Last month, I published The Latest Thoughts From American Technology Companies On AI (2026 Q3). In it, I shared commentary in earnings conference calls for the third quarter of 2026, from the leaders of US-listed technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large. 

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

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

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

Alphabet (NASDAQ: GOOG)

Alphabet’s AI investments are driving growth in Search, including commercial queries; the growth rate in queries in Search is the highest seen in the last 5 years; management is building a seamless experience across AI Overviews, AI Mode, and Search; queries in AI mode are 3x longer than traditional searches; 20% of AI Mode searches globally are multimodal, and Alphabet has crossed 50 billion visual searches per month; management is looking to bring agentic experiences to Search; many queries on AI Mode are upper funnel, which allows Alphabet to capture new, high-value commercial intent; management is also seeing that the majority of AI Mode queries are incremental, which creates more monetisation opportunities; management has found that users are comfortable with advertising on AI Mode and AI Overviews, with similar click-through rates between sponsored and organic product list items within AI Mode; management is now scaling high-quality ads within AI Overviews and AI Mode; management is taking the monetisation of AI Overviews and AI Mode global for the 1st time; AI Mode is currently monetising at a lower rate than traditional search, but management feels very good about the trajectory 

Search has strong momentum. Our AI investments are driving increased usage and overall query growth, including commercial queries. In fact, our year-over-year query growth rate is the highest it’s been in the last five years…

…We are building one seamless experience across AI Overviews, AI Mode, and traditional Search. People are using our AI features more, searching in entirely new ways. Their queries are longer, personalized, and multimodal. Queries in AI Mode are on average three times longer than traditional searches, and one in five AI Mode searches globally use voice, images, or documents. We have now crossed over 50 billion visual searches per month…

…People already put their trust in Google to get things done in their daily lives, and our leadership here remains an important differentiator. We focus first on getting these agentic experiences right with the Gemini agent for enterprise, because the bar for security, safety, and governance is extremely high. As a next step, we are bringing these features to Search…

…We think of Search holistically, a unified product spanning traditional results and AI-powered Search. On AI Mode, many of these queries are upper funnel queries, allowing us to capture new, high-value commercial intent. In addition, the majority of AI Mode queries are incremental, creating more monetization opportunities over time. Additionally, our user research shows that people are comfortable with ads on these surfaces and find them highly relevant. Click-through rates are already similar between sponsored and organic product list items within AI Mode. Based on these collective results, we are now scaling high-quality ads within AI Overviews and AI Mode to more users and queries. This includes taking our monetization footprint global for the first time. Today, we are announcing the expansion of ads in AI Mode into international markets alongside multi-turn ads in AI Mode in the U.S…

…It’s worth noting that the strong growth in AI Mode queries had an impact because of the lower monetization we were seeing in AI Mode. To be clear, we feel very good about our trajectory here. Our ad experiments have shown promising results, and we are now scaling ads within both AI Overviews and AI Mode to more users and more queries, including, as I mentioned, international markets and multi-turn ads in the U.S.

Alphabet’s 1st-party model APIs processed 32 billion tokens per minute in 2026 Q3, up 40% sequentially (was 22 billion tokens per minute in 2026 Q2), and up 4x from a year ago; Alphabet’s 2 latest models, Gemini 4 Argon and Gemini 4 Carbon, are leading the frontier; Argon’s usage in its 1st week ramped up 3.5x faster than Gemini 3, despite capacity constraints; management sees Argon performing well in finance, legal, cybersecurity, and science; customers are using Argon to untangle legacy code bases, accelerate scientific research, improve products, and more; Alphabet has used Argon internally over the last few weeks to ship 35% more code with 30% fewer errors; Gemini 4 Carbon is currently Alphabet’s strongest frontier model and it ranks No.1 on the recently updated Artificial Analysis Intelligence Index at 59; Carbon outperforms Argon across nearly all domains; management is rolling out Carbon to a trusted cyber defenders and the US government first, before expanding access; management recently introduced Genie 4, Alphabet’s most advanced world model; management sees Alphabet being at the point where AI is accelerating the company’s AI training and launch speed, and management expects continued acceleration because of RSI (recursive self-improvement); Alphabet will soon be releasing more efficient models, including the Carbon family of models; Alphabet’s Gemini Flash cyber models are frontier standard for cybersecurity and can patch vulnerabilities 2.6x more reliably than other models; the Gemini Flash cyber models will become even better when Argon and Carbon are rolled out

Our first-party model APIs are now processing nearly 32 billion tokens per minute, 40% more than last quarter and more than four times what we processed a year ago. With Gemini 4 Argon and now Carbon, which we announced today, we are excited to be leading at the frontier…

…We are seeing strong demand and usage for Argon, which we launched two weeks ago. In fact, on our developer platforms, usage in the first week ramped up 3 and a half times faster than Gemini 3, even with capacity constraints. Argon is proving to do really well in areas like finance, legal, cybersecurity, and science. Developers and Cloud customers are already using it for things like untangling legacy code bases, accelerating scientific research, robotics, discovering product improvements, and domain-specific tasks requiring in-depth reasoning. At Google, just in the last few weeks, teams across Chrome, Ads, and Search have used Argon to write and optimize code much faster, shipping 35% more code with 30% fewer errors. 

Argon is just the start of the Gemini 4 series. Today, we announced Carbon. It is our strongest frontier model with state-of-the-art cybersecurity and long-horizon capabilities. It ranks number one on the recently updated Artificial Analysis Intelligence Index at 59. Carbon outperforms Argon across almost every domain, like full-stack web development, multimodal understanding, complex knowledge work, and scientific reasoning. We are rolling Carbon out first to a subset of trusted cyber defenders before we expand access, and it is being tested by the U.S. government.

Beyond our Gemini 4 series, yesterday we announced Genie 4, our most advanced world model yet that simulates safe, realistic, interactive worlds with native audio in real-time. This is an important building block for training robots and agents…

…We are at the point where AI is helping us increase our training and launch velocity. I would expect the next 12 months to see continued acceleration because of RSI. We have more models coming soon, including more efficient versions to help developers optimize for speed and cost and to deploy across our billions of users…

…We are working on distilled versions of our Carbon models so that we can deploy it across a billion-user scale efficiently, both from a compute, from a latency standpoint, and from a quality standpoint…

…Our Gemini Flash cyber models are frontier models that automate vulnerability discovery across 20 programming languages and patch 2.6 x more reliably than other models. There is even better performance to come as we roll out Argon and Carbon.

The Gemini app is now at 1 billion users, and is Google’s fastest-growing product ever; management is attempting to bring agentic experiences to the Gemini app; the integration of the Gemini 4 models into the Gemini app has been a big step forward for Alphabet in introducing agentic experiences into the app

The Gemini app is now at 1 billion users and is the fastest-growing product in Google’s history…

…People already put their trust in Google to get things done in their daily lives, and our leadership here remains an important differentiator. We focus first on getting these agentic experiences right with the Gemini agent for enterprise, because the bar for security, safety, and governance is extremely high. As a next step, we are bringing these features to Search and also, of course, to our billion users on the Gemini app. People are already using the Gemini app to do more than ask questions. They are using it to do things, including complex multistep agentic queries. Our Gemini 4 series has been a really big step forward here and has already been handling these complex tasks really well.

Alphabet’s management is focused on providing the right agentic experience with the Gemini agent for enterprise; management thinks agents will be a unifying layer for Alphabet’s products; the Universal Commerce Protocol (UCP) for agentic commerce currently enables direct checkout for hundreds of thousands of brands; management is combining UCP with the Agent Payments Protocol for agentic commerce; management has launched Universal Cart for retailers to maintain their relationships with customers while helping to capture sales and avoid abandoned carts in the agentic commerce experience; management wants to enhance the product discovery experience for users through the agentic experience; management is confident that Alphabet can provide compelling advertising models for agentic use cases; management sees subscriptions and payments as other ways of monetising agentic use cases; management sees agentic use cases as an expansion opportunity for Alphabet; management thinks safety and trust are very important considerations for consumers when engaging with agentic experiences

We focus first on getting these agentic experiences right with the Gemini agent for enterprise, because the bar for security, safety, and governance is extremely high…

…Agents will operate as a unifying layer across our products. You will see us extend these agentic experiences beyond Search in the Gemini app to Chrome, Android, and our Workspace products as well…

…The Universal Commerce Protocol, UCP, is being rapidly adopted by retailers. Today, UCP enables direct checkout for hundreds of thousands of brands across Google, including Chewy, Nike, Tapestry, and many more. We are combining this with the Agent Payments Protocol as we build for the future of commerce. Our focus spans two fronts, unlocking agentic capabilities that act on behalf of the consumer, and designing practical tools that gives shoppers confidence and control. We launched Universal Cart this quarter to support that second track, mirroring how people actually shop today across Google. This allows retailers to maintain their relationships with customers while helping to capture sales and avoid abandoned carts…

…We also want to enhance, not just automate away the discovery experience that people frankly love in so many cases. Users like choice, and they want to consider things way beyond price, like brand loyalty, customer service and so on. We’re really architecting these agentic workflows in a way that we can intentionally design the time and the space for users to explore those choices. So simple example, an agent could respond, “Here’s the insurance product we found, and here’s another strong option, one of which is a higher premium, maybe a lower deductible, and they are currently offering a very special discount.” Given that there’s almost never only one perfect option for a decision, we’re actually confident that we can actually provide really interesting, compelling advertising models for agentic use cases. And obviously you can imagine going beyond us with subscriptions and other payment methods…

…I would view it as an expansion opportunity, right? Any time, just like a few years ago, being able to incorporate AI chat use cases in Search allowed people to ask more and do more in Search, which is what is playing out in our usage. I think agents would again be an opportunity to expand the types of problems we solve for our users… I’m excited about agents and I would view it as a new frontier, which brings in a new set of use cases to Search, and so we are excited to work through that…

…When people are approaching these agentic experiences, we clearly see people are concerned about safety and trust. It is a very high factor in how they want to use these things. Over decades, the trust we have built by delivering these experiences in a safe way across many consumer products with scale in billions of users, as well as this putting in end-to-end as we are building our models and agents, deploying them both with extensive testing, the kind of real-time monitoring we do as we deploy these at scale, I think all of that will end up being a huge differentiator.

Waymo now has more than 300 million fully autonomous miles; Waymo is now operating in 15 major cities in the USA and will be rolling out internationally soon

Waymo is on a great trajectory. We have now driven more than 300 million fully autonomous miles. Waymo now operates in 15 major U.S. cities and just recently opened up service to the public in San Antonio, San Diego, and Tampa, with planned expansions into Canada, Germany, Singapore, and Spain.

Among Alphabet’s other long-term initiatives, Isomorph is using Gemini 4 Agron for drug research

Isomorphic is using Argon to support drug design research.

Alphabet’s management continues to deploy Gemini models across Alphabet’s entire advertising infrastructure; the Gemini models are improving the performance of Alphabet’s advertising business in advertising quality, advertiser tools, and user experiences; in advertising quality, the integration of Gemini models into ads infrastructure is improving how Alphabet decodes user intent and drive ads relevance; in advertising quality, management upgraded product listing ads in 2026 Q3, which drove a 19% increase in satisfaction and a 50% drop in dissatisfaction; in advertising quality, management has delivered 18% incremental advertiser value and an 8% increase in user sentiment on Discover Ads in 2026; in advertiser tools, management is introducing a way to test different budgets and ROI targets across multiple Search campaigns in a single AI Max A/B test; in advertiser tools, management has integrated Gemini-powered AI creative tools into AI Max, and advertisers have generated over 1.5 billion campaign assets in 2026 Q3, up 7x year-on-year; in advertiser tools, Telstra used AI Max to achieve a 52% increase in conversions and 36% reduction in cost per order

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

First, ads quality. The integration of Gemini models into our ads infrastructure is improving how we decode user intent and drive ads relevance. For example, in Q3, we made the biggest update to product listing ads in a decade, adding new AI capabilities to help users make more confident purchase decisions. Already, we have seen satisfaction increase by 19% and dissatisfaction drop more than half. We are also seeing AI models enable better prediction of clicks and conversions, stronger overall performance, and deeper personalization of ads. For instance, through several innovations in personalization modeling, we delivered 18% incremental advertiser value and an 8% increase in user sentiment on Discover Ads in 2026. Overall, AI is accelerating our ads development and drives the majority of our product launches.

Second, advertiser tools. We are ensuring every advertiser, from global brands to small businesses, can seamlessly participate in our AI services. AI Max and Performance Max continue to see strong adoption. To give marketers even more control, we are now introducing a way to test different budgets and ROI targets across multiple Search campaigns in a single AI Max A/B test. To drive better performance, we integrated our AI creative tools directly into AI Max. In Q3 alone, Gemini models helped advertisers generate over 1.5 billion campaign assets, up over 7x year-over-year. For example, by using AI Max to capture long-form and multilingual queries, Telstra saw a 52% increase in conversions and a 36% reduction in cost per order, showing up in key moments however their customers search.

Google Cloud had 103% revenue growth in 2026 Q3 (was 82% in 2026 Q2) driven by growth in AI infrastructure and solutions; Google Cloud is seeing huge demand and management is working hard to bring more capacity online; Google Cloud is seeing strong demand for TPUs across a wide range of customers; Google Cloud now has 18 products with more than $1 billion in annual revenue; more than 2,600 customers have used at least 100 billion tokens in the last 12 months (was 2,000 in 2026 Q2); 75 customers have consumed at least 10 trillion tokens in the last 12 months; nearly 90% of the Fortune 100 are using Gemini Enterprise; customers who used Gemini Enterprise in 2025 Q3 have 4x-ed their spend in 2026 Q3; Google Cloud has sold more than 100 million seats for knowledge work across all its AI services; Gemini Enterprise now includes the Gemini agent that can research and answer questions, write and run code, and automate process flows; BNP Paribas is deploying Gemini Enterprise to 65,000 employees to streamline workflows; Tata Steel deployed 300 specialised AI Agents with Google Cloud in 9 months and cut service times for customers by 50%; Ulta Beauty used Google Cloud to build the Ulta AI shopping assistant, which has a 3x higher conversion rate than traditional browsing; Gemini is driving acceleration for Google Cloud’s database, analytics, and cybersecurity products; Etsy is using Google Cloud’s BigQuery and Icceberg products to perform critical data joins up to 60% faster; 

Google Cloud revenues grew 103%. Our AI infrastructure and solutions are the major drivers for Cloud growth, and our customers see us as the leading provider here, so there’s huge demand. We are working hard to bring more capacity online to meet this long-term demand. At the same time, we are seeing strong demand for our TPU systems across a wide range of customers, and we expect that to continue as we scale…

…Google Cloud now has 18 product lines with more than $1 billion each in annual revenue…

…Over the past 12 months, more than 2,600 customers have used at least 100 billion tokens, and nearly 75 at least 10 trillion tokens on Gemini Enterprise. Nearly 90% of the Fortune 100 use Gemini Enterprise, and customers who use Gemini Enterprise in Q3 2025 more than quadruple their spend with us year over year…

…Across all our AI services, we have sold more than 100 million seats for knowledge work. Gemini Enterprise now includes the Gemini agent that is deeply integrated across our entire Cloud portfolio. It can research and answer questions, write and run code, and automate process flows with one agent, one API, and a single prompt window. It gives companies strong security and cost controls and the widest choice of frontier models. Customers include BNP Paribas, who is deploying Gemini Enterprise to more than 65,000 employees to streamline workflows while maintaining strict enterprise-grade security and data governance. Tata Steel, who deployed more than 300 specialized AI agents in just nine months to enhance manufacturing safety, optimize supply chains, and cut service times for customers by 50%. Ulta Beauty, who built the Ulta AI shopping assistant, resulting in a 3x higher conversion rate than traditional browsing on their site.

Gemini is also driving acceleration for our database, analytics, and cybersecurity products. Gemini can now help users find, analyze, and visualize information from structured and unstructured data. Gemini uses our Knowledge Catalog to find the right sources and definitions of information, improving accuracy, lowering token cost, and making responses more reliable. Gemini uses our unified lakehouse to access information from multiple systems with zero copy. To respond to agentic analysis, BigQuery improved query speed over 40% year-over-year, while reducing query processing cost by over 50% year-over-year. Customers include Deutsche Telekom, which uses BigQuery Cloud Storage and Knowledge Catalog to build a unified data lakehouse across 40-plus legacy on-premise systems. Etsy, which migrated three petabytes of data to unified lakehouse. By using BigQuery and Iceberg, they now perform critical data joins up to 60% faster.

The growing risk of cybersecurity threats is also driving strong demand for our AI-powered security platform. Gemini and Google Threat Intelligence autonomously analyzes millions of external events every day with 98% accuracy. More than 95% of Wiz customers are adopting AI-powered features, and growth continues. These security tools protect critical infrastructure, including financial institutions like VanOorte and Jack Henry, and telecommunication providers such as AT&T. All of this is powered by our leading AI infrastructure, spanning our TPU systems, Axion CPUs, the latest Nvidia GPUs, and our Agent Compute Engine, which is specifically designed for AI agents.

Alphabet’s TPU 8t and 8i have up to 2.7x better training performance and 80% better inference price performance than previous TPUs; Alphabet recently partnered Meta Platforms to deliver TorchTPU, which allows users to train, serve, and deploy PyTorch models on TPUs; nearly 100 customers use TPUs today; Anthropic and Apple are both users of TPUs, with Apple performing pre-training of its foundation models with the latest generation TPUs; new frontier model companies Mirendil and Physical Intelligence are users of TPUs; Alphabet will be delivering TPUs to Oracle

TPU 8t and 8i offer up to 2.7 x better training performance and 80% better inference price performance, respectively, versus our previous generation of TPUs. To make it easy for developers to port workloads to TPUs, we recently worked closely with Meta to deliver TorchTPU, which allows users to train, serve, and deploy PyTorch models on TPUs using the tools they are already familiar with and rely on. Nearly 100 customers use TPUs today, and we are seeing strong and growing demand from many others. In addition to our work with Anthropic, both directly and via Broadcom, and public reference customers like Apple, who are scaling pre-training of their foundation models on the latest generation of TPUs, we are also working with new frontier model companies, including Mirendil and Physical Intelligence, capital markets firms including Citadel Securities and Jump Trading, universities like Caltech, and public sector service providers, including Cassava and Cirrascale. Given continued strong and accelerating demand, we have recently entered into partnerships to deliver TPU systems with both Crux AI and Oracle, with more in the works.

Alphabet had $924 billion in pre-commencement leases, and other obligations of $152 billion, at the end of 2026 Q3; these reflect maximum obligations, and the actual amounts can vary

At the end of the third quarter, we had future purchase commitments of $924 billion in pre-commencement leases and other obligations of $152 billion. These dollar amounts reflect the maximum contractual obligations. However, a number of these agreements have variable pricing and volume flexibility, which could result in lower spend.

Google Cloud had 103% revenue growth in 2026 Q3 (was 82% in 2026 Q2), driven by growth in GCP (including AI infrastructure, AI solutions, and core GCP) and TPU system sales; Google Cloud’s revenue growth was still very strong even if TPU system sales were excluded; Google Cloud operation margin was 36.2% (was 35.6% in 2026 Q2 and 23.7% in 2025 Q3); Google Cloud backlog grew 9% sequentially to $561 billion in 2026 Q3 (was $514 billion in 2026 Q2); most of Google Cloud’s backlog are GCP contracts and just over 55% of the backlog is expected to be recognised as revenue in the next 2 years; a gigawatt of TPU systems appear to cost $35 billion; when Alphabet sells TPUs to 3rd parties, the inventory is built only after a contract is signed; Alphabet receives cash for the TPU system sales after the systems are delivered

The Google Cloud segment delivered record growth in the third quarter. Cloud revenues were up 103% to $30.7 billion, driven by strong growth in GCP, with strength in AI, including both AI infrastructure and AI solutions, and core GCP. TPU system sales were also a meaningful contributor. GCP accounted for the vast majority of the revenue generated in Q3. The Cloud revenue growth rate, excluding the impact of TPU systems, was again incredibly strong. Cloud operating income was $11.1 billion, more than tripling year over year. Operating margin increased from 23.7% in the third quarter last year to 36.2%. The third-party bridging capacity, which we discussed on our prior call, came online only at the very end of Q3 and therefore had limited negative impact to our margin in the quarter. Google Cloud’s backlog was $561 billion in the third quarter, with the majority of the backlog related to typical GCP contracts. We expect to recognize just over 55% of the total backlog as revenues over the next 24 months…

…[Question] If a gigawatt of TPU full system costs around $35 billion for the compute and all the components around it, what percent of that are we actually recognizing as revenue?…

…When we sign an agreement with a customer to sell TPU systems, once we sign that agreement you’ll first see that showing up in our backlog. Then we start building inventory. Think about this from a CapEx versus cash perspective. We build the inventory ahead of getting the revenue stream in. I think I’ve alluded to this on the previous call. You see inventory balances go up, where previously inventory balances primarily were some of our hardware products. Now those TPU systems that are not yet sold will be showing up, so you see cash going out, and we’re building up the inventory base. As we start delivering and billing customers for these TPU systems, then you see cash coming in…

…We procure components to build inventory, we only do that after we have signed contracts. All of the inventory for TPU systems are associated with committed contracts.

Alphabet’s management expects TPU system sales to continue scaling in 2026 Q4, followed by a meaningful ramp in 2027; management expects Google Cloud margins to be pressured in the near term because of the use of bridging compute capacity that will mostly come online in 2026 Q4; management continues to expect 2026 capex to be $195 billion to $205 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 continues to expect 2027’s capex to be much higher than 2026’s; management expects Alphabet’s free cash flow to remain under pressure because of AI-related capex, but the AI-related capex is expected to generate an attractive ROIC that meaningfully exceeds Alphabet’s cost of capital

In Google Cloud, we expect TPU system sales to continue to scale in Q4, followed by a meaningful revenue ramp in 2027. With respect to Cloud margins, given the supply-constrained environment, we started utilizing third-party compute capacity at the end of Q3 and will ramp usage in Q4. We expect margin headwinds in the near term as we utilize this capacity…

…Our full year 2026 CapEx guidance range of $195 billion to $205 billion remain unchanged. We continue to expect our CapEx to increase significantly in 2027 and will provide further details on our Q4 call. 

In terms of expenses, the significant increase in our investment in technical infrastructure will continue to weigh on the P&L in the form of higher depreciation expenses and related data centers operation costs such as energy…

…Let me say a few words on how we think about return on invested capital and free cash flow. We are in a unique position to be able to monetize our AI investments across many products that reach billions of users and enterprises. Our AI investments in Search are driving our highest query growth in half a decade. Google Cloud has generated tens of billions of dollars in AI revenues year to date, and Google One is our fastest-growing subscription product, driven by demand for AI plans. Throughout this investment cycle, we expect to generate an attractive ROIC that remains meaningfully above our cost of capital and anticipate generating strong cash flow from operations. Our sizable investments in technical infrastructure and inventory build of TPU systems as we scale that business will put pressure on free cash flows.

Alphabet’s management saw a step-change in the company’s productivity with the introduction of the 3.8 Flash internally; the introduction of Argon internally has led to a huge inflection point in productivity, and Carbon will be a meaningful step up from Argon

[Question] Sundar, on a recent podcast, you said that 2027 could be the golden age of productivity for Google because of the new class of models and how they will integrate with your other products?

[Answer] We definitely started seeing a step change, I would say, with 3.8 Flash internally as our models got better at long-horizon tasks, agentic coding, et cetera. But definitely, it’s seeing a huge point of inflection once we’ve started deploying Argon internally. It is one of the most meaningful internal productivity levers we have seen on software engineering, directly tracking high-quality code that’s getting checked in, the rate at which it’s getting checked in, reducing the number of rollbacks. But more importantly, people are tackling massive problems as well as we are beginning to see innovation on the product and the consumer experience side from deploying those models. But all this happened only about four weeks ago. We are now deploying Carbon internally, and Carbon is a meaningful step up from Argon.

Amazon (NASDAQ: AMZN)

AWS grew 39.3% year-on-year in 2026 Q3 (was 36.7% in 2026 Q2), the 6th straight quarter of acceleration, and is now growing at its fastest pace in 19 quarters; AWS’s backlog is $578 billion in 2026 Q3 (was $496 billion in 2026 Q2), up triple-digits year-on-year; AWS customers are taking up compute capacity as soon as it’s installed; AWS’s AI business has rapidly improving operating margins; AWS’s non-AI (core compute and storage) revenue grew at the fastest rate in several years in 2026 Q3; the operating margin of AWS’s AI business is currently well ahead of where the non-AI business was at a similar age, despite heightened costs and massive depreciation, but the AI business’s current margin is still lower than the overall AWS business; management has continued to accelerate the capex payback for AWS’s AI business and they have even more conviction now compared to a few years ago that the AI investments will yield strong revenue, free cash flow, and ROIC (return on invested capital); management sees AWS having a strong flywheel where its (1) functionality and performance drives customers to run apps and store data on AWS, which is core-AWS (2) leading to customers running AI workloads on AWS where their data and apps are, (3) leading to more usage of AWS because AI workloads require core-AWS; management sees the flywheel as driving attractive long-term economics for AWS regardless of the level of compute supply; management still sees 85% of global IT spend being on-premises and that the spend will flip to the cloud over the next 10-20 years, as AI-driven migration capabilities make the transition easier, and being on the cloud benefits enterprises; overall demand for AWS is exceptionally strong; management sees enterprises as still being early in using inference at scale, but yet inference is already half of AWS’s AI revenue; AWS is now at a $184 billion revenue run rate (was $169 billion in 2026 Q2); management thinks AWS can reach $1 trillion in revenue in the next 5-10 years, and management has a plan to fund this growth; AWS’s AI revenue grew triple-digits year-on-year in 2026 Q3; big chunks of AWS’s capacity continue to be reserved well in advance, and management has line of sight to some of 2029; management has very good visibility into AWS for the next few years

Despite lapping a comparable year-over-year period where we added a lot of capacity, AWS grew 39.3% year-over-year, accelerating for the sixth straight quarter, our fastest growth in nearly five years. Backlog is $578 billion, growing triple digits year-over-year and well-diversified. We’re installing an AI accelerator about every 14 seconds, and almost as fast as we install capacity, customers are gobbling it up. Our core compute and storage revenue grew at their fastest rates in several years.

Our operating margins have long been strong on our core offering, but rapidly improving on the AI side too, well ahead of where they were on core at a similar time of evolution, even with heightened component costs like memory and significant depreciation from the CapEx we’re investing. We continue to accelerate our CapEx payback and have even more conviction than we already had that the investment we’re making in AWS will yield a much larger revenue and free cash flow-generating business than we would have imagined a couple of years ago, with strong return on invested capital…

…We are happy with the progression in both core and AI margins, and as Andy said earlier, they are ahead of where the core was at a similar point in time, but they are still lower, so there is going to be that mix issue….

…In AWS, having the leading functionality, security, and operational performance can’t help but drive customers to run their apps and store their data in AWS, which can’t help but drive AWS AI demand because customers prefer their production inference to be near the rest of their data and apps, which can’t help but drive core AWS demand because AI uses core for work like reinforcement learning, post-training, and tool usage, which can’t help but drive reinvestment back into our broader AWS functionality…

…It also yields very attractive long-term economics for us, regardless of supply scarcity…

…On the core side, it’s important to remember that the vast majority of global IT spend, about 85%, is still on premises, and we’re in a 10- to 20-year cycle where that equation will flip to the cloud. Conventional wisdom has been that linchpin workloads like mainframes or storage area networks would never move to the cloud, but these workloads are moving as AWS has built unique cloud substitutes and AI-driven migration capabilities to make this transition much easier. Enterprises get the benefits of less CapEx, lower operating costs, faster innovation, better developer productivity, and data accessibility to more easily leverage AI…

…Overall demand for AWS capacity remains exceptionally strong. Enterprises are still very early in using inference at scale, and yet inference already accounts for well over half of AWS’s AI revenue and growing, and we have a lot of training revenue as well…

…AWS is a $184 billion annualized revenue run rate business, and yet the opportunity remains extraordinary…

…We believe that AWS can grow north of 40% in each of 2027 and in 2028 and be a trillion-dollar revenue business in the next five to 10 years…

…Would it be more convenient if this type of acceleration and opportunity happened over 25 years and you could spread the CapEx over 25 years? Yeah, it might be more convenient, but that’s not the world that we live in, and that’s not the opportunity. We can fund this plan. I think it will forever change the size of Amazon…

…Our AI offerings are gaining traction with customers, with AI revenue growing triple digits year-over-year…

…Despite how much capacity we are providing, the big chunks of capacity that people take, the hundreds of megawatts or sometimes up to a gigawatt of capacity, those big chunks are reserved well in advance because you cannot leave a gigawatt of capacity hanging around. We have very good visibility into 2027 and 2028 and some of 2029 too, with respect to what customers want in those really big chunks of capacity…

…We have very good visibility into the next few years on what AWS looks like. 

Amazon has acquired DuckLabs, and DuckDB will become the SQL dialect of AWS Storage and Analytics; DuckDB will enable customers, for the 1st time in analytics, to enjoy zero migration or portability costs between query engines; management thinks the zero migration and portability cost feature will help AI agents to efficiently query data

With our acquisition of DuckLabs, DuckDB will become the universal SQL dialect of AWS Storage and Analytics, simplifying data processing with a modern analytics engine that is fast, simple to use and ubiquitous, and where customers will be able to write SQL queries once and then execute using any AWS analytics service. For the first time in analytics, customers will have zero migration or portability costs between query engines. This is a big deal, and you can imagine how it will also help agents do efficient querying as well.

Bedrock is AWS’s fully-managed service for companies to build upon frontier models; Fortune 500 companies are spending 7x more on Bedrock year-on-year in 2026 Q3 with token volume growing 17x; Bedrock is the fastest growing multi-billion dollar service in AWS’s history; Bedrock recently added managed versions of the latest Kimi, GLM, and SpaceX models; the data of customers that use Bedrock is secure and private; management thinks that the future of AI will (1) consist of 6-8 models that are good enough for nearly all workloads, and each of them will have their strengths, and (2) have customers that use multiple models; management sees Bedrock has being relevant for the future of AI; management thinks the Western world are very hungry for Western open sourced models; most of Bedrock’s workloads today are being powered by Trainium

We continue to expand the capabilities of our inference service, Amazon Bedrock, and it is leading to remarkable growth, with Fortune 500 companies spending nearly 7x more on Bedrock year-over-year and total Bedrock token volume growing nearly 17x year-over-year. Bedrock is the fastest growing multi-billion dollar service in AWS’s history. We also just added Bedrock-managed versions of Kimi, GLM, and SpaceX models…

…We have designed Bedrock so customers’ data remains secure and private. Inputs and outputs are never shared with model providers or used to train base models. When customers tune a foundation model, we create a private copy specifically for their use, and data is fully encrypted both in transit and at rest…

…One of the lessons that we’ve learned, I’ll say for 20+ years in offering technology to customers, is that there is not one tool to rule the world. People always assume that whatever is in the lead early on is going to be the only thing or things that people use, and it’s never the case. It’s not true in database, it hasn’t been true in analytics. In the beginning of AI, everyone said the only framework people use is TensorFlow. It turns out PyTorch is much bigger now. It’s not going to be true with models either.

I’ll say in the next two to three years, I think there are going to be 6- 8 models that are about as good as each other, or at least good enough for 95%+ of the workloads. Some will be better at reasoning, some will be better at coding, some will be better at visual pieces, some will be lower latency, and some will be lower cost. Most customers are going to use multiple models, and many will use multiple models in the same generative AI application. What customers really want to do, and we see this validated over and over again when we talk to customers, is they want a service that has really broad selection of those frontier models, and that has very good price performance, and that has very strong security and access control. They want that service so that they can decide what combination of models they want to use and to swap them back and forth as they leapfrog each other, as different constraints of their applications require them. That’s what we see with Bedrock…

…I think the Western world is very hungry for additional open models that are Western open models…

…The majority and most of Bedrock today is being run on top of Trainium. We do that in meaningful part because we really like the price performance that we are getting, and it allows us to both keep prices as low as possible for customers, as well as our ability, we like the margins associated with it.

Amazon’s management is seeing frontier models surface previously unknown software vulnerabilities, and at a much higher frequency; management has built AWS Continuum for customers to address software security; AWS Continuum already has established enterprises as customers; AWS Continuum is being integrated into popular vibe coding tools such as Claude Code, Codex, and Kiro; AWS Continuum is enjoying unprecedented customer interest for a new security service; some people think 20% of all AI spending in the next 1-2 years could be in security

As frontier models get more powerful, they are surfacing software vulnerabilities previously not found and at unprecedented rates. We built AWS Continuum to discover, prioritize, validate, and remediate code vulnerabilities at machine speed. It is already being used by customers like Capital One, IBM, Infor, Luminate, and Robinhood. In Q3, we announced that we will integrate it into the tools where code is being written, Anthropic’s Claude Code, OpenAI’s Codex, and AWS’s Kiro. The interest from customers exceeds anything we have ever seen for a new security service…

…There are some people who think that the total amount of tokens used over the next year or two, 20% of it is some significant percent of it may end up being just security.

AWS customers are using AgentCore to deploy agents at an accelerating rate; AgentCore has tens of thousands of customers, and they deployed 2x more agents in 2026 Q3 than the prior 3 quarters combined; management is seeing companies build their own agents and use turnkey agentic services; AWS has agentic services that customers love; management thinks every modern enterprise production application will be rewritten to be agentically capable

Agents continue to be deployed at accelerating rates, many of which are being built on AgentCore’s building blocks. Tens of thousands of customers currently use AgentCore, deploying 2x more agents with it in Q3 than the prior three quarters combined. In addition to building their own purpose-built agents, most companies will also use turnkey agentic services, and we offer several that customers love. Kiro for coding, Amazon Connect for contact center agents, and AWS Transform for migrations. They are all iterating quickly and picking up customer momentum. As is Amazon Quick, our intelligent AI work companion, which is growing very rapidly as large enterprises like Mondelez, EchoStar, 3M, Southwest Airlines, and DXC Technology are choosing Quick because it is purpose-built for work with no compromises on customer data…

…I believe virtually every modern enterprise production application will be rewritten to be agentically capable, because that’s the way you’re going to have to deploy if you want to move quickly or you want to actually guard in the right way against some of the security risks.

AWS’s chips business grew triple-digits year-on-year in 2026 Q3; revenue commitments for Graviton and Trainium in 2026 Q3 both increased more than 20x year-on-year; management sees AI as being a tailwind for AWS’s Graviton CPU chip, because AI workloads, including agent tool use, requires CPUs; management sees Graviton as the strongest CPU chip for AI, offering 30%-40% better price performance than alternatives; customers using AWS’s 1st-party chips saved more than $2 billion in 2026 Q3; management recently open sourced TorchNeuron for developers to run existing PyTorch code on Trainium without rewriting code; the backlog for prior generation of Graviton chips still grew more than 100% sequentially in 2026 Q3 even after Graviton 5 was launched; short-term capacity for prior generation Trainum chips are selling at a premium compared to 2026 H1; management intends to sell Trainium to 3rd parties outside of AWS in late-2027; Amazon’s chips business is helping AWS to optimise capacity and improve its cost efficiency; management is seeing that enterprises are eager to save on price-performance as they deploy inference into production, and this is where Trainium has an advantage; management believes that lowering the cost of inference is important for enterprises to start deploying inference at scale, and Trainium is an important part of this view; selling Trainium externally will lead to more revenue in the short term, but selling Trainium through AWS will lead to more absolute revenue and free cash flow over time; managemen thinks selling chips is a very different business than running a cloud

Our chips business continues to grow triple-digit percentages year-over-year, and revenue commitments for Graviton and Trainium have each increased more than 20x year-over-year. AI continues to be a tailwind for Graviton because post-training reinforcement learning and agent tool use is mostly done on CPUs versus AI accelerators, and Graviton is the strongest CPU chip, offering up to 30%-40% better price performance than alternatives. Customers using our custom silicon saved more than $2 billion in Q3 alone, and we just open sourced TorchNeuron so developers can run existing PyTorch code, which most already use on Trainium without rewriting a thing. As we launch new generations of chips, demand for prior generations remains strong. After our launch of Graviton5, backlog for prior generation Graviton instances still more than doubled quarter-over-quarter. Short-term capacity for prior generation Trainium instances sell at a premium compared to the first half of the year. With Trainium demand so strong, customers continue encouraging us to make it available outside of AWS. We intend to do so late in 2027…

…Our investments in custom chips and custom networking, along with software and process improvements, are helping us optimize capacity and build a more cost-efficient infrastructure…

…Increasingly, what you’re finding right now is most customers, every customer that uses AI at scale, but particularly enterprises now as they’re starting to deploy more inference into production, they are very eager to save on price performance. Trainium, with the meaningful advantage it has in price performance versus other alternatives, is very compelling to people…

…I think if we all want enterprises to be able to use inference the way we all believe they will, we have to keep working on taking the cost of inference down. That is a huge mission for AWS. It’s one of the significant reasons why we’re building our own custom silicon in Trainium, is that we just really believe it’s incredibly important for customers to be able to use inference in a much more cost-effective way…

…If you sell externally, you are able to realize more revenue in the short term because you get to recognize the revenue as soon as it leaves your inventory. On the flip side, if you decide to use it inside your cloud in the form of EC2 instances or Bedrock, you actually will end up driving more absolute revenue and free cash flow from those chips because you get to use them over five to six years and drive revenue for a longer period of time…

…We believe we are going to have a very significant business selling chips externally, and we are going to start in late 2027. We will be thinking through that balance, I think, probably every year. They will both be very significant. Our ability to create price, performance, scalable chips is going to be a very significant strategic advantage for us at scale, both in our own cost structure as well as in building a separate business and selling external chips. Selling external chips really is a very different business than your cloud infrastructure business.

Amazon’s management continues to expect capex for 2026 to be $220 billion (capex was $128 billion in 2025, and $83 billion in 2024); the majority of Amazon’s capex is for AWS; management expects capex to increase meaningfully in 2027; management has clear line of sight to strong financial returns on the company’s capex

We still expect 2026 cash CapEx of approximately $220 billion, the majority for AWS, and expect to spend meaningfully more in 2027 with a clear line of sight to attractive financial returns,

Robotics and AI are delivering measurable gains in Amazon’s fulfilment network; Amazon doubled its fleet of robotic arms in 2026; Amazon is developing foundation models for robotics to deliver useful robotics at scale; Amazon has over 1 million robots in its fulfillment network; Amazon’s safety record has improved in the past 4-5 years, driven by robotics; management sees the opportunity to deploy robots for delivery for the last 100 yards; Amazon’s new designs for its fulfilment network are robotics-heavy

Robotics and AI are delivering measurable gains through productivity and employee safety, which we’ll expect to compound in the future. In 2026, we doubled our fleet of robotic arms like Cardinal and Sparrow. We’re also developing robotics foundation models that enable faster programming and teach robots increasingly complex physical tasks, with the goal of delivering useful robotics at scale. Looking ahead, we see meaningful opportunities to improve productivity across our global fulfillment network and lower our cost to serve while continuing to get products to customers faster…

…We have over 1 million robots now in our fulfillment network, and while they’re having very significant impact on our cost structure and on our productivity and on safety. If you look at our safety numbers in our fulfillment network, they’re at record low numbers. We’ve made huge progress over the last four to five years, and robotics has been a big piece of that…

….I think we have the opportunity to also assist on some of the delivering of the last 100 yards to customer doors…

…If I look at the new designs that we have coming in our fulfillment network, including our expansion of same-day facilities, they are very heavy on robotics.

Over 400 million Amazon customers (350 million in 2026 Q2) have used Alexa for Shopping (previously known as Rufus) in the last 12 months; customer interactions with Alexa for Shopping was up 570% year-on-year in 2026 Q3 (was up 5x in 2026 Q2); US customers using Alexa for Shopping spend 50% more per order than those who don’t; management anticipates working with other agents (Meta Platforms’ Muse?), but they think Alexa for Shopping will be the primary shopping agent on Amazon

Over 400 million customers have used Alexa for Shopping, our agentic shopping assistant, in the last 12 months, with customer interactions up 570% year-over-year. U.S. customers using Alexa for Shopping are spending 50% more per order than those who don’t…

…Though we anticipate working with horizontal agents, we believe Alexa for Shopping is going to be the primary shopping agent customers use to shop Amazon.

Amazon’s management thinks agentic shopping from horizontal agents has no meaningful impact on Amazon’s advertising business; management will soon launch Amazon Ads Agent, which brings all of Amazon’s advertising offerings into a single AI-first platform; management has launched full-funnel campaigns in Amazon Ads Agent, where advertisers provide initial inputs, and Amazon’s AI does the rest

The natural question is whether agentic shopping from external horizontal agents is meaningfully impacting Amazon Ads. The answer is no. Few customers are doing much shopping this way yet, as the experience is still not great nor more convenient than coming to a retailer directly to research or one-click order there…

…Headlined by Amazon Ads Agent, which brings together our full advertising offering under a single AI-first platform. Backed by trillions of shopping, browsing, and streaming signals, it supports advertisers at every step with insights, answers, and the ability to optimize and act on their behalf. We also launched full-funnel campaigns in Amazon Ads Agent, bringing sponsored ads, display, video, and streaming TV into a single campaign. Advertisers provide initial inputs, including budget and duration, and Amazon’s AI plans, executes, and continually optimizes the rest.

Intel (NASDAQ: INTC)

Intel’s Granite Rapids CPU is ramping really fast while the Clearwater Forest CPU is shipping for revenue; Intel’s CPU products is seeing significant demand for 2027, driven by the compute needs of agentic infrastructure; Intel’s Diamond Rapids CPU is on track to ramp in 2027; Diamond Rapids is seeing strong interest from customers for inference and agentic workloads; Intel’s CPUs will be deployed by Oracle in its AI buildout; Intel is facing some headwinds in meeting the extremely high AI-driven demand for its CPU products; the headwinds include an industry-wide shortage of advanced organic substrates for EMIB (embedded multi-die interconnect bridge) packaging, and a factory incident in Israel; management sees CPUs as far more efficient at performing tasks compared to GPUs; management sees the TAM (total addressable market) for CPUs growing from $40 billion today to $200 billion in 5 years, because of agentic workloads; management sees competition in the CPU market for Intel, but thinks Intel has unique strengths, namely, (1) strong pipeline of CPU innovation, (2) willingness to customise CPUs for custom workloads, (3) the only advanced semiconductor fab in the USA; management sees demand signals from customers continue to strengthen, but supply is expected to be constrained up to 2028; Intel’s CPUs play a central role in enabling agentic workloads, and management expects growth of more than 30% in its server CPU units in both 2026 and 2027, with higher growth to be achieved if supply were available; management expects the DCAI (data center and AI) segment to achieve revenue in 2026 that’s $10 billion higher than initial expectations, driven by broad-based growth and purpose-built silicon

Within our server business, we are making steady progress on our roadmap. Granite Rapids on Intel 3 has become one of the fastest-ramping server products. Clearwater Forest, our first Intel 18A server products, is shipping for revenue. Demand for 2027 is significant, especially in CPU dense agentic rack scale infrastructure. Diamond Rapids, our first Intel 18A-P server product, is sampling and on track to ramp in 2027 with strong interest for inference and agentic workloads. We were pleased to recently announce an agreement with Oracle Cloud Infrastructure to deploy our latest generation CPUs to support their AI build-out.

I also want to acknowledge headwinds limiting our progress to meet this extremely high AI-driven demand. First, Intel 3 yield and cycle time continue to improve, but I am demanding significant execution from the team. Second, even as our Intel 3 wafer outputs ramps quickly, an industry-wide shortage of advanced organic substrates for our EMIB packaging is also impacting our ability to scale output. Finally, early this month, an unfortunate and tragic accident led to an extended shutdown in our Israel factory. While the teams have worked quickly to address the situation, it will impact our Q4 10-nanometer server shipments…

…Increasingly, energy efficiency is becoming the most important metric for the AI infrastructure build-out. While GPUs are great at thinking tasks, deterministic CPU are far more efficient at doing tasks. While GPU reasoning query run as high as multiple watt-hours of energy, a single CPU query could cost only joules of energy. Applying even conservative estimates to the total number of digital agents and agents served per CPU, it is easy to anticipate a massive dollar and unit TAM for CPUs over the coming five years. By our estimates, server CPU TAM, largely driven by agentic workloads, could increase as much as five times from where it is today, from $40 billion to more than $200 billion over five years…

…We are, of course, mindful of fierce competition underway as nearly every fabless company race to meet this unprecedented demand with their own offering. But Intel is uniquely positioned to benefit from this demand in three ways. First, as a custodian of x86, the most widely deployed ISA in the world, we are doubling down on x86 innovation, have an exciting Tick Tock roadmap of CPU innovations in the pipeline. In addition, for the first time, we are making fully modular, verified instances of our x86 CPUs optimized for the custom workloads available to our customers. Second, as one of the just three advanced node semiconductor foundries in the world, and the only one domiciled in the U.S., we are well positioned to support not only our own demand but also the demands of wider fabless industry. We are also open to building any CPUs at scale for our customers fully agnostic to underlying ISA. Third, as modern computing platforms increasingly become complex system in the package, our leadership in advanced packaging offers highly differentiated solution, especially to meet the requirements of the massive AI infrastructure build-out underway…

…Demand signals from our data center customers continue to strengthen. We expect the industry to remain supply constrained throughout 2027 and into 2028…

…The adoption of agentic AI is broadening the role of CPU compute. AI workloads are moving beyond prompt-based responses toward multi-step reasoning, orchestration, and autonomous persistent action. Our CPUs play a central role in enabling these workloads. As a result, we continue to see strong demand for our DCAI products. We now expect industry server CPU units to grow more than 30% in both 2026 and 2027. Growth would be higher in both years if additional industry supply were available…

…Based on our current outlook, DCAI revenue is on track to finish 2026 nearly $10 billion above expectations entering the year. Q3 growth was broad-based across hyperscale, enterprise, and Neo Cloud customers. We also continued to see strong momentum in purpose-built silicon, with revenue increasing more than 50% sequentially and more than doubling year over year.

Intel’s management sees a tripling of Intel’s purpose-built silicon business over the next few years after tripling over the last 3 years 

Purpose-built silicon remains our fastest-growing business segment, tripling over the last three years and poised to triple again over the next few years. We see further growth opportunity with both existing and new potential customers, including AI labs, and expanding our IP portfolio to meet the needs of a wider array of customers…

Intel’s management recently announced the company’s D-Series inference-optimized GPU; the new GPU has encouraging early feedback and performance is tracking well

We recently unveiled our Intel GPU D-Series inference-optimized GPU. Early feedback is encouraging. Performance is tracking well. This is new market for us, and it will take time to scale, but customer engagements are progressing towards firm commitments.

Intel’s PC (personal computer) business is seeing a slowdown because of memory prices and a shortage of memory chips because of AI-related demand for memory chips; management expects the slowdown in the PC business to last for a few quarters

In recent weeks, our customers began signaling a PC slowdown based on the memory prices and availability. Year-to-date system endpoint prices have increased significantly, placing additional pressure on already limited IT budgets. The pace of this PC correction has accelerated over the last two months as our OEM partners are aggressively managing inventory to match the softer sell-through. We expect the current environment to persist until memory availability and prices begin to normalize. While the next few quarters will be challenging for the PC market, our roadmap continues to strengthen, and our long-term outlook remains positive…

…The industry continues to navigate one of the most significant supply shortages in the history, driven by the unprecedented demand for super intelligence.

Intel Foundry’s Intel 3 and Intel 18A yields both increased in 2026 Q3, with Intel 18A’s yield improving at a double-digit rate; wafer output of both Intel 3 and Intel 18A exceeded management’s expectations; Intel 18A-P is on track for launch by end-2026 for Intel’s internal products; management is engaged with a few highly strategic customers on Intel 18A-P; management is confident Intel 18A-P will be an attractive node for external Intel Foundry customers; Intel 14A is on track; management recently released Intel 14A’s 0.9 PDK (process design kit); management is pleased with Intel 14A’s progress with multiple potential customers; management is pleased with Intel Foundry’s progress with EMIB and EMIB-T for advanced packaging for internal use; both EMIB and EMIB-T have significant external customer traction; management is seeing robust output for HBI (hybrid bonding interconnect); Intel Foundry is bringing additional capacity online, ithe US and Asia; Intel Foundry is still overwhelmingly serving Intel and not external customers; Intel 3 costs improved 15% from 2026 Q1, while volume increased mid-teens sequentially; Intel 18A costs have fallen by 40% since 2026 Q1, and its yield has improved ahead of internal projections; management expects Intel 3 to be a profitable node for Intel Foundry in 2027; management thinks Intel Foundry will have some external customers to announce in the next 2 quarters for advanced packaging

Intel Foundry execution continued to improve consistently. During the quarter, Intel 3 and Intel 18A yields both increased again, with Intel 18A improving at a double-digit rate. Wafer output on both nodes exceeded expectations. Intel 18A-P remains on track for launch by year-end across our internal product portfolio, and external customer engagement continues to build. We are engaged with a couple of highly strategic customers, and I am increasingly confident that our Intel 18A-P will be an attractive node for external foundry customers. Intel 14A also remains on track. In October, as committed, we released our industry-standard 0.9 PDK to select customers. It is our highest quality 0.9 PDK, meeting industry standard yield and performance criteria at release. As is standard practice in the foundry business, I have consistently maintained that we will not disclose customer details. That being said, I remain highly encouraged by the progress we are making with multiple potential customers on 14A.

On advanced packaging, we are pleased by the progress we are making with EMIB and EMIB-T for our internal products. Both continue to see significant external customer traction as well. We continue to improve on execution, working with our supply chain to secure the capacity to meet the needs of our advanced packaging customers. We are also seeing robust progress in the readiness of HBI, which is an enabler of 3D chip stacking for our upcoming server products. Across our manufacturing network, additional capacity continues to come online. Intel 3 tool installation is progressing well in Ireland, as is in the ramp of the 18A in both Oregon and Arizona, including Fab 62, our second high-volume 18A fab. In New Mexico and Malaysia, we are adding capacity to support strong customer ramps for advanced packaging next year. We are also seeing robust progress in the readiness of HBI, which is an enabler of 3D chip stacking for our upcoming server products.

Across our manufacturing network, additional capacity continues to come online. Intel 3 tool installation is progressing well in Ireland, as is in the ramp of the 18A in both Oregon and Arizona, including Fab 62, our second high-volume 18A fab. In New Mexico and Malaysia, we are adding capacity to support strong customer ramps for advanced packaging next year. In New Albany, Ohio, the team recently achieved important construction milestone with installation of the first major structural truss. And we continue to evaluate unique and highly differentiated opportunity to accelerate our Ohio build-out…

…Intel Foundry revenue was $6.7 billion, up 16% sequentially, driven by better-than-expected growth on Intel 3 and Intel 18A. External foundry revenue was $411 million, an increase of $118 million sequentially, primarily due to a non-recurring customer program…

…Intel 3 costs improved approximately 15% from Q1 levels, while volumes increased in the mid-teens sequentially. Intel 18A costs have declined approximately 40% since Q1, reflecting continued yield and cycle time improvements. Intel 18A yields continue their upward trajectory to industry-standard levels ahead of our internal projections…

…If you look at Intel 7 today, kind of 10 nanometer 7, that’s already a profitable node to us. Intel 3 is going to be profitable next year for us…

…We are going to have couple of customer ready to announce in next two quarter. Clearly, we always want to wait for the customer, if they want to announce, we will respond to that.

Intel’s management continues to expect capital expenditures to be flat in 2026 (around $20 billion); management expects capital expenditures to be up strongly in 2027; management has increased firm purchase orders to equipment suppliers by 40% sequentially in 2026 Q3; despite the expectation of much higher capital expenditure in 2027, management is still committed to deleveraging in 2027; management will only spend on capital expenditure if there’s clear evidence of customer demand; most of Intel’s capital expenditure will be on equipment

Our 2026 gross capital expenditures remains on track to be $20 billion, and we expect 2027 gross capital spending to be up strongly. We have made good progress securing the equipment required to support our growth plans with firm purchase orders to equipment suppliers increasing nearly 40% sequentially. We’re also accelerating clean room build-outs, including Fab 62 in Arizona, and exploring opportunities to accelerate in Ohio. Despite this increase in capital spending and working capital, we remain committed to deleveraging in 2027. We will remain disciplined as we invest, and we will not spend a dollar of capital unless we have confidence in the health of our products and process technologies and clear evidence of customer demand…

…We have, I think, a significant ramp in equipment spend this quarter, and it is expected to be even more significant next quarter. A lot of what we are spending on now is really equipment. We have made the investments. I would call it, in a lot of cases, putting the finishing touches on some of the space and most of the spend is going towards equipment, with the only exception, we are still in the heavy construction phase of Ohio. But outside of that, it is really about equipment.

Mastercard (NYSE: MA)

Mastercard’s new Digital Risk Protection product helps identify vulnerabilities in an organisation’s digital environment; Digital Risk Protection has nearly 800 enterprise customers after being launched for a few months; Mastercard’s enhanced risk offering combines RiskRecon and 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); management is seeing AI change the demand for Mastercard’s security solutions

Within cybersecurity, we continue to scale one-to-one platform sales, but even more so new network products like our newly launched Digital Risk Protection. This capability helps organizations identify vulnerabilities in their own digital environments. After only a few months in market, it now serves nearly 800 enterprise customers. Additionally, our enhanced third-party risk offering brings together RiskRecon and Recorded Future capabilities in one solution. It helps identify, prioritize, and respond to risks before they become business disruptions. AI is not only changing the demand for our security solutions, it’s also changing how commerce happens.

Mastercard’s management is seeing how AI change the way commerce happens; management sees 2 forms of agentic transactions, machine-to-machine, and traditional e-commerce; management thinks both forms of agentic transactions are significant opportunities for Mastercard; management thinks agentic transactions could eventually lead to an entirely new addressable market of micro-transactions; management sees consumers starting to use agents to discover products and make purchases; it’s still early days in consumers’ using agents for purchases, but management expects the trend to ramp up in the holiday season; management is seeing agentic transactions ramping up at impressively weekly growth rates; management believes 4 things are necessary for agentic commerce to function at scale, namely, trust in the payment, the agent, the ecosystem, and ubiquitous reach; management sees Mastercard as having solutions to solve for all 4 elements; management is helping merchants be ready to accept agentic commerce with the new Agent Connect solution; Shopify’s Shop Pay is already using Agent Connect’s features; Mastercard signed a partnership with OpenAI in 2026 Q3 for Agent Connect; management thinks that agentic commerce solutions that take VCNs (virtual card numbers) are not a good long-term solution

AI is not only changing the demand for our security solutions, it’s also changing how commerce happens. The foundation I described earlier, broad participation, global acceptance, and trusted capabilities, positions us to help shape the next evolution of commerce. That includes agentic commerce, where a buyer uses AI agents to discover, select, and purchase a product or service. There are two forms, machine-to-machine and traditional e-commerce. Combined, they create significant opportunity for Mastercard, from more transactions to new B2B carded payments as businesses utilize agents. Eventually, an entirely new addressable market of micro-transactions, all the while creating more opportunity for our services from fraud to longer-term potential in insight and loyalty. Consumers are already beginning to use agents to discover and purchase products. We expect that trend to build as holiday shopping ramps up and to continue well beyond the season. It is still early days, and the numbers are not yet significant in the grand scheme. As the AI agentic assistants have emerged, agentic transactions are ramping up at impressive weekly growth rates. We’re now seeing the early stages of consumer adoption.

For this type of agentic commerce to function at scale, we need four things. Trust in the payment, the agent, the ecosystem, and ubiquitous reach. Mastercard has built capabilities for each of these four. For trust in the payment, tokenization is embedded in Agent Pay and backed by the franchise rules everyone expects today. For trust in the agent, Agent Pay ensures agents are verified and authorized by the buyer and that the merchant and issuer are aware. For trust in the ecosystem, we bring transparency and continuity through the payment flow. Verifiable Intent captures the buyer intent, and our recently launched Agentic Trust Intelligence solution helps banks and merchants evaluate transactions initiated by any AI agent. That allows these transactions to occur wherever Mastercard is accepted. That is the unparalleled and growing reach I talked about earlier.

At the same time, we are helping merchants ready themselves and be more discoverable to agents with our new Agent Connect solution. Many partners are putting all of this into practice. For example, Shopify, through Shop Pay, is using Agent Pay and our network tokens to make agentic payments more secure and seamless by preserving the integrity of the consumer’s credential end-to-end. Shop Pay is already available on multiple agentic platforms with more to follow. With OpenAI this quarter, we signed a broad strategic partnership that includes the use of Agent Pay and goes well beyond. Our agreement establishes collaboration across payments, joint services growth, and AI transformation…

…It is in everybody’s best interest to use Agent Pay and equivalent. We are not supporting solutions that take a one-time VCNs out there as you’re fronting a transaction. That fundamentally breaks a transaction that is important from a consumer perspective. They made a choice. It’s important from a rewards perspective, from a control perspective, and so forth. I think we’re at this turning point where everybody is pushing in the same direction. We are very clear that fully tokenized all the way through agentic pay or an equivalent is the way to go.

Microsoft (NASDAQ: MSFT)

Microsoft’s management announced the biggest update to Copilot in 2026 Q3 (FY2027 Q1); management thinks of Copilot as the OS for work; automated tasks on Copilot has nearly doubled since July; daily Copilot users are up 50% sequentially in 2026 Q3 (FY2027 Q1) and up 5x year-on-year; user satisfaction of Copilot is up 20% sequentially in 2026 Q3 (FY2027 Q1); 90% of Copilot queries are on auto mode, which dynamically routes tasks across models; customers with more than 50,000 Copilot seats increased 8x year-on-year in 2026 Q3 (FY2027 Q1); Copilot is helping to drive momentum with E7; management is bringing Dynamics 365’s business logic and capabilities into Copilot; management introduced 13 new Copilot skills across Dynamics 365 in 2026 Q3 (FY2027 Q1)

We announced our biggest update to Copilot to date this quarter, bringing together chat, co-work, autopilot, and code into one unified experience. It’s one product experience across commercial and consumer and comes with all of Office built in. We think of Copilot as the new OS for work. Customers are increasingly turning to Copilot to get real work done. Automated tasks has nearly doubled since July. Daily users are up nearly 50% quarter-over-quarter and over 5x year-over-year. User satisfaction improved nearly 20% quarter-over-quarter to another all-time high, and 90% of Copilot queries use auto mode, which dynamically routes tasks across models optimizing for quality, speed, and cost. These improvements are translating into larger purchases. The number of customers with more than 50,000 Copilot seats increased over 8x year-over-year, including Capgemini, HCLTech, Hitachi Group, Truist Financial, and U.K. Revenue and Customs Department. Just last week, Volkswagen announced plans to deploy Copilot to more than 330,000 employees, one of the largest deployments to date in Europe.

We’re also seeing strong momentum with E7, which brings together Copilot, E5, Entra, and Agent 365. Thousands of customers in every industry have purchased the suite to date, including Amadeus, BlackRock, Fiserv, New South Wales Health Department, and ZEISS…

…We are extending this vision of Copilot as the new OS for work, bringing the business logic and capabilities of Dynamics 365 directly into Copilot and helping every organization stand up their own infinite SaaS factory. This quarter, we introduced 13 new Copilot skills across Dynamics 365, which enables customers to access CRM and ERP capabilities directly in the flow of work.

Microsoft’s business model now includes consumption, which management thinks expands the company’s TAM (total addressable market) and average revenue per user (ARPU); management thinks the consumption model opens up business-funded demand, compared to the subscription model, which addressed IT-funded demand; Copilot customers on usage-based billing grew 75% sequentially in 2026 Q3 (FY2027 Q1); credit consumption of agentic features on Github has more than 2x-ed since usage-based billing was introduced

Our business model is expanding from per-seat subscriptions to include consumption, expanding our TAM and ARPU. The subscription brings increasingly capable everyday intelligence to every user. Typically, IT funds this, consolidating frontier advances for everyone across the organization. As agents take on higher-value work, consumption opens up business-funded demand, with spending tied directly to the value these agents deliver. The number of Copilot customers actively consuming credits through usage-based billing grew over 75% over the quarter…

…Credit consumption from agentic features on GitHub has more than doubled since usage-based billing went into effect.

Agent-generated PRs (pull requests) on Github have grown 6x in the 6 months ended 30 Sep 2026; agents now open 1/3 of all PRs on Github; management is rebuilding the core Github architecture to address agentic use; management recently announced Copilot Fusion on Github; Copilot Fusion brings multi-model orchestration capabilities, delivering frontier quality up to 40% faster and at 77% lower costs; Github Advanced Security revenue grew 50% year-on-year; Copilot Fusion is ranked highly by Artificial Analysis’s latest benchmark for coding agents

When it comes to software development, all major coding agents have shown up to work on GitHub. Agent-generated PRs have increased 6x over the past six months, and agents now open more than a third of all PRs on the platform. We are very hard at work on reliability as agentic activity on GitHub soars. At GitHub Universe this morning, we shared how we are expanding capacity and rebuilding the core Git architecture for the agentic wave. Also at Universe, we announced broad availability of Copilot Fusion, which is all about bringing multi-model orchestration to software development. It delivers frontier-level quality up to 40% faster and at up to 77% lower cost…

…GitHub Advanced Security revenue increased over 50% year-over-year as customers use its AI capabilities to scan entire repos and fix vulnerabilities in real time…

…In fact, this morning, Artificial Analysis had what I think is the real benchmark, which is the coding agents using our Fusion, and I think we were number 2 or number 3 there, which is awesome to see. Which is you can now get frontier performance 45% faster, 75% cheaper. Who would want to not take that?

Foundry is becoming more important to Microsoft’s growth; Foundry customers reached 120,000 in 2026 Q3 (FY2027 Q1); Foundry revenue more than tripled year-on-year in 2026 Q3 (FY2027 Q1), driven by efficiency gains across Microsoft’s GPU fleet; Foundry helps customers optimise across quality, cost, and performance across models; the number of Foundry customers in 2026 Q3 (FY2027 Q1) using multiple models to build agents grew 5x year-on-year; Foundry’s revenue from open source models increased 10x year-on-year in 2026 Q3 (FY2027 Q1); Foundry customers can use Microsoft’s harness behind Copilot; Rolls-Royce used Foundry to build an agentic system to manage its supply chain; management thinks enterprise AI workloads will be very dynamic in terms of switching between models

It starts with Foundry, which is becoming an increasingly important driver of our growth. Foundry helps organizations build learning loops grounded in their business context and deploy agents safely at scale. We now have over 120,000 Foundry customers, with revenue more than tripling year-over-year. Foundry’s model choice and feature set, coupled with its deep integration across our stack, is driving differentiation. Azure OpenAI directly benefited in the last quarter with big advances in OpenAI models. When you couple this with our model choice built into Foundry, customers are able to build solutions optimizing across quality, cost, and performance. In fact, the number of customers building agentic solutions with multiple models increased 5x year-over-year, and revenue from open source models on Foundry increased 10x…

…Foundry revenue more than tripled year-over-year, with upside driven by efficiency gains across our GPU fleet that allowed us to allocate additional capacity to customers, as well as higher revenue per token that we benefited from in the quarter…

…Beyond models, we are making the harness behind our own Copilot experience available to customers in Foundry. It gives customers a Microsoft-managed way to build agents that reason, use tools, complete complex, long-running work while preserving their choice of model…

…Rolls-Royce deploys a system to really manage all of their maintenance of whatever their jet engines with 30,000 parts are pretty complex supply chain, both planning, execution, inventory management system, all built on Foundry agentic systems…

…A lot of customers are saying, hey, look, take the Unilever one. It is their system. They will ultimately take even the traces after having done even a bunch with frontier models, and they just want to train their own model. So it can be open weights or, in this case, it was MAI model. So I think all of that needs to be as options that are available. Then depending on pricing, depending on the need of the customer, they will exercise it. There is a temporality to it. It is never like, oh, I start one place and that is where I stop. It is like, I may start at the frontier. I may go to an open weight. I may go to an MAI model. I may go back to the frontier for the next drop for a few months, come out. That is how I think how dynamic these workloads are.

Microsoft’s management thinks the company is making good progress with its 1st party models; management’s goal is to deliver capabilities comparable to frontier models within 3-6 months of their release, at a fraction of the cost; Microsoft’s MAI-Cyber model now powers over 40% of the scans in Microsoft’s MDASH code scanner at 18% lower cost per scan; MAI-Transcribe is the cheapest and most accurate transcription model in the world; MAI-Code-Flash has 3 million users in Github Copilot in September 2026; more than 4,000 customers have used Microsoft-Decision-1, a model for structured decision-making; Microsoft recently completed pre-training of MAI-Base-2, which has 4x-12x better training compute efficiency compared to other open weight models; Unilever’s agentic system was built with MAI models

We also continue to make rapid progress with our first-party models. Our goal is to advance the cost to outcome frontier, which means delivering capabilities comparable to leading models within three to six months of their release at a fraction of the training cost, thereby allowing us to transfer those cost benefits to customers. You see this in our products today. MAI- Cyber now powers over 40% of the scans in our agentic MDASH code scanner at 18% lower cost per scan. MAI- Transcribe is the most accurate and cheapest transcription model in the world, supporting Dragon Copilot’s multilingual workflow. MAI- Code- Flash has three million users in GitHub Copilot last month alone, helping developers get more out of their AI credits. Microsoft-Decision-1 is our new model for structured decision-making, outperforming both LLMs and other decision models in latency and quality. Over 4,000 customers, including Atos Group, Equinor, and RBI, have already used it in the first month since its release. We recently completed pre-training MAI- Base- 2, which outperforms other open weight models we tested with 4x- 12x greater training compute efficiency. This base model will be used in the upcoming MAI models across thinking, code, and cyber…

…Take the Unilever one. It is their system. They will ultimately take even the traces after having done even a bunch with frontier models, and they just want to train their own model. So it can be open weights or, in this case, it was MAI model.

Fabric customers grew 50% year-on-year in 2026 Q3 (FY2027 Q1) to 44,000 (was 40,000 in 2026 Q2); 19,000 customers now use both Fabric and Foundry (was 17,000 in 2026 Q2); management is finding that customers are rapidly deploying Microsoft’s AI-optimised databases; PostgreSQL’s growth accelerated for 4 consecutive quarters in 2026 Q3 (FY2027 Q1); the number of PostgreSQL customers who also use Foundry was up 90% in 2026 Q3 (FY2027 Q1); Microsoft has WorkIQ, which makes an entire organisation’s work knowledge available to an agentic workload; Microsoft IQ combines Work IQ, Fabric IQ, and Foundry IQ, and it has more than 60,000 customers in 2026 Q3 (FY2027 Q1), up 25% sequentially 

We now have 44,000 paid Fabric customers, up more than 50% year-over-year…

…Over 19,000 customers now use Foundry and Fabric as enterprises connect and operate agents over real-time operational, analytical, and unstructured data.

Customers are also rapidly deploying our AI-optimized databases, including PostgreSQL, SQL DB, Hyperscale, Cosmos DB, to give agents fast, secure access to the data and context they need. PostgreSQL growth accelerated for the fourth consecutive quarter, and the number of PostgreSQL customers also using Foundry accelerated to 90% year-over-year.

We also have one of the most important databases for any enterprise, which is the knowledge underneath Microsoft 365, spanning people, their relationships, documents, emails, meetings, chats, projects, and other artifacts. Work IQ makes this context available to every agentic workload. With Microsoft IQ, we are bringing together Work IQ, Fabric IQ, and Foundry IQ as a unified context for agentic systems. Nearly 60,000 customers are accessing context across Microsoft IQ, up 25% quarter-over-quarter.

Microsoft has Agent 365 for AI observability and it has over 50 million agents in the registry in 2026 Q3 (FY2027 Q1), up 30% sequentially; Microsoft’s management recently expanded Perception for customers to secure agentic insiders; management recently introduced guardian agents that monitor agentic activity for unsafe actions; management is seeing encouraging feedback on Perception; customers have used Microsoft Purview to audit more than 7.6 billion agent activities in 2026 Q3 (FY2027 Q1), up 5x sequentially; management recently introduced Microsoft Execution Containers to govern and contain agents; agentic products from OpenAI, Anthropic, and Meta Platforms are starting to adopt Microsoft Execution Containers

 With Agent 365, we provide a platform for AI observability, enabling customers to manage, secure, and optimize the performance and cost of their agents. Over 50 million agents are now in the registry, up over 30% quarter-over-quarter…

…Last week, we expanded Perception with new capabilities to help customers secure and contain agentic insiders, including expanded agent discovery across local and cloud environments, dynamic permissions that enforce least privileged access for each task, and continuous risk assessment. We also introduced teams of guardian agents that continuously monitor agent activity to help prevent unsafe actions before they occur. We are seeing encouraging feedback on Perception overall from preview customers, including KPMG, Nationwide Building Society, Northern Trust, Celentis. More broadly, we are helping customers secure and govern AI use across the enterprise. Customers have used Microsoft Purview to audit over 7.6 billion agent activities to date, up 5x quarter-over-quarter…

…We introduced Microsoft Execution Containers, a policy-driven execution layer that governs and contains agents across Windows and other platforms integrated with Agent 365 for governance and observability. Agents like OpenAI Codex, Anthropic Claude Code, Meta Muse are adopting MXC, and customers like Meta are starting to deploy it across their enterprise environments.

Microsoft’s management is building the company’s compute infrastructure to be fungible, such that it can be dynamically allocated to the highest value opportunities; Microsoft added 24 new data centers across 5 continents in 2026 Q3 (FY2027 Q1); Microsoft has reduced dock-to-live times for new GPUs by 20% sequentially in 2026 Q3 (FY2027 Q1); management has increased the throughput for Copilot workloads by over 50% sequentially in 2026 Q3 (FY2027 Q1); Microsoft’s AI infrastructure continues to utilise chips from NVIDIA, AMD, and itself (Maia); Microsoft has docked the first Vera Rubin GPUs from NVIDIA; Microsoft’s Maia 200 is now delivering 1.7x more tokens per watt across Teams and Copilot, with Maia 300 expected to drive a further 40% more tokens per watt compared to leading GPUs

We are building against a growing book of business with a fungible fleet that allows us to dynamically allocate capacity to the highest value opportunities across compute, data, Foundry, and Copilot. Accelerating demand led to us adding 24 new data centers across five continents this quarter. We are bringing capacity online faster, reducing dock-to-live times by about 20% quarter-over-quarter for new GB300s in our biggest regions, which make up the majority of the GPU deployments. At the same time, we continue to get more from the infrastructure we already have by optimizing across the data center design, silicon systems, software, and models. For example, we increased the throughput for Copilot workloads by over 50% quarter-over-quarter.

We continue to scale our fleet with the latest innovations from NVIDIA, AMD, alongside our own Maia systems. We have docked our first NVIDIA Vera Rubin GPUs and are very excited about it. Maia 200 is already delivering up to 1.7x more tokens per watt across the AI experiences in Teams and Copilot, and Maia 300 will drive further gains, delivering 40% more tokens per watt compared to the leading GPU-based systems.

Microsoft is working to create new PCs that can run powerful AI models locally

As more capable models run locally, we see an opportunity to make it more intelligence effectively unmetered. We are working with the broader hardware ecosystem to bring these capabilities to a new generation of PCs across AMD’s Ryzen AI 400 Series, Intel Core Ultra Series 3, Qualcomm Snapdragon X2 Series. Earlier this month, we also announced new PCs powered by NVIDIA’s RTX Spark and DGX Station for Windows. With these new devices, you can run last year’s Frontier models on your laptop and last quarter’s model at your desk.

1/4 of Microsoft’s cloud and AI-related capex in 2026 Q3 (FY2027 Q1) are for long-lived assets, while the other 3/4 are for CPUs and GPUs; management sees the potential for each dollar of Microsoft’s capital expenditure to support more revenue and higher returns over time

Capital expenditures were $45.2 billion, as some third party capacity deliveries we expected in Q1 were delayed. Roughly three quarters of our CapEx was for short-lived assets, primarily CPUs and GPUs, including higher memory and storage costs, as we continue to invest in the infrastructure required for our customers to build and scale AI applications. The remaining spend was for long-lived assets. This quarter, finance leases were $7.4 billion. As a reminder, we recognize the full principal value of finance leases in CapEx at the time of lease commencement. Cash paid for PP&E was $39.4 billion…

…As we continue to improve the efficiencies of our infrastructure, each dollar of CapEx has the potential to support more revenue and higher returns over time.

Azure’s growth was broad-based across business-size and workloads; Azure grew revenue by 48% in 2026 Q3 (FY2027 Q1) (was 43% in 2026 Q2); Azure’s revenue growth was better than expected because of efficiency gains in the GPU fleet, which drove outperformance in Foundry, and in the CPU fleet; Azure’s growth in 2026 Q3 (FY2027 Q1) benefitted from higher token pricing; management is starting to see the breadth of enterprise AI workloads starting to emerge; Azure’s demand continues to exceed supply

All-up Azure growth was broad-based across both enterprises and small and medium business segments and spanning both core cloud services and AI infrastructure….

…Azure revenue was ahead of expectations and increased 48%, even with the 3P capacity delays noted earlier, driven by Foundry and improved CPU monetization. Foundry revenue more than tripled year-over-year, with upside driven by efficiency gains across our GPU fleet that allowed us to allocate additional capacity to customers, as well as higher revenue per token that we benefited from in the quarter. Across our CPU fleet, engineering work enabled us to identify and monetize underutilized capacity this quarter, helping us serve more of the growing demand with our existing capacity. The timing of engineering efficiencies like these, as well as 3P GPU lease deliveries, our own capacity build-outs, and ARPU changes from model mix can create quarterly variability in the year-over-year growth rate…

…We had higher token pricing than we expected in the quarter. With some high-quality models that we are able to take advantage of and see good margins on do benefit us in the quarter. That did happen in Q1…

…I was even in Europe just last week, and it was great to see the quality of the agentic systems that are just broadly getting deployed. When Unilever deploys what is their automated order taking and trade coupon dispute management stuff, all as a fully agentic touchless system. Rolls-Royce deploys a system to really manage all of their maintenance of whatever their jet engines with 30,000 parts are pretty complex supply chain, both planning, execution, inventory management system, all built on Foundry agentic systems. RBI, which is an Austrian bank, showed me 20+ business processes end to end. They were talking about whether it is in for credit or what have you, how they are able to now do that so much more optimally. So the breadth, the quality, their ability. Oh, by the way, the multi-model nature of it. They start with a frontier model. Many of them are, in fact, the Unilever one, they just dropped an MAI model trained using our traces. So even the degree of sophistication of what I think of as model optimization, workload optimization, token optimization, was just great to see. So the breadth is starting. Finally, it does feel like the enterprise workloads.

They feel like to me, like the database applications enterprises built in the past that I grew up with are happening now when it comes to AI. In fact, if anything, some of the shiny object stuff is waning, which is it is not about the next big thing. They are really more about, wow, can I build a system? Can I own the weights? Can I manage my costs? Can I have reliability? What about security? So in that sense, the enterprise breadth motion is starting…

…In Azure, we expect revenue growth of 49%- 50% in constant currency, with continued efficiency gains across our CPU and GPU fleet, helping to offset some 3P GPU as-a-service contracts that have been delayed to H2. Customer demand continues to exceed available supply. 

OpenAI accounted for 11% of Microsoft’s revenue in 2026 Q3 (FY2027 Q1)

[Question] At the same time, you have OpenAI at 11% of revenue in the quarter.

Visa (NASDAQ: V)

Visa’s management is seeing AI be one of the top 3 focus areas of its clients; empowering agentic commerce is one of management’s top priorities; management thinks agentic commerce requires trust, and that the trust barrier can be solved by using a Visa credential each time an agentic commerce user pays or gets paid; management is currently seeing a limited number of agentic transactions, even with the introduction of Muse and Instinct; Visa has nearly 50 AI partners going live with Visa Credentials and management expects the number to double in the coming months; Visa is collaborating with Anthropic to bring its commerce blueprint to Visa’s merchant network; management thinks B2B is an area for fast adoption of agentic commerce, and Visa has already launched with several partners; Ramp and Airwallex are using Visa Intelligent Commerce to provide B2B agentic commerce solutions; Visa Intelligent Commerce provides trust for agentic commerce users, trust for agents, and trust for the transaction; cross-border e-commerce volume was up 17% in 2026 Q3 (FY2026 Q4), up from 16% in 2026 Q2, driven by retail, including spending on AI platforms; management thinks that agentic commerce will bring incremental opportunities for Visa, but it will take some time to show up; Visa Intelligent Commerce’s role is to ensure that issuers, acquirers, and other ecosystem participants are ready to enable agentic transactions at scale

It continues to be among the top three focus areas for our clients, alongside AI and cybersecurity…

…One of the key areas of innovation for Visa has been empowering agentic commerce. It is clear that in agentic, trust is the biggest barrier to scale. We believe the best way to break through that barrier is with one simple but powerful idea. Every time someone pays or gets paid, it is with a Visa credential. As we look at the albeit limited number of agentic transactions today, Visa credentials are being used by consumers, businesses, and agents. We have nearly 50 AI partners live and in production, including more recently, Shopify’s Shop Pay, with that number expected to double in the coming months…

…I think both Muse and Instinct experiences are good examples of that. I think it is still very early. If you just look at just even the user numbers that are public for Muse, it is still very early in terms of the number of consumers that are doing this, and certainly in the big scheme of things in our overall business, it is a very small amount…

…We are also collaborating with Anthropic to bring their commerce blueprints to our merchant network, combining the intelligence of Claude with the trust, security, and global reach of the Visa network. One of the areas we have identified for fast adoption of agentic has been B2B, and we have had several partners launch and expand offerings. Ramp’s agentic bill pay use case is now generally available, and as Ramp businesses use Visa Intelligent Commerce to automate bill payments volume is growing. Airwallex is testing Visa Intelligent Commerce-enabled credentials and integrating them into their native wallet, Airwallex. BILL’s payment agent is piloting Visa Intelligent Commerce to pay invoices, and Billtrust’s supplier portal will identify the agent and accept payment. Our Visa Intelligent Commerce solution is enabling all of this. It provides trust for the user with credentials, agentic tokens, and transaction identifiers, trust for the agent with the Trusted Agent Protocol and Agent Directory, and trust for the transaction. Once we have verified the user and agent identity, we need to make sure that the purchase instructions are clear by capturing consumer-authenticated intent. Of course, like on any Visa transaction, our rules clarify the roles and responsibilities and processes should something go wrong. It is clear that agentic commerce is here, and Visa’s role is clear: delivering trust…

…Cross-border e-commerce volume was up 17%, more than one point above Q3, primarily driven by retail, including spending on AI platforms…

…[Question] I wanted to ask kind of a follow-on question related to how you are investing in agentic commerce and all the new money movement capabilities. As these flows continue to develop, how much of an opportunity do you expect it genuinely be incremental to Visa versus potentially a migration of existing card volumes?

[Answer] We do have conviction that there is real incremental opportunities. But as we think about the next four quarters in 2027, it all remains very small in the big scheme of the enormous size of our business. A couple other notes in terms of where we see incremental opportunities. One, you mentioned some of the work we have been doing on Visa Direct and things like that. Those are big incremental opportunities. Two, when you think about machine-to-machine payments and the CLI as a commerce interface, there is a lot of incremental opportunities there. Then just more broadly, in the big scheme of things with agentic commerce, are agents finding more buying opportunities for us? Are agents making more purchases at more seller locations because they are optimizing based on value, speed, delivery, whatever it is important to the user? Those are opportunities. I think we remain very excited and have conviction about the opportunities, but I think we all just need to realize that the development of all of this is still quite early…

…[Question] I want to hit on this a little bit more on the agentic network token. Our understanding is that this token is sort of a bolstered version of the network token. It’s more of a scoped network token, if you will. It will have all the programmatic aspects, meaning a time window, merchant category code, dollar amount. It will have the upfront intent, as you mentioned in your prepared remarks. All of that gets kind of bundled up into the agentic network token. The question is, how ready are the issuing banks, and what do they need to do in order to say, “Yes, I’m willing to have an agent issue an agentic network token for one of my customers.” Related, what about the merchant acquirers that are out there, the PSPs? Are they ready to accept the tokenization handshake, if you will?

[Answer] That was a great summary of a lot of the objectives of Visa Intelligent Commerce and our agentic tokens. In short, we are doing the work across the ecosystem to ensure that issuers, acquirers, and other ecosystem participants are not only ready but are ready to enable these transactions at scale. We know how to do this work. If you look at our history, even just with tokenization pre-agentic commerce, this is the work that we did broadly across the world. I think I mentioned in my prepared remarks now that we have tokenized e-commerce transactions in 200 countries and territories around the world. We know how to do this work to enable the ecosystem. I also just want to come back and build on your points around these agentic tokens and just put it in context of what we are delivering through Visa Intelligent Comme

Visa’s new agentic-AI-driven way of working has made big impacts on its initiatives for stablecoins; the Visa Stablecoin Platform enablement for OUSD was designed, built, and shipped in seven weeks with 12 people with the new agentic-AI toolchain and would have taken much longer otherwise; more than 60% of Visa’s global product development and engineering teams have adopted the new agentic-AI-driven way of working; management intends for 100% of the teams to adopt this new way of working; Visa strengthened its defenses in FY2026 with the Visa Vulnerability Agentic Harness, which has been open sourced; Visa has used AI to deliver over 350 product releases in FY2026, up 25% year-on-year

I spoke last quarter about the fundamental changes to the way that we build and ship products enabled by utilizing our agentic toolchain. For example, the Visa Stablecoin Platform enablement for OUSD was designed, built, and shipped in seven weeks with 12 people. This would have taken much, much longer and required many more people without utilizing our agentic toolchain. Today, more than 60% of our global product development and engineering teams have adopted this new way of working with the intent of reaching 100% this year…

…This year, we tested and strengthened our defenses through our Visa Vulnerability Agentic Harness, which we then open-sourced to the world…

…We demonstrated our ability to significantly accelerate product development with the true birth of tokenized agentic commerce transactions powered by Visa, with blockchain infrastructure and platforms designed for payments, with the increased utilization of AI across our business to deliver over 350 product releases, up 25% year-over-year.

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