Last week, I published The Latest Thoughts From American Technology Companies On AI (2026 Q2). In it, I shared commentary in earnings conference calls for the second 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 second 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

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

Airbnb (NASDAQ: ABNB)

Airbnb’s management has rebuilt the company into an AI-native one; management thinks AI is the best thing to happen to Airbnb, as it has allowed the company to reduce the time from concept to launch by as much as 60%, and increase the features and improvements shipped in 2026 H1 by 80% compared to 2025 H1; AI has accelerated improvements in Airbnb across search, sign-up, checkout, and payments, leading to more conversion of traffic into bookings; management has introduced AI-generated listing highlights; management is using AI to surface review highlights; management will introduce AI home comparisons later in 2026; management sees AI as an existential risk to Airbnb but it ended up being a good thing for Airbnb, especially after the hire of CTO Ahmad Al-Dahle, and now management thinks Airbnb is one of the most AI-native companies around outside of the frontier labs and hyperscalers; management thinks the AI-native nature of Airbnb will enable it to enter many new businesses within and outside of travel; management thinks being AI-native will not affect Airbnb’s cost structure; Airbnb does not need major capital investments to become AI-native; management expects Airbnb’s inference cost to be much lower compared to incremental revenue that can be generated; Airbnb has not been tokenmaxxing, as management has been thoughtful about the outputs of the company’s token-usage; in Airbnb’s updated guidance, management expects higher expenses because of AI spending, but the company’s margins are also expected to increase

We’ve rebuilt Airbnb from the ground up to be an AI-native company…

…AI is transforming how we execute and build products. Said simply, AI is the best thing to ever happen to Airbnb. Today, we’re building, testing, and iterating faster than we could just a year ago. Across some of our key initiatives, we’ve reduced the time from concept to launch by as much as 60%. Compared to the same six months last year, we’ve increased the number of features and improvements we shipped this year by nearly 80%. The acceleration from AI allowed us to make hundreds of improvements across Airbnb for hosts and guests…

…I’ve talked in past quarters about Project Y, which is our innovation blueprint, where hundreds of improvements compound over time. AI is accelerating this work across search, sign-up, checkout, and payments. By reducing friction across the guest journey, we are converting more traffic into bookings, and that’s become one of the biggest drivers of our growth. We improved search and discovery, making it easier for guests to find and book the right home, hotel, service, or experience, and it’s meaningfully improving conversion.

We also introduced AI-generated listing highlights so guests can quickly understand the key details about a home. We also launched AI-powered review highlights, surfacing what guest reviews say about a home’s location, amenities, and more. Later this year, we’ll introduce AI home comparison, allowing guests to compare homes side by side before booking…

…Last year, I told our company that AI is an existential risk to us. It was the only existential risk to this company. Now, policy is a risk, but it’s not an existential risk. It’s a risk that we will manage forever. The existential risk to everyone was AI. Is AI good for you? Is AI bad for you? I think the moment of truth happened this year. Moment of truth happened. First, we hired our CTO, Ahmad Al-Dahle. He was the leader of Meta Llama models. He came in, I think we went from a company that was a middle-of-the-pack company for AI to a leader in AI, at least amongst companies that are not frontier labs or hyperscalers. I think we are amongst the most AI-native companies now in all of Silicon Valley. I think because of that, this allows us to go into many new businesses in travel and eventually beyond travel that we might not have been able to go into except for the opportunity that AI affords us…

…[Question] With AI helping drive faster velocity of innovations, I was hoping to get your perspective on how this transition to AI native could impact your product costs and if there are any operational adjustments you’re making to help minimize the impact.

[Answer] It won’t affect us that much… We do not need to make any major capital investments. We are not buying up a whole bunch of GPUs. Second, the inference costs of Airbnb are de minimis relative to the ROI of our business model. Right? We’re not in the business of information where we’re trying to monetize. Our transactions are very high-dollar transactions, if AI can just increase our conversion rate just a little bit, the inference cost is so outweighed by the amount of money we make on that increased ROI. I think that what you’re seeing is the cost of tokens to develop products and the inference costs to run the models pales in comparison to the incremental revenue we generate and the incremental output or throughput we’re seeing…

…We’re not so-called token maxing, which I think is this thing where I think all these CEOs at the beginning of the year have this mandate. “I want to see everyone use AI,” with a vanity of have people use as many tokens as possible. Luckily, I have a great technology leader in Ahmed, we’ve been really, really rigorous and thoughtful about it’s not about how many tokens you use, it’s about the throughput of your product and the quality of your product designs and how much you’re shipping…

…In the updated guidance that we provided, it obviously does assume a material increase in terms of the AI spend over the course of the year. I would note that, yes, we are expanding margins while absorbing that increased cost.

AI is making it easier for property owners to become Airbnb hosts; AI is helping Airbnb improve the pricing information and hosting tools provided to hosts; management thinks Airbnb’s AI tools will for pricing will be very powerful

AI is also making it easier to host. We know that as hosts are more successful when they have the right price, the right insights, and the right tools, and AI is helping us improve all three. We made it easier for hosts to set competitive prices and get more bookings. We also gave hosts more actionable insights to help them improve their listings and increase their earning potential. We’re rolling out AI tools that help new hosts get started faster and better understand their pricing and earning opportunities…

…Most people don’t have a single price. They have different prices for every day, and the best way to price your home, like a hotel, is to have different prices on different days, and for those prices to be dynamically changed. I think that it’s very possible that, right now, hotels have very sophisticated pricing management. They have entire teams of people doing that. I don’t think anyone is going to be better than AI at doing this. I think that our models are going to be very, very powerful, and I hope in the future, hotels can even use that.

Airbnb’s management thinks AI is transforming the company’s customer support; Airbnb’s AI assistant is now available in more than 50 languages; the AI assistant has resolved 45% of issues without a human agent, and at much faster times; management will introduce AI voice support later in 2026; the AI assistant helped Airbnb reduce customer support costs per booking by 16% year-on-year in 2026 Q2; management expects the AI assistant and AI voice support to further reduce Airbnb’s customer support costs per booking

AI is also transforming customer support. Our AI assistant is now available in more than 50 languages. Nearly 45% of issues that start with our AI assistant are now resolved without a human agent, while delivering much faster resolution times. Later this year, we will begin introducing AI voice support, extending the experience to phone call…

…In Q2, customer support costs per booking declined about 16% year-over-year, driven in part by improvements by our AI assistant. We expect those costs to continue to decline as our AI assistant resolves more and more issues, and of course, as we bring it to voice.

Airbnb’s management is able to use AI to understand whether a user wants homes or hotels

We have really, really good personalization, and we know now with our personalization, and really driven by AI, whether someone wants to see just homes, just hotels, or both.

Airbnb’s management started testing AI Search recently on a tiny percentage of the company’s overall traffic; the test results for AI Search have so far been extraordinary; AI Search will initially be turned on only by a toggle, and management expects Airbnb to take months to instill a new habit among users; management sees AI Search as having 4 major natures, namely, (1) the ability for users to search with natural language, (2) users receiving responses in natural language, (3) visual titles, and (4) AI-generated personalised highlights; management envisions AI Search to be conversational, visual, and personalised; management expects AI Search to meaningfully improve Airbnb’s conversion rates

on AI search. Good news, we are beginning to put it in test this month. That test is going to be a very small % of our traffic, and based on those results, we are going to then begin to expand it to more traffic over the course of this year. I just want to point out that the tests that I’ve seen, I think, are extraordinary. They’re really great. That being said, we just have to remember that people come to Airbnb, often, most people, a handful of times a year, and they have an expectation that they see a search box with a location. It’s going to take some time, months and months, to retrain the customer. The way we’re initially going to roll it out is the default is going to still be the core search. Above, you’ll see a toggle. Once you’ve turned the toggle on, you’re going to be able to try the new AI search. We’ll have to see how it converts. I think for people who toggle it on, it’s going to convert very, very well. We don’t want to impose that on everyone…

…AI search, you can actually think about as really three or four major features. One is the search input. I can type in natural language whatever I want. The second thing is it can essentially respond to you in natural language. Rather than just saying, “300 search results,” it can respond to you in natural language. Then the title. The titles could actually be AI-generated, and they can be conversational, as if you’re reading a chatbot, but much more visual. Then you get to the product description page, and the highlights are AI-generated in real time, personalized to you. You go down the page, you have a question, you can ask the PDP through AI. You see the entire journey, not just AI search, is going to be powered by AI. What this will feel like is it’s going to feel as, or almost as conversational as a chatbot. Hopefully less chatty, in fewer words, because we think travel’s more visual. Very personalized. What this will mean is much higher conversion rates.

Airbnb’s management thinks that a lot of AI companies have not figured out how to make money on the consumer side because massive capital expenditure is needed, and inference costs are high

We are coming on the near four-year anniversary in three months of ChatGPT. In the nearly four years, almost all the actual business that’s been generated is on the enterprise…

…Part of the reason why is a lot of companies have not figured out how to make money on the consumer side. Why is this? Because the inference cost is not cheap, and there’s huge capital expenditures.

Arista Networks (NYSE: ANET)

Arista Networks’ Etherlinks switches now has more than 100 cumulative customers, up from just 4-5 in 2024

Our AI fabrics momentum with Etherlink switches now exceeds 100 cumulative customers from the initial 4 to 5 customers I spoke of in 2024.

The maximum possible scale for an AI network depends on the number of tiers and the number of ports; increasing tiers and ports is expensive and eats power; Arista Networks’ 7800 AI Spine allows users to achieve high scale without adding tiers; the 7800 AI spine has an important use case in scale-across; management sees scale-across switching and routing as a $15 billion to $20 billion market by 2030; Arista Networks’ scale-across solution can provide near instantaneous recovery in the event of transient congestion, packet loss or a physical failure of AI clusters independent of their geographical location and management expects this scale-across use case to be 30% of Arista Networks’ overall AI target of at least $3.6 billion in 2026; Arista Networks’ management continues to see 3 types of AI fabrics for the company to participate in, and they are scale-up, scale-out, and scale-across; management thinks competing whitebox solutions are seen more in simple scale-up or scale-out use cases where software requirements are low; Arista Networks’ solutions stand out for massive training and inference clusters; Arista Networks’ scale-across technology is mostly used by AI titans at the moment, along with some neoclouds

The maximum possible scale for an AI network generally depends on two things: the number of tiers in the network and the number of ports per device, often known as Radix. Increasing the tiers and ports is expensive and power hungry. Our customers deploy and often chose the Arista flagship 7800 AI Spine to achieve that high scale without adding additional tiers. 

Scale-Across is an important application. The scarcity of compute capacity, physical space and gigawatts of power mandate that the AI infrastructure must be designed thoughtfully. The Arista 7800 platform continues to be the flagship spine for distributed scale across applications, providing traffic isolation, contextual routing and security. The Scale-Across switching and routing TAM is forecasted to be roughly $15 billion to $20 billion in 2030, and Arista is well poised in this segment. 

Our Scale-Across AI innovations deliver programmable and deterministic routing, SRv6 multi-plane forwarding, multi-tenancy and traffic engineering as well as load balancing across the regions. We are capable of providing near instantaneous recovery in the event of transient congestion, packet loss or a physical failure of AI clusters independent of their geographical location. This Scale-Across use case is expected to be approximately 30% of our overall AI target of at least $3.6 billion in 2026…

…There’s 3 types of AI fabrics that are critical for us to participate in, scale-up, scale-out, and scale-across…

…White box is certainly a tactical solution that we tend to see more in use cases that are simple, scale-up or scale-out where the actual amount of software and system requirements are low. But when you look at traditional network topologies, you can slow down the job completion significantly if you go with a box-by-box approach. And so this — as you rightly point out, the Etherlink system-wide portfolio with all of the features Ken alluded to for reliability, MRC, SRv6, traffic engineering, synchronizing elephant flows, low latency, this massive training and inference does put more pressure on the combination of our hardware and software, and we feel very well recognized and ready to achieve that…

…[Question] Where we are with the scale-across build-outs. Like is it concentrated to maybe a couple of the cloud titans? Or can we see a world where this extends out to some of the Neocloud customers as well?

[Answer] Our scale-across is dominated by the cloud and AI titans. But I see no reason why it wouldn’t apply to — and we’re already seeing it apply to several Neoclouds. So I would say less in the enterprise and definitely more in the Neoclouds and titans.

There are 3 important recent innovations in AI networks, namely, Smart System Upgrade (SSU), Multipath Reliable Connection (MRC), and Segment Routing v.6 (SRv6); SSU is a recent Arista Networks innovation that enables customers to upgrade switch software without any disruption; frequent upgrades of software is important because AI is uncovering security vulnerabilities and creating tools to exploit the weaknesses; maximising XPU utilisation requires MRC, which enables senders to spray a single XPU-to-XPU flow across many paths through an AI fabric; SRv6 enables senders to control which paths an XPU-to-XPU flow will take in MRC; SRv6 is not a new innovation, but using it to load balance an AI fabric is; Arista Networks’ EOS (Extensible Operating System) provides a unified operating system, so it can support SRv6 from scale-out to scale-across; Arista Networks uses explicit SRv6 probes for multipath and multiplane monitoring for reliable accelerator communication

I have never witnessed the combination of rapid innovation and scale deployment that we are seeing in AI networks. I’d like to call your attention to 3 innovations: SSU, MRC and SRv6…

…SSU is Arista’s Smart System Upgrade, the ability to upgrade switch software without any disruption. Frequent upgrades are a hard reality today, especially as AI both uncovers security vulnerabilities and creates tools to exploit them. While many competing systems require a full reboot to address these issues, leading to expensive and disruptive downtime, Arista’s EOS handles these upgrades seamlessly. We ensure our customers stay secure without sacrificing even a single minute of valuable XPU cycles…

…To maximize XPU utilization, you need MRC or Multipath Reliable Connection. See in first-generation AI networks, every packet on an XPU to XPU flow has to take the same path. That means if 2 flows hash to the same link, they both run at half speed. MRC enables senders to spray a single flow across many paths through the fabric, where receivers reassemble any data that arrives out of order, eliminating the performance hit from fabric hash collisions. But how is the sender supposed to control which paths the flow will use? And that’s where the third innovation comes in.

SRv6 or Segment Routing, it’s not new, but using it to load balance an AI fabric, that’s the game changer. The sender tags each packet with a stack of SRv6 segment IDs, dictating the exact path the packet will take. The system then uses real-time congestion signaling to dynamically shift packets away from hotspots. Because Arista EOS provides a single unified operating system, we support this SRv6 intelligence all the way from the scale-out fabric to the long-distance scale across routing. It gives our customers the combination of high quality, top performance and operational simplicity that Arista is known for…

…Arista’s multipath and multiplane monitoring with explicit SRv6 probes ensures that reliable performance for accelerator communication.

Arista Networks’ management sees the semiconductor industry’s supply chain challenges persisting, but the company has made solid progress in hardening its supply chain; management thinks the industry’s supply chain problem will last until 2028; as part of the improvements made to Arista Networks’ supply chain, the company has tripled its purchase commitments to $9.7 billion from a year ago (was $8.9 billion in 2026 Q1); Arista Networks’ purchase commitments are mostly for chips for new products and AI deployments; management has secured Arista Networks’ memory supply for 2026 and for parts of 2027; management has built capacity in a 12-month window for PCBs (printed circuit boards) and optics; management has established a supply chain for liquid cooling for the next generation of AI infrastructure; management has increased Arista Networks’ manufacturing and distribution capacity; management thinks the semiconductor industry is in a constant state of scarcity across 3 dimensions, namely, power, space, and compute, and getting all 3 solved simultaneously is very difficult; the supply chain challenges in the semiconductor industry has not increased management’s visibility with customers

While the industry-wide supply tightness and rising component costs persist, Arista has taken individual and aggressive proactive steps. Arista is making solid progress here in addressing our tight supply chain…

…The industry is going to have a 2-year problem. I don’t think we get out of it as an industry until 2028…

…Arista is leaning in with our increased multiyear purchase commitments, now almost tripling from a year ago at $3.6 billion to approximately $9.7 billion by the end of Q2 2026…

…Our purchase commitments at the end of the quarter were $9.7 billion, up from $8.9 billion at the end of Q1. As mentioned in prior quarters, this expected activity mostly represents purchases for chips related to new products and AI deployments…

…We’ve secured multiyear agreements with leading vendors of strategic components, qualified new suppliers in key areas to limit risk and built out supply chains for next-gen AI technologies. Our capacity has been increased in both manufacturing and distribution, and we’ve negotiated better component delivery terms to drive up both factory efficiency and capital deployment. Relationships with our strategic silicon vendors continue to be strong with really excellent collaboration in both supply chain and technical engagements. Our memory supply has been secured for 2026, and we have extended visibility well into 2027 across DDR4, DDR5 and NAND memory. And importantly, we’ve increased our resiliency through optionality and expanded vendor qualification. For PCBs and optics, we’re now able to build capacity in a 12-month window and have strengthened our engagement and commitments from key suppliers. We’ve improved our lead times and inventory management of thousands of component SKUs, improving subcomponent pipelining and multisourcing and providing increased flexibility with reduced inventory risk. In a new area, we’ve now established a liquid cooling supply chain capable of driving and delivering the next generation of AI infrastructure. This includes cold plate, quick disconnect and tubing vendors with capacity agreements for cutting-edge new AI technology.

And to match our capacity with customer demand, we’ve increased both our manufacturing and distribution capacity. We have now 3 contract manufacturers and 3 distribution facilities, providing geographic diversity in the U.S., in Asia and in Mexico…

…We’re going to be in a constant state of scarcity for power, space and compute. Getting all those 3 to lock in, whether you’re a cloud titan, an AI titan or a Neocloud is going to be very, very difficult…

…[Question] With the tightness in the overall supply environment and your demand that you’re seeing, I’m wondering how far out your visibility with customers is extending?

[Answer] We still have the same kind of 2 quarters of visibility that we referred to.

Arista Networks’ ramp of new products and new use cases in AI is contributing to growth in deferred revenue

Our total deferred revenue balance was approximately $6.9 billion, up from $6.2 billion in the prior quarter. The majority of the deferred revenue balance is product related. Our product deferred revenue increased approximately $600 million sequentially versus last quarter. We remain in a period of ramping our new products, winning new customers and expanding new use cases, including AI. These trends have resulted in increased customer-specific acceptance clauses and an increase in the volatility of our product deferred revenue balances.

Arista Networks’ management has raised its revenue guidance for 2026 to growth of 40% (previous guidance was for growth of 28%), but kept its AI fabrics guidance unchanged at $3.5 billion; management thinks the AI fabrics revenue will increase, but is unsure by how much; Arista Networks’ business performance for 2026 will be gated by supply

Reflecting our strong momentum, we are raising our 2026 fiscal year outlook to 40% revenue growth, equating to approximately $12.6 billion. Within this guide, our 2026 campus revenue goal is at least $1.25 billion and our AI fabrics goal is at least $3.5 billion…

…[Question] You took up the calendar year ’26 revenue guidance substantially from last quarter. But if I remember correctly, you got the AI target unchanged and only touch the campus. Can you kind of explain kind of the thought process there?

[Answer] If you had to ask me whether our AI number or campus number will go up, I think Chantelle, Todd, Ken and I absolutely believe it will. The question is not whether it will go up. The question is what is that number? And that I would like to reserve that $1.1 billion question to, well, it depends on how we ship. If we ship more front-end AI or we ship more WiFi or wired or Etherlink switches or routing. So I’d like to give our customers the priority and Todd’s team the flexibility to ship what we can. And that’s why we’re not holding ourselves to a number…

…Where we are at this time of year, Jayshree and I are guiding based on what we’re confident we can get the supply for. And if the supply was to release a little more, there is an opportunity to do better in the year.

Arista Networks’ management thinks it’s a bad idea for there to be a variety of different technologies for networking in scale-up use cases; management has decided for Arista Networks to commit to an open CPO (co-packaged optics) technology; management thinks the industry will still stick with pluggable optics and copper in the near future, but CPO will appear in 2028 and 2029

Let me take the scale-up use case, which we are less prevalent in. I think there’s very much a philosophy there on copper if you can, optics if you must. So I think you’re going to see a lot of copper in that 2-meter, 3-meter distance, well within a rack, that type of thing and the importance of pluggable optics. But in some cases, there is a number of instances of proprietary implementations of traditional co-packaged optics that’s been floating around. Arista is not a fan of 5 different proprietary implementations. They’re going to have and we’re going to commit and Andy and the team have been working hard at this to really solve one, which is an open CPO.

And we don’t think open CPO is going to happen overnight. But the idea here is to use socketed optical engines, pigtail fibers and allow these modules to be fully pretested. And whether they started on the board or nearby, the idea is to have a truly open interface that can operate with multiple vendors and multiple switch configurations. And sometimes those open CPOs are often called NPO too, [ nearby ] optics, right? So we’re big fans of that. It’s very early stages. It probably comes into examples and trials next year. And probably from our perspective and the industry perspective with all the supply chain shortages, majority of the world will still remain pluggable optics and copper, but there will be some amount of co-packaged optics in 2028 and 2029.

Arista Networks’ EOS (Extensible Operating System) has the lowest security vulnerabilities; management thinks regardless of what model is being used, the network must have robust security; management thinks there’s to be a lot of diversity in agents and models; management thinks the more diversity there is in models and AI infrastructure providers, the better it is for Arista Networks

I just want to acknowledge Ken’s architectural design of EOS and how we have the lowest vulnerabilities and we’re absolutely committed to building that rock-solid foundation…

…In terms of our own approach to models, every day, it’s in the news, there’s a China model, whether it’s a DeepSeek or whatever. But from our standpoint, the network must be robust no matter what the model. There’s going to be a lot of enterprise AI agents. There’s going to be a lot of diversity of models…

…we are extremely excited about the growth and diversity in the industry. The more diversity there is among models and model makers and between AI infrastructure providers, the better for us.

Arista Networks’ management thinks AI workloads will be shifting to inference eventually

It’s going to be training at some point, but it’s going to move to inference.

Arista Networks’ management expects more large customers to become 10%-customers for the company, and join the ranks of  Meta Platforms and Microsoft

[Question] Can you revisit your thoughts on adding a 10%? I think in the past, you’ve talked about 1 or maybe even 2 additional 10% plus customers.

[Answer] we’re going to increase the number by $1 billion or more. And undoubtedly, we remain very committed to our 2 longest partners, Microsoft and Meta. And I fully expect there to be 1, maybe 2 10% customers.

Arista Networks’ management sees the AI accelerators market as currently being dominated by NVIDIA; in the NVIDIA-dominated AI accelerators market, the scale-up networking technology is typically provided by NVIDIA; Arista Networks does better in scale-out or scale-across networking technology with NVIDIA accelerators; management is excited about non-NVIDIA AI accelerators, such as AMD’s MI series and Google’s TPUs; management is working with non-NVIDIA AI accelerators to build both scale-up and scale-out networking technologies; management thinks it will take time for Arista Networks to win more share of networking technologies in NVIDIA AI accelerators, but the wins will happen; management thinks NVIDIA’s Infiniband networking technology is no longer widely discussed

We live in an NVIDIA world. And I think we all can safely say it’s a high percentage of the GPUs we connect to. So there’s really 2 use cases there. One is where NVIDIA provides the full vertical stack and usually, that’s an NVLink. So there’s very little participation from Arista or anybody else’s scale up. And generally, it Arista does better there in the scale-out or scale-across domain.

The second is the non-NVIDIA accelerators, and you’ve heard me talk about our enthusiasm with the MI series from AMD. We’re excited about the Google TPUs. Increasingly, we see that as a formidable training processor. And we’re very excited about the range of inference accelerators as well. And we have a number of partners who are working with — you can imagine, many of our customers are building their own in-house.

So now parsing your question a little bit. In that sector, where it’s non-NVIDIA, Arista will be excited and will be working more closely, both in the scale-up and scale-out, in some cases, to build custom racks with their custom processors so that we can better tune our network with the behavior for their inference or training engine. In the NVIDIA cases, it’s going to take a bit longer. I feel a little bit like 2, 3 years ago when we were talking about InfiniBand and now we don’t mention InfiniBand, but it took 2, 3 years to move to Ethernet. So it will take time to go from a proprietary scale-up that’s been around a long time with NVLink to these other alternatives, even if Ethernet is really good. But I expect us to do much better there.

Arista Networks’ management thinks customers value the company’s EOS (Extensible Operating System) for its operational excellence, AI features, and combination with the NetDL diagnostics layer; with the current scarcity in the supply chain, customers are turning more to Arista Networks’ integrated networking technology that comes with quality hardware and the EOS, as opposed to going with white boxes (generic networking hardware) or blue boxes (Arista hardware with generic software)

The 3 things they value greatly with EOS is operational excellence. They don’t have to put a lot of staff. Most of these Neoclouds don’t have staff. And even if they’re paying a little more for the CapEx, it more than makes up for the operating cost that they would incur. So that’s a huge piece. The second is the AI features themselves. You heard Ken talk about a few of them. There’s a tremendous depth and breadth to EOS that they can’t recreate in a white box or they can figure out how it is. And the third is reliability and vulnerability, which is becoming top and center. You can’t put these things in the middle and have them blow up. So — and our combination of both EOS and NetDL, our diagnostics layer, which plays in both the hardware and software has been very compelling. So we continue to see that while it’s interesting to talk about these open NOSes that more and more customers want either EOS itself in its entirety or a hybrid combination of open NOSes and EOS…

…[Question] You’ve talked in the past about kind of new customers who have come in who have maybe wanted to start down the road of blue box and then discovered with the complexity that they actually need to go down the road of EOS. Can you just talk about kind of the latest trends that you’re seeing there, particularly as you continue to add customers?

[Answer] I think in this in this current scarcity of supply chain and people and needing to deploy AI fast, Arista is really winning out with the system-wide approach on EOS and good hardware. 

Cloudflare (NYSE: NET)

Cloudflare’s management sees the company providing the new kind of cloud that’s needed for an agentic future; management thinks Cloudflare’s Workers Developer Platform is the fastest, most secure, and most cost-effective place to build and deploy agents; management sees Cloudflare occupying the pole position to lead the internet’s agentic AI phase; management is seeing a lot of code from vibe-coding platforms going to Cloudflare; management thinks agents need ephemeral, low-cost places to do their work; management thinks the ideal case is for an organisation’s agents to be running in many different places, and for the network to become the computer; management thinks that if every knowledge worker ran an agent that ran in a container, there would not be enough CPUs to power that; Cloudflare has a container-less version called Isolates that is more lightweight; management is not interested in simply renting out commodity AI compute because that is not an attractive business

It’s clear that the agentic future needs a new kind of cloud. Developers are flocking to Cloudflare because our Workers Developer Platform gives them what they need to build that agentic future. We’re the fastest, we’re the most secure, and the most cost-effective place to build, deploy, and scale agents and the code they generate…

…It’s the same one that has propelled Cloudflare into the pole position to lead the next phase of the Internet in the age of agentic AI…

…Cloudflare Workers is turning out to just be the perfect platform for building agents and agentic workloads. It’s extremely lightweight. You only get charged for when it’s actually doing work. You can spin things up and spin them down very, very quickly. It has become the go-to place for sophisticated developers to be able to launch code…

…If you look at companies like Lovable and Replit and Base44 at Wix and others, that a lot of times that code is actually getting deployed and where the preferred target of that code is going is actually to Cloudflare…

…What I think that we’re seeing is what agents need is the ability to have very ephemeral, low-cost places to create code, do inference, access the network, coalesce information, store some things, and pack all of these things together. It’s not simply the inference that matters. The real key is how do you orchestrate all of those pieces together. In the ideal case, your agent doesn’t run just in one place. It runs in many places. It might be operating in literally different places around the world where it has to access information. It has to get different things. You want, effectively, for the entire network to, as the old Sun saying was, become the computer. The network is the computer…

…I think that’s exactly what agents need. They need the network to be the computer…

…If every knowledge worker on Earth ran an agent that was running in a container, we don’t have enough CPU to actually power that. We’d need to increase the amount of CPU that’s accessed by many orders, many times. What we’ve done, which is a new version, which is much lighter weight, in terms of our sandboxing technology, which we called Isolates, that gives you the ability and the scale to actually deploy and run code in a way that is going to keep up with the demands that agents have…

…If you’re selling what is just commodity compute, if you’re basically letting an AI company use your balance sheet and your credit rating in order to buy servers that are the same as everybody else’s servers, then that’s just not attractive business for us…

…I don’t think we want to get into the business of renting servers, because over time, it’s a commodity business and it’s not very attractive.

A leading digital native media company signed a deal with Cloudflare to combat aggressive scraping, despite competitive pressure to use its incumbent hyperscaler

A leading digital native media company expanded their relationship with Cloudflare, signing a five-year $31.8 million contract for application services and Zero Trust. To combat aggressive scraping and accelerate global performance, this customer chose Cloudflare for our best-in-breed edge capabilities and operational velocity. Despite competitive pressure to consolidate spend with their incumbent hyperscaler, this customer’s long-term commitment is a proof point that when performance and security are non-negotiable, enterprises choose Cloudflare’s unified platform.

A Global 2000 European technology company expanded its relationship with Cloudflare for future AI workloads; the European technology company is using Cloudflare to replace 5 incumbent point solutions, with up to 7 solutions targeted

A Global 2000 European technology company expanded their relationship with Cloudflare, signing a three-year, $11 million contract for application services and Zero Trust with our developer platform seeded for future AI workloads. After years of acquisitions resulted in a fragmented IT footprint, this customer chose Cloudflare to eliminate a stack of five incumbent legacy point solutions with up to seven targeted on their long-term roadmap in favor of our single unified platform as the foundation for their entire organization to build on.

A rapidly-growing generative AI company signed a deal with Cloudflare for the Developer Platform; the generative AI company pulls a lot of videos and images, so its egress fees would have been way too high if it was using hyperscalers’ cloud computing platforms

A rapidly growing generative AI company signed a one-year, $7.5 million pool of funds contract for our developer platform. This customer’s workloads pull an enormous volume of images and video. At that scale, a hyperscaler’s egress tax would break the economics and create vendor lock-in, limiting their choice of inference tools and GPUs. Their engineering team evaluated multiple providers and chose Cloudflare as the only one that pairs a zero egress model with the reliability, scale, and comprehensive capabilities of an enterprise-grade platform. By structuring this as a pool of funds deal, the customer can solve their immediate storage needs while retaining the flexibility to expand across our entire developer platform.

A rapidly-growing technology company in the Asia Pacific region expanded its relationship with Cloudflare after signing a 2-year deal in 2026 Q1; the company chose Cloudflare over a hyperscaler

A rapidly growing technology company in APAC expanded their relationship with Cloudflare, signing a one-year, $4 million pool of funds contract for our Workers Developer Platform. This deal accelerates a powerful partnership, building on an $8.7 million application services contract signed just last quarter. In only one year, this customer has standardized on Cloudflare end-to-end, from application security and Zero Trust to now our developer platform, directing every request through a Cloudflare Worker and using KV and Durable Objects as the routing and tenant configuration layer for their entire platform. They chose Cloudflare over their incumbent hyperscaler to avoid added latency, proving the flywheel of our unified offering.

A Fortune 1000 company expanded its relationship with Cloudflare and is now adopting Workers Developer Platform; the Fortune 1000 company is using Cloudflare to replace multiple products

A Fortune 1,000 technology company expanded their relationship with Cloudflare, signing an 18-month, $15.9 million contract for application services and our Workers Developer Platform. This customer serves hundreds of thousands of businesses, which requires an architecture that can act as their global front door for security and performance without adding latency. By standardizing on Cloudflare over legacy alternatives, they eliminated multi-product complexity and secured long-term operational predictability as they build an AI-first customer platform.

A leading technology company signed a deal with Cloudflare for the Workers Developer Platform; the leading technology company needed an elastic, secure container infrastructure for its agentic workloads, and chose Cloudflare over hyperscalers

A leading technology company expanded their relationship with Cloudflare, signing a one-year, $6 million pool of funds contract for our Workers Developer Platform. As this customer scales their new AI agent capabilities, they needed an elastic, secure container infrastructure that could scale with their rapid growth and ship new capabilities in weeks, not quarters. They chose to build on Cloudflare over legacy hyperscalers and point solution competitors because of our built-in threat intelligence that actively prevents compute abuse, rapid pace of innovation, and the ability to deliver FedRAMP compliance. This win also shows how the most sophisticated AI builders are increasingly selecting Cloudflare as the agent cloud of the future.

In 2026 Q2, Cloudflare’s management saw agentic traffic on the internet exceed those of human users for the first time; management is seeing unabated growth in requests from AI agents; management sees the Internet shifting towards machine-to-machine traffic, and thinks Cloudflare is well-positioned for the paradigm shift; management recently announced Monetization Gateway, which allows customers to sell any resource behind Cloudflare; management thinks Monetization Gateway will empower the next set of business models over the Internet; management recently announced Wallets, which allow buyers to pay autonomously through agents; management recently announced cloudflare.pay, which allow merchants and buyers to identify themselves with AI agents; management recently announced a research pilot with OpenAI to figure out a sustainable ecosystem for content creators and AI companies; management has more to announce in the coming months on the sustainable ecosystem; management initially thought that agentic traffic would surpass human traffic only in 2027, but the event happened faster than expected; management expects agentic traffic to be 1000x higher than human traffic in 5 years, meaning human traffic will become a rounding error; management expects some of the agentic traffic to be malicious in nature, such as hackers or AI companies trying to take content; in the case of AI companies taking content, Cloudflare will block such malicious traffic; management recognises that some companies will want traffic from AI companies, so Cloudflare has to be more efficient at supporting this; management is seeing that the give to get of agentic traffic is different with human traffic; management expects agents to pay only fractions of a penny for every request, but there are still unanswered questions on who foots the various bills for the traffic; management thinks the business model of the internet for the next 27 years will be very different from the Google-defined model of the past 27 years; Cloudflare handles 500 million requests per second on its network and management thinks 1%-10% of the requests could be monetised through a micro transaction; the amount of micro transactions to be processed is many orders of magnitude higher than what traditional payment networks can handle; management is thinking of even letting free users of Cloudflare monetise through micro transactions, because this would really accelerate Cloudflare’s business

For the first time in human history, in Q2, more than 50% of the traffic flowing across Cloudflare’s network was not human. The number of requests on our network from AI agents continues to grow unabated. With the web shifting from human-driven browsing to AI answer engines and agent-driven commerce, we are witnessing a fundamental rewrite of the Internet for machine to machine traffic. Cloudflare is positioned at the center of this paradigm shift, building the scalable infrastructure, the controls, the developer tools, and the payment rails to power the agentic Internet…

…During these, we unveiled the key building blocks for a two-sided agentic marketplace. Monetization Gateway allows our customers to sell any resource behind Cloudflare, whether it’s a web page, an API, a dataset, or an MCP tool. This will empower new business models that will define the next generation of the Internet. In addition, we announced Wallets, which will offer a way for buyers to pay autonomously through their agents, and cloudflare.pay, which will provide merchants and buyers an agent-friendly means to identify themselves and establish trust. Not only are we building the foundational elements for agentic commerce to succeed, we also believe AI companies and content owners should thrive together. That’s why we recently announced a first-of-its-kind research pilot with OpenAI that we believe may help pave the way to a sustainable ecosystem of content creators and AI companies. Over the coming months, we’ll announce more ways that AI companies, content creators, and businesses large and small can thrive together. The business model of the Internet is changing, and there is no company better positioned to define its future than Cloudflare…

…I was asked in the end of 2025, in November of 2025, when I thought that non-human traffic would pass human traffic. We pulled all the data, we ran all the numbers, and we were pretty confident that it was going to be the second half of 2027. I was asked the same question again in March of 2026, and we did the same exercise, and we were surprised to see that it had moved up, that it would cross in the first half of 2027. I was quite surprised when in May of this year, our team came to me and said, “You won’t believe it, but non-human traffic has now passed human traffic online.” To give you a sense of how this trend is playing out, and with the big caveat that I have called it wrong at every point along the way, if the current trends continue, we think in five years, non-human traffic will be as much as 1,000 times as much as human traffic. In other words, humans will be a rounding error on the Internet, not because human traffic goes down, but that’s just how fast we’re seeing non-human traffic grow…

…Some of that non-human traffic, it’s malicious. That could be malicious like it’s hackers or bad guys. It could also be it’s malicious from the perspective of a particular customer’s business model, where it’s traffic that is maybe an AI company trying to take the content from a media company that relies on advertising. In those cases, we block that traffic, and we don’t charge the customers anything more for blocking that traffic because we think that that’s the right thing for us to be doing and delivering, and that’s part of being a security company. At the same time, though, there are some people who want that traffic, and so we’re doing everything we can not only to serve that, but to make it as efficient as possible to serve it. If we’re going to have 1,000 times as much traffic online, we’ve got to get a lot more efficient, and companies like Cloudflare are critical to be able to support that for customers, whether they’re large and small…

…It’s clear that as agents are accessing all of these sites and the volume that they’re accessing them on, the sort of give to get that you have with human traffic is different…

…Things like Cloudflare.pay, that’s us setting the foundation to be able to say, how do we charge agents some, again, what will be a very, very small fee, fractions of a penny for every request that goes through, but for the requests that pass through that traffic. Somebody has to pay for the bandwidth, somebody has to pay for the server, somebody has to pay for the people doing the work to create the content.

…I think that the business model of the Internet for the last 27 years has been largely defined by advertising and really defined by Google. I think the business model of the next 27 years of the Internet is going to be very different…

…We handle, let’s say, about half a billion requests per second through Cloudflare’s network. We roughly estimate that somewhere between 1% and 10% of those you could monetize through some sort of a micro transaction. Again, these would be tiny fractions of pennies. That means that you, day one on launching something like this, you’d need to be able to support, call it 10 million financial transactions per second, and be able to scale up to call it 100 million financial transactions per second. To give you some sense, Visa, and again, these are from memory, but Visa, which is the largest payments network in the world, at peak during the holidays, handles about 20,000 transactions per second. You have to build something that’s three orders of magnitude bigger than Visa in order to pull this off…

…One way to think of this is a lot of that is our free customers. What if we made it less than free? What if being part of Cloudflare, we actually sent you money for being part of us because we were generating through a series of micro-transactions, largely serving agents. I think that then just continues to accelerate the flywheel across all of our business.

More than 80% of the major AI companies are Cloudflare customers

Over 80% of the major AI companies are Cloudflare customers and rely on us.

Cloudflare’s management is hearing from customers that their key concern with deploying AI is security; the lack of Zero Trust policies for agents from security vendors is causing organisations to cancel deals

The number one thing that’s causing our phone to ring from big companies is them saying, “Listen, we know we have to do AI, but we need to do it more securely.”…

…I was just in London a few weeks ago, meeting with a large government agency there that was well down the track with one of the sort of first generation Zero Trust companies to implement that across a big chunk of the U.K. government. We sort of started talking about agents and what their plan was and how they were thinking about it. Very quickly, it became clear that the vendor that they were considering really hadn’t thought about this, whereas it’s been core because of the fact we have a developer platform to how we did things. They literally canceled the RFP and are now reevaluating this with a sort of agents first approach.

Cloudflare’s management is seeing the company’s customers increasingly move their business models away from a typical SaaS model into one with consumption-based structures

As we said at Investor Day, their business model is also going to evolve. It’s moving away from a purely ratable SaaS model towards a much more diversified mix of pool of funds, consumption-based structures, and also what we call T-shirt sizing. As the business accelerates, that also leads to more and more customers burning through their T-shirt sizing faster. Pool of funds are getting consumed faster and getting renewed.

Cloudflare’s management thinks the GPU utilisation of the hyperscalers are really low; management thinks Cloudflare has been able to get as much as 10x the utilisation of every capex dollar; management sees Cloudflare as being in  

We know what the typical kind of inference loads are at the hyperscalers, and their GPU utilization. It’s super low, and that’s not the hyperscalers’ fault. It’s that what they’re selling is just a box, and it’s up to the customers in order to actually maximize the Customers I think don’t have the diversity of traffic or the ability to really schedule things or get the highest possible utilization…

…If we can, and in some cases, get 10 times as much utilization out of every CapEx dollar. 

Coupang (NYSE: CPNG)

Coupang’s management expects the company’s margin to continue expanding because AI improves discovery, personalization and service for customers, and productivity and cost to serve for Coupang

The long-term margin drivers keep compounding. Automation continues to improve productivity across our fulfillment and logistics network, and margin-accretive offerings like advertising and FLC are still early in their scale. And AI raises the ceiling on both. We think of AI as a multiplier and what it multiplies is a set of assets we’ve been building for 15 years, the physical network, operating data from billions of orders picked, packed and delivered and direct relationships with tens of millions of customers. Applied to the customer experience, AI improves discovery, personalization and service. Applied to operations, it compounds productivity and lowers the cost to serve. And applied to margin-accretive offerings, it raises the returns for the merchants and brands who are using them, which expands the addressable opportunity itself. 

Coupang’s management thinks that the winning experience in agentic commerce has yet to emerge; Coupang is exploring agentic commerce; management thinks that Coupang is in the best position to win, whatever form agentic commerce takes

On the agentic AI part that you’ve brought up specifically, we think this is still a work in progress. We think the industry’s direction or the — it’s not clear that the winning experience has emerged but we’re investing. We are investing in teams and the research, as you mentioned, to explore it while being thoughtful about it and investing with the same discipline that we do and all the other initiatives that we have on the exploration front.

Whatever form agentic shopping takes, we believe we’ll be in the best position to provide the winning experience which we believe will combine AI with all the other aspects of customer experience to provide a complete and seamless buying experience that customers trust. And to build a complete and seamless buying experience, you need more than AI. AI is one input, but there are many other assets that will be part of it. And we believe we should be in a position to provide the best of all worlds.on that front. 

Datadog (NASDAQ: DDOG)

Datadog’s management is seeing its base of customers, from startups to large enterprises, adopt AI; the customers’ adoption of AI is accelerating their usage of the cloud and Datadog; Datadog has more than 750 AI customers in 2026 Q2; all 10 of the largest AI companies are Datadog customers; management is seeing AI activity growing across its non-AI customer base; the number of MCP tool calls has quadrupled sequentially again in 2026 Q2, and is up 22x from 2025 Q4; the 750 AI customers includes AI startups and hyperscalers; 31 customers in the AI native cohort now spend more than $1 million annually (was 22 in 2026 Q1), with 8 spending more than $10 million annually (was 5 in 2026 Q1); the hyperscalers are different companies from the AI labs mentioned in Point 14

Our broad base of customers, from the most nimble startups to the largest and most established enterprises, are all adopting AI. We think this is accelerating their usage of cloud and modern technologies, as well as their usage of the Datadog platform to observe, secure, and act on their cloud and AI workloads…

…As of Q2, over 750 AI customers use Datadog to monitor and improve their tech stacks. When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers. Beyond AI natives, we see AI activity growing across our broader customer base. We are also seeing signs of rapid growth in agentic activity with a number of MCP tool calls quadrupling again quarter-over-quarter and growing more than 22x when compared to Q4 2025…

…This 750-strong customer group includes a broad range of AI startups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which eight customers spent more than $10 million annually…

…[Question] If I’m thinking about the two seven-figure AI labs that you landed this quarter, and then going to David’s commentary around winning some of these in-house AI labs with the hyperscalers, are those one and the same here?

[Answer] These are different customers. The ones we mentioned on the new lands are new lands.

Datadog’s management recently expanded Bits AI to automate the DevOps loop; for the DevOps loop, Bits AI can now create and maintain monitors, identify root causes quickly and recommend and implement fixes, follow safety guardrails, continuously learn, and detect symptomatic behaviors early; management recently announced Bits AI Products to improve the development loop; for the development loop, Bits AI can now analyse code changes and run end-to-end checks, generate code fixes, and automate synthetic test generation and maintenance; Bits Security Analyst now works with Datadog Cloud SIEMs (Security Information and Event Management), and users can gain insights no matter which SIEM they use; management is not worried about Bits AI using automation to reduce the volume of activity that drives Datadog consumption, because they think if Bits AI can provide great value to customers, it will yield a great outcome for the company; customers who use Bits AI tend to use more of Datadog’s products; management has significantly expanded the use cases of Bits AI and the new use cases are seeing a lot of adoption; management has a new model with AI credits for Bits AI; management thinks Bits AI could eventually become an AI SOC (Security Operations Center) platform; see Point 14 for AI labs using Bits AI

We expanded Bits AI to accelerate and automate the DevOps loop. This is the loop that goes from detection to investigation to remediation that engineers go through each time something breaks. At DASH, we announced a lot of new Bits capabilities for the DevOps loop. Bits can now create and maintain monitors, identify root causes within minutes of a negative signal, recommend and implement fixes, follow guardrails to add safety and controls, continuously learn and improve from prior incidents, and detect symptomatic behaviors early to repair infrastructure issues before they escalate…

…We announced Bits AI products to address the development loop. This is the loop that goes from coding to delivery to evaluation that developers navigate to get code to production. For this loop, Bits Release now acts as an AI release validation agent, analyzing the impact of code changes, running end-to-end checks, and verifying production rollouts. Bits Code generates code fixes, guaranteeing every fix and reproduction behavior, Bits Testing also automates synthetic test generation and maintenance…

…We expanded Bits Security Analyst to run on known Datadog Cloud SIEMs so customers can benefit from the smarts and the learnings of a broad data set regardless of which SIEM they deploy…

…[Question] Just thinking that Bits AI is going to be increasingly automating activities that historically has created observability workflows, I’m curious and really wonder, how do you ensure that greater automation that might be driven by Bits AI doesn’t eventually reduce the volume of activity that traditionally drove Datadog consumption?

[Answer] If we provide more value, as I was saying earlier in the call, we sell more software by helping customers make more money or save money or both. I think if we can automate more and let them do more, we’ll provide more value. That’s as simple as that. I think the future of observability is not just observing, it’s fixing. It’s not waking up people in the middle of the night because something broke, but fixing it for them. It’s not letting people do damage control on a security incident because an attacker is in. It’s preventing the attacker from getting in to start with by auto-remediating issues. We’re very busy building all of that. We’re super confident that this will yield great business outcomes for us in the end. That’s what we see from customers in the market. When they use Bits AI, they use more of our product. They deploy more of it. They create more dashboards and alerts and everything else. They have more users inside of our product. It’s not a zero sum game…

…Bits AI used to be fairly specific. It used to be dedicated to alerts. Like, Bits AI would pick up an alert and would run an investigation for you. Now the surface of contact is a lot wider with the customer. Bits AI, you can access it through chat. You can, of course, still do the investigations, and we’ve done quite a bit more there. You can have Bits AI manage your monitoring and manage your detection for you. You can have it code for you. You can have it generate managed tests. There’s all sorts of different use cases that we built into it that broaden the surface of contact, and we see a lot of adoption across all of those different areas. We also are changing the way we package it. We have a new model with AI credits that we’re rolling out just because the surface of contact is so much wider now than the specific feature…

…[Question] I want to follow up on the questions around Bits, which sound super interesting. I guess longer term, as you try to push deeper also into the security side of things, could this evolve into a broader AI SOC automation kind of platform?

[Answer] That’s definitely, we’re taking moves towards that, right? Initially we built the SIEM first for that, then we built the agent into the SIEM. Our Bits Security Analyst. Now we’ve actually separated the agent from our SIEM so customers can use it with other SIEMs. We do that because the agent performs just so well, and it’s been such a differentiator when we pitch the SIEM that we think we’re limiting our sales market-wise if we just go after customers that want to re-platform their SIEM, and it can have a much broader appeal as an AI SOC.

Datadog’s management recently expanded the products under its Datadog for AI bucket; Data Observability enables companies to trust the data being used by AI; Bits Data Analysis answers business questions with data content; Agent Console provides visibility into agentic use; Agent Observability identifies agentic quality and cost issues; Bits Evals automates repetitive parts of agentic development

We expanded Datadog for AI, our products that observe, secure, and optimize the AI stack from end to end. Data Observability enables companies to trust the data being used by AI with lineage quality monitoring and jobs monitoring. Bits Data Analysis uses a rich data context to accurately answer business questions. Agent Console provides visibility into AI agent usage, cost, and effectiveness. In Agent Observability, our patterns capability automatically clusters user interactions into behavior groups to identify quality or cost issues. Bits Evals handles the repetitive parts of the agent development loop in order to improve the outcomes of agents.

Datadog has Bits Database Optimizer within Database Monitoring to simulates and evaluates the impact of AI-generated changes; Datadog has Infinite Cardinality Metrics within Custom Metrics Data, which allow users to answer complex questions without incurring extra costs; Infinite Cardinality Metrics solves a long-standing source of frustration with customers, where they unpredictably get higher bills because they send more data or more fine-grained data; Infinite Cardinality Metrics has gotten great feedback so far, but it’s still early

Within Database Monitoring, Bits Database Optimizer now automatically simulates and evaluates the impact of AI-generated changes in order to optimize slow queries… 

…For custom metrics data, we introduced Infinite Cardinality Metrics, which allow our users to answer arbitrarily complex questions as they generate larger amounts of data with AI agents without incurring any extra costs…

…In terms of Infinite Cardinality, I would say it’s been one of the longest-standing source of frustration for customers, when sometimes they send more data or they send more fine-grained tags with their data, and they get some unpredictability on the bills because of that, because it increases the cardinality of the data we’re getting. We’ve solved that from a technical perspective and from a commercial perspective by packaging our metrics a little bit differently. We think it’s particularly important and relevant as customers are building more applications with AI and as they want to send basically more tags and more information and ask more complex questions and get more fine-grained answers to those questions. That fits well within their plans, basically. We’ve got great feedback on that so far, but it’s still early.

Datadog’s management recently launched products to protect users against AI-powered attacks; AI Guard Agent Discovery finds and maps all known and unknown custom agents; AI Guard For Custom uses real-time observability data to block attacks; AI Guard For Coding Agents blocks malicious skills and packages in code; management thinks that cybersecurity in the AI age requires a complete change in how security products work, where the security needs to be a lot closer to the application and infrastructure, and this plays into Datadog’s strengths; management is now seeing new classes of security issues popping up weekly; management is building these insights into Datadog’s security product

We launched a number of innovations to secure the AI stack and defend against a new class of AI-powered attacks. AI Guard Agent Discovery finds and maps every known and unknown custom agent so security teams can see what is protected and what is not. AI Guard For Custom Agents provides runtime protections to block attacks that can only be detected with real-time observability data. AI Guard For Coding Agents applies the same deep observability to block malicious skills and packages in code. We also announced Runtime Prioritization Engine to cut vulnerability noise by over 95%…

…There’s a complete switch in the way the security products need to work. You can’t wait basically for putting humans in the loop. You can’t have the typical path when you have 12 or 15 different products that are going to aggregate signal, then you put that signal into a system to aggregate, to prioritize them for humans, then humans review them when they can. You need to integrate everything a lot more. You need to operate a lot closer to the application and to the infrastructure. You need to have AI agents solve the issues first. It’s a complete reveal for most of the industry, I think it plays into our approach, which is to have an integrated platform and have all of the different data streams come directly from observability straight into the security agent, and have all that be integrated from end to end. Obviously, this is a field that’s moving very fast. We see new classes of issues pretty much every week at this point. We are quite busy building that up, we think it displays into our strength and into where we are basically already are, and we’re building for our security product.

Example of 7-figure land deals with 2 neuro AI labs; the AI labs will use Datadog for visibility across their training infrastructure so they can train their models faster; the AI labs are using Bits AI to rapidly build their observability stack; the AI labs are different companies from the hyperscalers mentioned in Point 2; the AI labs are using Datadog for observing the training of their models; AI model training is a new business area for Datadog

We landed seven-figure annualized deals with two neuro labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches. By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context…

…[Question] If I’m thinking about the two seven-figure AI labs that you landed this quarter, and then going to David’s commentary around winning some of these in-house AI labs with the hyperscalers, are those one and the same here?

[Answer] These are different customers. The ones we mentioned on the new lands are new lands. These are companies that didn’t exist a few years ago. What’s interesting about them on the use case there is that very often we land customers when they go into production, they release products, and they start serving their customers. In this case, these are customers we’re getting as they are training models, they’re using us to observe and improve and optimize the training of the models. That’s an exciting new area that was not really a business area for us a couple of years ago.

Example of a 7-figure expansion deal with a large health insurance company; the health insurer is using Datadog to protect PII (personally identifiable information) across dozens of business units; the health insurer is using Bits AI Investigation to speed up incident resolution and reduce expensive escalations; the health insurer is expanding to 19 Datadog products

We signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 100 health insurance company. This customer’s biggest pain point is to deliver great experience to their members throughout their care while protecting PII across dozens of business units. Datadog’s HIPAA compliance and PII handling in RUM, Log Management, and Cloud SIEM allowed us to differentiate and win over competitive solutions. Bits AI investigation is already speeding up incident resolution and reducing expensive escalations. This customer will expand to 19 Datadog products.

Example of a 9-figure renewal deal with a leading AI company (most likely referring to OpenAI); the AI company is a long-time, and very large, customer of Datadog; the AI company is using 17 Datadog products for visibility on production workloads at very large scale; the AI company will have a user reduction starting in 2026 Q3, which management has incorporated into guidance; management cannot share much about the reduction in usage; the renewal with the AI company appears to cover many products it was previously using

We signed a nine-figure renewal with a leading AI company. This longtime, very large customer uses 17 Datadog products to enable unified visibility on production workloads at a very large scale, albeit with a user reduction starting in Q3, which we considered in our guidance…

…We don’t want to comment too much on any specific customer, because we also don’t really control what’s happening with any specific customer…

…It’s a longtime customer who uses many of our products, but there’s not a lot more we can share…

…I think there’s a lot of continuity in that renewal. It’s one way to put it.

Datadog’s management continues to believe that digital transformation, cloud migration, and AI adoption are long-term growth drivers of the company’s business; AI is already a tailwind for Datadog because it drives cloud consumption and thus more use of Datadog’s platform (see Point 2 for Datadog’s AI customers, and adoption of AI among customers); next-generation AI is introducing new complexity and observability challenges and Datadog is solving these problems with its Datadog for AI products; Datadog’s access to large volume of data has enabled management to build the second version of Toto, Datadog’s foundational model for time series forecasting; Toto version 2 has state-of-the-art performance on key benchmarks; Toto version 2 has true scalability for time series models, and management is targeting the same improvement path language models have followed since 2020; management is working on world models and larger dedicated models to power Bits AI; Datadog has acquired Adaptive ML to accelerate Datadog’s work in Toto, and larger models; management thinks inference will eventually be the dominant AI workload and that there’s opportunity for Datadog at every layer of the inference stack; Datadog’s GPU Monitoring and Agent Monitoring products are doing well; management thinks the AI-related focus of customers will change over time, as customers were busy prototyping with AI in 2025 and the focus in the last few months have switched to AI costs; Datadog just had 2 new AI labs use the company’s products for observing AI training and this follows on from 2026 Q1 when Datadog landed a hyperscaler customer who wanted to use Datadog for observing AI training; management is unsure if the agentic-led renaissance of CPU usage (the CPU-to-GPU ratio is much higher for agentic use cases than for model training) has led to the acceleration seen in the consumption of Datadog’s infrastructure products; management has seen an explosive in usage of Datadog’s AI-related monitoring products in recent quarters, and the usage is coming from both non-AI and AI-natives

There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. We now have an additional growth driver with AI as we help our customers deliver value with this transformative new technology. We are tremendously excited about our opportunities in AI…

…AI is a tailwind for Datadog today as cloud consumption grows and drives more use of our platform…

…Next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI to observe and secure the AI stack from end to end. This includes GPU Monitoring, Agent Observability, Agent Console, Data Observability, AI Guard, and many other products…

…Our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research. We have shown some of our work already with the second version of our time series model, Toto, in May. Toto version 2 was exciting for two reasons. First, we’ve shown it to be state-of-the-art on key benchmarks. More importantly, we’ve demonstrated for the first time true scalability for time series models, allowing us to target the same improvement path language models have followed since 2020. Now beyond Toto, we are working on larger and more ambitious dedicated models, post-training models to power Bits AI and bringing other modalities beyond time series data into world models that we think can lead to a step change in capabilities for our customers. We plan to accelerate these research efforts with the acquisitions of Adaptive ML, which will close in June…

…There’s opportunity at every layer of the stack in inference. We do think at the end of the day, inference will be the dominant workload. That anytime you train, you probably will want to infer more than you train, as a rule of thumb. We see opportunity at the low level, when it comes to the infrastructure, the GPUs, and the consumption you have there. There’s opportunities at the very top end, when you measure what the agents are doing and whether you’re getting the right outcomes or whether you’re getting the right alignment. There’s opportunities at every layer in between, just looking at the LLM itself, just looking at the tool calls and the applications that are being called by the agents…

…We mentioned our GPU Monitoring product is actually getting quite a bit of usage in a number of neuro labs and very AI-first types of customers. We’re also seeing an explosion of volume in our agent monitoring product, we’re well-positioned there. We think this market is going to change quite a bit. The preoccupations of customers, they also change over time. For example, last year, our customers were mostly trying to validate correctness and validate that they were getting some form of outcome that it could then scale up. I would say three to six months ago the focus has moved quite a bit towards cost. Customers were spending a lot on AI, and they were wondering how to optimize cost…

…When we have a concern with customers, that’s the one thing they kept mentioning is, “Hey, how can you help me rein in my AI costs? This is growing very fast, and I don’t have any control on it, and I don’t know whether I’m reaching the right outcomes with that.”…

…[Question] If I’m thinking about the two seven-figure AI labs that you landed this quarter, and then going to David’s commentary around winning some of these in-house AI labs with the hyperscalers, are those one and the same here?

[Answer] These are different customers. The ones we mentioned on the new lands are new lands. These are companies that didn’t exist a few years ago. What’s interesting about them on the use case there is that very often we land customers when they go into production, they release products, and they start serving their customers. In this case, these are customers we’re getting as they are training models, they’re using us to observe and improve and optimize the training of the models. That’s an exciting new area that was not really a business area for us a couple of years ago. We’ve seen a number of new proof points around that. In addition to that, we’ve mentioned in previous calls, we’ve also landed the AI Lab or super intelligence labs of a number of hyperscalers. I would say the workloads are similar in that it’s largely training of the models, the customers are a bit different…

…[Question] CPUs have had a renaissance lately driven by agentic AI. Would be great to hear your thoughts on this topic if it can be an incremental growth driver for your infrastructure monitoring.

[Answer] We do see an acceleration of consumption of our infrastructure products in general. At a high level, we do see that across the customer base. I don’t know that if we see specifically the CPUs that get attached to GPUs in the new build-out. I think a lot of it has more to do with the fact that the AI agents are largely spending a good amount of their time, like sometimes the majority of their time, coding tools. Tools are just applications that already existed, and those applications typically run on CPUs, so we see quite a bit of that…

…[Question] Did you see rising demand for the AI monitoring tool, particularly with open source tools being deployed across enterprises?

[Answer] There used to be very little volume a year ago. It started growing quite a bit into the second half of last year. Now it’s been very rapidly accelerating over the past couple of quarters. We’ve seen an explosion, basically, of the volume we’re getting there. We get more usage from different kinds of companies, so we definitely see that. We see it also across traditional companies and some more recent AI natives. We see a little bit of both.

Datadog’s management sees the company helping customers save a lot of money on AI initiatives

What we do for our customers today, especially as they keep adopting AI, is we help them save a lot of the money they would spend on building, running operations or running AI agents.

Datadog’s management thinks it’s great for enterprises that there are many model options to choose from; management thinks a multi-model world is great for Datadog because having options (1) leads to more complexity for enterprises, and Datadog helps customers deal with complexity, and (2) means customers are likely to do a lot more training of AI models on their own, and observing AI training is a new opportunity for Datadog; management has long held the view that AI will be a multi-model environment

[Question] I’d love to just get your thoughts on the impact of diversification of AI model usage in your customers and what you’re seeing there.

[Answer] We think it’s great. There’s a lot more options for customers to choose from in general. That opens up a lot of doors and opportunities for them. It also creates a lot of complexity, and we’re here to help deal with that complexity. For us, these are great opportunities. By the way, we’ve had that thesis since the early days of AI that we would not just end up with one or two big AI companies and everybody using them, the same way we didn’t just end up with one or two big cloud companies and everybody just using software from them. The ecosystems are very rich. There are lots of providers. There are very large providers. There are smaller providers, and everything in between… We think the same is going to happen in AI. We think also that the multiplication of models, and open source models in particular, opens the door to customers doing a lot more training on their own. That’s a new market for us.

MercadoLibre (NASDAQ: MELI)

Mercado Ads’ AI Advisor reaches sellers on Whatsapp and can automatically analyse campaigns and deliver budget and ROA (return on advertising) recommendations; AI Advisor now reaches tens of thousands of sellers per month, and 1/3 of sellers engaging with AI Advisor makes a change to a campaign; the number of sellers using Mercado Ads’ AI-powered budget orchestrator was up 63% sequentially in 2026 Q2; Mercado Ads has a new AI-powered search architecture that surfaces more relevant ads and increase click-through-rates

Mercado Ads’ AI Advisor reaches sellers on WhatsApp, analyzes campaigns in real time and delivers fully automated budget and ROAS recommendations with zero human intervention. It has scaled from small pilots to tens of thousands of sellers per month, and one in three sellers who engage with it go on to make a change to a campaign directly within the conversation. The number of sellers adopting our AI-powered budget orchestrator, which helps sellers manage spend across multiple campaigns, grew 63% QoQ in Q2’26. These initiatives lower barriers to entry, and enable sellers to invest more in advertising. We also improved how we rank ads on product pages, and our new AI-powered search architecture helps us to surface more relevant ads and increase click-through-rates. 

Handwritten code is now the exception at MercadoLibre; in 2026 Q2, MercadoLibre’s code submissions were up 110% year-on-year, merged code was up 100%, deployments were up 75%, and rollbacks fell; agents at MercadoLibre have autonomously reviewed more than 0.5 million code submissions and migrated 9,000 services; AI agents are accelerating MercadoLibre’s product roadmap; MercadoLibre has 20,000 developers who are using AI

Today, human-written code has become the exception, and productivity has soared as a result. In Q2’26, code submissions were up 110% YoY, merged code was up more than 100%, and deployments were up nearly 75%, all while rollbacks fell YoY. Agents are doing genuinely autonomous work, having independently reviewed more than half a million code submissions and migrated roughly 9,000 services to newer platforms over the past year. This is a structural gain in productivity that is accelerating our product roadmap…

…We have 20,000 developers that are using AI. 

In 2026 Q2, MercadoLibre’s management completed the rollout of its AI-powered marketplace search architecture across its largest sites; management used different models for different countries, but they are delivering comparable uplifts in conversions and click-through rates at lower cost; even when MercadoLibre used more expensive 3rd-party models, the uplifts in conversion and ads click-through-rate more than offset the cost of using the models; sellers representing 50% of MercadoLibre’s GMV are using Seller Assistant, and DAUs (daily active users) were up 21% month-on-month in June 2026; the Seller Assistant resolved more than half of the requests in 2026 Q2 without any human intervention; MercadoLibre has a Shopping Assistant but it’s still under early testing, although management is very excited about the early results; MercadoLibre used to have 10,000 customer service reps 4 years ago, but today the reps have declined to 7,000 even though the business is 3x larger, because 90% of customer service interactions are now done without human intervention

In Q2’26, we completed the rollout of our AI-powered marketplace search architecture across our five largest sites. We deployed this architecture with different models in certain countries and they are achieving comparable results – uplifts in conversion and click-through-rates – at a lower cost. In all cases – even where we use more expensive models – the uplifts in conversion and ads click-through-rate more than offset the cost of using third-party LLMs. Engagement with our Seller Assistant continues to rise, with sellers representing almost half of our GMV using it, interactions per seller rising, and DAU growing 21% MoM in June. As engagement rises, our assistant is resolving a growing share of interactions: in Q2’26, more than half of the requests on our Help Portal were solved by the assistant without any human intervention…

…Our shopping assistant, we are just AB testing that one, nothing to really share in terms of engagement and results. We’re very excited with the early results we’re seeing on the shopping assistant, which is on live for some consumers in the marketplace…

…Four years ago, we used to have 10,000 reps in customer service. Today, we have 7,000 reps, even though the business grew by 3x in that period of time. That’s because 90% of the interactions are done without a human participating on the issue.

MercadoLibre’s management has built technology to maximise results while lowering cost when using AI models; management has obtained better commercial terms with AI suppliers; management has lowered MercadoLibre’s cost per token on a year-on-year and sequential basis in 2026 Q2; MercadoLibre’s AI investment is split between cost of goods sold and product development; MercadoLibre’s AI investment grew in dollar-terms in 2026 Q2, but product development as a percentage of revenue declined year-on-year; MercadoLibre was able to grow its business even when slowing down the pace of hiring engineers, because of AI-driven productivity gains; 2026 is the first year in many years where MercadoLibre is not growing its engineering team

We have built technology that analyzes usage patterns across teams, helping us improve our mix of models, reduce inefficiencies in how they’re used, and refine the tools built on top of them – all aimed at maximizing results at a lower cost. This, combined with better commercial terms negotiated with AI suppliers, has resulted in a reduction of cost per token QoQ and YoY. AI investment grew roughly $80mn YoY in Q2’26, split between cost of goods sold and Product Development. Despite this, Product Development expenses fell from 8.4% of net revenue in Q2’25 to 7.2% in Q2’26, as the productivity gains described above allow us to grow without adding engineers at the pace we once would have…

…AI is definitely contributing to cost efficiency. 2026 is probably the first year in many years in which we are not growing our engineering team. That’s also coming from the fact that AI is driving developer productivity up consistently.

Shopify (NASDAQ: SHOP)

Shopify’s management sees Catalog as a source of truth for AI product discovery; Catalog has over 1 billion products in its search index, and structures product data so any AI partner’s agents can access it directly; management believes Catalog will be among Shopify’s most important assets in the future; AI searches powered by Catalog converted at twice the rate of those using scraped data in 2026 Q2; management sees Catalog as the discovery engine for AI; dozens of retailers and platforms have adopted UCP (Universal Commerce Protocol) since its launch in early-2026; every Shopify merchant is UCP-ready, such that agents can access their product data and check out; every Shopify merchant’s products are automatically listed in Catalog; any developer can access UCP and the Catalog API; Catalog is built with Shop sign-in, so agents can recognize returning buyers and surface personalized recommendations, and this is a feature no other catalog API can do; management is now building taste-driven attributes into Catalog

Catalog, which you can think of as the authoritative source of truth for AI product discovery of the world’s best products and best brands. For nearly two years, we’ve been investing in the search index, ensuring over a billion products and 20 years of commerce experience is distilled for agents. It structures merchants’ product data so every and any AI partner can access it directly, giving agents the ability to discover, understand, and recommend our merchants’ products. Let me say this, Catalog will be one of Shopify’s most important assets for years to come, here’s why.

We’re seeing that AI searches powered by Catalog converted twice the rate of those using scraped data. That is because with Catalog, merchants’ products show up complete, accurate, and with the right context when someone is ready to buy. Put simply, Catalog is the discovery engine for the future, Shopify built it and owns it…

…We introduced UCP at the start of 2026, already industry players across the commerce stack and beyond are converging on this unified protocol with dozens of retailers and platforms adopting it to date…

…Every Shopify merchant is UCP-ready. Agents and builders can access their product data, create carts, and even check out using the protocol. Everything flows through Shopify, so their checkout logic and fulfillment rules are perfectly preserved. Their products are also automatically listed in Catalog. Every builder can now access UCP and the Catalog API across millions of merchants, so they can build commerce experiences with the same infrastructure as our major AI partners. We built our Catalog the way only Shopify could, integrated with Shop sign-in so agents can recognize returning buyers and surface personalized recommendations based on their purchase history. No other Catalog API can do this…

…I don’t know if you tuned in to Editions.dev two weeks ago, but we talked about these taste-driven attributes that we’re now building in to Catalog things like, is it formal enough for a wedding? Does it have breathable fabric? What’s the wrinkle tendency? Are these sneakers suitable for an endurance run? These sort of taste-driven attributes are things you can only get with Catalog.

Shopify’s management recently rolled out a new agentic section in the Shopify admin, which provides cross-channel attribution for agentic selling; agentic commerce volume is still small for Shopify, but it’s growing rapidly, with AI-driven traffic and orders to Shopify stores tripling year-on-year in 2026 Q2 (AI-driven traffic to Shopify stores was up 8x year-on-year in 2026 Q1 and orders from AI-powered searches were up 13x year-on-year); new buyer orders from agentic channels are coming in at nearly 2x the rate of other channels; traditional search is still the largest source of traffic for Shopify merchants and it’s growing, up 1.3x in the last 2 years; management sees AI as a complement to search for commerce; management is seeing that AI search has been particularly helpful for smaller merchants that form the long tail of commerce; 75% of Shopify’s AI-attributed orders in 2026 Q2 came from outside its top 100 categories; the phenomenon of long tail merchants doing well in AI search can be attributed to AI agents using Shopify’s catalog to find the product that works best for the consumer; half of all AI-referred sessions are landing directly on product description pages, 2.5x more than with traditional search, and is a strong tailwind for Shopify merchants; agentic transactions have the same economics for Shopify merchants as online store transactions, with no extra fees; management thinks Shopify merchants will benefit disproportionately from agentic channels; conversion from AI search is 80% higher than traditional organic search; management is seeing that large retailers are being pushed to figure out their agentic strategy, and this is where Shopify can help 

In May of this year, we rolled out our new agentic section in the admin, the first cross-channel attribution for agentic selling. Merchants can manage AI channels, they can track performance, and they can get specific recommendations on what to improve, all from a single interface. While the volume from agentic commerce is still small relative to our massive GMV, the growth trends are impressive. Both AI-driven traffic and also orders to Shopify stores tripled year-over-year in the second quarter. New buyer orders are coming in at nearly twice the rate of other channels…

…Search remains one of our largest sources of buyer traffic to our merchants, and it’s still growing. Traditional search sessions are up 1.3x over the past two years, holding roughly a third of all storefront sessions. That is AI as a complement to search rather than a substitute for it…

…Early indications show that AI search has been particularly helpful to some of the smaller brands that form the long tail of commerce. These are brands that also happen to make up the majority of Shopify’s merchant base, smaller businesses with specialized products built for a particular customer. We saw that AI search was starting to disproportionately benefit the long tail in 2025, and that trend has continued, with 75% of AI-attributed orders in the second quarter coming from outside our top 100 categories in Q2.

The explanation is simple. While search engines rank by popularity against a handful of keywords, AI agents make multiple calls into Shopify’s Catalog, working with richer, structured data to match products with the buyer’s specific intent rather than just keywords. When a buyer asks an AI assistant for the best car seat that fits three across a sedan, traditional search focuses on the keyword car seat. An agent, however, understands the actual need, the dimensions, the vehicle type, and the fact that they need three. It searches across all of those constraints at once to find the product that actually works, not just the one that ranks highest. In this world, relevancy reigns. Specific products made for a specific buyer do particularly well…

…Buyer shopping journeys are being compressed as half of all AI-referred sessions are landing directly on a product description page. That is 2.5x more than what we see with traditional search. This is a serious tailwind for our merchants and in turn for us at Shopify…

…Agentic transactions carry the exact same economics as an online store transaction. There’s no new fees. There’s no separate pricing…

…I mentioned a car seat that fits three across a sedan. These are real Shopify products discovered because an AI agent understood what the buyer actually needed, That’s a structural advantage for these small, specialized, independent businesses. That’s our base. That’s our sweet spot. We think that these trends suggest that merchants on Shopify will disproportionately benefit from this new surface area…

…When you zoom it even further, conversion from AI search runs nearly 80% higher than traditional organic search as well…

…On the merchant side, yeah, I meet these very large retailers and the executive teams there literally every single week. Every one of them is being pushed to figure out what their agentic strategy is. By coming to Shopify, we take their agentic strategy off their plate.

Daily active merchants using Sidekick was up 3.6x year-on-year in 2026 Q2, with daily sessions up 4.8x; in 2026 Q2, Sidekick handled 34 million conversations and created 36,000 custom apps, up from 12,000 in 2026 Q1; Sidekick’s personalised guidance for new merchants led to an 8% increase in merchants reaching 5 orders within 15 days; Sidekick can now access data and take action on 3rd-party apps without the merchant leaving Sidekick; merchants of all sizes are adopting Sidekick; the way new merchants and established merchants use Sidekick is different; most of Shopify’s AI costs from merchant use of Sidekick appear in the Subscription Solutions’ segment’s gross profit; management was able to hold gross margin for the Subscription Solutions segment steady even as Sidekick usage scaled; management believes that investments in Sidekick will translate into more merchants joining Shopify and more merchants achieving greater success; power users of Sidekick are pushing it into areas such as advanced design, content, SEO etc.

In the second quarter, daily active merchants using Sidekick were up 3.6x year-on-year, and daily sessions were up 4.8x. It handled nearly 34 million conversations, and it was used to create more than 36,000 custom apps, up from 12,000 in Q1…

…Sidekick’s personalized guidance for new merchants during onboarding led to an 8% increase in merchants reaching five orders within 15 days…

…It can now access data and take action through extensions to third-party apps like Klaviyo without the merchant ever leaving Sidekick. Adoption is widespread across merchants of all sizes…

…In a merchant’s first 30 days, roughly half of their conversations with Sidekick are about store setup, design, and theme configuration. For merchants five years in, that drops to about 8%, while analytics and reporting climbs past 40% as they use Sidekick as their intelligence layer to interrogate their own data and make better decisions…

…As a reminder, the vast majority of AI costs related to merchant use of Sidekick appear in subscription solutions gross profit. We were able to hold gross margins at a relatively consistent level quarter-over-quarter while Sidekick usage scaled, which reflects some cost efficiencies and support, as well as our ability to continue providing merchants unique AI solutions like Sidekick while diligently managing cost. We are big believers in Sidekick and the value that it can deliver to merchants. We believe these types of investments in our platform will translate into more merchants joining the platform, and those merchants having even greater success. That translates to more gross profit for us, but more importantly, it is helping our merchants accelerate their businesses…

…We’re already seeing power users, if I can use that term, of Sidekick, pushing it way further into things like advanced design or content or SEO or even product creation.

Shopify’s management has built connectors to agents from 3rd-party platforms, so Shopify merchants can build on Shopify however they choose; Shopify has integrations across vibe coding platforms for developers to build in

We’ve built connectors to agents including Claude, ChatGPT, Perplexity, Manus, Replit, and Vercel with our AI Toolkit. Our merchants can build on Shopify however they choose…

…Our integrations across vibe coding platforms like Lovable, AI chat agents, and CLI IDEs show Shopify’s commitment to meeting builders where they are, however they choose to get there.

Shopify’s management thinks the company has moved from reflexive use of AI to having AI being a source of leverage; management wants to be thoughtful about AI costs and will use the appropriate models for different use cases; management thinks widespread adoption of AI tooling already is and will continue to yield benefits for Shopify; most of Shopify’s internal AI spend goes into R&D, and R&D as a percentage of total revenue has been declining; Shopify has built distilled models by teaching a large frontier model about specific use cases; the distilled models run faster, are less costly, and can even be better than large frontier models at narrow tasks

We’ve moved from a place of just reflexive use of AI to a place of AI leverage. Our AI philosophy is straightforward: maximum leverage paired with thoughtful cost management. We use the best model for the job, frontier intelligence where it matters, less expensive models where it doesn’t. We believe widespread adoption of AI tooling already is and will continue to yield benefits in the quality of our output…

…R&D, the majority of our internal AI spend is allocated here, so you’ve seen a modest uptick in year-over-year growth. Overall, we’ve driven substantial leverage in R&D as a percentage of total revenue and will continue to be disciplined in managing this spend…

…We now have a number of distilled models where we take a teacher model, usually a big frontier model, then teach it a specific use case to a smaller model, which results in much faster, less costly, and actually sometimes even better at the narrow task.

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

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

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

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

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

Alphabet (NASDAQ: GOOG)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

discovered and booked right when it matters most…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Amazon (NASDAQ: AMZN)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Apple (NASDAQ: AAPL)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ASML (NASDAQ: ASML)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Intel (NASDAQ: INTC)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mastercard (NYSE: MA)

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

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

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

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

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

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

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

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

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

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

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

Meta Platforms (NASDAQ: META)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Microsoft (NASDAQ: MSFT)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Netflix (NASDAQ: NFLX)

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

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

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

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

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

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

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

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

Taiwan Semiconductor Manufacturing Company (NYSE: TSM)

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

Demand for our leading-edge technologies is very strong…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[Answer] Yes…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

All the silicon-roads for AI lead to TSMC

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

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

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

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

Tesla (NASDAQ: TSLA)

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

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

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

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

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

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

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

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

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

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

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

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

We will soon start production with Optimus…

…I think Optimus will be the biggest product ever…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Visa (NASDAQ: V)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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From Mastercard

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

From Visa

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beaver’s revenue and net profit fell in 2023

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[4:14 pm, 25/10/2024] Ser Jing: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Thrift IPOs have attractive economics for investors

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

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

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

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

Thrifts that stray far from traditional mortgage lending are risky

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

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

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

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

An example of a thrift conversion that looked attractive to Klarman

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

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

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

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

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

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

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

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

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

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

Adobe (NASDAQ: ADBE)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Oracle (NYSE: ORCL)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I’ve written numerous articles about stock-based compensation in the past for a few reasons. 

For one, it’s super common. Almost every major tech company in the world pays some form of stock-based compensation to employees. Two, stock-based compensation is the silent killer that destroys shareholder value beneath the surface. 

Yet despite these two facts, shareholders still do not seem to fully understand stock-based compensation and the massive impact it has on shareholders.

In this article, I want to show you just how bad stock-based compensation can be for shareholders and why it deserves more attention from investors.

Covid darling

Let’s use the one-time Covid darling, Zoom Communication Inc, as an example. As you probably know, Zoom is a video conferencing software company whose business simply exploded during the COVID lockdowns. Revenue soared and its share price rocketed. 

But as the world reopened, Zoom’s growth also stalled and its share price has since come back down to pre-COVID levels. 

Today, Zoom is guiding to generate free cash flow of US$1.7 billion for FY2027 (fiscal year ending January 2027). That’s still a decent number, which shows that Zoom continues to be a strong business in the aftermath of COVID.

Investors love using free cash flow to measure a company’s profitability as free cash flow is the cash generated from operations minus cash spent on capital expenses. 

In theory, this is cash that can be returned to shareholders via dividends. However, free cash flow does not take into account stock-based compensation. 

The hidden cost

Stock-based compensation is the hidden cost that eats into shareholder returns. 

In FY2026, Zoom granted 10 million shares to its employees. These are shares that will vest over the next 3-4 years. We can assume that based on Zoom’s grant history, around 10-11 million shares will vest each year. In FY2026, for example, 11 million shares vested.

Zoom has an active share buyback plan. In aggregate, it is buying back more shares than is vesting. But that also means Zoom is actively using its free cash flow to offset the shares that vest – the cost to shareholders is immense!

To buy back the 11 million shares that vested in FY2026, Zoom has to pay around US$1.14 billion (based on its current share price of US$104). 

Earlier, I mentioned that Zoom is expecting to generate US$1.7 billion in free cash flow in FY2027. If management decides to offset the stock-based compensation by conducting buybacks, the remaining cash left over for shareholders is less than US$600 million.

Valuations change

Stock-based compensation can, hence, make a huge difference to how we value a company. 

In Zoom’s case, the company’s free cash flow of US$1.7 billion looks healthy on the surface and its current market cap of US$31 billion represents a somewhat decent valuation of 18 times free cash flow.

But if you account for the cash that will simply vanish from shareholders’ hands just to offset dilution, the company now only has around US$600m to return to shareholders.

This changes the picture completely. After making this adjustment, at a US$31 billion market cap, Zoom trades at much less palatable 51 times adjusted free cash flow.

The Good Investors Take

Stock based compensation is often the silent killer that destroys shareholder value – more so for companies that rely heavily only on stock-based compensation. As such, the headline free cash flow figure may not present the full picture of how profitable a business is. 

Zoom is already a slow growing, mature company. Yet it is still off-setting stock-based compensation with a large part of its free cash flow. 

The key thing for investors to note is how much cash can a company actually return to shareholders, once all employees’ stock-based compensation is offset. Only then, can investors truly gauge how much cash is left over for investors.

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

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

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

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

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

MongoDB (NASDAQ: MDB)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Nu Holdings (NYSE: NU)

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

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

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

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

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

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

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

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

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

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

NVIDIA (NASDAQ: NVDA)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Okta (NASDAQ: OKTA)

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

The future of technology is agentic. For Okta,, this represents a tremendous opportunity and an even greater responsibility. Every agent inside an enterprise is a new identity. Today, AI agents are the fastest-growing identity in the enterprise but also the least governed. Okta helps bring agents under control by treating them as first-class identities that can be managed and governed by their existing identity management system. We believe, over time, most large enterprises will have more agentic identities than human ones. This shift broadens the attack surface because every agent comes with credentials privileges, and the ability to act on a user’s behalf. In turn, this raises the strategic value of the identity layer because governing autonomous systems requires the kind of control, audit, continuous intent-driven authorization and real-time enforcement only an identity platform can deliver…

…I’ve spent the last 6 months, I’m on this goal to talk to in-person face-to-face with our top 100 customers, about 75 customers in. And when you mix that with a bunch of other conversations, here’s what’s going on, everyone is deploying agents in some way, shape or form. But they’re really just starting to think about and put in programs in place to lay out the rails of governed managed adoption. So a concrete example is you’ll have a development team that is using cloud code, but it’s connected to GitHub and their JIRA system with static tokens in the local developer box. So that company is viewing agents, but they’ve really done it in a haphazard nonsecure way. And what’s happening now is they’re figuring out those rails. They’re figuring out how they’re going to have secure connections, have a system to monitor where all the agents are, have the ability to support it for multiple platforms…

… I think what I’m seeing is that Boards and CEOs are saying, we know this agentic thing is real. We’ve got to put the guardrails in place for that. and we know that security is real, and we’re going to spend money on that. And it’s, the reality of it, Brian, is that it’s the fundamentals. It’s identity. 80% of breaches are go through identity. And you know you have to patch your systems. You know you have to have a good multilayered defense and Zero Trust so you can defend for multiple ways…

…there’s a few fundamental truth right that are going to play out. I think, one is that they’re going to get agentic capabilities from many, many companies. They’re going to have different platforms. They’re going to have hyperscaler platforms. They’re going to have Foundation model platforms. They’re going to have open source platforms. They’re also going to get agentic capability from apps. Salesforce is going to have there. Workday is going to have their ServiceNow is on and on…

…Customers have a problem today. They have a problem today where over 90% of them have agents in production, and only 22% of them are confident to have them governed.

Okta’s management sees 3 advantages the company has in securing AI agents, namely, (1) distribution, where Okta can extend its identity system to AI agents, (2) product breadth, where Okta is the only vendor that address both sides of the agent security problem, and (3) neutrality, where Okta allows customers to choose whichever cloud provider and agentic platform they want; Okta’s 3 advantages in securing AI agents are mutually reinforcing; Okta as a neutral identity layer, can help customers avoid vendor lock-in for agentic capabilities

To help our customers confidently secure this shift, we’re building on 3 unique advantages, each with powerful network effects: distribution, product breadth and neutrality…

…In the agentic era, identity becomes even more foundational. When a customer secures their agents with Okta, they are not taking on a new platform; they are extending the trusted foundation they already rely on with Okta. We’ve already seen how our customers benefit from this expansion in other parts of our business. Customers are finding value in Okta’s unified identity system as Okta in governance was once again the leading contributor among our new products. This distribution flywheel is evident in our results…

… Our second unique advantage is product breadth. We are the only vendor with solutions that address both sides of the agent security problem…

… The third unique advantage is neutrality, which is more important than ever. The AI landscape is opting rapidly. Customers need an identity solution that frees them to choose whatever technology serves their business best without fear of vendor lock-in. As the leading independent and neutral identity platform, Okta gives organizations the flexibility to do exactly that. In the same way, enterprises run workloads across multiple clouds, they are deploying agents across various platforms like OpenAI, Anthropic, Google, Microsoft, Salesforce and a growing set of open source frameworks. Managing and securing an autonomous workforce requires a neutral, independent identity layer that others can’t provide. In practice, cloud providers, model providers and agent platforms are partnering with Okta to securely manage agent identities as they continue to proliferate across the enterprise…

…These 3 advantages are unique and mutually reinforcing. The more organizations use Okta to secure their agents, the more identity signals flow into our platform and the stronger our governance and detection becomes, and our neutrality allows us to secure current and future agent frameworks for customers, allowing Okta to capture more of the addressable market…

… I think, one is that they’re going to get agentic capabilities from many, many companies. They’re going to have different platforms. They’re going to have hyperscaler platforms. They’re going to have Foundation model platforms. They’re going to have open source platforms. They’re also going to get agentic capability from apps. Salesforce is going to have there. Workday is going to have their ServiceNow is on and on. Everything is going to be agentic — have agentic capabilities. But we know they’re going to have a directory of these things or roster everything, a policy layer and they’re going to have to make sure they can connect to things. And so we’re seeing our customers — it’s a kind of a no-regrets move to pick this independent and neutral identity layer that can solve those fundamental problems without locking them in 

Okta has two product categories to address both sides of the agent security problem; Okta for AI Agents became generally available in April 2026 and provides enterprises with centralised visibility into agents with identity governance capabilities; Auth0 for AI Agents is for developers building AI agents and it helps developers ship secure agents inside their products; Okta had strong pipeline generation in 2026 Q1 (FY2027 Q1), driven partly by Okta for AI Agents and Auth0 for AI Agents; the opportunity for Okta for AI Agents is not limited to existing workforce customers, and it extends to every enterprise with a multi-platform AI strategy; Okta for AI Agents is integrated with ServiceNow and Amazon Bedrock; there is a lot of interest in Okta for AI Agents and Auth0 for AI Agents, but they are still early and are currently not contributing materially to the business; management believes Okta for AI Agents and Auth0 for AI Agents will become really big products; Okta can give agents specific access to different apps based on access management; the pipeline for Okta’s agentic products is bigger than anything management has ever seen; the pipeline for Okta for AI Agents is bigger than that for Auth0 for AI agents because companies are further along with deploying internal agents than building agents into products; management is already starting to see some pull-through of demand for Okta’s non-AI products because of Okta for AI Agents

Okta for AI agents, which became generally available last month, gives enterprises a single control plane to discover, govern and manage agents across their organization. It is the first and best implementation of the blueprint for the secure agentic enterprise, an industry framework for bringing agents under control by answering the three questions that have dominated my customer conversations over the past several months. Where are my agents, what can they connect to and what can they do? Enterprises need to maintain visibility and control over their sprawl of agents, ensuring they have governed identities, consistent access policies and ways to shut them down to secure every agent into end. Okta provides customers with centralized visibility into agents with identity governance capabilities, including ownership assignment and life cycle management while giving IT and security teams, critical security controls to deactivate rogue agents. For developers building AI agents, Auth0 for AI agents provides the identity foundation to ship secure agents inside their products. Auth0 for AI agents secures agents, APIs and users effortlessly for B2B, B2C and internal apps, all backed by the enterprise grade Auth they already trust. In tangible terms, pipe generation in Q1 was strong, driven in part by these 2 new products…

…Okta is the only modern identity platform purpose-built to sit above the agent ecosystem, and it federates with whatever identity provider a customer runs. That means the opportunity for Okta for AI agents is not limited to our existing workforce customers. It extends to every enterprise with a multi-platform AI strategy…

…We’ve entered into a partnership with ServiceNow that integrates their AI control tower product with Okta for AI agents…

…Okta for AI agents now integrates with Amazon Bedrock Agent core to provide customers with identity governance capabilities for their agents…

…They’re figuring out how they’re going to have secure connections, have a system to monitor where all the agents are, have the ability to support it for multiple platforms. And that’s why you’re seeing the record interest and the record pipeline for what we do with Okta for AI agents and Auth0 for AI agents. The reality is of these products, it’s still early. They’re not materially contributing to the business in Q1. In fact, we’re still being prudent in our guide. They’re not even — they’re a little bit in the guide, but not significant in the guide but it’s going to be big…

…So it’s very natural to say, who can really manage these connections and give me these governed rails for all these secure connections, where my agents are, what they’re doing, what can they do? It’s a natural fit for us. So I think as they build out this infrastructure, we’re in this really great position to have to be a super, super meaningful part of the business and TAM over the next several quarters and several years…

…We tell you who your agents are. There’s a directory of agents. We can scan multiple platforms and multiple systems and give you that source of truth of where your agents are and we can help you set a policy on what they can connect to. Agents can this from teams and they can read this from Slack, and they can read this information from Snowflake and they can you read this from GitHub. So it’s like a single sign-on or access management…

…[Question] You mentioned a building pipeline on AI. I wonder if you might hope with the size of this maybe relative to other products in the past

[Answer] The pipeline is bigger than anything we’ve ever seen…

…[Question] The difference between AI for agents in Auth0 versus Okta, the 2 different platforms. Maybe just help us appreciate the technology aspect of that? And is there like a big difference in size of pipeline between the 2? 

[Answer] They’re both healthy, the Okta pipeline is bigger. And I think that’s because it’s a little bit of a — I think the companies that are figuring out how to manage and deploy internal agents are further along than people building agents into their products and into their websites…

…We’re seeing that the products we’ve offered for AI agents in this blueprint, this vision we have for the industry and agents is raising the strategic level of conversations, which is pulling in other products and helping us displace legacy faster and sell more of our existing products and our newer products into new customers in the base than we would be otherwise. I say that because to make it clear that the AI agent products are still, is still immaterial, the contribution with Okta for agents going GA in April. They had a good quarter, but it’s still a small base. So the pull-through is real already though.

Okta’s management believes that no single company can address the agentic security market; Okta has entered into partnerships with AI leaders ranging from ISVs (independent software vendors) to AI vendors and hyperscalers; the ISVs include ServiceNow, while the AI vendors include Anthropic and OpenAI; Okta is partnering with Anthropic for its Project Glasswing cybersecurity initiative

Neutrality becomes even more important when it comes to technology partnerships and integrations, like the traditional cybersecurity landscape, no single company can address the agentic security market alone. That’s why we’ve partnered with AI leaders from ISVs to hyperscalers to frontier AI vendors, and I’d like to highlight a few of those partnerships today.  We’ve entered into a partnership with ServiceNow that integrates their AI control tower product with Okta for AI agents. Our partnership with Google brings centralized identity guidance and access control to Google’s agent gateway. Okta for AI agents now integrates with Amazon Bedrock Agent core to provide customers with identity governance capabilities for their agents. We were a launch partner for OpenAI’s release of GPT 5.5 trusted access for cyber. And finally, we’re collaborating with Anthropic in a number of ways to testing Anthropic’s preview model as part of Project Glasswing to a new integration between Okta Identity Security Posture Management and the Cloud compliance API.

Okta’s management is pricing agentic products as an increase to a user’s monthly price because (1) management is seeing customers want to consume agentic products via this pricing model, and (2) agents are currently mostly deployed on behalf of users; management thinks pricing models for agentic products will evolve over time and the software industry is still figuring it out; management is seeing that the average deal size for AI-specific deals is much larger than the average deal size for other types of deals; Okta does not have unlimited-consumption AI deals

And so the way we’ve done pricing for our products is exactly in line with how our products have been priced in the past. They’re priced on, it’s an uplift to a named user or it’s an uplift to a monthly active user. Now you might say, “Hey, Todd, but agentic — agents are this new thing and why are you pricing them on an active user or a named user price?” And that’s for two reasons. One reason is that’s the way customers want to consume it right now. And two, the majority of concrete use cases in the world right now for agents, it’s on behalf of the user. It’s an agent working on behalf of a software developer. It’s an agent working on behalf of a support rep. It’s an agent working on behalf of someone in accounting. So it’s very natural how they want to buy it and how they’re actually being used. So it’s an uplift on a named user, and it’s uplift on an active user.

Now we fully understand that, that’s going to evolve. And there will be more autonomous agents that have to be priced not by user base or not an extensive user. They have to be the unit has to be the number of agents. It’s a little bit tricky because it’s very hard to define the number of agents because some person might say, “Oh, I have 1,000 agents, but it’s really kind of 1,000 copies of the same agent or 1,000 instances of the same agent. In other cases, it might be literally 1 instance of an agent acting for many, many different use cases. So the industry is kind of figuring that out, and we’ll figure that over time how to monetize and price that now…

…The average deal size for these AI-specific deals is significantly larger than the average deal size for the rest of the company…

…[Question] You guys are doing deals where basically the contract is for an unlimited number of agents. The good thing is in those deals, I’m hearing that the spend is very, very high relative to your existing spend and other products. But the risk there is what if the customer doesn’t get to unlimited agents, so there’s downside renewal or other things that could happen. So how are you approaching that dynamic with customers in factoring in the contracts?

[Answer] There’s no unlimited. If there is unlimited, it’s time bound. So there have been some deals where we’ve done like a year, and then it’s like we’re going to figure out after a year what the — how the use case really unfolded and how to snap it back to the kind of normal pricing model. But there’s no — it’s not unlimited in the sense of time and volume.

Okta’s management is seeing the leaders of AI companies being worried about the durability of their revenues

If you look at the — particularly the AI landscape, I was having dinner with a bunch of CEOs of companies, different sizes, and everyone is super worried about their spend in their products and their revenue in their products being not durable because it’s token spend, and they worry about the products being used and then and maybe someone is going to look at the spin and stop spin the token spend

Okta’s governance-related product portfolio is still performing well; Okta’s privileged access product is not as mature as the governance portfolio

We’re very excited about our AI products. But governance continues to be a strength for us. We talked over the past couple of quarters about how governance has evolved from being primarily a cross-sell add-on product to also now being a land product. And we are seeing sizable land opportunities, starting with governance at some companies that are displacing systems that they’ve had in place. So we’re very excited about the enterprise readiness and robustness of our governance product and the rural deployments…

…Privileged access is further behind governance on that maturity curve. It came to market a little bit later. We’re continuing to invest heavily in it and we did an acquisition back Q3 at Axis to add capabilities to that. And we’re continuing to invest in that breadth of portfolio, kind of rounding out the identity security fabric in addition to all the momentum that we’re seeing with our great success in the AI product.

ServiceNow wanted kill switches for rogue AI agents; Okta can help sever connections and access that any rogue AI agent has

ServiceNow is, as you mentioned, super interesting. They are — their product strategy is they want to be the control tower for all AI agents. And what is, what they were really interested in was this kill-switches capability. When agents go awry and agents aren’t following the policy, how do you shut them down, and that can mean a lot of different things. That can mean actually stopping the running of the agent that can mean quarantining the agent at a network level, there’s many different strategies. The one thing we do really well and that they wanted from us is the ability to sever the connections, the access tokens, the actual logical connection at the authorization layer to the back-end resources, and we’re really good at that. That’s kind of the core of our product. What can these things connect to, what can they do.

Okta’s management thinks that cybersecurity in the future will take multiple companies to secure, and the large AI model providers cannot do it by themselves

I think in terms of the model providers, how they’re going to play in the broader cyber ecosystem, it’s going to take a village. I think we’ve seen that in cyber forever. I think consolidation in cyber never seems to work. All seems to be — gets to a certain point and then new threats emerge, and the companies that are trying to consolidate cyber have such a hard time integrating amongst themselves. It kind of fractures a part. And I think that will continue. I think cyber in the agentic world is going to take a village, and we’re going to have to make sure it’s integrated together and make sure we have layered defenses. And that’s why I think it’s really healthy to be coming into this conversation with this open mindset of, hey, we have our lane, we’re going to try to provide the best identity foundation in the world and then connect around that in a standard way that helps customers get great outcomes.

The cost of inference is real at Okta, and management thinks more companies using AI models will be scrutinising their inference costs in the near future; management is optimistic that Okta can manage inference costs and drive positive ROI (return on investment)

The cost thing you’re talking about is real, the inference costs and the AI tooling and what it’s driving in terms of expenses. And I think you’re going to see at Okta, and then over the whole industry over the next 6 to 12 months, you’re going to see a little bit more scrutiny in terms of what are you getting from all this, the inference cost you’re spending, how is it translating, which is not surprising given the amount it’s rising across the industry.  And we’re going to come out the other side with more balanced ROI-driven investment portfolio of how we spend these things. And we’re optimistic about how it’s going to work out very well for us.

Salesforce (NYSE: CRM)

Leading AI companies are all Salesforce customers, in particular, Slack customers; Slack was half of Salesforce’s $1 million-plus wins in 2026 Q1 (FY2027 Q1), up 80% year-on-year; Slack is AI startup Anthropic’s core operating system; Slackbot is also a MCP (model context protocol) client; Slack MCP has seen 1 million users in 6 weeks; Slack’s agentic work units (AWUs) was up 350% sequentially in 2026 Q1 (FY2027 Q1); management thinks that in 2 years, there will be more agents using Slack than people; management thinks agents need the context and data that resides in Slack; internal usage of Slackbot by Salesforce has led to 3.8 million hours of annualised productivity gains; Anthropic is one of the biggest users of Salesforce’s Sales Cloud; Slackbot has increased the productivity of Salesforce by around 3%; management sees Slack as the place where humans and agents work together; management sees the work graph of enterprises living in Slack, which is already one of the richest work contexts, becoming even richer over time; 3 million custom apps were built by the community on Slack in 2026 Q1 (FY2027 Q1), up 8x sequentially, and 250,000 of the custom apps were 3rd-party AI agents, which doubled sequentially; management sees Slack on a fast track towards being a $10 billion cloud

OpenAI, Anthropic, Google, companies building the future of AI, all of them Salesforce customers, all of them Slack customers, building these incredible new capabilities with Agentforce…

…Slack, which every AI company in the Bay Area here is using to run their business, including OpenAI and Anthropic, transforming our customers into agentic enterprise. Slack was nearly half of our 1 million-plus wins this quarter, up 80% year-over-year…

…Anthropic calls Slack its core operating system, and that’s what Slack is becoming for every enterprise. All of our apps are Slack first. So now a service agent can summarize a case, update the record, escalate to a human right in Slack. And Slackbot is also an MCP client, so you can tell it to create a purchase order in NetSuite or update a project in Jira, and it happens, no switching tools. We’ve seen 1 million users of Slack MCP in the first 6 weeks, and Slack AWUs grew nearly 350% quarter-over-quarter.

In 2 years, there’ll be more agents using Slack than people. Every one of those agents needs the context and the data and the insights directly from Slack. Every workflow needs the data. Every action needs the integration and every customer needs to see what’s happening across the entire business. We have the largest collection of trusted CRM context ever assembled between Data 360, Informatica, MuleSoft, Tableau manage and deliver all that context so that any agent can reason, act, and deliver real outcomes…

…Slackbot, which is embedded directly into the flow of work, is now our fastest adopted AI tool in Salesforce’s history, driving 3.8 million hours of annualized productivity gains for our employees…

…Anthropic is one of our biggest users of CRM of Sales Cloud…

…Slackbot is our personal assistant. It has increased the productivity of the whole company around 3% more or less…

…When we say agents and humans work together, you experience it in Slack. When you’re in a channel and suddenly in a lot of these — especially I see it now in my engineering channels, like half the time, somebody puts a question or a request on a Slack channel and the agent is listening and answering it, developers do a PR request in Slack. And then suddenly, the agent is picking up and trying to do it. They want status reports. So I think Slack is where people can really understand the manifestation and they’re all asking questions as a human and Slackbot is even a better way of articulating that in a packaged way…

…Because that work graph that will become one of the richest work context in the enterprise is getting richer and richer. So we build — I mean, the community built 3 million custom apps on Slack in Q1. That’s 8x quarter-on-quarter. I mean there is a huge boom. Out of those custom apps, there were 250,000 that were AI agents that were built, third-party AI agents, and that grew more than doubled in quarter-on-quarter, grew eightfold year-on-year…

…I’m not giving guidance by what I’m saying, but sales is a $10 billion cloud already. Service is a $10 billion cloud already. Data is already a $10 billion cloud. I think when we see the growth rate that’s happening inside Slack, you saw the ACV was incredible in the first quarter. This is going to be fast track from something we bought with less than $1 billion that I’m sure we’ll be talking in short order about Slack being a $10 billion cloud as well.

Agentforce ARR reached $1 billion in 2026 Q1 (FY2027 Q1) (was $800 million in 2025 Q4, up 169% year-on-year); Agentforce and Data 360 reached nearly $3.4 billion in ARR (annual recurring revenue) in 2026 Q1 (FY2027 Q1) (was $2.9 billion in 2025 Q4, up 200% year-on-year); 50% of Agentforce and Data 360  bookings in 2026 Q1 (FY2027 Q1) were from expansions by existing customers; management recently announced Agentforce Coworker, where every Salesforce application now comes with a built-in autonomous agent; bookings for A1E and A4X, Salesforce’s premium SKUs that include agentic capabilities, was up 60% year-on-year in 2026 Q1 (FY2027 Q1); top 10 customers by AWUs (agentic work units) in 2026 Q1 (FY2027 Q1) increased their total Salesforce spend by 1.5x in the last 12 months; Agentforce allows every user of Salesforce to create agents

We’re seeing incredible demand for Agentforce with ARR now greater than $1 billion. And combined with Data 360 and Informatica Cloud, we’ve delivered $3.4 billion in AI and Data ARR. 50% of Agentforce and Data 360 bookings were from existing customers expanding their commitment.

…Very excited about our new Agentforce Coworker, which we announced last week. If you haven’t heard about that, every single one of our Salesforce applications now comes with a built-in autonomous agent. No complex configuration. You just turn it on. It becomes your coworker, finding answers, taking action, getting work done fast. To give you an idea of the impact that Coworker will have, people search for information inside Salesforce 1 billion times a month. Coworker turns search into answers and answers into action…

…Agentforce ARR surpassed the $1 billion mark this quarter. Our largest applications, sales and service saw year-over-year seat growth with humans and agents both expanding on the platform. Bookings for A1E and A4X, our premium SKUs anchored in sales and service, including the value from our agentic capabilities, grew nearly 60% year-over-year. As customers adopt Agentforce, they expand across our platform. On average, our top 10 customers by Q1 AWU usage have increased their total Salesforce spend by 1.5x in the last year…

…Those of you who are Salesforce users, the millions of people who use Salesforce every day, the search bar is a critical part of how the application operates. Now Agentforce is that search bar. So you can not only search and aggregate and get insights into information throughout every single app we have, but also create agents, and those agents can appear in Slack and Microsoft Teams and other applications, even in an app that’s going to run directly on your phone called Salesforce Coworker.

Salesforce has processed 28.6 trillion tokens to-date in 2026 Q1 (FY2027 Q1), up 152% sequentially (was 19 trillion to-date in 2025 Q4); Salesforce has delivered 3.8 billion AWUs (agentic work units) to-date, up 111% sequentially (was 2.4 billion in 2025 Q4)

To date, we processed 28.6 trillion tokens, up 152% quarter-over-quarter and converted them into 3.8 billion, as I mentioned already, agentic work units for our customers, up 11% — sorry, up 111% quarter-over-quarter. 

Salesforce acquired Qualified in 2026 Q1 (FY2027 Q1); Salesforce has integrated Qualified’s sales development representative (SDR) agent, Piper, into Salesforce; more than 700 customers are already using Piper; Piper is deployed on Salesforce’s website and is engaging with 50% of the website’s traffic, delivering 45% more pipeline than traditional web agents

In Q1, we completed the acquisition of the Qualified and integrated Piper, their SDR agent, into Salesforce. Brought all those great Salesforce alumni back home. More than 700 customers are already using Piper. It’s an incredible success, and we deployed Piper on salesforce.com, as I mentioned. So you’re going to be able to use it firsthand. I think that’s so great. It’s engaging 50% of our traffic and qualifying thousands of leads and delivering 45% more pipeline than traditional web agents.

Salesforce’s management recently announced Headless 360, which makes all of Salesforce accessible through MCP (model context protocol) clients, APIs, and CLA (command-line agent) prompts; since Headless 360’s launch in April 2026, Salesforce has already processed 4.5 million MCP calls and 1 trillion API calls; management thinks Headless 360 expands Salesforce’s addressable market into previously unmonetised areas; management is excited about Headless 360 in 2 areas, namely, (1) Headless 360 making it easier to implement Salesforce with coding agents, and (2) customers getting more value out of Salesforce through Headless 360; management is not seeing customers build in-house applications with Headless 360 to replace Salesforce; Slackbot is an example of a Headless 360 experience; the Headless MCP server for Slack has done 50 million tool calls; Headless is a way for agents to connect to Salesforce with APIs, because agents require slightly different types of APIs than what human developers used when connecting with Salesforce in the past

This quarter, we also announced Headless 360. Again, making all of Salesforce accessible through our MCP clients, APIs, CLA prompts. Headless 360 bringing together the human agents and headless platforms so you can use Salesforce with any coding agent across any surface. It’s going to speed implementations, drive consumption, more actions, more workflow, more data, more intelligence, all compounding across Salesforce. We’re meeting our customers where they are. Since launch in April, we’ve already processed 4.5 million MCP calls into our platform. Q1 alone, we processed nearly 1 trillion API calls, incredible…

…Looking ahead, the Headless 360 strategy that Marc walked through expands our addressable market into surfaces we’ve never previously monetized…

…I think what’s so exciting about Headless is 2 things. One, it’s having a real impact on making it easier to implement with Salesforce. So building out with Salesforce has now become easier than ever because we’ve seen these coding agents, Claude and Codex from OpenAI. As you use these things, what you realize is you need to be able to connect the underlying APIs, which you do through this layer that’s called MCP. And if you can connect those into the coding agents, it makes it faster than ever to implement and deploy Salesforce. And I think we’re seeing that show up in the numbers. Just this quarter alone, Agentforce customers in production grew by 50%. So I think we’re starting to see a little bit of that impact as not just our customers, but also our global SIs across the entire platform, absolutely implementing Data 360, implementing Agentforce, implementing a service. All of this — life sciences, all of this now becomes really just a conversation. So that’s one end.

But the other end is really what we heard from Miguel, which is this is really changing how people get value and consume Salesforce. In my experience, we’re not seeing people take this capability and the coding agents, for example, and try to build all of this stuff themselves. What they want to do is they want to take this capability and they want to use Salesforce in different ways and get more value out of it. So rather than logging into this discrete application and this application and this application to get an answer to one question that might span multiple applications or multiple kind of sources of information, you can now just take these MCP servers and plug them into any tool that you want…

…If you’re a Slack customer, you can get to it right with Slackbot. That’s really a Headless experience as well…

…We announced the Headless MCP server for Slack and Slack has done 30 million — 50 million tool calls…

…When you’re a builder, when you’re out there building something, and this is especially true today because there’s now an ocean of builders that have been created as a result of this coding agent boom. When you go to build something for your business, you, at some point, are likely going to want to connect to Salesforce that is what we see. And it doesn’t matter what platform you’re doing it on. You can be building something on a competitive platform to Salesforce or on Google or AWS or one of our partners. But at some point, you’re going to want to connect into Salesforce. And that’s why those APIs have always been hugely, hugely used. But when you are building with an agent, you need a slightly different type of API. That’s what we call MCP. And so by really putting those MCP servers out and saying, yes, this is how we want people to build.

Informatica has been a successful acquisition, performing the heavy lifting and data management that customers need to move agentic workloads from pilot to production; Informatica has helped drive an acceleration in revenue growth at Salesforce; Informatica’s bookings growth has accelerated significantly since being acquired by Salesforce

Informatica has an amazing acquisition. It performed incredibly well this quarter. It’s doing the heavy lifting and data management that every customer needs to move from pilot to production…

…And now with Informatica as part of Data 360, we’re already unlocking synergies with revenue growth accelerating since the acquisition. This is the flywheel we laid out at our Investor Day, and it’s working. Those signals show up in the headline numbers…

…Informatica was a business that was growing single digit, both on bookings and revenue. In just 2 quarters, we have significantly reaccelerated that the bookings of the chart beyond anybody’s expectation because data is king.

Salesforce deployed Agentforce on its support website 15 months ago and it has already handled 4 million inquiries autonomously; Agentforce now handles 2x what human agents are handling on Salesforce’s support website; Agentforce Sales worked 220,000 leads for Salesforce autonomously in 2026 Q1 (FY2027 Q1), generating a $42 million pipeline; Agentforce Coworker is able to quickly answer questions that would have taken an hour to do so in the past

Since we deployed Agentforce on help.salesforce.com and on 1-800-NO-SOFTWARE, well, only 15 months ago, it’s autonomously handled now 4 million inquiries. It’s now double what human agents are handling…

…Over 25 years, Salesforce has generated tens of millions of leads. We never called back. In Q1 alone, Agentforce sales worked 220,000 leads autonomously, generating $42 million in pipeline, awesome…

…Agentforce Coworker was able to pull together and navigate our complex sales and ERP data to answer questions that just yesterday would have been 60 minutes of swivel chairing between screens and systems. It was pretty cool to see that.

Wine company Vivino is using Agentforce to support 74 million users with just 37 reps; Agentfore has helped Vivino reduce resolution time of customer queries by 70%; cyber security company McAfee has replaced ServiceNow with Salesforce’s Agentforce IT Service; Florida Prepaid is using Agentforce to autonomously handle 75% of business hour calls, and 100% of after-hour calls; cyber security company Fortinet is using Agentforce Sales for predictive lead scoring; Agibank built a sales development representative (SDR) agent with Agentforce Sales

Vivino, the world’s largest wine company supporting 74 million users with only 37 reps, kind of hard to believe, but it’s possible because its agent, Vivina, autonomously handles order status, lookups, account questions more autonomously slashing resolution time by 70%. McAfee has selected our new Agentforce ITSM product or what we call Agentforce IT Service to replace ServiceNow. They are using it for everything, ticket deflection, hardware provisioning, incident management. Florida Prepaid, a college savings plan provider with more than 200,000 accounts is using Agentforce voice to autonomously handle 75% of business hour calls and 100% of after-hour calls…

…Cybersecurity leader, Fortinet using Agentforce sales to power predictive lead scoring. Financial leader, AgiBank now built an SDR agent that instantly qualifies leads on WhatsApp.

Indeed is using Headless 360 to build and deploy Agentforce agents directly from Cursor; Just Eat is using Headless 360 to bring agents into WhatsApp for engaging 350,000 partners across 15 countries; Adecco is excited that agents they are building outside of Agentforce can now leverage Salesforce because of Headless 360; Anthropic’s usage of Slack through 2026 Q1 (FY2027 Q1) has grown 5x partly because they are using Sales Cloud via Headless 360; the presence of Headless 360 has made Sales Cloud even more strategic for Anthropic

With Headless 360, Indeed is building and deploying Agentforce agents right from Cursor and Just Eat Takeaway, one of the leading online food delivery platforms in Europe, we just had them speak to our entire management team with such an amazing story, is using Headless 360 already to bring agents into WhatsApp and other channels, engaging with 350,000 partners across 15 countries…

…Adecco, great customer across the board. They use pretty much every cloud. They went into Data Cloud and Agentforce last year. They did a big commitment in Q1, at the beginning of Q1. They are basically design and AELA, wall-to-wall. They have amazing recruiter agents going there, millions of transactions. They’re moving into voice. When we announced Headless, they called us and they are like, “Wait a minute, this is — let me try to understand what you’re doing.” So now because they are also using other platforms to develop other agents. So they have agents with some of the AI labs that they’re also trying to access our data. Are you saying that now these agents that we are building outside Agentforce can also leverage Salesforce? And we said, exactly, we did it for that. So now there’s going to be a lot of new agents that are going to be accessing our platform…

…Anthropic is one of our biggest users of CRM of Sales Cloud. And obviously, Slack, their usage through Q1 has exploded fivefold because now they are using Sales Cloud from a Headless perspective, and they are approaching it from Coworker, from other applications from Slack, they’re hitting Sales Cloud. So Sales Cloud has become more prominent and more strategic for them than ever because of Headless.

PenFed Credit Union handles 500 transactions every second, and 160 million member transactions annually, and wanted to deliver hyper-personalisation for customers; PenFed Credit Union chose Salesforce to enable the hyper-personalisation and now has 76 agents across various functions; PenFed Credit Union chose Salesforce because it has the products, engineers, and reputation that PenFed Credit Union was looking for in a vendor; PenFed Credit Union built Agent Wingman with Salesforce; in 2026 (FY2027), Agent Wingman will help PenFed Credit Union (1) save $1.6 million, (2) lower call handle time by 10%, (3) lower after-call work time by 50%, and (4) lower held calls by 40%; PenFed Credit Union has agents listening to a phone call with members for transcription; PenFed Credit Union only developed its agentic vision about 2 years ago

[PenFed Credit Union CEO] When we’re competing against 8,000 other firms, we got to deliver hyper-personalization and every transaction, we do about 500 transactions a second, 160 million member transactions a year. They have to be right anywhere in the world real time. So we built our entire platform over the last few years. We went from about 400 platforms down to literally 12 strategic partners. Our call center, our mobile, our web, and our branches all run on Salesforce. Every additional partner or tech siloed capability is a tax on innovation, it’s a tax on speed, and it’s a tax on security. So by building it around Salesforce, I really think it’s taking me 25 years to realize Jim Collins’ Flywheel Effect, we have 76 agents now running across operations, mortgages, IT, HR. All of our areas are adopting it to make our employees be more productive. We like to say they’re bionic employees now. We’re not losing employees. We’re able to add more volume at scale, industrialized scale with the same number of people, and we’re very proud of that…

…How is the decision really made? First of all, does the firm, in this case, Salesforce have the product and service that we need? Second, do you have the engineers, the architects, the professionals to work with my team in order to bring that vision to reality? And then lastly, even if another firm had those first 2, who is the firm standing behind it that can be there through good times and bad times that’s going to stand behind that product or service. When you line up all 3, that’s where a good trusted partnership exists. That’s why we went with Salesforce. So we work with your team literally hand in hand. We said we want to streamline processes. We want to take out latency in the code. We want to do X, Y or Z. Your team was there in the trenches at every level, engineers, architects, building out the vision. But then it’s not just pie in the sky on the white [ sheet ], it’s implementable. We have 76 agents running side by side with our employees. 

A good example is in our call centers. We have Agent Wingman. I’m an aviator, so I think they named it because I like Wingman. Agent Wingman is going to save me nearly $1.6 million this year, has decreased our call handle time 10% this year, 50% reduction in after-call work time and 40% reduction in held calls. So better experience for the member…

…I want my employees to do the knowledge work, building trust in the relationship, not entering what just happened on the phone call. We have agents that listen to the phone call, transcribe it. The human is still in the loop. They approve what was just talked about, but then it’s 360, if the transaction occurred in the branch, web, mobile. So the next person that deals with that consumer, that member, they know exactly the relationship. They know what we might want to sell them next or what they need next for their daughter, their graduation…

…We had the vision when we saw what was possible 2 years ago. You can build it quickly. The most important thing is having the right partner and not to have too many partners. Too many partners slow things down.

UCLA Health has been working with Salesforce for some time; UCLA Health recently consolidated into a single instance of Salesforce’s Health Cloud; UCLA Health recently launched its first experiment with Agentforce, which is a customer-facing virtual concierge; UCLA Health was very cautious about launching a customer-facing virtual concierge

[UCLA Health executive] We’ve been working with Salesforce for quite a few years. But most recently, we’ve consolidated into one single instance of Health Cloud, and we’ve built on top of that with Marketing Cloud, Data 360, and most recently launched our first experiment with Agentforce, and that’s a customer-facing chatbot that just — it’s — right now, it’s only scraping our website to act as a little bit of a virtual concierge to direct patients to where they need to go. It’s helping with find a provider. It’s helping with general inquiries. It’s helping with clinical trials…

…I would say it took a while for us to sort of dip our toe in the water in the customer-facing space. We’re doing a lot on the back end when it comes to research, but this really has an impact on our operations. And we took a lot of precautions. This particular product really helped us from a testing perspective. There were a lot of protocols in place that allowed us to validate every step that we were taking. And that offered a lot of certainty for senior leadership to kind of sign off on the first experiment that we took here.

The use of AI coding tools by Salesforce employees has doubled the amount of features and codes shipped in 2026 Q1 (FY2027 Q1) compared to a year ago; Salesforce’s engineering team has been kept at 15,000 for the past 2 years because of the higher efficiency of the engineers through the use of AI coding tools

In Q1, AI coding tools enabled us to double the amount of features and codes shipped year-over-year, while simultaneously reducing incidents and defects…

…Srini is here at the table. He’s got what about 15,000 engineers, and you’ve had the 15,000 engineers for about 2 years, it’s been mostly flat, right? And I would say that the reason it’s been mostly flat is because we have been using AI to create more efficiency for our engineers. And especially this year, now with these new coding agents, we’re seeing even more dramatic capability.

The biggest way for Salesforce to monetise AI is by selling Flex Credits

The biggest way that we have to monetize AI is with customer-facing use cases by selling Flex Credits, by putting fuel in the tank 6 of the top 10 deals, 6 of the top 10 deals were AELAs, unlimited enterprise license agreement, where we threw in a bunch of Flex Credits and customers are deploying use case after use case, channel after channel.

Salesforce has been able to protect its margins despite investing in AI because it’s not hiring more engineers as a result of higher productivity; Salesforce’s headcount is growing only because of an expansion of the sales team, and that is because AI agents cannot actually sell; Salesforce’s margins are protected despite the company spending a lot with OpenAI and Anthropic

Srini is here at the table. He’s got what about 15,000 engineers, and you’ve had the 15,000 engineers for about 2 years, it’s been mostly flat, right? And I would say that the reason it’s been mostly flat is because we have been using AI to create more efficiency for our engineers. And especially this year, now with these new coding agents, we’re seeing even more dramatic capability. So that’s a key part of our margin story is that we’re not hiring more engineers. We’re not hiring more GA. We’re mostly expanding only in one area.

You can see head count has grown, but it’s mostly growing in Miguel’s area in sales because I think we all realize the one thing that we’re doing here with you selling and communicating that agents are not exactly doing that. They can qualify, okay? They can provide service. But in sales, we still scale because there are so many different parts of the market that we have to get to. So that will be a critical part of expanding our company, but at the same time, expanding our margins…

…It’s not that we’re not spending a lot with OpenAI. We are. We’re using their platform. We’re using Codex, their coding tool. We’re using Anthropic. We’re using their platform and their coding tool Cowork. We’re using both of these platforms.

Sea Ltd (NYSE: SE)

Sea’s management has used AI in Shopee’s search and recommendation systems to improve product discovery; Shopee has AI content tools for sellers to create better product listings, which has helped the purchase conversion rate improve by 14% year-on-year in 2026 Q1; AI-powered advertising personalisation and targeting contributed to Sea’s 80% advertising revenue growth in 2026 Q1; management is exploring an AI shopping assistant for buyers that can deliver personalised recommendations and cost savings; management is building an AI agent for sellers that can be a business advisor; the AI shopping assistant and AI agent for sellers are both in the early stages

We have taken a practical resource-oriented approach, embedding AI into our operations to drive better outcomes for our users and greater efficiency across our platform. This is already making a meaningful impact. AI-powered enhancements to our search and recommendation algorithms have led to better product discovery. Our AI-generated content tools are helping sellers create more compelling product listing. These efforts supported a 14% improvement in purchase conversion rate year-on-year in the first quarter. And AI-driven personalization and targeting helped to contribute to the strong year-on-year ad revenue growth we saw this quarter…

…For buyers, we are testing an AI shopping assistant that leverages purchase history and preferences to deliver personalized recommendations and optimize savings.  For sellers, we are building an AI agent that acts as a virtual business adviser, providing diagnostic and actionable insights on shop performance. Both are in early stages with plans to roll them out more widely over time.

Around 80% of Sea’s customer queries are now handled by its AI chatbot; AI has reduced Sea’s customer service cost per contact by 30% year-on-year in 2026 Q1 while maintaining satisfaction

Around 80% of customer queries are now handled by our AI chatbot. AI usage helped reduce customer service cost per contact by around 30% year-on-year, while maintaining high satisfaction rate.

Tencent (OTC: TCEHY)

Tencent has made significant progress in its Hunyuan large language model in the last 6 months; Tencent has overhauled its foundation model team and system and processes for pretraining and reinforced learning; management has moved away from from chasing public model benchmarks that can be gamed and has chosen to evaluate Tencent’s models with the latest exams, human tests, product feedback and in-house tasks; management launched Hunyuan 3 Preview in April; Hunyuan 3 Preview was designed to deliver comprehensive intelligence with cost efficiency; Tencent reduced Hunyuan 3 Preview’s inference costs significantly by designing inference together with the model; Hunyuan 3 Preview is already deployed across 131 of Tencent’s products, including Yuanbao, QQ, and WorkBuddy; Hunyuan 3 Preview has been ranked 1st on OpenRouter by token usage since April 28, even after its free period ended on May 8; the Hunyuan team is already working on a larger parameter model; Hunyuan 3 Preview is a smaller model, but is still very capable; Hunyuan 3 Preview is significantly better than Hunyuan 2 for agentic work; Hunyuan 3 Preview’s total token usage is at least 10x compared to earlier generations; Hunyaun 3 is currently not fully integrated into Weixin because it depends on Weixin’s own evaluation on what’s the best model for users; the adoption of Hunyuan 3 Preview in actual use cases has been much better than management expected 

Over the last 6 months, we have made significant progress on our Hunyuan large language model..

…We started the initiative by completely overhauling our foundation model team, centering around newly added elite AI researchers and engineers with deep expertise in large language models. Our new team is young, energetic and cohesive, enabling us to make progress quickly in this highly dynamic AI era. 

In February, we reengineered the system and process for pretraining and reinforced learning from the ground up. We rearchitected the infrastructure to support robustness, scalability and efficiency across pretraining, data and reinforcement learning. On data, we expanded our data set significantly and strengthened our data collection, cleansing and synthesis capabilities with a focus on data quality. On training, we upgraded the process for pretraining and supervised fine-tuning, and we scaled up reinforcement learning. And for evaluation, we’re moving away from chasing public benchmarks that can be gamed. Instead, we evaluate our model through the latest exams, human tests, product feedback and in-house tasks to see how the model actually performs in the real world.

In April, we launched Hunyuan 3 Preview. When we set out to build this model, the purpose was to build a cost-efficient and solid model for diverse applications and derisk scaling toward larger models. The core design principles behind Hunyuan 3 Preview was to deliver comprehensive intelligence and cost efficiency, optimizing it for real-world deployment. We moved beyond narrow expertise and towards comprehensive intelligence such as integrating reasoning, long context understanding, instruction follow, dialogue, coding and tool-use capabilities. And by codesigning inference with model, we’re able to reduce costs significantly so that the intelligence is economical enough to be used at scale. Hunyuan 3 Preview has delivered on these expectations.

The model has already become a leading reasoning model in China and has proven effective in real-world software engineering and other productivity agent tasks. Internally, the model has been deployed across 131 widely used internal products, including Yuanbao, QQ and WorkBuddy, providing valuable feedback and iterative improvement vehicle design process. And externally, Hunyuan 3 Preview has been well received by users and developers in real applications. It has ranked first among all models available on OpenRouter by token usage since April 28 and continued its lead even after its free period ended on May 8…

…Our Hunyuan team is already working on a larger parameter model, leveraging our infrastructure and learnings from Hunyuan 3 by aggregating bigger and better data sets and scaling more powerful reinforcement learning, we can strengthen the model’s contextual understanding, enhance its agent capabilities in areas, including coding and increase the model’s general intelligence. Through codesigning and collaborating with other Tencent product teams, we are optimizing data set selection and focusing reinforcement learning for high-value use cases…

…We have given a pretty comprehensive overview of Hunyuan 3. And as you can see from the prepared remarks, it’s more intelligent and it’s actually very strong in terms of reasoning despite being a smaller model. And at the same time, it has significant improvement vis-a-vis Hunyuan 2 on agent capabilities…

…The total token usage is actually at least 10x compared to Hunyuan, so that’s the clear indication that Hunyuan 3 is actually well designed…

…In terms of the integration into the Weixin workflow, I think it will be a step-by-step process. And Weixin itself actually sort of have been always using some part of their products, Hunyuan 2 and they upgraded already to Henyuan 3. And in some cases, they use different models and they evaluate different models and evaluate what’s the best model to use for their users, right? So as Henyuan 3 continue to be getting better and better, then they will be adopting more…

…if you look at how this is received in the actual use cases, it’s actually better than our expectation by quite a bit.

Tencent’s management thinks agentic AI is a breakthrough use case for AI; management thinks agentic AI first delivered value in coding through enhanced productivity and is now shifting to more workloads and occupations; management thinks Tencent’s apps, such as Weixin, Yuanbao, and more, are great avenues for users to control AI agents; in the future, management will enable AI agents to access Tencent’s Mini Programs as AI skills; management sees Tencent having a lead in agentic AI deployment through the leading DAU (daily active users) of WorkBuddy; Tencent’s agentic products, CodeBuddy and WorkBuddy, are still early in their lifecycle but currently have strong organic growth and high retention rates; the high usage of Tencent’s agentic products is a virtuous feedback loop for the company, as more usage leads to insights for product development, which leads to more agentic usage, and as agentic usage grows, token usage in Tencent Cloud also grows; management thinks the breakthrough of agentic AI as a use case is a very recent phenomenon

It has become increasingly evident that agentic AI represents a breakthrough use case after AI chatbots have become popular. Agents are more valuable in uplifting productivity from initial use cases supporting programmers in creating code, such as with our product CodeBuddy to now catering to a wider range of workloads and occupations such as with Claws and WorkBuddy. These breakthroughs were made possible by more powerful models and by the hardness infrastructure that allows models to utilize tools and act as interfaces that enable users to manage agents effectively.

Our platform inherently has many benefits of hosting AI agents as users can control AI agents through our communications and browsing interfaces such as Weixin, WeCom, QQ, Yuanbao and QQ Browser in addition to third-party applications…

…And in the future, AI agents will be able to access our Mini Programs ecosystem using Mini Programs codes as AI skills.

Tencent has established an early lead in agentic AI deployment evidenced by the leading DAU of our product, WorkBuddy. While early in adoption cycle, CodeBuddy and WorkBuddy are already achieving strong organic growth and high retention rates among active users and paying users. The high time spent and high-frequency interaction with AI agents among early adopters act as a virtuous feedback loop to Tencent, enable us to identify and provide complementary software and services, which in turn drives increased AI agent usage among a broader enterprise and prosumer user base. As users utilize more AI agents for more complex tasks, paying user conversion increases, resulting in rapid growth in token usage on Tencent Cloud in recent weeks…

…The upturn in sort of productivity AI is really something that’s happened not in the last few quarters or even last few months, but last few weeks. And I think that’s true globally actually, that really, it’s since late in or since the end of the first quarter that the Agentic AI has broken through in terms of its ability to create code, in terms of its ability to make people more productive.

Tencent’s management believes Tencent Video has competitive advantages in creating animated series, partly because of the use of generative AI for storyboarding and producing animation

We believe Tencent Video possesses competitive advantages in creating animated series, including our ability to cross over IP from China literature and our games into animated IP and our use of technology tools such as Unreal Engine and generative AI for storyboarding and producing the animated content. Tencent Music subscription revenue increased 7% year-on-year, driven by growth in ARPU and subscribers.

Tencent’s management has improved the content recommendation model for video accounts, which has led to a 20% year-on-year increase in total time spent on video accounts; management has upgraded the developer toolkit architecture for Mini Programs to enable users to better leverage AI plug-ins; Weixin Search’s query volume was up 25% year-on-year in 2026 Q1, driven by foundation model powered ranking and broadening AI search coverage to include image-based queries

We scaled up the number of parameters and enhanced the algorithm for video accounts content recommendation model, enabling deeper understanding of users’ interest to recommend more personalized and relevant content and total time spent on video accounts increased over 20% year-on-year. For Mini Programs, we’ve upgraded the developer toolkit architecture so users can better leverage AI plug-ins, including CodeBuddy to create and debug Mini Programs…

…Total query volume on Weixin search increased over 25% year-on-year, benefiting from foundation model powered ranking and broadening AI search coverage to include image-based queries.

Tencent’s management sees AI being really helpful for game production in areas such as accelerating 3D asset production and animation, improving the player experience, and delivering better graphics; the use of AI in game production can be directly revenue-generating, and management has seen this happen; management sees Tencent as a global leader in utilising generative AI to improve game production; management’s objective with generative AI in the games business is to speed up content creation and generate incremental revenue; management is not intentionally using AI in the games business to expand margins, even though operating leverage should happen in the games business if AI is applied correctly to boost revenue

AI provides increasingly helpful tools, facilitating our game developers to deliver more content and enhanced experiences. Currently, AI for games is most beneficial in areas, including accelerating 3D asset production and animation, enriching player experiences with intelligent in-game guides and delivering more realistic graphics via AI rendering techniques…

…Generative AI enables us to produce more content faster. And that content is, in some cases, to enhance the overall player experience. But in some cases, it results in direct monetization. For example, if the content is a virtual outfit. And so that’s what we are doing, and that’s what we are seeing. And we think that we’re a China leader and to some extent, even more so a global leader in terms of deploying that capability and achieving that benefit. And the objective at this point is really faster content creation and incremental revenue generation. We’re not prioritizing margin expansion per se. It’s more that as we deliver the revenue uplift that we’re seeing and if we can keep headcount fairly stable, then I suppose mathematically, that combination would tend to result in higher margins over time, but that’s sort of a happy output rather than the intention of the process.

Tencent’s AI Market Plus automated campaign management solution, powered 30% of total advertising spend; management has upgraded Tencent’s runtime advertising recommendation models with a unified transformer-based architecture; Tencent’s video accounts ad impressions grew rapidly year-on-year in 2026 Q1

Our automated campaign management solution, AI Marketing Plus powered around 30% of total marketing services spending from advertisers with us in the quarter. We upgraded our runtime advertising recommendation models with a unified transformer-based architecture. This upgrade provides deeper understanding of user context and the intent while balancing model complexity with system efficiency. By inventory, video accounts ad impressions grew rapidly year-on-year, supported by increased total time spent video views and ad load. We released more inventory of rewarded ads, which deliver high click-throughs for advertisers.

Within the Fintech and Business Services segment, Business Services revenue grew 20% year-on-year in 2026 Q1, driven by higher demand and a better pricing environment for cloud services; Tencent Cloud benefited from AI-related demand across GPUs, CPUs, and storage; management had upgraded Tencent Cloud’s AI agentic solutions, which led to rapid usage growth and token monetisation; Tencent Cloud’s international business increased revenue by 40% year-on-year in 2026 Q1; Tencent Cloud finally has sufficient GPUs to serve all the external demand it’s seeing; previously, management had prioritised Tencent’s internal AI use cases for its AI compute but newer AI compute capacity will be focused on meeting external demand for Tencent Cloud

Turning to Business Services. Revenue in the first quarter grew 20% year-on-year, driven by increased demand and better pricing environment for our cloud services alongside rising technology service fees generated from mini shops e-commerce. For Tencent Cloud, AI-related demand contributed to increased revenue year-on-year across GPU, CPU and storage. We upgraded Tencent Cloud’s AI agent solutions with proprietary security infrastructure, skill hubs and interfaces, contributing to rapidly increasing usage and initial token monetization. Tencent Cloud’s international business grew its revenue over 40% year-on-year as we expanded our global footprint and captured demand for our Platform-as-a-Service solutions, including media processing services and TDSQL cloud database…

…For Tencent Cloud, where until now, we actually haven’t had sufficient GPUs to begin to service the external demand, the KPIs will be more revenue and market share related…

…We’ve already made the choice and paid the price in that we have prioritized a multiplicity of internal services ahead of Tencent Cloud…

…And the reason why we have been able to support all of these at once is because we have not been active in leasing out GPU capacity in Tencent Cloud. Now looking through the rest of this year, as the supply of China design GPUs progressively ramps up, then we’ll be remedying that situation, and we will be making more capacity available in Tencent Cloud and consequently driving up Tencent Cloud’s rate of expansion. But that’s where the trade-off has been made that we have been consciously late to monetize the AI opportunity through Tencent Cloud because we’ve been simultaneously supporting a number of AI initiatives internally.

Tencent’s operating capex in 2026 Q1 was up 18% year-on-year and up 84% sequentially because of higher server investments; non-operating capex was down 36% year-on-year (was RMB 1.1 billion in 2025 Q1); free cash flow was up 20% year-on-year, and up 67% sequentially

Operating CapEx was RMB 31.2 billion, up 18% year-on-year and 84% quarter-on-quarter as we accelerated investment in server infrastructure. Nonoperating CapEx was RMB 0.7 billion. Free cash flow was RMB 56.7 billion, up 20% year-on-year, driven by growth in games, gross receipts and advertising billings, partly offset by higher server infrastructure and compute spending. On a Q-on-Q basis, free cash flow was up by 67%, reflecting seasonally higher game gross receipts and the timing of certain seasonal accounts payable settlements, partly offset by higher server infrastructure and compute spending.

Tencent’s management thinks it’s still too early to determine the impacts that agentic AI can have on the e-commerce industry, but they don’t see agentic AI as a risk to Tencent’s advertising business

[Question] With agents increasingly potentially replacing the traditional click-throughs on the web pages and also the apps, could management share your view on the future advertising pricing and also the resulting impact on advertiser budget?

[Answer] It’s certainly more of an issue potentially for e-commerce companies than it is for us because users actively choose and desire to spend their time watching short videos or listening to music or consuming content or chatting with their friends versus generally speaking, when users spend time on e-commerce, it’s because they’re trying to find the lowest price. It’s not because they necessarily enjoy that process. So to the extent that AI agents play a bigger role in the future in facilitating price comparison, then it’s possible that users will spend less time on e-commerce sites and be less exposed to ads than they are today, while the AI agents can scan infinite listings and therefore, not influenced by ads the way that human beings with a finite attention span are influenced. All of that said, there’s been many prior iterations of price comparison services, including search engines and the big e-commerce companies are generally thrived despite the existence of those price comparison services. So I think it’s premature for us to sort of have a definitive view at this point on how it will affect our friends in the e-commerce industry. But we don’t see it as a primary risk for Tencent.

Tencent’s management continues to see Tencent increasing capex substantially in 2026, especially in 2026 H2, to meet AI-related demand; Tencent’s AI-related capex in 2026 will be focused on AI chips designed by Chinese companies; the KPIs management is looking at to determine the ROI (return on investment) of AI-related capex includes (1) revenue and profit for the advertising and games businesses, (2) intelligence, usage, and token consumption for the new AI products, and (3) revenue and market share for Tencent Cloud; Tencent Cloud finally has sufficient GPUs to serve all the external demand it’s seeing; in management’s eyes, the ROIs on AI-related capex have both near-term and long-term components, with advertising being a near-term example and Hunyuan being a long-term example; previously, management had prioritised Tencent’s internal AI use cases for its AI compute but newer AI compute capacity will be focused on meeting external demand for Tencent Cloud

We are seeing increased demand, both from internal products as well as from external users of our model for our AI-related services. And we had previously guided that we’ll be increasing CapEx this year versus last year, and we’re now more affirmative, more confident in that guidance. And we and you should expect a substantial increase in CapEx, especially in the second half of this year as more China designed ASICs become available to us month by month through the year…

…At a high level, for our existing activities such as advertising and games, the KPIs would be more revenue and profit related. For our new AI products, the KPIs would be more capabilities, how intelligent is our foundation model and usage, how much token consumption is happening on world body related. And then for Tencent Cloud, where until now, we actually haven’t had sufficient GPUs to begin to service the external demand, the KPIs will be more revenue and market share related…

…AI includes a range of sort of shorter cycle investments as well as longer cycle investments. And so if we buy GPUs and we deploy them into our ad tech, then that’s a relatively short-cycle investment. The GPUs yield better targeting, higher click-through rates and higher revenue and profit on a pretty accelerated basis. On the other hand, when we deploy GPUs into our Hunyuan foundation model, that’s something which we view as important for our franchise and where we’re taking a longer-term view…

…We’ve already made the choice and paid the price in that we have prioritized a multiplicity of internal services ahead of Tencent Cloud…

…And the reason why we have been able to support all of these at once is because we have not been active in leasing out GPU capacity in Tencent Cloud. Now looking through the rest of this year, as the supply of China design GPUs progressively ramps up, then we’ll be remedying that situation, and we will be making more capacity available in Tencent Cloud and consequently driving up Tencent Cloud’s rate of expansion. But that’s where the trade-off has been made that we have been consciously late to monetize the AI opportunity through Tencent Cloud because we’ve been simultaneously supporting a number of AI initiatives internally.

Tencent’s management thinks society is still at a very early stage in terms of AI diffusion; management thinks many new kinds of products will appear, beyond agentic AI; management believes that it’s much more important to find high-value use cases in AI as compared to focusing on gathering users because AI is expensive to produce for each user, unlike the internet which supports infinite scaling of users; management thinks building a subscription model for consumer AI in China is very difficult compared to the USA because the USA’s living standards are high and its population has a habit of paying high prices for subscriptions; management thinks the consumer AI market in China will not be a winner-takes-all market; management thinks it’s still early days for monetisation of AI in e-commerce and advertising even in the USA

In terms of how we think about the different products, we felt this is actually sort of a very early stage in terms of AI diffusion, right? And we would see many different products coming up going forward. Initially, it was chatbot and everybody felt chatbot is actually the king of the product. And then suddenly, you have a coding that came up and this becomes sort of even more eye-catching and less significant use case because it’s very high value, right? And now we are seeing sort of agentic capability proliferating right? And I think that would actually allow AI to be diffused to different industries, and you have many different agents coming up, which can help you to do work, right? And there’s going to be new products coming up. So I think that would continue to propagate.

And I think to some extent, right, you actually have to — in the AI world, you actually have to find a high-value use case as opposed to sort of just purely focused on DAU because the difference between the AI revolution and Internet is that this is about intelligence and intelligence manifest its value in sort of how much people are willing to pay for it. And at the same time, the intelligence is not free, right? In the Internet world, you basically sort of have mostly existing information. And then you also create some new information and content, but then that’s a fixed cost and then sort of the variable cost for delivering is actually very small, right? You only have to pay for bandwidth. and the compute sits on people’s devices, right? And as a result, you can almost like go for infinite scaling. But in this case, right, every single delivery of a DAU actually cost you quite a bit. And as a result, you can’t just apply the same logic as Internet and apply it to AI. And I would say the ability to find high-value use cases is going to be as important, if not more important than just sort of blindly get a lot of use DAU and user time…

…In terms of the 2C monetization, I would say it’s actually not easy, right? If you look at global standard in the Western market when the paid service is actually very well penetrated and the living standard is actually very high. So the subscription price in the Western market is multiple times of what the equivalent service in China is like, be it music service or be it video service. The paying penetration is probably in the single digit, right? And — and when you sort of applied it to China, I think the subscription model is not going to be that big for the China market…

…I think the more important implication is that when you have to have payment to support a service, then most likely the service is not going to be a winner take-all business. It would basically sort of be supporting multiple players who would have a share of the market and each one of them would sort of have some kind of users and some share of subscriptions…

…When we look at e-commerce or advertising as a way to monetize, I think it’s also very early for even the U.S. players where the eCPM is actually much higher, right? The leading player has not been able to roll out very robust advertising model.

Tencent’s management sees Tencent as having many more flagship internal use cases for AI as compared to the hyperscalers in the USA

And so I think most big tech hyperscale companies with cloud businesses have one flagship internal use case where they’re allocating a large number of GPUs. We have multiple flagships. We have the foundation model. We have agentic developments within Weixin. We have — support. We have the AI deployment for advertising for games, now also for the WorkBuddy and CodeBuddy use cases. 

Tencent’s management thinks policy restrictions from the USA and limited manufacturing capacity in China are the reasons why there was a supply shortage of GPUs in China; the GPU supply shortage in China is now easing because there’s more capacity from China fabs and other foreign fabs to manufacturing China-designed AI chips; management does not see any supply shortage in China for CPUs and other networking chips; management is seeing that the suppliers of CPUs and networking chips are not raising prices indiscriminately over the short-term; management is seeing that the suppliers of CPUs and networking chips are negotiating long-term contracts with customers, and they are looking for a variety of customers 

The reason why there’s been a GPU bottleneck that’s been much more pronounced in China than elsewhere is a combination of policy restrictions on certain foreign design GPUs being brought into China and then the China design GPUs facing limited fab capacity within China. And as a result, the country has really been short of GPU or ASIC capacity. And that’s now being addressed because the China designed ASICs are seeing more supply from fabs within China as well as more supply from fabs in neighboring countries.

But by contrast, we haven’t faced those sort of artificial additional constraints CPU or networking chips. We’ve been a big buyer of CPU and networking chips for many years before GPUs became such a big presence in data centers. We have very long-term relationships with the companies that supply the CPUs and supply the networking chips. And on their side, while one might think that these suppliers would be sitting back and just selling at the highest possible price into the spot market, that’s not actually the reality. The smart suppliers are taking very conscious 3- to 5-year forward views and negotiating long-term agreements in order to give them certainty of their revenue outlook over the next 3 to 5 years. And when they’re deciding with whom to sign those long-term agreements, they’re looking to work with a number of partners, not just a single partner, and they’re looking to work with partners who have been there for many years already and will be there for many years to come and ideally with partners whose demand they believe will grow substantially over time. And happily, we fulfill all of those criteria. We’ve been a big customer for the Intel and AMD and so forth for many years. We’ve been progressively growing our volume with them for many years, and they believe it will continue to progressively grow our volume for many years to come.

Veeva Systems (NASDAQ: VEEV)

Veeva’s management sees the company changing from an industry-specific application provider to an industry-specific application and agent provider; management wants Veeva to support both human users and agentic users; management is seeing pharmas leaning into a new technical architecture called MAAP (models, agents, and applications); management sees pharmas wanting to see AI in Veeva’s applications; management is thinking of building very specific agents that would go the last-mile and automate standardised actions for pharmas, and management thinks Veeva can lead in this area

Veeva is moving from an industry-specific application company to an industry-specific application and agent company. In our first chapter, we became the leader in applications. In this next chapter, we intend to also lead in industry-specific agents. This includes agents that support human users, as well as agentic labor, which represents an entirely new market and type of application user…

…[Question] As we think about pharma appetite for AI applications more broadly, I’m curious what areas you think they lean into first

[Answer] It’s not that they’re thinking mainly about transition from applications into AI applications. What they’re really leaning into is this new technical architecture, we call it the MAAP architecture of Models, Agents and Applications. So the applications that they get from Veeva, they’re looking for them to be more efficient, to have AI in there and help the users. What they really want to get to be is an agentic biopharma so that agents can do a lot of the work. And so the humans can do the more higher value work…

…Let’s just say there’s 100 million documents collected from clinical research sites around the world every year having to do with clinical trials, they have to be checked for quality and they have to be sorted into the right places. That’s work that agents can do, it’s difficult, specific work, but we can make agents that are very specific on that. Agents that take in a bunch of free text via e-mail or other channels and have to sort it out to see, is this a product complaint? If so, how to handle that? And categorize that? Or no, this is an adverse event. This is the issue with a medicine-making somebody potentially ill, okay? Well, what is that illness? Is that a headache or a throbbing headache? How serious is that? Is that involved in the clinical trial? What drug is that involved with? We will make agents to do that and do those very standard things. And this is an area where I’m enthused because Veeva can lead. 

This is where — just like for cloud applications, you got the very specific industry-specific cloud applications could add tremendous value if you went to the last mile and solve the thing. In industry-specific agents, agentic labor, we may be able to go the last mile and make specific agents that just do the thing for life sciences because we’ll go to that last mile and make it work, we may make agents that are better safety case processors and more reliable than humans.

That’s a heck of a lot of work, but we have a structural advantage to do that because we’re deep in life sciences, we have a consulting in life sciences, and we have the applications that those agents can use, it’s the same reason why Claude is getting very good at Claude Code because they have the agent, the coding agent and they have the model, and they have 2 layers. We don’t have a model we use, but we have applications and the agents. So that is a structural advantage.

Veeva’s agentic products will have different pricing depending on the type of agent

Pricing and packaging also vary by agent. Some agents are charged by usage, while others are part of a fixed-price subscription license.

Veeva recently acquired Ostro, which provides conversational AI for brands to provide patients and doctors with immediate, compliant answers; management believes Ostro can be a significant revenue driver for Veeva; Ostro had no material impact on Veeva’s financial results in 2026 Q1 (FY2027 Q1), but accounted for 25% of headcount growth; the buyer of Ostro’s product is the biopharma company, but the user is a healthcare professional or patient; Ostro is a brand engagement platform; management thinks it’s really hard to do what Ostro is doing; management thinks Ostro will be a really significant acquisition for Veeva; management has organised Ostro smartly so it can retain the speed of a startup

In March, we acquired Ostro, the leader in conversational AI for brands to provide patients and doctors with immediate, compliant answers through an easy-to-use chat experience. Ostro operates as a startup within Veeva and is now an important part of our Commercial Cloud. Things are going well, revenue and pipeline are growing as anticipated, and we have an ambitious product roadmap. We believe Ostro can be a significant revenue driver for Veeva and transformative for the industry, fundamentally changing how patients and doctors get information…

…We also acquired Ostro in the quarter, which had an immaterial impact on Q1 financial results and accounted for about 25% of net headcount growth…

…The buyer of Ostro is the biopharma company, the user of Ostro is the health care professional or the patient. So it’s a brand engagement platform for biopharma companies to help HCPs and patients ask questions and get answers instantaneously and do that in a compliant way. That’s very, very hard to do. It’s hard to do that at scale. It’s hard to do it in a compliant way, and that’s exactly what Ostro does…

…It’s going to play a bigger and bigger role in Commercial Cloud over time, and we see it as a really significant acquisition and a potential long-term growth opportunity for us…

…In an operating model for Veeva, we have a notion of the start-up models in the core models. And in the core models, we’re organized functionally like the central sales team, engineering team, things like that. In the startup model, it’s all fully contained under CEO, and we use that either when the market is very different or when the product really needs to evolve. So Ostro is in the start-up model. Everybody who works on Ostro is fully reporting to the CEO of Ostro. There’s guidance and help from other functional areas of Veeva, but it’s — and they’re certainly inroads like, okay, Ostro doesn’t have to use their own master subscription agreement anymore and all that type of stuff. So it operates as a start-up, they can retain its speed, but it has a really smooth ramp up.

Veeva’s management will soon release standard agents and the ability to build custom agents for all Vault applications; management will soon release Veeva Falcon, an agentic platform and for clinical, regulatory, and safety; Veeva Falcon is on track to be released in November 2026; Veeva Falcon will be the first agentic solution for the industry; management recently talked about Veeva Falcon to Veeva’s customer base, and it was very well received; management envisions Veeva Falcon to be replacing jobs that humans used to do; the presence of Falcon means Veeva’s applications need to be headless; agents within Vault applications are meant for human users and to improve the productivity of human users; Veeva Falcon is not a platform for pharmas to build custom agents; the platform for pharmas to build custom agents would be Vault AI or other 3rd-party agentic platforms; nobody is asking for the kind of solution Veeva Falcon presents, but management believes it’s the way to go; management is very positive on Veeva Falcon; Veeva Falcon will be tackling the simplest and highest volume labour, specifically the processing of documentation related to clinical trials, and processing safety cases; management’s still unsure how Falcon will be priced, but they’re toying with the idea of pricing Falcon on a per document or per case basis; management sees Veeva Falcon as being completely accretive to Veeva; management expects small biopharmas to be among the first customers of Veeva Falcon because the small biopharmas are running all their processes on Veeva; Veeva Falcon reports directly to Veeva’s CEO; the kind of labour Veeva Falcon is designed to replace does not involve CROs (contract research organisations), and Veeva Falcon could in fact even benefit CROs

In August, our standard agents and the ability to develop custom agents will be generally available across all Vault applications. 

We also announced Veeva Falcon, our agentic platform and standard agents that provide agentic labor for clinical, regulatory, and safety. Many of the processes in these areas are ripe for automation. We are on track with our plan to release Falcon for early adopters in November. Delivering agentic labor in this area will be a first for the industry and the quality and control requirements will be significant. Falcon is a disruptive technology trying to solve a very hard and valuable industry-specific problem. It’s an outstanding fit for Veeva…

…Veeva Summits bring the industry together and are key to driving customer success and product excellence. It was a milestone event as we talked about Falcon to a broad audience for the first time. Falcon was very well received, and customers are excited about the potential to lower costs and increase speed in drug development…

…Falcon specifically is at the agent layer and that’s agentic labor. So fully replacing parts — jobs that people used to do. People who used to do these jobs using our applications, now will deliver the agentic labor to do that. So it’s a big new area for Veeva. It’s something we haven’t done before, and that’s why it’s disruptive. Those agents have to become users of our applications, which means our applications have to become very good in operating at a headless manner. Now at the same time, we have agents inside of the Vault applications. So that’s Vault AI inside of the applications. That’s where when people are actually using the application because there’s definitely things that people still need to do in our applications, that’s where the AI agents can help them do it more efficiently, much like you might use ChatGPT or Gemini at your work, okay, that helps you do it more efficiently…

…For Falcon, the actual effort there is taking the path less traveled. So that’s a platform for us to build and operate standard agents to actually solve the problem for the industry. So it’s not really a platform for customers to develop their custom agents. For custom agents that live inside of our applications, of course, they can use Vault AI for that. For custom agents that are outside the applications, there are many agent building tools, and they will dip into the Veeva applications operating in a headless manner…

…In 2012 for the first time we laid out our first visions for Development Cloud. 2014, they got sharper; in 2016 that really became apparent what we were doing. We’re trying to simplify and standardize and integrate the tech of the development area of life sciences. That’s not anything that anybody asked us for, right? That’s the vision that we have, and that’s not anything that anybody has tried to do before. Falcon is the same thing. It’s the same magnitude of disruptive innovation. It’s not giving tooling to people to design agents. This is to designing and operating the standard agents for the industry rather than the industry having to hire humans for those specific jobs…

…I think Falcon is just going to deliver value. It’s going to be great revenue for Veeva, but it’s going to deliver value far above and beyond that for the industry, and that’s going to allow the industry to grow. It’s a disruptive thing. It’s not an incremental thing or a tool…

…[Question] How are you deciding which labor roles to address or to attack with Falcon agents? 

[Answer] I think the most right there ones are actually the simplest and the highest volume. And actually, when you look inside of life sciences, those are the areas where they have a tendency, some of the companies to do some outsourcing today already. So that makes it also — they’re used to outsourcing. Of course, they would outsource that to humans. In Falcon, the first ones we’re looking at are processing of documentation involved with clinical trials, specifically the stuff that comes from clinical sites, the millions and millions, hundreds of million, tens of millions of documents that come from research sites. They need to be collected, inspected for quality, categorized, the metadata pulled out of them, filed in the TMF the right way. So that’s one, the intake and control of documents. Another one is the safety cases, the safety cases that come in, the triage and the categorization and the collection of the safety cases. So those are the 2 main ones, we’ll also take on regulatory health authority correspondences because that’s another high-value one, and there’ll be more…

…[Question] How are you pricing Falcon?

[Answer] You can imagine most likely that Falcon will be charged by the document, most likely. We haven’t fully decided that. You can imagine that safety will be most likely charged by the case. So that’s how that is…

…[Question] On the Veeva Falcon. You’re mentioning the displacement of potential roles at these larger firms. I’m just wondering, is there anything that you would consider timing-wise from an economics perspective. So let’s say, these roles were to move in another direction? Do you think it could potentially cannibalize some of the revenue that you get from those customers?

[Answer] Definitely all accretive because this is not a market we address today. We don’t play in that market today. This is not type of labor or work that we supply. So it’s definitely going to be accretive. And these agents, they need a system of record. You can’t operate them without a system of record. So it definitely doesn’t cannibalize the systems of record…

…Veeva Basics, small biotechs. We continue to win a lot of those that are going on Veeva Basics. And by the way, those will be some of the first consumers of things like Falcon and our other AI solutions…

…Basics are smaller companies, very nimble. Also, they’re running not only our products, but they’re running our processes. So they have an absolute standard configuration of Veeva, where they’re running our processes. So we don’t have to wonder how they have configured Vault or MAAP Vault or done this Vault or with that Vault. They’re running absolute — let’s say, we have over 100 Basics customers in the clinical area, their configuration is exactly the same. How they’re using product is exactly the same. And we operate those systems in a way for the customers. So that’s — if we have our agent working on for one Basics customers, it will work for them all. With the enterprises, the larger companies, our agents have to be a little more adaptive. They have to first go through a phase of, okay, understanding how that customer is using that Vault, testing it out. Okay, I’m going to classify these documents that they’ve previously classified. Do I get the same of what they got. And if so, that’s good. If not, what happened there? Basics is just going to be smoother, very, very smooth…

…Falcon, for example, reports directly to me. This is our first step into digital labor. You can’t — you have to operate that effectively, back when we were the CRM company, way back when before we went public, Vault was this tiny little thing that reported directly to me. Falcon is like that…

…In terms of where can agentic labor play and what can agents do. The best places to do are high-volume repetitive work that actually gets outsourced. So that type of work actually it’s not so much the CROs, it’s other specialized labor providers that do that. So I think this could actually be beneficial for the CROs because that — we can do that lower volume work, which is generally done by the pharma company or a specialized outsourcer. We can do that cheaper, faster, better. That will hopefully allow pharma companies to run more trials, and that’s where the higher margin work is for the CROs.

Veeva’s management believes AI will change the commercial model for pharmas, and Veeva is well-positioned to bring the right solutions; Veeva’s Agentic Call Report in Vault CRM and Ostro help biopharmas capture compliant Commercial Evidence for the first time at scale; there are currently 10 customers live with Vault AI for PromoMats’ Quick Check Agent; management will be focused on commercial content for AI investment to solve the MLR (medical, legal, and regulatory) review bottleneck; management thinks agents on the commercial side will not be a full replacement for field salespeople

While it is early days, AI will fundamentally change the commercial model. This represents a major transformation, and we believe Veeva is well-positioned to help the industry bring the right medicines to more patients through new and better ways of working with AI. With major innovations like the Agentic Call Report in Vault CRM and Ostro’s conversational AI on brand websites, biopharmas are now able to capture compliant Commercial Evidence for the first time at scale. It’s a real breakthrough that allows companies to gain insights and take actions that were simply not possible before AI…

… I am also excited about the progress of Vault AI for PromoMats. We have 10 customers live today for Quick Check Agent, across both small and large biopharma. Commercial content will be a key area of AI investment as we look to solve the MLR content review bottleneck for the industry…

…In commercial, that won’t be — agentic labor there will not be — you’re not going — you’re going to have helper agents that help the field teams do things, but I don’t think you’ll have — you will — you’re not going to replace a field person. That’s about managing relationships, things like that. There may be some things in commercial for example, there’s a medical legal regulatory process that is burdensome and expensive and occupies many parts of people’s time in Life Sciences. I think that can largely be automated, 70% or more with the right agents over time. But the actual field person, I think, it’s going to augment them. 

Veeva’s management expects immaterial AI revenue and margin-impact in 2026 (FY2027)

For this year, our overall expectation had been for AI to be fairly immaterial outside of Ostro. And we’re really focused on getting AI live in all of our customer areas, getting the product excellence, getting to customer success. It starts with that deep value creation for customers. So on the margin side, you also don’t see a material impact, Craig. And in Vault AI, where its usage based on tokens. I think we have a pretty good understanding of what that dynamic looks like, and it’s factored into our guidance. But I don’t expect there to be a material impact on margins driven by AI this year.

Veeva is using AI throughout the company, including general-purpose tools and specific tools; Veeva is using Claude Code from Anthropic and finding great efficiency, which has led to Veeva needing to hire less; management thinks the productivity from AI tools, and the need to hire less, outweighs the cost of tokens

We use AI throughout the company, we’ve got general-purpose tools and then also specific tools and major functional areas. Probably the most significant place for using it is around the product because that’s where we spend the most. And so you heard Peter mention earlier, in product engineering, we use Claude Code, and it’s come a long way. So we’re seeing great efficiency from that tool. And I think in general, that means we’ll hire a little less than we would have and accomplish more than we would have and go a little bit faster. But for us, it’s more about productivity and the combination of hiring a little less, accomplishing a little more, we think easily outweighs the token cost, and that’s all factored into our guidance.

Wix (NASDAQ: WIX)

Base44 has reached $150 million of ARR, or annualised recurring revenue (was $100 million in March 2026); Wix Harmony and Base44 can now be accessed within popular AI chatbots such as ChatGPT and Claude; management recently released Superagents inside Base44; Superagents allow users to build and deploy autonomous AI agents without coding; Superagents can run continuously in the background without any manual intervention; Base44 users can interact with their Superagents through popular messaging apps such as WhatsApp and Telegram; Base44 now has better app-design tools; Figma is now integrated with Base44; Base44 is currently incurring significant AI processing and compute costs as usage ramps, but management believes the costs are front-loaded as new Base44 users tend to consume more AI inference bandwidth during their initial build phase; Base44’s user behaviour and cohort quality look positive, with retention improving, and monetisation steadily increasing; management has been lowering inference costs in the core Wix business through optimising 3rd-party models, open source models, and building a proprietary LLM, and management expects to apply the same strategy to Base44’s AI costs; use-cases in Base44 remain wide, but management thinks specialisation will happen over time; some use-cases seen in Base44 are also applicable for business owners on Wix

Base44, which is now the leading AI-powered application creation platform in North America (per Similarweb data) with ~$150 million of ARR as of May…

…Both are now accessible within ChatGPT, Microsoft Copilot, and Anthropic’s Claude. Users can type “@Wix” or “@Base44” in these platforms, describe their idea, and a full website or application is created in conversation and managed there too, without any context switching…

…In March, we unveiled Superagents, a new experience inside Base44 that lets anyone build and deploy their own autonomous AI agent simply by describing what they want it to do. Base44 automatically builds the underlying workflows, connects the necessary tools, and deploys the agent. No coding, no configuration and no infrastructure to manage. Once deployed, Superagents run continuously in the background, responding to triggers, schedules, and real-time events, executing tasks without the need for any manual intervention. It can connect to third-party platforms and applications, remember preferences and priorities across conversations, and become more effective over time. Users can also interact with their agents directly through iMessage, WhatsApp and Telegram – wherever they are already messaging…

…Base44 now includes a fully rebuilt set of tools for shaping how an app looks and feels. Users can set colors, typography, and overall style across their entire app from one place, with any change carrying through automatically. Images, documents, and data files can be uploaded to an asset library or generated on the fly, and pulled into any app directly from the visual editor or chat…

…Design screens in Figma, paste the frame link, and Base44 builds a working app on top of it. The layout stays intact, and users go straight from design to a live app…

……Creative Subscriptions non-GAAP gross margin was 80% in Q1’26, down from 84% in Q1’25. Creative Subscriptions non-GAAP gross margin in our core Wix business was stable in the first quarter as AI costs remained minimal while we carefully controlled costs as we scale our platform, particularly Harmony… Creative Subscriptions non-GAAP gross margin was driven by accelerating contribution from Base44, which is incurring significant AI processing and compute costs as demand and usage continues to ramp. We believe these AI costs to be front-loaded as new Base44 users consume more AI inference bandwidth during their initial build phase…

…We also saw positive signs in the user behavior and cohort quality of Base44. Retention is improving as more users are choosing annual subscriptions, either through new purchases or renewals. Monetization is also steadily increasing, resulting in stable TROI even as marketing spend stepped up in the first quarter…

…We have been lowering inference cost of users by optimizing third-party AI model usage, leveraging open source models and most recently building our own LLM to power Harmony. As we apply this strategy to more of our products, particularly Base44, we believe that the large majority of these AI costs will be firmly in our control…

…About the Base44, I think we’re happy actually to say that we’re still using — we’re seeing a very wide variety of use cases. And it’s really — some of it is personal uses, some of it is solopreneurs, some of it is small businesses. And we think that there’s going to be more and more specialization that’s going to go and happen throughout the platform over time as we understand what is — where there is differentiation happen between those different use cases and where everyone can benefit from the generalized platform…

… I think there’s another opportunity that is very interesting, which we’re seeing is that some of the more small business-oriented use cases can also be relevant to applications needed by business owners that on Wix.    

Wix’s management thinks the differentiation for website builders is not in the AI models, but in the experiences built around the models, and Wix has the necessary knowhow

As powerful AI models become increasingly accessible across the industry, I believe differentiation will come not from the models themselves, but the experiences built around them. The real value lies in the capabilities layered on top of the models: the backend infrastructure, agent orchestration, tooling, integrations, and everything that comes after turning a prompt into a website or app. With our deep infrastructure, world-class distribution, product expertise and years of technological innovation and market intelligence and understanding, this is where I believe Wix is uniquely positioned to win in today’s AI world. 

Wix’s management recently built Wix’s first proprietary large language model (LLM) that’s designed to power Wix Harmony, the company’s first-of-its kind website builder blending visual editing with vibe coding; Wix’s proprietary LLM is faster and has fewer errors when building websites; having its own LLM means Wix can move faster, and operate with lower inference costs; Wix is currently experiencing only tiny benefits from using its own LLM, but management expects the company’s own LLM to drive the company’s profitability over the long run; the LLM is just the first in a broader portfolio of AI models that Wix will release; Wix can build websites with its own LLM at just 5% of the cost of 3rd-party alternatives; management thinks the AI advantage in website building belongs to the most specialised model; Wix Harmony is now in all of Wix-supported languages; Wix Harmony and Base44 can now be accessed within popular AI chatbots such as ChatGPT and Claude; AI has made creating a website easy, but the real work is done after the website is published; despite having its own LLM now, Wix still has the flexibility to use the best 3rd-party models when appropriate; Wix Harmony was rolled out to the company’s main geographies in late-January 2026; management thinks Wix’s own LLM could eventually be used for Base44, but there’s no exact time line; Wix spent only a small sum of money to train its own LLM, so ongoing training costs will also be reasonable 

We recently built our first proprietary LLM, purposefully designed to power Wix Harmony – a significant milestone in our innovation journey and a project I am personally very proud of. Thorough A/B testing is showing that our Wix-built model is faster while resulting in fewer errors and significantly better results when applied to building Wix Harmony websites. Having our own model means that we can accelerate the cycle of improvement, which we believe creates a continuous flywheel for our platform that general-purpose models can’t replicate with success.

Importantly, building and relying on our own LLM means significantly lower inference costs that sit completely within our control as we scale the Harmony platform. While the margin benefit is small today, we expect this model to drive profitability over the long term. We expect this to be just the first in a broader portfolio of proprietary AI models across a number of use cases as they become increasingly central to our product roadmap…

…Big LLMs optimize for broad scopes and with limited feedback; we’re optimizing for one thing, every day, with millions of real users building real websites. This gives us full control over our roadmap, reduces dependency on external vendors, and significantly accelerates our iteration cycle. The result is a model that’s faster and more accurate, and we will be able to create beautiful websites that are optimized specifically for our users’ needs at approximately 5% of the cost of third-party alternatives. We believe that the AI advantage won’t go to the biggest model; instead it will go to the most specialized one…

…Wix Harmony is now available in all Wix supported languages…

…Both are now accessible within ChatGPT, Microsoft Copilot, and Anthropic’s Claude. Users can type “@Wix” or “@Base44” in these platforms, describe their idea, and a full website or application is created in conversation and managed there too, without any context switching…

…AI has made building online simple and anyone can generate a simple good-looking website in minutes. But that’s as far as it goes. The real complexity begins the moment you hit publish. How does it drive engagement? How do you host it, get found on search engines, run your storefront, secure your customers’ data and actually operate a business day-to-day. These are the hard problems, and we’ve been solving them for 20 years through continuous product innovation and user feedback…

…Still, we also have the flexibility to continue to leverage the best third-party models for the right use cases. So we are never constrained…

…Harmony, which was rolled out in late January across our main geographic markets…

…Where can we expect to have the same thing on Base44. The answer is that I don’t have an exact time line. Obviously, it’s a bigger or more complex undertaking than the Harmony one just because it is much more generalized the — the Harmony use case. That being said, it is something we believe and our top engineers are the ones who are dealing with it…

…In terms of the spend on the Harmony LLM and again, we’re not breaking out the exact number, but it’s quite small, okay? These are not like massive research costs and GPU investments that you can consider when you think about big frontier models. This is something that we managed to do at a very reasonable cost, which also means that for us to continue training it and improving it, should not be something that puts any real weight on our expenses.

Wix websites are now optimised with agentic AI

Wix collaborated with Microsoft to enable users to connect their sites to NLWeb directly from their Wix Dashboards, making Wix sites agentic-optimized. Now available through the Wix SEO & GEO Dashboard, the integration allows structured, continuously updated site data to be queried by AI systems using the ASK protocol, delivering accurate, context-aware answers in real time. 

Wix has ramped up the use of AI in its customer-care organisation for the last 3-plus years, and this has led to a 40% decrease in headcount since 2022, while maintaining or improving service; management is shifting Wix’s R&D (research & development) to be more aligned with Base44’s

We have ramped the integration of AI over the past 3-plus years. This has allowed us to optimize headcount, which has decreased by more than 40% since 2022, while maintaining or even improving in some areas, our services to users…

…We are working to shift our Wix R&D structure to align more closely with that of Base 44, which has been a leader in leveraging AI to drive productivity since day one. We are learning from them and working to implement those same operating principles at Wix. As we execute on this strategy with good line of sight, we expect faster output will more than balance out the cost of AI usage across our organization.

Wix’s Partners are using other AI platforms as well as Wix; the Partners are generally happy with Wix Harmony, but are also pointing out specific areas for improvement; a decent amount of Wix’s Partners are also using Base44

I also think in terms of what they’re using, they are using some AI platforms. By the way, some of them are using Harmony and are very happy with it on one end. And also they’re pointing out to us specific holes, if you may, or missing capabilities that are obviously there because we build Harmony for self-creators and not in the view of partners, but it gives us great visibility into what kind of innovation, what do we need to do next on the partner side in order to make them more successful and happier…

…I’m not going to share percentages, but I can say that we are seeing like there is a decent amount of partners’ usage on Base44. So it’s not marginal.

Wix’s management has no current plans to change the pricing strategy for the core Wix product

I think on Wix at this stage, we think the current structure is the right one. Obviously, if at some point, we introduce something which is very intense on token consumption, then we’ll have to charge for that as well. But at least for now, that’s not the case.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I have a vested interest in Alphabet (parent of Google), Amazon (parent of Amazon Web Services), Meta Platforms, Microsoft, MongoDB, Nu Holdings, Okta, Salesforce, Sea, Tencent, Veeva Systems, and Wix. Holdings are subject to change at any time.

The S&P 500 index is up about 8% in US dollar terms so far in 2026. That’s a decent return for less than half a year.  The index is also on track to surpass its historical 10% annual return for the year.

Similarly, the tech-heavy NASDAQ index is up 13% year-to-date.

Both indexes are also sitting near an all-time high.

Despite this, there is an interesting phenomenon happening.

Usually when the indexes are at an all-time high, you’d expect to see most companies to be up year-to-date. But that’s not the case this year

Although the indexes have performed well, nearly half of the indexes’ components are currently underwater for the year.

Of the 503 stocks in the S&P 500 index, 218 are negative for the year, while 45 of the NASDAQ’s 101 component stocks are down. The depth of some of the drawdowns are also quite steep.

Of the 218 stocks that are down in the S&P 500, 122 are currently more than 10% below where they started the year. And 56 are 20% or more underwater for the year. Meanwhile, more than 20% of the NASDAQ components are down more than 20% in 2026 thus far.

This has created a really interesting scenario where despite the indexes being near all-time highs, there are pockets of the stock market, even in the large cap arena, that are spotting materially cheaper valuations than they did just five months ago.

Hunting for value

Lower share prices naturally mean lower valuations and could be a good hunting ground for value investors. 

With the index at all time highs, searching amidst beaten-down individual names could be a great way to gain exposure to the market.

The list of stocks that are down year-to-date include some well known companies such as FICO, Lululemon, Tractor Supply, and Accenture to name a few. The four listed companies are down 35%, 43%, 39% and 37% respectively this year. 

This being said, just because a company is trading at a lower stock price does not automatically make it good value. Even seemingly stable companies can run ahead of fundamentals and corrections may just be stock prices coming down to more sane valuations.

Previously “deep-moat” companies can also run into trouble or face disruption, as is the fear surrounding software-as-a-service companies as they are potentially facing disruption from artificial intelligence.

Nevertheless, as an investor, seeing that there is a substantial list of big cap stocks that are trading down for the year does excite me.

Why is the stock down?

When hunting for value, it is important to understand why the stock is down. There could be a legitimate reason for a stock to fall.

For instance, FICO, the company behind the FICO credit score that banks in the US use to assess whether to provide loans to someone, is facing a potential new competitor in the form of Vantage Score which could lead to market share losses in the future. (Vantage Score has existed for many years, but there are recent regulatory changes that have eaten away at FICO’s previous monopolistic status.)

A stock could also be down simply because its price had run ahead of its fundamentals.

Take Palantir for instance. The company just reported stellar revenue growth of 85% in the first quarter of 2026 and is guiding for revenue growth of more than 100% for 2026.

Yet the stock price is down 25% year-to-date. This could simply be because Palantir was trading at an overly expensive valuation of 100 times its 2026 free cash flow guidance. With the aforementioned year-to-date decline, Palantir now trades at a more reasonable but still expensive 74 times its 2026 free cash flow guidance.

Happy hunting

Although there are companies that are facing challenges and so have stock prices that are down for a reason, there are potentially also companies that may be mispriced after a steep drawdown.

This could provide a nice entry point for patient investors who are willing to ride out the negative sentiment and potential downward momentum.

It is also a great way to enter the market if you are not keen to buy directly into the index which is trading at an all-time high price.

Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. I don’t have a vested interest in any company mentioned. Holdings are subject to change at any time.