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…

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.”…

[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.

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.