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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