applications, and with enterprises on the other end using AI for productivity purposes; now, management is starting to see enterprises using AI for brand-new experiences

The AI labs are spending an incredible amount of money on compute at this point and in compute, both on the AI side as well as on the core side. And the models that they’re building and the companies that have successful generative AI applications are certainly spending a lot. And there are several of those labs. But we also see quite a bit of enterprise adoption and usage of AI. As I’ve said before, the largest absolute place that we see enterprises having success is in projects that are around cost avoidance and productivities. These are things like automating customer service or business process automation or fraud or things of that sort. But the number of projects that we’re working with across enterprises and that we’re now starting to see to come to production around brand-new experiences, trying to figure out how to reinvent their current experiences, but using inference and AI to be smarter, also very significant. So we’re seeing the adoption in both of those segments.

Amazon’s management sees a giant impact on how AI will shape Amazon’s business internally; management believes AI will completely reinvent Amazon’s current customer experiences in the fullness of time; management is aware of the innovator’s dilemma that can trap Amazon in reinventing AI-native customer experiences, and is actively avoiding the trap; Amazon swapped the engine of a service running at full tilt with a team of just 5 people who used agentic coding tools to build the new engine in 65 days; the engine would previously have taken 40-50 people a year to rebuild

On the use of AI internally and for our current businesses, I think that the shortest first summary I could give you, Colin, is that I do not see a place in any of our businesses or any of the ways that we do work where we’re not going to have giant impact on what we do. I think I’ve long had this belief that while you can add incrementally to a lot of your existing customer experiences, different agentic and AI experiences, I really believe that in the fullness of time, and I don’t know if that’s 3 years from now or 5 years from now or it could be sooner, too, that all of these customer experiences we know are going to be completely reinvented…

…It’s tricky for — if you have an existing business that’s doing well. But you have to look at every single one of your customer experiences and you have to be able to carve off resource for that team to think anew about what would the future customer experience look like if you started from scratch today, and if you had all the technologies like AI available to you when you started. And that is what we’re doing in every single one of our experiences…

…If you look at one of our services, we swapped out the engine of the service while we are also running the service full tilt. And normally, that would have taken 40 or 50 people about a year to do, and we took 5 really smart people, AI forward-thinking people building on agentic coding tools and those 5 people rebuilt it in 65 days. Like that is a very different world of operating. And that’s the world I think we’re heading to over the next few years.

Apple (NASDAQ: AAPL)

The iPhone 17 family contains the A19 and/or the A19 Pro chips, which include neural accelerators to deliver strong AI capabilities

During the quarter, we welcomed iPhone 17E, the newest addition to what is already the strongest iPhone lineup we’ve ever had. It brings outstanding performance and core iPhone experiences at a remarkable value for everyone from enterprise teams to consumers. Across the lineup, this is the most powerful, capable and versatile iPhone family we’ve ever created. That starts with the latest in Apple silicon for iPhone, A19 and A19 Pro, which include neural accelerators in the GPU to deliver a huge boost to AI performance

Apple’s management thinks the Mac is the best platform for AI, with Apple’s in-house chips giving Macs the ability to run advanced AI models on-device; the MacBook Air now comes with the M5 chip, which enables the product to run AI models on device; the MacBook Pro has even more advanced versions of the M5 chip in M5 Pro and M5 Max

From Mac Mini to MacBook Pro and everything in between, Mac is the best platform for AI with Apple Silicon delivering exceptional performance, industry-leading efficiency and the ability to run advanced models locally in ways that simply weren’t possible before…

…We’ve also further improved MacBook Air, already the world’s most popular laptop with M5, making everyday tasks faster and more responsive than ever. MacBook Pro reaches new heights with M5 Pro and M5 Max, delivering extraordinary performance and dramatically advancing what users can do with AI on a portable system…

Apple’s new AirPods Max 2 has Apple’s most advanced active noise cancellation technology; AirPods can now do live translation, thanks to Apple Intelligence

During the quarter, we introduced customers to a new level of audio experience with AirPods Max 2, delivering stunning sound quality and our most advanced active noise cancellation yet…

…AirPods can bridge languages too, thanks to Live Translation powered by Apple Intelligence.

Apple Intelligence now has more powerful capabilities such as visual intelligence for cleanup; management is looking to launch a more personalised Siri later in 2026 ; Apple Intelligence is powered by Apple’s self-designed chips; management is not treating AI as a standalone feature but is instead treating AI as an essential experience

In addition to live translation, Apple Intelligence brings together dozens of powerful capabilities from visual intelligence to cleanup and photos that are seamlessly integrated into the moments that matter most to our users every day. And we look forward to bringing a more personalized Siri to users coming this year. What truly sets Apple apart is how Apple Intelligence is woven into the core of our platforms, powered by Apple Silicon and designed from the ground up to deliver intelligence that is fast, personal, and private. This is not AI as a stand-alone feature, but AI as an essential intuitive part of the experience across our devices. It builds on years of innovation from the neural engine to advanced on-device processing, enabling capabilities that are not only incredibly powerful, but also respectful of user privacy.

Reminder that in 2025, management committed to invest $600 billion over 4 years (was a $500 billion commitment in 2025 Q2; Apple has around $190 billion in gross profit per year, for perspective) in the USA in areas such as advanced manufacturing, silicon engineering and artificial intelligence; Apple now has Mac mini production in the USA; in March 2026, management brought 4 new companies to Apple’s American manufacturing program; Apple is on track to buy over 100 million advanced chips from TSMC’s Arizona fab; later in 2026, Apple will open its advanced manufacturing center in Houston to provide hands-on training for students, supplier employees and American businesses

We’re also making great progress in advancing American supply chain innovation. As part of our $600 billion commitment to the U.S., we were pleased to share recently that Mac mini production is coming to America later this year, expanding our factory operations in Houston with a brand-new facility. In March, we were thrilled to welcome 4 new companies to our American manufacturing program to help manufacture essential materials and components for Apple products sold worldwide. These include sensors that support key iPhone features like camera stabilization and integrated circuits essential for features like crash detection and activity tracking. These efforts build on the progress we’ve made in the American manufacturing program, including the work we’re doing to advance an end-to-end silicon supply chain across the U.S. At TSMC’s Arizona facility, for example, Apple is on track to purchase well over 100 million advanced chips.

As we’re accelerating our long-standing support for U.S. innovation, we’re also investing in America’s workforce. We’re looking forward to opening the doors to an all-new advanced manufacturing center in Houston later this year, which will provide hands-on training led by Apple experts and tailor-made for students, supplier employees and American businesses.

The Mac Mini and Mac Studio models are great devices for AI and agentic AI, and so demand from consumers was greater than management expected; management thinks the supply constraints with the Mac Mini and Mac Studio will take a few months to resolve; management’s guidance for 2026 Q2 already embeds significantly higher memory costs; management thinks memory costs will have an increasing impact on Apple’s business

You look forward to the June quarter, the majority of our supply constraints will be on several Mac models given the continued high levels of demand that we’re seeing. And we have less flexibility in the supply chain than we normally would. For Mac, in the June quarter, there’s 2 factors that are driving the constraints. One is that on the Mac Mini and the Mac Studio, both of these are amazing platforms for AI and Agentic tools. And the customer recognition of that is happening faster than what we had predicted. And so we saw higher-than-expected demand. The second reason is that the customer response to Mac Neo has just been off the charts, with higher-than-expected demand…

…We think looking forward that the Mini and the Mac Studio may take several months to reach supply-demand balance…

…I’ll go back to December for a moment and just walk you through the chronology. In the December quarter, we really had a minimal impact due to memory, and you can kind of see that in the gross margin results. We said it would be a bit more in the March quarter, and we did see higher memory costs in the March quarter, and they were partially offset by benefits from carry-in inventory that we had. For the June quarter and what’s embedded in the guidance that Kevan went through earlier, we expect significantly higher memory costs. They are also partly offset by the benefit of carry-in inventory. And then where we don’t give color beyond June, I can tell you that beyond the June quarter, we believe memory costs will drive an increasing impact on our business.

Apple’s management has been investing more in AI in both products and services, and this shows up in the company’s operating expenses, specifically in R&D (research and development); the increased investments in AI include building Apple’s own foundation models, and in the collaboration with Google; Apple’s collaboration with Google on foundation models is going well

[Question] As we think longer term, do you think Apple will invest more? Where will Apple invest more heavily over the next several years? And is this at all related to your net cash comments in terms of perhaps building out more infrastructure as we enter an AI-centric world?

[Answer] We are clearly investing more. You can see that in the OpEx numbers. And if you click down on those a step deeper and look at the R&D area separate than SG&A, you’ll find that R&D is even accelerating much higher than the company is. And so we are clearly investing. We’re investing in products and services, and we see opportunities in both of those…

…We believe AI is a really important investment area for Apple, and we’re going to be doing that incrementally on top of what we normally invest in our product road map…

…[Question] Last quarter, you did talk about Apple foundational models and sort of the two-pronged strategy there of the collaboration with Google as well as continuing to internally sort of work on your own models. Hoping you can sort of give us an update in terms of how you’re able to balance those 2 priorities as well as do you feel like you need to double down and invest more to be able to balance those 2 priorities side by side?

[Answer] We are investing more. You can see that in the OpEx numbers. And as I’ve mentioned before, the R&D, in particular, is — has scaled rather significantly on a year-over-year basis. The collaboration with Google is going well. We’re happy with where things are, and we’re happy with the work that we’re doing independently as well.

ASML (NASDAQ: ASML)

ASML’s management is seeing the semiconductor industry’s growth continue to solidify, driven by AI investments, and this applies to both advanced Memory and advanced Logic; management thinks semiconductor supply will not meet demand for the foreseeable future, and this is creating constraints in end markets, including AI; management is seeing ASML’s Memory customers being asked to ramp supply; ASML’s memory customers are sold out for 2026, with supply constraints extending beyond the year; management is seeing ASML’s Logic customers building capacity, including for the 2nm node to meet AI demand and mobile demand; management is seeing ASML’s customers increasing their capital expenditure to ramp up their capacity, and this capacity is supported by long-term commitments from their customers; management is seeing ASML’s Memory customers and Logic customers increase their adoption of EUV and DUV immersion lithography; the level of demand for ASML’s DUV immersion lithography systems in 2025 was significantly lower what’s currently seen; besides DUV immersion, management is also seeing health in the DUV dry lithography business; management has seen major adoption of EUV by ASML’s DRAM customers in 2025 because EUV provides better performance; DRAM has been a really good story for lithography intensity in 2025; ASML’s customers have been very open with the company on their expansion plans

We see that the semiconductor industry growth continues to solidify. This is still very much driven by investments in AI infrastructure. So, this translates into a lot of demand for advanced Memory, for advanced Logic. We expect in fact that the supply will not meet the demand for the foreseeable future. So, this is creating a strong constraint in the end markets from AI to mobile and PC. As a result our customers are strongly invited to create more capacity. So if we look at Memory, what our customers tell us is that they are sold out for 2026. And their supply constraints will last beyond 2026. For advanced Logic, we see our customers building capacity for several nodes, while they also continue to ramp 2 nm in order to address the AI products…

…We see our Memory and Logic customers increasing their capital expenditure and trying to accelerate basically their capacity ramp in 2026 and beyond. What’s also very interesting is that a lot of this demand is supported by long-term commitment from their customers. On top of that, we see both Memory customers, DRAM customers and advanced Logic customers continuing to increase their adoption of EUV, but also immersion. So this translates basically into higher lithointensity and a higher litho demand for ASML…

…When it comes to immersion DUV, we actually had a bit of a slow start because in the course of last year, we were looking at a significantly lower demand for immersion. That has now reversed itself…

…I already mentioned what we’re doing on immersion, but also the dry business is doing quite nicely…

… In the Logic business, our customers are adding capacity across multiple advanced nodes to support demand while continuing to ramp the 2-nanometer node in support of next-generation HPC and mobile application…

…We have seen a major adoption of EUV in DRAM in 2025. And you may have noticed that our, I will say, U.S. DRAM customer also made this announcement that they were shifting also pretty strongly on EUV. And the reason for that is, of course, performance, but it’s also capacity because if you are going to use more EUV layers, you are going to need less multi-patterning and multi-patterning takes a lot of space also in the fab. So I think this is also definitely another argument in favor of EUV. I think this was mentioned, by the way, by this U.S. customer in their call. So I would say the first results of that is, first, more adoption of Low NA EUV…

…DRAM has been really a good story when it comes to litho intensity in ’25…

…Customers are very, very open. By the way, that’s also the case on the Logic side. But very — customers are very open to us, and they’re very openly discussing with us also their expansion plans for this year, but also beyond.

ASML’s management does not want EUV systems to be the bottleneck in building compute capacity for AI; EUV systems are not the bottleneck today

We do not want EUV to be the bottleneck. So I think I’d like to say that very, very strongly…

…I know the question of bottleneck comes back very often. I think we don’t feel at all that we are the bottleneck today.

Intel (NASDAQ: INTC)

Intel’s management expects sustained momentum for the company’s Xeon server CPU products in 2026 and 2027, with the Xeon 6 being Intel’s fastest new product ramp in 5 years alongside the Core Series 3 products; Xeon’s momentum is powered by the reinsertion of CPUs as a foundation for AI where the CPU-to-GPU (accelerators) ratio is swinging back to the CPU’s favour; management thinks the CPU’s resurgence in AI is great news for Intel’s x86 CPU ecosystem; Intel saw strong ASIC growth in 2026 Q1 sequentially and year-on-year; Intel’s DCAI (Data Center and AI) segment, signed multiple long-term agreements in 2026 Q1; Xeon 6 was recently selected as the host CPU for NVIDIA’s DGX Rubin NVL8 systems; Xeon remains the most deployed host CPU for AI systems; DCAI recently started a multiyear collaboration with SambaNova to design a next-generation AI inference architecture; management’s confidence in the sustained growth of CPUs for AI is growing; management’s outlook for server CPU demand has improved in 2026 Q1; management expects the server CPU industry to have a strong year of double-digit unit growth in 2026, extending to 2027; the long-term agreements signed by DCAI have volume and pricing terms, and last 3-5 years; Intel’s customers are telling the company that CPUs are more important in AI inferencing and agentic AI than AI training, with the ratio of GPUs-to-CPUs flipping from 8:1 to possibly 1:more-than-1; management believes Intel’s CPUs will be very effective competitors to the likes of ARM, AMD, and the hyperscalers

Demand continues to run ahead of supply for all our businesses, especially for Xeon server CPUs, where we expect sustained momentum this year and next. Intel 3-based Xeon 6 and Intel 18A based Core Series 3 products are now in full volume production ramp and each represents the fastest new product ramp in 5 years…

…For the last few years, the story around high-performance computing was almost exclusively about GPU and other accelerators. In recent months, we have seen clear signs that the CPU is reinserting itself as the indispensable foundation of the AI era. CPU now serves as the orchestration layer and critical control plane for the entire AI stack. This is not just our wishful thinking, it is what we hear from our customers, and it is evident in the demand profile for our products. Xeon server demand is seeing strong and sustained momentum. Customers are deploying server CPUs along accelerators in the ratio that is moving back towards CPU. The accelerator remains central to Frontier AI, and we will continue to participate, innovate and partner in that category. Our recent announcement with SambaNova Systems is an example of such partnership on heterogeneous compute architectures. But the backbone of AI computing in production remain a CPU anchored architecture. That is good news for the x86 ecosystem. It is great news for Intel…

…We also saw strong ASIC growth with revenue up more than 30% sequentially and nearly doubling year-over-year…

…Within the quarter, DCAI signed multiple long-term agreements, including Google, supporting our view that the current business momentum is sustainable. In addition, Xeon 6 was selected as the host CPU for NVIDIA’s DGX Rubin NVL8 systems, and Xeon remains the most deployed host CPU due to its industry-leading memory, security and networking orchestration. Lastly, DCAI also established a multiyear collaboration with SambaNova to design a next-generation heterogeneous AI inference architecture combining SambaNova’s RDUs and Intel Xeon 6 processors…

…Our confidence in the sustained growth of CPUs driven by the AI infrastructure build-out is growing. Our outlook for server CPU demand has improved over the last 90 days, and we expect a strong year of double-digit unit growth for the industry and for us with momentum extending into 2027…

…Most of these agreements are structured with volume and pricing, and they are usually somewhere between 3 and 5 years…

…The feedback from the customer, CPU is very important when you move from training to inference. Inference side, I think in terms of orchestration, control plane and also managing all the different agent with data, CPU is much more efficient. So I think the ratio of CPU to GPU used to be 1 and 8, and now it’s 1:4 and I think towards parity or even better…

…One statistic that we look at is the ratio of CPUs to GPUs. And if you look at training solutions, they’re generally running in the kind of 7 to 8 GPUs to 1 CPU. As we look into inference, it’s probably getting into like the 3 to 4:1 kind of level. And as you get into agentic and multi-agent, it’s one potentially even flip in the other direction a little bit…

…[Question] On server CPU competition. So both when we look at competition versus x86 against AMD, do you think you are gaining share? Do you expect to gain share against them? And then broader, I think the competition against Arm because NVIDIA is planning to launch a stand-alone Vera CPU Rack. Recently, we heard Amazon talk up their Graviton option. I think Google yesterday said they would launch Axion and connect it with every TPU. So just kind of near term, how do you look at competition versus AMD and x86?

[Answer] The CPU is a great demand right now. I think we all enjoy that. And then in terms of our product road map, we have been fine-tuning the last year… We are laser-focused on execution. Multithreading, I think we are putting in. So we’re going to have Coral Rapid, have the multithreading that we can compete effectively with AMD. And we try to accelerate that Coral Rapid ahead. And then the other part is we’re also looking at some of the architecture, CPU and GPU architecture… In all, I think we have the team, we have the technology road map. I think we’re going to be — over time, going to be a very effective competitors to them.

Intel’s management sees the semiconductor industry’s addressable market approaching $1 trillion, driven by AI demand, and the company is well positioned to benefit

Driven by tremendous demand for AI, the semiconductor industry TAM is now approaching $1 trillion. Intel is well positioned to benefit from this demand with 3 strategically important assets: our x86 CPU franchise, our advanced packaging technology and our vast manufacturing network.

Intel’s management sees AI moving into the real world, with more distributed inference

Artificial intelligence is now moving into the real world towards a more distributed inference and reinforced learning workloads like agentic, physical AI and robots and edge AI.

Intel’s management is pleased with the progress of the company’s foundry technology development, but it will be a long journey; the manufacturing yields of the Intel 3 and Intel 18A process technologies are now running ahead of management’s projects; Intel continues to make progress in advanced packaging technologies, with additional customer backlog growth in 2026 Q1; Intel’s 14A process technology is now at a higher level of yield compared to 18A at a similar point in time, and the company is developing PDKs (process design kits) with multiple customers; management expects to see design commitments for 14A in 2026 H2 and 2027 H1; the progress of Intel Foundry has driven the company to land more of its own future product tiles on the Intel 14A process; Intel Foundry will be supporting TeraFab, the huge semiconductor project undertaken by Elon Musk’s companies; management wants to work with TeraFab to improve the manufacturing efficiency of semiconductors; rising prices for memory chips and other materials are a headwind for Intel Foundry’s gross margin in 2026 H2; management will continue to utilise a multi-foundry approach for Intel; Intel Foundry’s advanced packaging business is seeing demand in the billions of dollars; Intel Foundry’s advanced packaging is a differentiated offering – it allows customers to use larger reticles – and so it’s getting attractive pricing; Intel Foundry’s 18A yields are going to hit management’s end-2026 targets by the middle of the year; most of Intel Foundry’s supply is for internal demand at the moment, but management expects it to win customers over time

The accelerating deployment of AI infrastructure creates a meaningful opportunity for us as we continue to build our external foundry business. I’m pleased with the progress we have made in foundry technology development over the last year, even though I will continue to remind you this will be a long journey for us. We have made steady progress with Intel 4 and Intel 3 and 18A yields are now running ahead of the internal projections, representing a meaningful inflection in our execution and our factory finished good output.

We also continue to make steady progress on our advanced packaging technologies, including additional growth in customer backlog in the quarter.

Intel 14A maturity yield and performance are outpacing Intel 18A at a similar point in time, and we continue to develop PDKs with multiple customers actively evaluating the technology…

…We expect to see earlier design commitments emerge beginning in the second half of 2026 and expanding into the first half of 2027…

…I’m particularly pleased that our progress today has driven us to land more of our own future product tiles on Intel 14A as well. At a time when advanced wafer capacity is in the short supply, this enables us to have better control over our supply chain…

…As we look to continue challenging the status quo, I can think of no better partners than Elon Musk. We recently announced our partnership with SpaceX, xAI and Tesla to support Terafab. Elon and I share a strong conviction that global semiconductor supply is not keeping pace with the rapid acceleration in demand. We are excited to explore innovative ways to refactor silicon process technology, looking for unconventional ways to improve manufacturing efficiency that will eventually lead to a dynamic improvement in the economics of semiconductor manufacturing…

…Our foundry team is delivering consistent yield and throughput improvements across all process nodes, which will help gross margins. With that said, Intel 18A is still early in its ramp and rising input costs, especially in memory, present growing headwinds in the second half that we need to overcome…

…I’d say the one cautionary concern I have on gross margin in the back half of the year is just some of the materials have gone up in terms of cost, substrates are going up, T glass. We’ve got memory going up, as you know. So those things offset some of the improvements that we’re having through the year…

…TSMC is a very important partner for us. Morris and C.C. have decades of friendship. And then clearly, with our product group will decide which is the best foundry. So I think we’re going to use a multi-foundry approach, our own internal and also external. And so we really have good relationship, continue to build from both sides to benefit the customer…

…[Question] I would love to kind of level set where we are on the advanced packaging front. You talked about rising backlog. Anything you can share in terms of what that number looks like?

[Answer] We have been really pleased with our traction there. And I think maybe naively, I had thought that these opportunities would come in the hundreds of millions of dollars level. But so far, what we’re seeing is that their demand is more in the billions of dollars per year kind of level. So this is going to be a big part of the foundry revenue as we get through this decade. And the good news is advanced packaging really is a differentiated offering for us, and it does a lot for the customer in terms of allowing them to use larger reticles. So there’s real value to the customer. And as a result, we get very attractive pricing relative to some of the other areas of the foundry business…

…18A yields are somewhat a closely guarded proprietary piece of information for us. So we don’t typically — I would just say Lip-Bu had a target as we came into the year for the end of this year, and we’re probably going to hit that probably the middle of this year…

…[Question] As we think about your capacity tightness, the leading edge foundries are also quite tight as well. Has this driven any near- to medium-term share gains?

[Answer] All the supply right now or the lion’s share of the supply is all internal, but we do expect, obviously, to win customers over time.

Intel’s AI-driven businesses are now 60% of revenue, and was up 40% year-on-year in 2026 Q1

AI-driven businesses now represent 60% of revenue and grew 40% year-over-year.

Intel’s management now expects capital expenditures to be flat in 2026, but the actual dollar-amounts spent on tools will be up 25% in 2026, as management is seeing a lot of demand and wants to catch up on supply

We forecast capital expenditures in 2026 to be flat to last year versus our prior expectation of flat to down, reflecting increased capacity investments to support committed demand and a continued emphasis on improving fab productivity and output. We now expect expenditures to be roughly equal across the year and still to be heavily weighted towards the equipment that directly grows wafer outs to support growth this year and next…

…In the last few years, a lot of our CapEx spending was space. And I think we’re actually in a pretty good position in space. We wanted to have white space available to move into when needed. And I think Lip-Bu and I both feel like we’re in a good place. So we actually will be bringing the space spend down pretty materially, even though the total is flat. And so what that means is the tool spend is actually increasing pretty significantly. In fact, tool spending will be up year-over-year 25% or so. And so that’s, I think, a function of the fact that we just see a lot of demand, and we want to make sure we’re catching up on the supply front.

Intel’s management thinks the ASIC business will be a fast-growing one for the company in the next 5 years; the ASIC business is already at a run rate of more than $1 billion

[Question] On the ASIC business, Dave, I think you said it doubled year-on-year. If you could maybe help us with what is included in that? I believe it’s IPUs, but I just want to get a better sense how big it is.

[Answer] Stay tuned on that one, the next 5 years is going to be a fast growing for us…

…One thing that people have been surprised about is how big the business is already. It’s at a run rate that’s north of $1 billion already.

Intuitive Surgical (NASDAQ: ISRG)

The da Vinci 5 captures real-world surgical data at greater scale and fidelity, enabling deeper surgical insights; the surgical insights captured by da Vinci 5 will be used by Intuitive Surgical for AI-enabled capabilities; management expects to add telesurgery and more automation to Intuitive Surgical’s robotic surgery platforms over the long term; management believes that AI will help Intuitive Surgical to move its Quintuple Aim forward; the data captured by da Vinci 5 includes video, kinematic, and force data; the AI-powered insights that management wants to deliver to customers can be in the form of operational guidance, learning of a surgeon/care team, and in the operating theatre

da Vinci 5 captures real-world surgical data at greater scale and fidelity, enabling deeper insight into how procedures are performed in practice. That insight paired with clinical context from connected electronic medical records, provides better understanding of variation, workflow and outcomes, and informs current and planned digital and AI-enabled capabilities…

…Collectively, these efforts are foundational to our long-term digital and AI road map where we expect to add telesurgery, deeper decision support and augmented dexterity, including aspects of future automation, all in pursuit of advancing the Quintuple Aim…

…We believe, yes, that AI will be a contributor to moving the Quintuple Aim forward…

…It starts with high-quality data, and that data will exist in video data from surgeries. It will exist in robotic data streams like kinematic data and force data. It will exist in connected electronic medical records, where we’re working with customers to do so. And once we have that high-quality data set, then the job of our AI and our data scientists is to turn that into meaningful insights…

…So there are, I think, ways in which this will show up to the customer. Some will be as operational guidance and assistance as they look at their hospital robotic program and want to increase efficiencies or understand costs. Some of it may show up in the learning of a surgeon and/or a care team. But a lot of it will show up in the operating room and I think show up in the surgery itself. And an example of this kind of first phase might be AI-enabled anatomy identification where you can see AI showing critical structures in the surgical field, showing tissue planes to help assist the surgeon. Then, over time, what we expect is that many of those same foundations that are being established and built in kind of that first phase, if you will, will support more advanced assistance around augmented dexterity and it will include — likely include aspects of automation. There, an example might be helping to control the camera as the surgeon is focused on the procedure.

Intuitive Surgical’s management thinks the company’s differentiation in AI comes from its installed base of da Vinci 5 systems, and the number of procedures performed by the systems annually which generates unique data

How do we sit, how do we exist within the AI ecosystem and how are we differentiated? I think part of that differentiation is around the installed base of systems that we have out there, including about the 1,500 da Vinci 5 systems, the 3 million and more procedures that are being done on an annual basis. And I believe that gives us the foundation to strengthen the differentiation over the next 3 to 5 years. If you look at the industry and you say, what is broadly available, broadly available to everyone, it’s things like edge and cloud compute, the math that underscores much of this, some of the training algorithms. Our advantage, we believe, lies is in the unique data sets that are available to us today through something like Force Feedback and will be increasingly available to us as we add capability to da Vinci 5.

Mastercard (NYSE: MA)

Mastercard is working with key players in the agentic commerce ecosystem, including Google, Microsoft, and OpenAI; Mastercard is partnering with OpenAI on Mastercard Agent Pay, which enables agent-to-agent payments; nearly all Mastercards globally are enabled for Mastercard Agent Pay; Mastercard’s management launched Verifiable Intent in 2026 Q1; Verifiable Intent is a temper-resistent record of authorisations a user has given to his/her agent; the FIDO Alliance is using Verifiable Intent as a foundation for security standards in agentic commerce; Crossmint, a leading blockchain infrastructure provider, will integrate Mastercard Agent Pay and Verifiable Intent so that it can enable secure Mastercard transactions for agents; Crossmint’s integrations will be launched initially on OpenClaw; management thinks Mastercard’s network will serve agentic commerce with tokenised credentials; management thinks agentic commerce will bring even more incremental opportunity in transactions and services over time; volumes with Mastercard Agent Pay are still low

On Agentic, the ecosystem continues to evolve. Our payment solutions are ready, and we are engaged, shaping what comes next with key players, including Google, Microsoft, OpenAI, and other partners across the ecosystem. We’re deepening our partnership with OpenAI, reinforcing their use of Mastercard Agent Pay, working to enable agent-to-agent payments and collaborating to embed our services across their solutions while using their tools as an enterprise customer. I’m also happy to share that nearly all Mastercards around the world are now enabled for Mastercard Agent Pay…

…In quarter 1, we launched Verifiable Intent, a tamper-resistant record of what a user authorized when an AI agent acts on their behalf. In fact, the FIDO Alliance is now using it as a foundation for setting security standards in this space. And earlier this month, we announced a partnership with Crossmint, a leading blockchain infrastructure platform. Crossmint will integrate Mastercard Agent Pay and Verifiable Intent to enable secure Mastercard transactions for AI agents in its ecosystem. This will initially launch on the OpenClaw platform with plans to expand…

…But as agent-driven commerce gains traction, our network is there with tokenized credentials, powering the payments, bringing the security, and trust, and reach that everyone is looking for. It’s very clear there is even more incremental opportunity in transactions and in services over time…

…[Question] In Mastercard Agent Pay. Michael, you talked about some of the partners and some of the activity on the ground, but can you just give us a little bit more detail on volumes or any surprises with respect to actual activity or actual demand?

[Answer] In terms of where volumes are, we’re still at early stage. So that is also true because a few things were not quite in place yet.

More than 500 customers are already engaged with Mastercard Threat Intelligence, which was launched in 2025 and powered by Recorded Future’s capabilities (Recorded Future was acquired by Mastercard in 2024 Q4 and it provides AI-powered solutions for real-time visibility into potential threats related to fraud); Mastercard Threat Intelligence have helped customers take down malicious domains responsible for the payment card test impacting over 10,000 e-commerce sites; Recorded Future puts Mastercard in a unique position to provide insights on threats faced by states 

Last year, we launched Mastercard Threat Intelligence, bringing Mastercard and Recorded Future capabilities together. In a short period of time, more than 500 customers are already engaged. Using the product, partners have taken down malicious domains responsible for the payment card test impacting over 10,000 e-commerce sites. That’s tangible value…

…Asymmetrical warfare, state actors, all of that is going on, and Recorded Future puts Mastercard in a very unique position to be a trusted partner to provide those kind of insights.

Mastercard has started to launch Mastercard Agent Suite, where Mastercard will design and deploy AI agents within customer environments; management thinks Agent Suite could be a much bigger opportunity than on the consumer side

You heard us talk about Agent Suite, which we started to launch, where we’re going to get into the business of building agents with our customers in the B2B space, et cetera. So early-stage on B2B earlier than on the consumer side, but I would think this is a much bigger opportunity, and it fits right into our focus on commercial payments. So early-stage ecosystem building, covering your basis, that’s what we’re doing.

Meta Platforms (NASDAQ: META)

Meta’s AI research lab, Meta Superintelligence Labs (MSL), has released the first model, MuseSpark, in its Muse family of models; MSL has built what management thinks is the strongest research team in the industry; MSL is already training even more advanced models than Muse; management thinks MuseSpark has already made Meta AI a world class assistant for users in many areas; management has heard very positive feedback on MuseSpark; management thinks Meta’s product team is now able to build products on top of the company’s models because the models are now strong, unlike in the past; management thinks models in the future will have to be able to improve themselves in order for them to be considered leading models; management is not focused on building coding capabilities with Meta’s AI models; coding is not the only ingredient needed for models to be self-improving

Our biggest milestone so far this year has been the release of our Muse family of models and our first model MuSpark along with a significantly upgraded new version of Meta AI. This was the first release from Meta Super Intelligence Labs, and it shows that our work is on track to build a leading lab. Over the past 10 months, we have built the strongest research team in the industry and established the scientific and technical foundations to scale very advanced models. Spark is just one step on that scaling ladder, and we are already training even more advanced models…

…Spark has already made Meta AI, a world-class assistant that leads in several areas related to our vision of personal super intelligence, including visual understanding, health, shopping, social content, local, creating games and more. We’re hearing very positive feedback on it so far…

…We have our product team, and that team is now really unlocked to be able to build things on top of our models because we now have a very strong model. So before this, we have been prototyping a bunch of things using other different models, whether it was our previous older models or kind of using the APIs from other companies. And now we’re unlocked to be able to go build things and get them to scale on top of our own models…

…You’re not going to have leading models in the future if your models can’t improve themselves, right? So you’re getting to a point where today, the models are still able to learn from people — and then I think at some point, the models will have to improve themselves. And that’s how the growth is going to — an improvement in the models is going to happen…

…Does that make us a developer tools company? Not necessarily. I mean, I’m not against having an API or coding tools or anything like that. But it’s not our primary focus. But I actually think people conflate coding with self-improvement more than they should. Coding is one ingredient for the model self improving. It’s not the only thing. And we are focused on all of the parts that are going to be necessary for self-improvement in service of the personal super intelligence vision that we have for people and businesses.

Meta AI has seen large increases in usage since MuseSpark was introduced, with double-digit percent increases in Meta AI sessions per user; the Meta AI app has consistently been near the top in app stores; MuseSpark is now powering Meta AI in chat threads in Facebook, Instagram, WhatsApp, and Messenger, as well as in the standalone Meta AI app

We’ve seen large increases in Meta AI use since releasing the updates, and the Meta AI app has consistently been near the top of the app stores as well…

…We’re seeing encouraging results within Meta AI since we began powering responses with the first model from MSL, Muhspark. In tests we ran leading up to the launch, we saw meaningful engagement gains that accelerated week-over-week with each new iteration of the model. We’re seeing similar games within Meta AI following the broad rollout of our new model with double-digit percent increases in Meta AI sessions per user. MuseSpark is now powering Meta AI in direct chat threads across our family of apps as well as the stand-alone Meta AI app and website, giving billions of people globally access to our latest model.

Meta’s management has a very view on AI than others in the industry; management thinks that AI will help people and improve many aspects of their lives; management wants to build AI agents that empower people and businesses; management thinks there are clear monetisation opportunities for personal superintelligence

My view of AI is very different from many others in the industry. I hear a lot of people out there talk about how AI is going to replace people. Instead, I think that AI is going to amplify people’s ability to do what you want, whether that’s to improve your health, your learning, your relationships, your ability to achieve your personal career goals and more. My view is that human progress has always been driven by people pursuing their individual aspirations. And I believe that this will continue to be true in the future. People will be more important in the future, not less. Meta believes in empowering individuals. And those are the kinds of products that we’re going to build, and I believe that they’re going to be some of the most important and valuable products of all time. We are building a personal agent focused on helping people achieve the diverse goals in their lives. We’re also building a business agent focused on helping entrepreneurs and businesses across the world, use our tools and others to grow their efforts, reach new customers and serve existing customers better. These agents will work together to form an ecosystem…

…The focus is on building personal super intelligence, building a consumer agent that can work for you and help you get things done. That right now is a consumer experience that we’re focused on, but we think there will be clear monetization opportunities over time. You can imagine commission structures or a premium offering.

Meta’s management has been testing business AIs and weekly conversations have 10x-ed (from 1 million to 10 million) since the start of 2026; the Meta AI business assistant was recently fully rolled out to all eligible advertisers on supported Meta buying services and performance has been strong, with common account issues being resolved at a 20% higher rate; the business AIs are tested in SMBs across Latin America and Asia Pacific; management will expand access to the business AIs in 2026 Q2; the business AIs are currently free, but management expects to monetise them over time

We’re already testing an early version of business AIs and weekly conversations have grown 10x since the start of this year…

…The Meta AI business assistant has now been fully rolled out to all eligible advertisers on supported Meta buying services, providing personalized recommendations to advertisers, resolving account issues, and servicing campaign insights to help optimize results. Performance has been strong since we began testing the assistant in Q4 with common account issues being resolved at a 20% higher rate…

…In Q1, we expanded business AIs on WhatsApp to SMBs across Latin America and Indonesia as well as on Messenger in Asia Pacific. We now have more than 10 million conversations each week being facilitated through business AIs, up from 1 million at the start of the year. We’ll further expand access to more countries this quarter while adding more capabilities to the AIs…

…Business AIs today are currently free for most businesses on our messaging apps. But as we make more progress, we expect that we will also work towards establishing a longer-term monetization model.

Meta’s management is working to incorporate MuseSpark in the company’s upcoming models used in its recommendation systems, core apps, and advertising products; the upcoming models will enable Meta to understand more of people’s goals for the first time in the company’s history; in the last few years, Meta has seen an increasing return on the amount that it can improve user-engagement, and this has encouraged management to continue investing heavily in this area 

We’re also working on using Spark in our upcoming models to improve our recommendation systems and core business in Facebook, Instagram and ads. Right now, our apps primarily help people accomplish 3 important goals: connecting with people, learning about the world and entertainment. But we’ve always wanted our apps to understand more of people’s goals so we can help improve their lives in all the ways that they want. These new AI models will let us understand this in more detail. So instead of just looking at statistical patterns of what types of people engage with what content, for the first time in Meta’s history, we’re going to be able to develop a first principles understanding of what you care about and what each piece of content in our system is about — is that way we can show you more useful things for what you’re trying to accomplish. And we’ll also be able to create personalized content specifically for people to help you achieve your goals as well. Since our recommendation systems are operating at such a large scale, we’ll phase in this new research and technology over time.

But the trend over the last few years seems clear that we are seeing an increasing return on the amount that we can improve engagement for people and value for advertisers. This encourages us to continue investing heavily in what we expect will provide increasing value over the coming years as well.

Meta will be rolling out more than 1 GW (gigawatt) of its own custom chips; Meta’s AI compute infrastructure will include large amount of its own chips and AMD chips, alongside NVIDIA chips; Meta is investing in more compute, partly through multiyear cloud deals; Meta’s contract commitments increased by $107 billion in 2026 Q1; the multiyear cloud deals support both Meta’s training and inference needs; management has consistently underestimated Meta’s compute needs even as the company has been ramping up compute capacity significantly; management expects compute to be even more central for the business going forward

We are rolling out more than 1 gigawatt of our own custom silicon that we’re developing with Broadcom, as well as significant amount of AMD chips to complement the new NVIDIA systems that we’re rolling out as well…

…We’re also signing cloud deals that will come online over the course of this year and 2027, allowing us to scale more quickly. These multiyear cloud deals and our infrastructure purchase agreements drove a $107 billion step-up in our contractual commitments this quarter. Our investments will support our training needs for future models and most importantly, provide us the inference capacity necessary to deliver personal and business agents to billions of people around the world, along with several other AI product experiences we’re developing…

…Our experience so far has been that we have continued to underestimate our compute needs even as we have been ramping capacity significantly as the advances in AI have continued and our teams continue to identify compelling new projects and initiatives. And now to, there are very compelling internal use cases. So our expectation is that compute will become even more central to the business going forward.

Meta’s AI glasses continue to perform well, with daily users tripling year-on-year in 2026 Q1; the AI glasses continue to be one of the fastest-growing categories of consumer electronics ever; Meta released new glasses for all-day wear in 2026 Q1; Met has new partnerships and styles for AI glasses coming later this year; all of Meta’s AI glasses are designed to easily update to Meta’s newest AI models and features; Meta’s AI glasses are evolving into a personal agent product; the sales of Meta’s AI glasses have shifted from the prior generation to the latest generation; management is seeing strong interest in the Meta Ray-Ban Display product that comes with neural bands; management thinks the Meta Ray-Ban Display product will be the next generation for how AI glasses evolve

Our AI glasses continue to perform well with the number of people using them, daily tripling year-over-year. This continues to be one of the fastest-growing categories of consumer electronics ever. We released Ray-Ban Meta optics this quarter designed for all day wear rather than primarily as sunglasses. And building on our release of Oakley last year, we have some exciting new partnerships and styles that I think are going to have the potential to reach even more people coming later this year. All of our glasses are designed to easily update to use our newest AI models and features. I’m also really excited to see the glasses evolve from being able to answer questions to being able to be a personal agent that’s with you all day long, helping you remember things and achieve your goals…

…We’re seeing sales shift now from the prior generation of Ray-Ban Meta’s to the latest generation, which I think speaks to the value of the improved features like extended battery life and higher features like higher resolution video capture…

…We see strong interest now in the Meta Ray-Ban displays with the Meta neural bands. So that’s an encouraging sign that there is consumer appetite for display glasses, which is kind of the next generation of how this product evolves.

Ranking improvements made in 2026 Q1 drove a 10% increase in time spent on Instagram Reels, an 8% increase in total video time on Facebook globally, and a 9% increase in video watch time on Facebook in the US and Canada; the ranking improvements are driven by a number of things, including (1) the doubling in the length of user interaction sequences for training on Instagram, (2) increasing the speed of indexing new posts by the ranking models, and (3) applying more advanced content understanding techniques; same-day posts are now more than 30% of recommended posts in Instagram and Facebook, up more than 2x from a year ago; management is now using AI to auto translate and dub videos into a viewer’s local language; more than 500 million users are watching translated videos weekly on each of Facebook and Instagram; management continues to invest in Meta’s recommendation capabilities, and the investments include near term ones such as scaling up models in size and complexity and incorporating LLMs, or large language models, to deepen content understanding, and long-term ones such as building foundation models for organic content and ads recommendations, and LLM-based recommendation systems; management thinks there is still a lot of room to continue improving recommendations on both Facebook and Instagram

We’re continuing to see significant gains from our content recommendation initiatives. On Instagram, the ranking improvements that we made in Q1 drove a 10% lift in Reels time spent. On Facebook, total video time increased more than 8% globally in Q1, the largest quarter-over-quarter gain in 4 years. Within the U.S. and Canada, ranking improvements we made drove a 9% increase in video watch time on Facebook in Q1. 

These gains are benefiting from advances we’re making across the full stack. Starting with data, we doubled the length of user interaction sequences we use for training on Instagram in Q1 and increase the richness of how each user interaction is described, enabling our systems to develop a deeper understanding of user interests. Within our models, we’ve significantly increased the speed with which our ranking models index new posts, which is enabling us to recommend them sooner after they are published. We’re also applying more advanced content understanding techniques, which is enabling us to quickly identify posts that may be interesting to someone even if they haven’t engaged with a lot of similar content. These and other improvements have enabled us to increase the diversity and recency of recommended content with same-day posts now representing more than 30% of recommended reels on both Instagram and Facebook more than double the levels 1 year ago.

We’re also using AI to unlock more inventory by auto translating and dubbing videos into a viewer’s local language, enabling us to recommend a more diverse set of content. Over 0.5 billion users on each of Facebook and Instagram are now watching AI translated videos weekly. 

Looking forward, we’re making several investments we expect will deliver more valuable recommendations. This year, we will continue scaling up our models in several dimensions, including their size and complexity, while incorporating LLM to deepen content understanding across our platform. This will enable us to better match people to a wider variety of content aligned to their interests. At the same time, we are executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that power organic content and ads recommendations as well as developing LLM based recommender systems. Our focus this year is validating the model architectures and techniques in these domains before we scale them out in future years…

…There is still a lot of room to continue improving recommendations over the rest of the year, and we expect we’ll be able to do that to drive additional engagement on both Facebook and Instagram.

Meta continues to enhance its systems to show advertising to users at the optimal time and location; improvements made to Lattice and GEM (Generative Ads Model) in 2026 Q1 increased conversion rates for landing page view advertising by more than 6%; management expanded coverage of Meta’s new adaptive ranking model, which was rolled out in 2025 H2, to off-site conversions and this drove a 1.6% increase in conversion rates across Facebook and Instagram’s major surfaces; Meta is introducing Meta Ads AI Connectors in open beta and it allows advertisers to connect their Meta advertising accounts directly to an AI agent; more than 8 million advertisers are now using at least one of Meta’s Gen AI advertising creative tools with very strong adoption among SMB advertisers; advertisers using Meta’s video generation feature are seeing 3% higher conversion rates in tests; Meta’s value optimisation suite, which maximises the return on advertising spend for advertisers by prioritising the highest value conversions, has seen strong adoption with the revenue run rate reaching $20 billion in 2026 Q1, more than double from a year ago; Meta’s new adaptive ranking model enables the company to leverage LLM-scale model complexity when it previously couldn’t

We continue to enhance our systems to show ads at the optimal time and location…

…In Q1, enhancements we made to Lattice’s modeling and learning techniques, along with advances in our GEM model architecture, drove a more than 6% increase in conversion rate for landing page view ads. In addition, we’ve been investing in more performing inference models for 1 more serving ads. In the second half of last year, we began rolling out our new adaptive ranking model, which is an LLM scale adds recommender model that we use for inference. This model improves our inference ROI by routing requests to more compute-intensive inference models when it determines there is a higher probability of conversion. In Q1, we expanded coverage of our adaptive ranking model to support off-site conversions, which drove a 1.6% increase in conversion rates across the major surfaces on Facebook and Instagram…

…This week, we’re also introducing Meta ads AI connectors in open beta, providing advertisers the ability to connect their Meta ad account directly to an AI agent. We’ve always supported advertisers both on our platform and through tools like the marketing API. And now we’re extending that to AI. So businesses and agencies can analyze and optimize campaigns with the tools they’re already using.

Usage of our ad creative tools is also scaling with more than 8 million advertisers using at least one of our Gen AI ad creative tools and particularly strong adoption among small- and medium-sized advertisers. These tools are benefiting performance as well with advertisers using our video generation feature seeing more than 3% higher conversion rates in tests…

…We also continue to invest in the value optimization suite, which helps advertisers maximize their return on ad spend by prioritizing the highest value conversions rather than optimizing solely for the most conversions at the lowest cost. Adoption by businesses has been strong following performance improvements we’ve made over the past year with the annual revenue run rate of our value optimization suite now over $20 billion, more than doubling year-over-year…

…The inference models are bound by strict latency requirements since they need to find the right ad within milliseconds, and that has, again, historically prevented us from meaningfully sizing up — scaling up their size and complexity. But in the second half of last year, we introduced a new adaptive ranking model, which enables us to leverage LLM scale model complexity of 1 trillion parameters, and we made advances in the model architecture and codesign the system with the underlying silicon, so it maintains the sub-second speed that is required to serve ads at scale. We also developed an approach that intelligently routes request more compute-intensive inference models if it determines that there is a higher probability of conversion and that lets us drive both better performance and increased inference ROI.

Microsoft (NASDAQ: MSFT)

Microsoft’s management has 2 priorities to capture the AI opportunity, namely, (1) build the leading cloud and AI infrastructure, and 2) build high-value agentic systems across core domains

We are at the beginning of one of the most consequential platform shifts that will change the entire tech stack as agents proliferate and become the dominant workload. This will drive TAM expansion and change the value creation equation across the entire economy. To capture this opportunity, we are executing against 2 priorities. First, we are building the world’s leading cloud and AI infrastructure for agentic computing era. Second, we are building high-value agentic systems across core domains such as productivity, coding and security

Microsoft’s management is optimising every layer of its technology stack and this is producing operational gains; Microsoft’s dock-to-live times for its data centers has reduced by 20% since the start of 2026; Microsoft has delivered a 40% improvement in inference throughput in Copilot’s most-used models

We’re optimizing every layer of the tech stack, from DC design, to silicon to system software, the model architecture as well as its optimization. This is translating into operational gains. We have reduced dock-to-live times for new GPUs in our biggest regions by nearly 20% since the beginning of the year. Our Fairwater data center in Wisconsin came online earlier this month, 6 weeks ahead of schedule, allowing us to recognize revenue earlier. And we delivered a 40% improvement in inference throughput for our most used models across Copilot, driven by our software and hardware optimization work.

Microsoft added 1 gigawatt of GPU compute capacity in 2026 Q1 (FY2026 Q3); Microsoft is on track to double its overall compute footprint in 2 years; management announced new data center investments across 4 continents in 2026 Q1 (FY2026 Q3)  

All up, we added another gigawatt of capacity this quarter and remain on track to double our overall footprint in just 2 years. We are moving aggressively to add capacity aligned to our demand signals we see and we have announced new data center investments across 4 continents.

Microsoft’s AI infrastructure utilises chips from NVIDIA, AMD, and itself (Maia); Microsoft’s Maia 200 chip has 30% better tokens per dollar compared to other leading AI chips, and is now live in 2 Microsoft data centers; Microsoft’s Cobalt server CPUs are deployed in half of the company’s data center regions; as Microsoft’s customers scale their AI workloads, they are increasingly using other Microsoft cloud services and are choosing Cobalt to run these services; management is expanding Cobalt’s supply significantly to meet demand

We also continue to modernize our fleet with our first-party innovation alongside the latest from NVIDIA and AMD. Across our fleet, millions of servers are powered by our custom networking security and virtualization silicon, including Azure Boost as well as our first-party CPUs and accelerators. Our Maia 200 AI accelerator, which offers over 30% improved tokens per dollar compared to the latest silicon in our fleet, is now live in our Iowa and Arizona data centers. Our Cobalt server CPU is deployed in nearly half of our DC regions running workloads at scale for customers like Databricks, Siemens and Snowflake. As our largest customers scale their AI deployments, they’re increasingly leveraging other services across our platform and choosing to run those workloads on Cobalt. And we are expanding Cobalt supply significantly to meet this demand.

Microsoft’s management thinks Microsoft offers the broadest selection of models among the cloud hyperscalers; over 10,000 customers have used more than 1 model on Foundry; the number of customers who used Anthropic and OpenAI models doubled sequentially in 2026 Q1, or FY2026 Q3 (was 1,500 in 2025 Q4, or FY2026 Q2); Bayer is using multiple models in Foundry to build its in-house agent platform; over 300 Microsoft customers are on track to process 1 trillion tokens each on Foundry in 2026, up 30% sequentially 

We offer the broadest selection of models of any hyperscaler, so customers can choose the right model for the right workload across OpenAI, Anthropic, open source and more. Over 10,000 customers have used more than one model on Foundry. 5,000 have used open source models, and the number who have used Anthropic and OpenAI models increased 2x quarter-over-quarter…

…Bayer is using multiple models in Foundry to create its own in-house agent platform with more than 20,000 active monthly users. All up, over 300 customers are on track to process over 1 trillion tokens on Foundry this year, accelerating 30% quarter-over-quarter.

Microsoft’s management is building a unified IQ layer for organisational intelligence; the IQ layer initiative is driving acceleration in Microsoft’s data businesses, with Cosmos DB revenue up 50% year-on-year in 2026 Q1 (FY2026 Q3), Fabric customers growing 60% year-on-year to 35,000, and Fabric OneLake data up 4x year-on-year; 15,000 customers now use both Fabric and Foundry, up 60% year-on-year; Fabric provides agents with operational, analytical, and unstructured data; Microsoft’s Copilot Studio is helping enterprises build agents; nearly 90% of the Fortune 500 have active agents built with Copilot Studio’s low-code and no-code tools; Copilot’s credit consumptive offer is up 2x sequentially in 2026 Q1 (FY2026 Q3); Agent 365 is a control plane for managing agents’ governance, identity, and security; tens of thousands of companies are already using Agent 365 to manage tens of millions of agents

Across Fabric, Foundry, Microsoft 365 and our Security Graph, we are building a unified IQ layer for organizational intelligence. Thousands of enterprises already are accessing context across these IQ layers. And as AI usage grows, so does the context layer, creating a flywheel that continuously improves the grounding, relevance and effectiveness of every agent they use and build, making our IQ layers an unmatched context engine for organizational intelligence. More broadly, our database business accelerated quarter-over-quarter. Cosmos DB alone saw 50% year-over-year revenue growth driven by AI app workloads. We now have 35,000 paid Fabric customers, up 60% year-over-year. And all up, the amount of data in Fabric OneLake data lake increased nearly 4x year-over-year. Over 15,000 customers now use both Foundry and Fabric, up 60% year-over-year as enterprises connect agents to real-time operational, analytical and unstructured data that Fabric brings together…

…We are also helping knowledge workers build agents with tools like Copilot Studio. Nearly 90% of the Fortune 500 now have active agents built with our low-code/no-code tools. And we are seeing fast growth of our Copilot credit consumptive offer, up nearly 2x quarter-over-quarter as customers increasingly extend Copilot with custom agents tailored to their workflows…

…With Agent 365, we offer a control plane that extends company’s existing governance, identity, security and management frameworks to agents. Tens of thousands of companies are already managing tens of millions of agents in Agent 365, and we expect this momentum to grow significantly as agents will increasingly need tools for identity, governance, security and more.

Microsoft’s management is turning its family of Copilots from synchronous assistance software to asynchronous digital workers; Microsoft 365 Copilot seat adds grew 250% year-on-year in 2026 Q1 (FY2026 Q3), the fastest growth since launch; there are now over 20 million Microsoft 365 Copilot paid seats; the number of companies with over 50,000 Microsoft 365 Copilot seats grew 4x year-on-year in 2026 Q1 (FY2026 Q3); WorkIQ grounds Copilot’s responses with an organisation’s full context; the data residing in WorkIQ now spans 17 exabytes, up 35% year-on-year; users can now access multiple models together in Microsoft 365 Copilot to generate the best responses; monthly active usage of Microsoft’s 1st-party agents in Microsoft 365 Copilot is up 6x year-to-date; Copilot queries per user was up 20% sequentially in 2026 Q1 (FY2026 Q3); weekly engagement of Microsoft 365 Copilot is now on par with Outlook

We are evolving our family of Copilots from synchronous assistance to async coworkers that can execute long-running tasks across key domains. In knowledge work, it was another record quarter for Microsoft 365 Copilot seat adds, which increased 250% year-over-year, representing our fastest growth since launch. Quarter-over-quarter, we continue to see acceleration and now have over 20 million Microsoft 365 Copilot paid seats. The number of customers with over 50,000 seats quadrupled year-over-year and Accenture now has over 740,000 seats, our largest Copilot win to date. And Bayer, Johnson & Johnson, Mercedes and Roche all committed to 90,000 or more seats…

…Work IQ grounds Copilot responses in the full context of an organization, including people, roles, documents and communications, all within the company’s security boundary. The system of work behind Work IQ alone now spans more than 17 exabytes of data growing 35% year-over-year. The liquidity and freshness of that data matters, with billions of e-mails, documents, chats, hundreds of millions of Teams meetings, and millions of SharePoint sites added each day. And that context is getting even richer as Copilot adoption grows, Copilot and Agent conversations and artifacts they create feedback into Work IQ, making it even more context-rich…

…In Microsoft 365 Copilot, you now have access in chat to multiple models by default with intelligent auto routing, in Agents with Critique and Council. You can use multiple models together to generate optimal responses. As of last week, Agent Mode is now default experience across Copilot in Word, Excel and PowerPoint. And with Cowork, you now have a new way to delegate and complete work using Copilot.

All this innovation is driving record usage intensity across Copilot. We have seen a surge in usage of our first-party agents with monthly active usage up 6x year-to-date. Copilot queries per user were up nearly 20% quarter-over-quarter. To put this momentum in perspective, weekly engagement is now at the same level as Outlook, as more and more users make Copilot a habit.

Microsoft’s management is observing a shift in pricing in business software from seat-based models to seat-plus-consumption models because of AI; nearly 60% of Microsoft’s service customers are already buying usage-based credits; HSBC is using pre-built agents to reduce issue resolution time for customer inquiries by 30%; LinkedIn Talent Solutions’ agentic products now have an annualised revenue run rate of more than $450 million; management thinks the pricing model for business software could yet evolve further to include business outcomes into the equation

When it comes to biz apps, we are seeing a new pattern emerge as customers shift from traditional seat model to seats plus consumption. The customer service category is at the forefront of this transformation as nearly 60% of our service customers are already purchasing usage-based credits. For example, HSBC uses prebuilt agents with Dynamics 365 to manage customer inquiries across products, markets, regulatory requirements, reducing issue resolution time by over 30%. And our agentic products in LinkedIn Talent Solutions, which help hirers automate time-consuming tasks like sourcing, screening and drafting messages have already surpassed a $450 million annualized revenue run rate…

…From a customer perspective, they’re going to evaluate it by evals. Where are they seeing the value of tokens, as simple as that. So where they see the outcome, the eval and the token, whether it’s improving revenue, improving efficiency, and that’s what will refine. Like when we talk about IT budgets, IT budgets are going to have to be reshaped by a combination of business outcomes, making their way into IT budgets and maybe reallocation from other line items on the income statement like OpEx.

GitHub is growing rapidly, driven by agentic coding; nearly 140,000 organisations are using GitHub Copilot; GitHub Copilot enterprise subscribers nearly tripled year-on-year in 2026 Q1 (FY2026 Q3); most users in GitHub Copilot use multiple models; usage of GitHub Copilot CLI (command line interface) nearly doubled month-on-month; management has shifted GitHub Copilot to a usage-based pricing model

GitHub itself is seeing unprecedented growth driven by proliferation of agentic coding, and we are hard at work to scale and meet this demand. We see this even with GitHub Copilot. Nearly 140,000 organizations now use GitHub Copilot and enterprise subscribers have nearly tripled year-over-year. The majority of users leverage multiple models. We’re also seeing rapid adoption of GitHub Copilot CLI with usage nearly doubling month-over-month. And earlier this week, we announced our move to usage-based pricing model for GitHub Copilot as we align pricing to actual usage and cost.

1/3 of Microsoft’s cloud and AI-related capex in 2026 Q1 (FY2026 Q3) are for long-lived assets that will support monetisation over the next 15 years and more, while the other 2/3 are for CPUs and GPUs; Azure is still capacity-constrained, and management wants to balance Azure demand for compute with 1st party demand for compute; Azure’s capacity-constrain is expected to last through at least 2026

Capital expenditures were $31.9 billion, down sequentially due to the normal variability from cloud infrastructure buildouts and the timing of delivery of finance leases. And this quarter, roughly 2/3 of our CapEx was for short-lived assets, primarily GPUs and CPUs. The remaining spend was for long-lived assets that will support monetization over the next 15 years and beyond. This quarter, total finance leases were $4.7 billion and were primarily for large data center sites. And cash paid for PP&E was $30.9 billion, roughly in line with capital expenditures as the impact from finance leases was partially offset by differences between the receipt of goods and payment…

…In Azure and other Cloud Services, revenue grew 40% and 39% in constant currency against a prior year that included accelerating growth. Results were ahead of expectations as we delivered capacity earlier in the quarter, enabling increased consumption across both AI and non-AI services. Strong customer demand across workloads, customer segments and geographic regions continues to exceed available capacity…

…Broad and growing customer demand continues to exceed supply, and we continue to balance the incoming supply we can allocate here against our other high ROI priorities, first-party applications, R&D and end-of-life server replacement…

…Even with these additional investments and continued efforts to bring GPU, CPU and storage capacity online faster, we expect to remain constrained at least through 2026.

Azure grew revenue by 40% in 2026 Q1 (FY2026 Q3) (was 39% in 2025 Q4); Azure’s revenue growth was better than expected because capacity was delivered earlier in the quarter; Azure continues to be constrained by capacity and the constraint is expected to last through at least 2026; management wants to balance Azure demand for compute with 1st party demand for compute; as Microsoft’s customers scale their AI workloads, they are increasingly using other Microsoft cloud services; Azure’s margin for its AI business remains better than the non-AI business when it was at a similar age

In Azure and other Cloud Services, revenue grew 40% and 39% in constant currency against a prior year that included accelerating growth. Results were ahead of expectations as we delivered capacity earlier in the quarter, enabling increased consumption across both AI and non-AI services. Strong customer demand across workloads, customer segments and geographic regions continues to exceed available capacity…

…Broad and growing customer demand continues to exceed supply, and we continue to balance the incoming supply we can allocate here against our other high ROI priorities, first-party applications, R&D and end-of-life server replacement. As a reminder, year-over-year Azure growth rates can vary quarter-to-quarter based on capacity, timing and contract mix…

…Even with these additional investments and continued efforts to bring GPU, CPU and storage capacity online faster, we expect to remain constrained at least through 2026…

…. As our largest customers scale their AI deployments, they’re increasingly leveraging other services across our platform and choosing to run those workloads on Cobalt…

…We’ve been talking about sort of where this AI business of ours has been in the cycle compared to even the cycle we saw with the cloud, which now seems very long ago. And how margins were actually better and they remained better in our AI business versus where we saw in the cloud transition, looking back.

Microsoft’s management has gained more confidence over the past 1-2 years that the economics of AI’s addressable market are in areas where the company has structurally strong positions in

One of the things that we have learned even in the last, whatever, 2 years or so in AI and also build more conviction and confidence on is where is the TAM and the category economics of the TAM. And so this, I mean, it’s fascinating that here we are in 2026 and the most exciting things are plug-ins in Word or Excel or CLIs in coding or — and so when you see that, that means we have a structural position in knowledge work, coding, security, which are the big TAMs.

Microsoft’s management continues to feel good about partnering with OpenAI after the recent change to the 2 companies’ agreement; Microsoft has full IP rights to OpenAI’s frontier models all the way to 2032; OpenAI remains a large customer of Microsoft

We feel good about our partnership with OpenAI. I’m always very, very focused on any partnership and ensuring that there’s a win-win construct at all times. I mean that’s how you can remain with partners. In this case, it starts with, quite frankly, IP, Amy referenced this. We have a frontier model, royalty-free with all the IP rights that we will have access to all the way to ’32, and we fully plan to exploit it…

…They’re a large customer of ours, not just on the AI accelerator side, but also on all the other compute side, and so we want to serve them well.

Netflix (NASDAQ: NFLX)

Netflix has been using generative AI to improve content recommendations for members; management is also leveraging generative AI to provide better tools for filmmakers; Netflix acquired InterPositive, a company providing AI-powered filmmaking tools, in March 2026; management thinks Netflix has significant and unique data for applying AI; management thinks even with AI tools, only great artists can make great art; Netflix’s content creation partners have been leveraging AI tools for many purposes, and these tools also help improve on-set safety; InterPositive contains proprietary technology created specifically for filmmakers and for filmmaking, so it’s different compared to other generative AI video apps; management is already seeing momentum around adopting InterPositive’s tools among Netflix’s content creation partners; management has been working on content recommendation and personalisation for many years, but they think generative AI provides plenty of opportunity for Netflix to continue improving in those areas; management thinks AI can be applied in Netflix’s advertising suite to make it easier to create new formats, customise ads, and improve contextual relevance 

We’ve been using machine learning and AI for many years, and as the technology advances with GenAI, we continue to find new opportunities to deliver an even more seamless experience for members and expand possibilities for storytellers. This includes using GenAI to improve recommendations for members through deeper content understanding so we can recommend the right title at the right moment, test conversational discovery experiences, and improve the breadth and quality of our promotional assets. Leveraging GenAI, we are enabling our creative partners with more and better tools to help them tell their stories, with the potential to make our single largest area of spend—content—even more impactful. To accelerate this opportunity, in March we announced our acquisition of InterPositive, the filmmaking technology company founded by Ben Affleck that develops AI‑powered tools built by and for filmmakers…

…Given our technology DNA, we have a significant and unique data assets here. We have tremendous scale. So we see that as all great opportunities to leverage new technical capabilities across every aspect of the business. So I think AI is going to deliver benefits for our members, for creators and for our employees…

…It takes a great artist to make great art and AI won’t change that. But AI will give those artists better tools to bring those visions to life in ways that we’re just scratching the surface on. So today, our talent leverages these tools for things like set references, pre visualization, visual effects, sequence prep, shot planning. All of these things, by the way, also improve on-set safety, which is something that’s not talked about enough…

…With our acquisition of InterPositive, we think it accelerates our GenAI capabilities because it’s a proprietary technology that was created specifically for filmmakers and specifically for filmmaking and that’s different than other GenAI video applications. So while our ownership of InterPositive is very new, we have generated a bunch of interest with our creators who spent time with the tools, and we’re seeing real momentum build around adoption…

…We’ve been in personalization and recommendation for 2 decades, but we still see tremendous room and opportunity to make it even better by leveraging some of these newer technologies. We see that recommendation systems based on these new model architectures, not only improve the current personalization, but it also allows us to iterate and improve more quickly to improve that velocity. Things like adding support for different content types going forward, that’s much more quick, much more efficient…

…We really see an opportunity to leverage AI within our Netflix ad suite. Makes it easier to design new creative formats, custom ads, improved — that improve contextual relevance. And the technology stack just allows us to roll them out more quickly, more effectively and allow partners to leverage those things in an easier manner.

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

TSMC’s capital expenditure is always in anticipation of growth in future years; management expects capex for 2026 to be near the high end of its previous guidance of US$52 billion to US$56 billion (growth at the high would be 37% from 2025’s capex of US$41 billion); management now expects TSMC to grow revenue by more than 30% in USD terms in 2026 (previous guidance was for growth to be nearly 30%); TSMC’s capex in the last 3 years was ~US$100 billion, and the next 3 years is expected to be much higher, although management does not expect a sudden surge in capital intensity; management thinks the AI accelerators business will have a CAGR for 2024-2029 towards the high end of the previously released growth forecast of mid-to-high-50% CAGR

At TSMC, a higher level of capital expenditures is always correlated with higher growth opportunities in the following years…

…We now expect our 2026 capital budget to be towards the high end of our range of between USD 52 billion and USD 56 billion, as we continue to invest heavily to support our customers’ growth…

…We maintain strong confidence for our full year 2026 revenue to now grow by above 30% in U.S. dollar terms…

…In the past 3 years, our total CapEx was $101 billion. This year, we’re already seeing is towards the high end, which is $56 billion, which is already over 50% of the past 3 years in total. So we have a strong conviction in the AI megatrend. So we expect the CapEx in the next few years, in the next 3 years will be significantly higher than the past few years…

…Now therefore, we do not expect in the next several years, a sudden surge in capital intensity…

…But again, let me say that is toward higher 50s of the CAGR that we observe.

TSMC has been sourcing helium (an element whose supply has been affected by the conflict in the Middle East) from different regions, and it has safety stock in hand; TSMC has been working with Taiwan’s government to secure power, and Taiwan has sufficient LNG supply through at least May; management does not expect any near-term impact to TSMC’s operations from the Middle East conflict in terms of materials and power supply

About the materials and energy supply update given the recent situation in the Middle East. TSMC operates a well-established enterprise risk management system to identify and assess all relevant risks and proactively implement risk mitigation strategies. In terms of material supply, TSMC’s strategy is to continuously develop multi-store supply solutions to build a well-diversified global supplier base and to improve the local supply chain. For specialty chemicals and gases, including helium and hydrogen, we source from multiple suppliers in different regions and we have prepared safety stock inventory on hand. We are also working closely with our suppliers to further strengthen the resiliency and sustainability of our supply chain. Thus, we do not expect any near-term impact on our operations for material supply.

In terms of energy, TSMC worked closely with Thai Power and the Taiwan government to ensure a stable and sufficient energy supply. With the recent situation in the Middle East, the Taiwan government has announced it has secured sufficient LNG supply through at least May. The government has also said it is actively working on securing further LNG supply, diversifying sourcing to other regions and other power backup plans. Therefore, we do not expect any near-term disruption or impact to our operations.

TSMC’s management sees very robust AI-related demand, as the shift from generative AI and queries (chatbots) to agentic AI is leading to a step-up in token consumption; management is seeing very strong signals and positive outlooks from TSMC’s customers’ customers, who are the cloud service providers; management’s conviction in the AI megatrend remains high

AI-related demand continues to be extremely robust. The shift from generative AI and the query mode to agentic AI and command and action mode is leading to another step-up in the amount of token being consumed. This is driving the need for more and more computation, which supports the robust demand for leading edge silicon. Our customers and customers of customers, who are mainly the cloud service providers, continue to provide us with a very strong signal and positive outlook. Thus, our conviction in the multiyear AI megatrend remains high, and we believe the demand for semiconductors will continue to be very fundamental.

TSMC’s management intends to ramp up new technology nodes in Taiwan because of the need for tight integration between production and R&D; TSMC’s N2 node entered high-volume manufacturing in 2025 Q4 in Taiwan with good yield; N2’s ramp is supported by strong demand from both smartphone and HPC AI applications; management believes that N2, N2P, and A16 will lead to the N2 family becoming another large and long-lasting node for TSMC; management has decided to add capacity for N3 even though TSMC has historically not added capacity to a node once it has reached its target capacity, because of the strong demand for N3 in AI applications; management is seeing robust multiyear demand for N3 nodes from end markets such as smartphone, HPC AI, and more; TSMC is adding a new N3 fab to its giga fab cluster in Tainan, with volume production expected in 2027 H1; TSMC is continuing to convert N5 tools to support N3 capacity in Taiwan; management is focusing on flexible capacity support among the N7, N5, and N3 nodes; the upcoming A14 node has 10-15 speed improvement at the same power or 25-30 power improvement at the same speed, and a nearly 20% chip density gain; the A14 node is on track and progressing well; management is seeing a high level of customer interest and engagmeent for A14; volume production of A14 is expected for 2028

Our practice is to prioritize the land in Taiwan to support the fast ramp of our new node due to the need for tight integration with R&D operations. Today, our new node, N2, has already entered high-volume manufacturing in the fourth quarter of 2025 with good yield. N2 is ramping successfully in multi phases at both Hsinchu and Gao Hsiung site supported by strong demand from both smartphone and HPC AI applications. With our strategy of continuous enhancement such as N2P and A16, we expect our N2 family to be another large and long lasting node for TSMC.

Historically, we do not add additional capacity to a node once it reached its targeted capacity. However, as a foundry, our first responsibility is to provide our customers with the most advanced technologies and necessary capacity to unleash their innovations. Based on our assessment, to meet the strong demand in AI application, we are stepping up our CapEx investment to increase our N3 capacity. Thus, we are now executing global capacity plan to support the robust multiyear pipeline of demand for 3-nanometer technologies, which are used by smartphone, HPC AI, including HBM based side, automotive and IoT customers. 

In Taiwan, we are adding a new 3-nanometer fab to our giga fab cluster in Tainan Volume production is scheduled for the first half of 2027…

…In addition to all the new fabs, we continue to convert 5-nanometer tool to support 3-nanometer capacity in Taiwan…

…We are also focusing on capacity optimization across nodes, which including flexible capacity support among the N7, N5 and N3 nodes…

…Figuring our second-generation transistor structure, A14 delivered another 4-node stride from N2, with performance and power benefit across to address the sensible need for high performance and energy efficient computing. Compared with N2, A14 will provide 10 to 15 speed improvement at the same power for 25 to 30 power improvement at the same speed and close to 20% chip density gain. Our A14 technology development is on track and progressing well. We are observing a high level of customer interest and engagement from both smartphone and HPC applications. Volume production is scheduled for 2028. Our A14 technology and its derivatives will further extend our technology leadership position and enable TSMC to capture the growth opportunities well into the future.

TSMC’s 2nd Arizona fab will utilise N3 technologies; the N3 nodes in the 2nd Arizona fab will begin volume production in 2027 H2; management has gained a lot of experience in Arizona, and expects to improve the cost structure of the Arizona fabs

In Arizona, our second fab will also utilize 3-nanometer technologies. Construction is already complete and volume production will begin in the second half of 2027…

…We already gained a lot of experience in Arizona. And so now we have much more confidence in last year that we can make good progress and moving aggressively forward and with, we expect we can improve the cost structure, of course.

TSMC’s management now plans to utilise N3 technology in the company’s 2nd fab in Japan; volume production is scheduled for 2028

In Japan, we now plan to utilize 3-nanometer technology in our second fab and volume production is scheduled in 2028.

TSMC’s management is open to including CPUs into its HPC (high-performance computing) AI calculation, but they will not do it right now, because TSMC is not able to tell where the CPUs it manufactures goes to

[Question] TSMC’s definition of AI revenue includes GPU, AI accelerator, HPM based maybe I up a few others, but it does specifically excludes data center CPU, I think you made that the definition very clear for a couple of years now. But with the CPU, there’s more and more conversation about CPU now becoming part of the AI infrastructure, especially for agentic workflows. Any chance for TSMC to maybe provide us revised numbers for AI revenue and maybe the AI revenue growth take a projection going 2029, 2030 and maybe hopefully give us some sense about the historical AI revenue numbers would have been if some of the data centers CPU numbers, especially for genetic AI workloads are included there.

[Answer] Certainly, CPUs becomes more and more important in today’s AI data center. But actually, let me share with you, this is a good question, by the way. Let me share with you that we are not able to identify which CPU goes to where, right? It’s a PC or it’s desktop or it’s AI data center. So today, we still not include the CPUs in our AI HPC’s calculation. Someday later, we might consider.

TSMC is working with NVIDIA for its next-generation LPU (language processing unit); the LPU comes with NVIDIA’s recent acqui-hire deal with Groq; Groq’s LPUs have historically been manufactured by Samsung

[Question] NVIDIA, of course, they recently added more CPU content to the overall but I think that most people are focusing on that brand-new LPU. They recently added — we understand I appreciate that the TSMC very strong institute and we’ll definitely participate in that upside in CPU. But the LPU business, it’s the acquired business, well, for historical reasons, it’s still at your competitors Samsung Foundry. And I think investors are looking at that and the thing that maybe looks like Samsung foundry finally made the first inroads into AI. So any thoughts from TSMC side, how should we think about whether and how TSMC will win back that LPU business or any future business coming from your customers?

[Answer] We are working with our customers for their next-generation LPU anyway. And we are very confident in our technology position, and we will work hard to capture every piece of business possible.

Tesla (NASDAQ: TSLA)

Tesla’s management is going to increase the company’s capital expenditure significantly, partly for AI-related investments; the increase in capital expenditure will last for a few years; management expects Tesla’s capex to be $25 billion in 2026, and thus cause the company to have negative free cash flow for the year

We’re going to be substantially increasing our investments in the future so you should expect to see significant — a very significant increase in capital expenditures, but I think well justified for a substantially increased future revenue stream…

…We’re investing in and improving our core technologies, battery powertrain, AI software, AI training, chip design, manufacturing — laying the groundwork for significantly increased manufacturing and production. We are also strengthening our supply chain across the board, batteries, energy, AI, silicon, everything, and laying the groundwork, like I said, for what we expect to be a significant increase in vehicle production in the future and, of course, a very significant increase — well, actually releasing Optimus…

…We are in a very big capital investment phase, which is going to start now and would last a couple of years. So based on that, our current expectation for 2025 — 2026 is over $25 billion of CapEx. And just to remind you, we are paying for 6 factories which were going to go into operation. Some have already started, some would go into operation later part of this year. We’re further increasing our investment in AI-related initiatives, including the AI infrastructure to support Robotaxi and the launch of Optimus. We’ve already started placing orders for the research semiconductor fab in Austin and for solar manufacturing equipment. While this may seem a lot and will have the impact of negative free cash flow for the rest of the year, we believe this is the right strategy to position the company for the next era.

Tesla’s management thinks Optimus can be useful outside of Tesla sometime in 2027; management continues to think Optimus will be the biggest ever product made; Tesla is preparing its Fremont factory for production of Optimus later this year; the production S-curve of Optimus will be very slow at the start, before ramping significantly in 2027; Tesla is building a 2nd Optimus factory, with production scheduled for mid-2027; v3 of Optimus (Optimus 3) is almost ready to be demonstrated, but management is hesitant because they have found competitors trying to copy Optimus’s design (in the 2025 Q4 call, management said Optimus 3 would be ready in a few months); management thinks Optimus can start production in July/August 2026, but it will take tremendous work to get there; management does not know what the production rate for Optimus will be in 2026; the production rate for Optimus will be limited by the slowest part in the entire Optimus supply chain; management wants to place a lot of intelligence locally in Optimus in the event that the robot loses wireless data; management thinks Optimus would need an orchestrator-AI and a voice AI, both of which can be Grok (a foundation model from one of Elon Musk’s companies, xAI)

But increasing our internal production for testing and then probably being able to have Optimus be useful outside of Tesla sometime next year. As you’ve heard me say a few times, I think, Optimus will be our biggest product — not just Tesla’s biggest product ever, but probably the biggest product ever. And I remain convinced of that conclusion…

…We’re preparing Fremont for start of production later this year with Optimus. Again, totally new supply chain, totally new technology. So therefore, the production S-curve is always very slow in the beginning, but it will ramp up to significant numbers next year. And we’re constructing a second Optimus factory in — at our Giga Texas location. And that will probably start production around summer next year.

The V3 Optimus design is almost ready to demonstrate. I think we want to just make sure it’s like polished. Like it works functionally, but there’s some aesthetic elements that need to be finalized. And I think probably middle of this year, we should be able to show it off. We’re also a little hesitant to show V3 off because we find our competitors do a frame-by-frame analysis whenever we release something and copy everything they possibly can. So I think there’s some value to not showing new technology until it’s close to production…

…We want to push the Optimus 3 unveil maybe closer to production. Start of production is — we’re assuming is somewhere around the late July, August time frame…

…The last S, X production will be in early May. But you have to look at the entire upstream portion of the production line. So you have to start with sales, battery packs, motor production, all the parts production. And so we’ve been dismantling the S, X production line from the more base-level parts — more basic level parts to — as you get to more larger subassemblies, you start dismantling the line from the small parts first, not from the final assembly first. So the final assembly line will — that will be dismantled next month and after the last of the S, X vehicle is done. You can’t dismantle some gigantic production line like overnight. It takes at least a few months to do so. And then you’ve got to install a new production line, and you’ve got to provide all of the wiring and communication, test out the machines of the new production line for Optimus. So that also takes several months. So frankly, if we’re able to go from stopping production on one line, dismantling that entire line, reinstalling a whole new line and turning that on in a matter of 4 months, that is an insanely fast speed. I don’t think any other company on earth has ever done that before…

…I don’t know what the production rate of Optimus will be this year. It is impossible to predict these things…

…when you have a brand-new product in an entirely new production line and you have 10,000 unique items, all of which have to go right into ramp production, it will move as fast as the least lucky, slowest, dumbest part in the entire 10,000. And this is a — Optimus is a completely new product with completely new production line. So it’s just literally impossible to predict, except that I think it will be quite slow at first as we iron out the 10,000-plus unique items that have to be sold for Optimus to reach volume production…

…We think we can put a lot of intelligence locally in the robot, and it certainly needs to be enough intelligence that if the robot gets disconnected, like if it’s a bad cellular signal or there isn’t WiFi, Optimus can’t just get stuck. It needs to have enough local intelligence that it can still do useful things even if it loses connection, kind of like a car…

…You can think of like Optimus needs kind of a manager to tell it what to do, broadly speaking, like if — otherwise it’s going to keep doing the same thing it did before. So I think you need kind of an orchestration AI, which Grok would be good for orchestration. And then for Optimus’ voice, having a low-latency intelligent voice AI, Grok is actually very good for that. So if you want to talk to Optimus and have kind of a Grok-level conversation, you kind of need to connect to a Grok-level AI for that.

All Tesla cars are autonomy-ready; supervised full self-driving is getting really good; v14.3 (version 14.3) of FSD was a major architectural update; management has a pipeline of improvements for FSD that they think will lead to unsupervised full self-driving being available globally; v15 of FSD is coming by end-2026 or early-2027; v15 of FSD will be a complete software architecture overhaul; v15 of FSD will run on Tesla’s AI4 chip; management thinks v15 of FSD will increase the safety level of FSD to way above human level; FSD now has 1.3 million paid customers globally (1.1 million in 2025 Q4); most of the growth in FSD customers in 2026 Q1 came from subscriptions, as management has removed the upfront-purchase option in some markets during the quarter; FSD recently received approvals in Netherlands; management is looking for EU-wide approval for FSD in 2026 Q2; FSD has received some approvals in China, although broader approval has yet to arrive; management hopes FSD can be fully approved in China by 2026 Q3; management has changed Tesla’s sales strategy to emphasise FSD as the product; management hopes to have unsupervised FSD in a dozen states by end-2026; management thinks unsupervised FSD revenue will not be material in 2026 but will be material in 2027; management thinks unsupervised FSD will reach customer-cars by 2026 Q4, but the release will be gradual; the FSD software deployed in Netherlands has the same exact architecture and the training procedure as the US version, but with more Europe data; management believes that the way Tesla solves full autonomy in the US can be applied to all parts of the world, if Tesla can add data from local regions; the Tesla customer fleet of vehicles is driving close to 10 billion miles on FSD in a few weeks; management thinks v14.3 of FSD is the last piece of the puzzle to enable unsupervised FSD; most Tesla drivers with Hardware 4 are already using FSD; FSD’s churn rate has improved

It’s always, I think, worth noting that a Tesla car is incredibly — incredible value for money, and they’re all autonomy-ready, depending on what part of the world you’re in. The supervised full self-driving is getting extremely good…

…For full self-driving and Robotaxi, version 14.3 was a major architectural update. And we have a whole pipeline of major improvements to full self-driving that, we believe, will lead to unsupervised full self-driving being available anywhere in the world that it is legal to do so. And then there’s a version 15, hopefully later this — hopefully by the end of this year, but certainly by early next year. And that will be a complete overhaul of the software architecture, and will run on AI4. That’s — and at that point, we’re really just increasing the safety level of FSD above human safety level, even more. Meaning, I think, even within version 14, we’re significantly safer than human, but v15 will take that to another level…

…On the FSD adoption front, we continue to see improvement, reaching nearly 1.3 million paid customers globally. The bulk of the growth came from subscriptions, while upfront purchases only increased 7% as we remove the purchase option in some markets in Q1.

We recently received approvals for FSD in Netherlands. This sets up us well for an EU-wide approval later in Q2, and we’re just gated by how the regulators go about it. Additionally, we’ve also received approvals in China. The broader approval is still not there, but we’re working with the regulators in the country, and we’re hoping that we can get approval by Q3…

…We have evolved our vehicle sales strategy, where we now emphasize FSD as a product and vehicle as only the delivery mechanism…

…We certainly hope to be — have unsupervised FSD/Robotaxi operating in, I don’t know, a dozen or so states by the end of this year…

…I think probably unsupervised FSD or Robotaxi revenue would not be super material this year. But I do think it will be material — it will be material probably in a significant way next year…

…[Question] When do you expect FSD unsupervised to reach customer cars?

[Answer] I’m just guessing here, but probably in the fourth quarter. It’s difficult to release this like to everyone everywhere all at once because we do want to make sure that they’re not unique situations in a city that particularly complex intersection or actually, they tend to be places where people get into accidents a lot because they’re just — perhaps there’s — and like I said, an unsafe intersection or bad road markings or a lot of weather challenges. So I think we would release unsupervised gradually to the customer fleet as we feel like a particular geography is confirmed to be safe…

…From a technology standpoint, what we deployed in Netherlands and Europe is the same exact architecture and the training procedure and so on, except it had more Europe data. And I suspect that same thing will be true for unsupervised FSD as well. Whatever we use to solve in the U.S. will work in other places and the rest of the world, too, provided we were able to add the data from the local regions…

…We are simultaneously solving the long tail of safety by monitoring the metrics across the entire Tesla customer vehicle fleet, which is close to driving 10 billion miles on FSD in the next few weeks…

…I think 14.3 is last piece of the puzzle for unsupervised FSD. Now the question is like degrees of safety. Like how — safety and convenience, I suppose…

…[Question] You have 180,000 new users, paying users this quarter, and I compare that to your overall installed base. It might be 15%, but then if I shrink that to the U.S. or to North America where most of them are, it’s probably more like 30%, 35%. And I’m trying to — and I compare that to what you sold, about 100,000 cars in North America in the quarter. So you’re winning twice more FSD users than you’re selling cars. And then if I add to that picture the fact that, I guess, it’s mostly Hardware 4 owners who subscribe to FSD, it sounds like most drivers in North America who have Hardware 4 would already be using FSD. Is that the right way to think about it and the kind of like success FSD is meeting today?

[Answer] You’re thinking about it the right way…

…We are actually seeing churn of subscribers also coming down, which again is a reflection of the product is getting better.

Tesla has started production of Cybercab, which are autonomous vehicles for the company’s Robotaxi fleet; the production of Cybercab will be a stretched-out S curve, ramping up only towards end-2026; the Robotaxi service has been expanded to Dallas and Houston; the expansion of the Robotaxi service is limited by management’s desire for really high safety levels; Robotaxi has, to-date, not had a single accident or injury; management hopes to have unsupervised Robotaxi in a dozen states by end-2026; management thinks Robotaxi revenue will not be material in 2026 but will be material in 2027; Robotaxi is currently running on FSD v14.3; Cybercab is 2-person vehicle; management thinks most of Tesla’s future vehicle production will be Cybercab; Tesla’s vehicles in the Robotaxi fleet sometimes get stuck because it’s programmed for maximum safety; the vehicles in the Robotaxi fleet can sometimes be stuck on infinite loops

We have just started production of Cybercab…

…Whenever you have a new product with a completely new supply chain, new everything, it’s always a stretched out S-curve. So you should expect that initial production of Cybercab and Semi will be very slow, but then ramping up and going kind of exponential towards the end of the year and certainly next year…

…We’ve expanded Robotaxi to Dallas and Houston using the same software source in the Bay Area. And the limiting factor for expansion is really rigorous validation, making sure things are completely safe. We don’t want to have a single accident or injury with the expansion of Robotaxi. And we have, to the credit of the team, not had a single one to date…

…We certainly hope to be — have unsupervised FSD/Robotaxi operating in, I don’t know, a dozen or so states by the end of this year…

…I think probably unsupervised FSD or Robotaxi revenue would not be super material this year. But I do think it will be material — it will be material probably in a significant way next year…

…So far, we have 0 incidents, and that’s what the NHTSA filing also shows…

…The version of Robotaxi that’s running in Austin, Dallas, Houston, et cetera, those are essentially 14.3 variants, and it’s obviously safe that, that’s why we’re able to launch in those cities…

…Cybercab is a compact vehicle. It’s actually — I mean, it’s very roomy, but it’s a 2-person vehicle. And we do think probably most of our production long term will be Cybercab because 90% of miles driven are with 1 or 2 people…

…A lot of what limits wider deployment of Robotaxi are actually not safety issues, but convenience issues or the car basically gets paranoid and gets stuck. Like sometimes it gets — because it’s programmed for maximum safety, so the problem is that then it sometimes just gets scared to do things. So like sometimes it gets scared to cross railroads, for example, or it’ll get stuck at a light or where there’s — the light never changes from red or, I mean, there was one kind of amusing situation where a whole bunch of Robotaxis got stuck in the left turn lane in Austin because, I kid you not, a Waymo had crashed into a bus. And so they could not turn left because the Waymo had crashed into the bus. And so you have this like long line of like, I don’t know, a dozen or more Tesla Robotaxis that were waiting for the bus to move, but the bus was never going to move because the Waymo crashed into the bus…

…We’ve also had literal infinite loops where the car might want to make a turn into a road, but there’s construction, and then it goes around the block, tries to turn into the road with construction, goes around the block, tries to turn into the road, and so you have to stop the infinite looping, the literal infinite looping.

Tesla has taped out its AI5 chip; management thinks the AI5 chip will be the best AI chip for inference at the edge, and will be the best value-for-money AI chip; Tesla is already designing the AI6 chip and is working on Dojo 3; management expects AI5 to go into Optimus and Tesla data centers, because AI4 is currently sufficient to achieve autonomy that is much safer than human drivers, so AI5 is not needed in the vehicle fleet; management thinks it will make sense at some point in the future to put AI5 into Tesla vehicles; management is planning to increase the memory and compute capacity of AI4, but the progress partly depends on Samsung (the fab for the chip)

Congratulations to — again to the Tesla AI chip team for taping out AI5. That’s going to be a great chip. I think probably the best AI inference chip for edge compute that exists. And certainly, I think, unequivocally the best value for money. The team did a great job. And we already have a lot of momentum for designing AI6, and we’ve begun to discuss ideas for Dojo 3…

…I do expect that AI5 will go into Optimus and into the data center because it’s looking like we’ll be able to achieve unsupervised self-driving with AI4 that is far greater than human safety levels. So — which means it’s not — certainly not immediately needed in the car. At some point, I think it will make sense for us to switch to AI5 in the car, but that’s — but there’s not a pressing issue to do so. So — but at some point, the AI4 hardware is going to get like so old that it’s like, okay, the only reason they’re keeping the factory open is for AI4.

We are planning an AI4 upgrade to use newer generation RAM. So it will go from 16 gigabytes to, I think, 32 gigabytes per SoC. So a total of 64 gigabytes, and probably a 10% increase in compute in sort of into — trillions of operations per second and in memory bandwidth. So that’s AI4.1 or AI4+, probably goes into production middle of next year, I think, depends. It depends on — Samsung is doing the modifications for us. So it sort of depends on when they’re able to finish that — finish those modifications and bring it to production.

Tesla’s management now thinks that Tesla vehicles with Hardware 3 will not be able to run unsupervised FSD; Hardware 3 has much lower memory capacity for Hardware 4, and memory capacity is needed for unsupervised FSD software to run; management is offering a trade-in for Tesla Hardware 3 vehicles to upgrade to Hardware 4; management is also considering setting up small factories to upgrade Hardware 3 on existing vehicles to Hardware 4

Unfortunately, Hardware 3 — I wish it were otherwise, but Hardware 3 simply does not have the capability to achieve unsupervised FSD. We did think at one point, it would have that, but relative to Hardware 4, it has only 1/8 of the memory bandwidth of Hardware 4. And memory bandwidth is one of the key elements needed for unsupervised FSD. And it’s just generally a thing that’s needed for AI. If you’re doing autoregressive transformer memory bandwidth, this is the choke point. So for customers that have bought FSD, what we’re offering is essentially a trade in — like a discounted trade-in for cars that have AI4 hardware. And then we’ll also be offering the ability to upgrade the car, to replace the computer, and you also need to replace the cameras, unfortunately, to go to Hardware 4.

So to do this efficiently, we’re going to have to set up like kind of micro factories or small factories in major metropolitan areas in order to do it efficiently. It’s — because if it’s done just at the service center, it is extremely slow to do so and inefficient. So we basically need like many production lines to make the change. And I do think, over time, it’s going to make sense for us to convert all Hardware 3 cars to Hardware 4 because that’s what enables them to enter the Robotaxi fleet and have unsupervised FSD.

Tesla’s research fab for the TeraFab project will begin construction this year at the company’s Giga Texas campus; management’s still working out details on TeraFab, which is a joint-venture between Tesla and other Elon Musk-related companies (xAI and SpaceX); the construction of the research fab will see Tesla spend around $3 billion, and the research fab is for Tesla to try out new ideas; SpaceX will be in charge of the initial phase of the scaled up TeraFab; Intel will be partnering the TeraFab for some of the core manufacturing technologies; TeraFab will utilise Intel’s 14A process, which is leading-edge but currently not fully mature; the TeraFab will be housing memory, logic, mask, lithography, and advanced packaging all under one roof, whereas the broader fab industry has separate facilities and companies for the different activities; management wants TeraFab to house all the different activities because they think it’s the fastest way to conduct R&D, but they are also aware it’s a long shot; management sees TeraFab as the only way to produce sufficient AI chips for the world, and not to press 3rd-party fabs on pricing; the TeraFab is also a great way for management to test out the radical ideas they have for improving AI chips

We’ve also finalized plans for the chip fab — the research chip fab on the Giga Texas campus, and we’ll start construction of that this year…

…We’re still working out the details of the Terafab deployment. In the near term, Tesla will be building the research fab on our Giga Texas campus. This is something we expect to be probably a $3 billion-ish initiative and capable of maybe a few thousand wafers per month, but it’s really intended to try out ideas, the research fab, both in terms of maybe — we have some ideas for improving the fundamental technology of how chips are made and some of the — there’s some new physics we’d like to test out. But we also want to test out the ability to see if something is working in production. So you need kind of like a few thousand wafer starts a month to make sure that a production process is sound. And then SpaceX is going to take care of like the initial phase of the scaled up Terafab. And that’s what we’ve figured out thus far…

…Intel is excited to partner with us on some of the core manufacturing technologies. So we plan to use Intel’s 14A process, which is state-of-the-art and, in fact, not yet totally complete. So — but given that by the time Terafab scales up, 14A will be probably fairly mature or ready for prime time. 14A seems like the right move…

…I think this will be unique in the world, or at least I’m not aware of any — a place where you have the lithography mask creation, the — and then logic, memory and packaging under one roof in one building. That’s about the fastest I could possibly imagine doing recursive research and development and being able to try out some pretty radical ideas, some of which have — it’s kind of long-shot stuff, but if some of these long shots pan out would be radical improvements in the way chips work…

…Terafab is not some sort of mechanism to generate leverage over our chip suppliers. It’s just literally we don’t see a path to having enough — a sufficient quantity of AI chips down the road as we scale production to high levels. Just the rate at which the industry is growing in logic, but even more so in memory, it just doesn’t — we just anticipate hitting the wall if we don’t make chips ourselves…

…I think that we do have some ideas for how to make maybe radically better AI chips. And these are kind of research ideas there — which means like long shot, but if long shot pays off, it’s maybe a giant improvement. And it’s just easier to do that if we have our own research fab and are developing our own production technologies. So — and if you look sort of long term at, say, having AI satellites, making chips for those. There’s just no way in hell the existing industry can keep up with that. It’s impossible.

Visa (NASDAQ: V)

Visa’s management believes agentic commerce will expand Visa’s market opportunity in 4 ways, namely, (1) accelerating the digitisation of commerce, (2) creation of significantly more transactions by agents, especially in a new category of commerce characterised by micro transactions, (3) accelerating the digitisation of B2B payments, with virtual cards and tokens becoming a preferred way to pay and be paid, and (4) accelerating overall GDP growth by 80-150 basis points

We believe AI and agentic commerce will expand our addressable market in 4 important ways. 

First, like eCommerce and mobile commerce before it, agentic commerce will accelerate the digitization of commerce around the world. And just like the acceleration from eCommerce and mobile commerce, Visa will benefit.

Second, agents will create significantly more transactions. Agents will intelligently split purchases across multiple transactions, optimizing price, timing and value to the buyer. And importantly, in some use cases, we expect agents will pay for their own data and resource consumption transaction by transaction and event by event, which creates an entirely new category of commerce with micro transactions.

Third, we will see accelerated digitization of B2B payments, where there is still enormous friction that AI agents can help remove. They will be able to automate payment initiation directly from invoices and contracts and manage approvals autonomously. In this context, virtual cards and tokenization will become a preferred way to pay and be paid.

And lastly, just like the advent of eCommerce and mobile commerce, agentic commerce will increase economic growth generally. Third parties estimate we are looking at a boost of 80 to 150 basis points of incremental GDP growth from AI and when GDP grows, spending grows and digital payments transactions grow.

Visa’s management believes the company is well positioned to win in agentic commerce for 3 reasons, namely, (1) the massive scale of Visa’s network, which means plenty of proprietary data to work with, (2) the tight security of Visa’s network, and (3) the high level of trust in it; Visa is a proven leader in tokenisation, and management believes tokens will become an essential element in agentic transactions; management thinks people will want their agents to pay with cards, just like how they prefer to use cards for physical and online payments; management recently launched Intelligent Commerce Connect, a network protocol and token vault agnostic on-ramp for agentic commerce; management is seeing early growth in agentic commerce transactions performed with Visa agentic tokens; management thinks the CLI (command line interface), which is effectively a chat box, is becoming a commerce platform, and cards will continue to have strong value in CLI-driven commerce transactions of all sizes; management recently launched Visa CLI as a proof-of-concept for developers to use their Visa credentials to make payments; early feedback for Visa CLI is very positive; management thinks agents will soon realise that no other payment methods, other than Visa cards, offer ease of use, broad acceptance, privacy, easy liquidity management, KYC, user security protection, and rewards; management thinks the limiting factor for agentic commerce is currently trust, which also means users will fall back on payment methods they already trust

Visa is extraordinarily well positioned to win in agentic for 3 important reasons. Our network, security and trust. Our network has enormous scale, more than 175 million seller locations, 5 billion credentials in 200 countries and territories with nearly 14,500 financial institution clients who have opted in to using this network. Payment security is only going to become more difficult and more valued. With our scale comes over 300 billion transactions annually, equating to an average of about 900 million transactions per day, and all of the data that comes with it. Visa has proven it knows how to manage transaction risk, identity risk and fraud, all enabled by this transaction data. And trust. Visa has well-established trust grounded in our standards and brand. We’ve set the standards that enable trusted payments in the digital and emerging agentic ecosystem.

And a big part of our network, security and trust are Visa tokens. Visa is a proven leader in tokenization, which is foundational in eCommerce and is set to become an essential element of trusted transactions in an agentic world.

People overwhelmingly choose to pay with cards face-to-face and online, and they will prefer their agents to pay with cards. And merchants want this, too. We recently launched Intelligent Commerce Connect, which acts as a network protocol and token vault agnostic on-ramp to agentic commerce for agent builders, merchants and enablers. Now while it’s early, we are seeing growth in agentic shopping and the emergence of early agentic commerce, real transactions with Visa agentic tokens.

And AI continues to evolve. With the AI landscape, we are seeing that Claude code and other agentic coding assistants will allow anyone to become a developer. It’s that easy to work in simple command-style tools like the command line interface, or CLI. These agentic coding assistants are a great example of how we see AI and agentic commerce increasing economic growth as they enable anyone to bring their new business ideas to life. We see a world where we will all design, build and launch digital products and experiences ourselves, engage with digital platforms and buy digital services using the CLI, or a slick consumer-friendly version of one as our interface. The CLI itself is becoming a commerce platform, and we believe that the preference and value of cards will be equally strong for all sizes of transactions, including micro transactions. A key to making this happen is enabling safe, simple and easy payments that are widely accepted by all API endpoints. We recently launched Visa CLI as a proof of concept, which shows how easy it is for a developer, soon all of us, to use their Visa credential to pay for digital services like an image, a website builder or more via the CLI. The early feedback we have been receiving from developers is very positive. And as we move forward, we plan to enable CLI commerce at scale, which means scaling the availability of command line tools and card acceptance by promulgating standards, products, rules and pricing…

…In all of these use cases, Visa cards are providing significant value. They’re easy to use, broadly accepted, integrated into the transaction flow, offer privacy, unlike most stablecoins, offer a way to manage liquidity in aggregate rather than funding millions of real-time micro transactions, offer an issuer KYC, user security protections if something goes wrong, and in many cases, cards offer rewards and benefits. We see no other payment method on earth that delivers all of these features. Buyers know this, sellers know this and soon so will agents. We expect more transactions, more value-added services and therefore, more revenue in the years ahead from agentic…

…I think the limiting factor for agentic commerce is trust. I think when we all think about ourselves as buyers and we all think about ourselves having agents go out and transact on our behalf, we are going to fall back on payment methods that we, as users, trust…

…When you think about yourself as a user, when you think about kind of who you’re going to trust your agent to make payments on your behalf, whether those are macro transactions, average transactions or micro transactions, we feel really good about our ability to win those transactions for our users using all of those capabilities.

AI is making Visa’s value-added services better; Visa’s new Large Transaction Model, which has a 5x increase in fraud value capture, is starting to be a foundational model for a variety of AI-powered fraud and risk services at the company; management has been integrating AI features across Visa’s VAS solutions; management thinks AI helps improve the differentiation of Visa’s VAS business even more; there are a variety of AI-driven products within the VAS portfolio that have helped the business perform well

Across Visa, AI is making what we do even better, especially for our value-added services. Our new Visa Large Transaction Model is beginning to act as the foundational model for a variety of AI-powered fraud and risk services at Visa. Early results have shown that it can power up to a 5x increase in fraud value capture. Our team has been integrating new AI-enabled features across our suite of VAS solutions, including the recent release of 6 dispute resolution capabilities. In fact, across all of our services, client adoption has been the fastest among AI embedded services such as Smarter Stand-In Processing and Visa Provisioning Intelligence…

…Our value-added services are highly differentiated and even more so in an AI world…

…We’ve been shipping new, especially AI-driven products in the issuing solutions space. We outperformed in the quarter in our AI-driven stand-in processing platform. We outperformed in our Visa supplier payment services platform. Those are two of the service — issuing solution platforms. In the acceptance side of the business, our Visa account updater platform outperformed. That’s one that allows merchants to automatically upstore credentials when you might have had fraud on your account and it was reissued or something like that. Look at our Risk and Security Solutions area, we saw outsized performance in VCAS, our Visa Consumer Authentication Service, or also in our VAA and VRM platforms, Visa Advanced Authorization and Visa Risk Manager. These are all products that we’ve been deploying in market, largely AI-driven products, and they’ve been driving broad-based out-performance across the value-added services portfolio.

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