Earlier this month, I published More Of 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 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:
Adyen N.V. (OTC: ADYEY)
The complexities faced by merchants are increasing because of AI and fraud
As the industry shifts, so does the complexity of our customers’ operations and the scope of what we solve. Today, merchants are competing for consumer attention while shoppers expect increasingly personalized experiences and have access to instant product and price comparisons. AI is reshaping enterprise operations, exposing the limitations of legacy systems. Fraud is more sophisticated and requires businesses to protect revenue without compromising the customer experience. It is becoming clear that modern businesses require solutions not only at the transaction, but also well before and after it.
Adyen’s management recently introduced Adyen Agentic, which allows merchants to participate in the AI commerce ecosystem; Adyen Agentic has the Agentic Feed module that allows AI agents to have accurate inventory data; Adyen Agentic has the Agentic Cart module that brings an AI agent’s selection into existing checkout, tax, fulfillment, and order management systems; Adyen Agentic is AI platform-agnostic; major payment companies American Express, Visa, and Mastercard are early strategic partners of Adyen Agentic; Adyen Agentic is already live with some merchants; management thinks that trust must be established instantly when AI agents make payments on behalf of consumers; Adyen Agentic’s payment module enables merchants to accept machine-to-machine payments in a safe and secure way; OpenAI is also an Adyen Agentic partner; navigating the agentic commerce landscape is currently what merchants are most focused on
We introduced Adyen Agentic, enabling merchants to integrate once and participate across the rapidly evolving ecosystem of AI commerce…
…Adyen Agentic is another way in which we expand our platform beyond payments. Two modules of the product suite extend our role earlier in the commerce journey: Agentic Feed structures and distributes real-time catalog, pricing, and availability data into conversational commerce environments so AI agents have accurate inventory. Agentic Cart then hooks the agent’s selection back into existing checkout, tax, fulfillment, and order management systems to build a dynamic, purchase-ready order. Because this architecture is AI platform-agnostic, merchants can integrate once and participate across the entire ecosystem. Early participants in Adyen Agentic include strategic partners American Express, Mastercard, Salesforce, and Visa, as well as enterprise retailers ESW, Scheels, Sézane, and SharkNinja…
… As AI agents begin making purchases on behalf of consumers, trust must be established instantly, without a human in the loop. The payment module within the Adyen Agentic product suite is built to enable merchants to accept machine-to-machine payments while maintaining the same high standards of authentication, fraud prevention, and payment performance that defined our platform for the past twenty years…
… I think it is good to point out on the OpenAI relationship that we work with them as an LLM, as we do with all the parties. That is to help our merchants…To be super clear it is an Adyen Agentic partner and also a customer…
…Currently, what’s top of mind is for merchants is “How are you going to help us through the Adyen Agentic threat for them?
Adyen recently won OpenAI as a customer for consumer payments
Recent wins like OpenAI show our ability to solve the most complex operational and financial challenges in these emerging business models…
…If you look at a company like OpenAI working for us, that is just for payments. That is for payments of their consumers…
…On the OpenAI, it is their payment. It is the payments which their clients pay to them, and no forward-looking statements on that.
Orb helps to reduce the complexities involved with usage-based billing and this is important for software companies because these companies are transitioning to AI-driven usage-based billing; management thinks Orb can open up a market for Adyen with very mature companies; management thinks Adyen can onboard AI native companies with Orb
Our acquisition of Orb extends this strategy beyond customer engagement into monetization. AI is transforming how software is consumed, making usage-based billing the default pricing model for many modern businesses. Traditionally, companies relied on fragmented systems for usage metering, billing, and payments, creating operational complexity. By integrating Orb with our payment infrastructure, we replace this fragmentation with a unified monetization engine spanning the entire revenue lifecycle — from usage tracking to settlement. Beyond basic billing, Orb enables merchants to continuously experiment with, optimize, and evolve their pricing strategies using real-time data. For software and AI companies, this eliminates underlying friction and turns billing into a strategic growth driver…
…Orb, if you look at AI native, that is billing and you see that opens up a market for us where we can land very mature companies…
…The reason to work with Orb is that we can onboard AI native companies. So that is separate from that. What you see is that the AI native companies grow very fast, and the billing sits in their infrastructure. So where we take that, you’ll see that in the future.
Nu Holdings (NYSE: NU)
Nu Holdings’ management introduced NuFormer, the company’s foundation model for financial behaviour around a year ago; since NuFormer’s introduction, management has focused on building a single AI platform that will power the entire Nu Holdings business; the work on building the AI platform includes growing Nu Holdings’ GPU fleet, expanding architecture research, and building on a decade of transaction history across more than 100 million customers; management recently updated NuFormer to a hybrid linear attention design, and trained the model with the Muon optimiser; any improvement to NuFormer can instantly upgrade performance across all of the company’s business lines without retraining; the latest generation NuFormer model has much higher context length and training and inference speeds, while having lower costs; Nu Holdings can now achieve the same predictive performance with NuFormer with 20 million fine-tuning data rows that previously required over 400 million, cutting development cycles from weeks to days; NuFormer now teaches nearly every decision Nu Holdings makes; NuFormer was first deployed in the credit portfolio in Brazil before it was deployed in Mexico, then unsecured lending in Brazil, and to core credit models; NuFormer is now being tested in credit cards for SMEs, and for Nu Holdings’ Colombian customers; NuFormer is also being used to handle more than 60% of customer support conversations in Brazil; NuFormer is being used to predict what a customer wants next and allows Nu Holdings to recommend relevant products; NuFormer is used to put relevant campaigns infront of customers most likely to find them useful; NuFormer is more powerful than traditional models, but managemnt is still tracking its performance
About a year ago, we introduced nuFormer, our foundation model for financial behavior. Since then,e we have focused on one objective, building a single AI platform that powers business and customer decisions across Nubank. That work spans every layer of the stack. We increased and upgraded our own GPU fleet, giving us full control of the compute layer. We expanded our architecture research efforts, and we continue building on one of our greatest advantages, more than a decade of transaction history across more than 100 million customers in three countries…
…We recently advanced NuFormer to a hybrid linear attention design, the same architectural approach behind frontier models like Kimi K3 and Qwen3.5, and we trained it with Muon, the same class of optimizer powering today’s most efficient large language models. By decoupling NuFormer’s core backbone from specific downstream decisions, any improvement to the central model can instantly upgrade performance across all our business lines without costly retraining.
The latest generation quadrupled context length, training speed, and inference speed, while reducing the cost of running models in production. As we have scaled pre-training, the base model’s understanding of how our customers behave has become deep enough to change how we build every model on top of it. To give you one example, today we can achieve the same predictive performance with 20 million fine-tuning data rows that previously required over 400 million, cutting development cycles from weeks to days.
The platform now reaches nearly every decision we make. We first deployed NuFormer in our flagship credit portfolio in Brazil. Through 2025, we replicated the model in Mexico, demonstrating that the platform generalizes across markets. During the first half of this year, we extended it to unsecured lending in Brazil and to the next generation of our core credit models. We are now testing it in credit cards for SMEs and for our Colombian customers…
…Today, AI agents handle more than 60% of customer support conversation in Brazil, with customer ratings at or above human parity.
Beyond underwriting and customer support, we are using artificial intelligence to optimize decisions across credit, deposits, and growth, moving from predicting outcomes to determining the actions that maximize value under real-world constraints. Now the same understanding of transactions that predicts credit risk also predicts what a customer wants next. It allows us to recommend the products that maximize long-term customer value, personalize the app experience, and move toward our vision of an AI private banker.
NuFormer is also improving how we grow. As the model learns our representation of how every customer behaves, we use it to put each campaign in front of the customers most likely to find it useful, and more than 100 campaigns have already run this way.
One AI platform now powers underwriting, customer support, optimization, and growth…
…It certainly is the case that our AI generated models and assisted models are more powerful than traditional logistic regression models. That is incontrovertible. We are tracking them, though, in the exact same way that we would have tracked our historical models. We are looking at the degree of predictability, the variance at the low- end and the high- end of the predictive range, as well as the outcomes across both back-testing as well as forward-testing of that model in production.
Management believes NuFormer can be translated very quickly from Latin America to the USA, but a specifically US-tuned model will take 1-3 years; management’s focus in the USA in the beginning will be to build out Nu Holdings’ dataset to prepare for expansion once management has the same level of confidence as they do in Brazil, Mexico, and Colombia
It will take us some time to build up the same confidence in our credit risk models in the U.S. as we have in Brazil and Mexico and Colombia, where we have been operating for years. The way to think about it is that the platform, the NuFormer platform for credit models and the credit risk expertise that we have in the company, will translate very quickly across the border. But the actual data richness and building the experience of foundational testing and having the models in place that are specifically tooled for the U.S. market will take somewhere between 12 and 30 months, depending on the degree of maturation of those curves. Our priority at the beginning of our entry into the U.S. market, when that happens, will be to test, learn, build out our data set, and then be ready to expand once we have that same level of confidence there that we do in our core markets.
NVIDIA (NASDAQ: NVDA)
NVIDIA’s management sees supply constraints for 2027 (FY2028)
We expect to grow revenue by approximately 70% in fiscal 2028. This is a supply-constrained outlook…
…Customers’ forecasts point to our growth doubling next year. However, as I mentioned earlier, we expect to grow approximately 70% as we are supply-constrained. NVIDIA Compute is fully utilized across every cloud we serve.
Hyperscale revenue grew 13% sequentially in 2026 Q2 (FY2027 Q2); ACIE (AI Clouds, Industrial, and Enterprise) revenue grew 25% sequentially in 2026 Q2 (FY2027 Q2); management expects the AICE segment, which includes neoclouds, to be nearly half of NVIDIA’s data center business; sovereign AI revenue, driven by neoclouds, was up 35% sequentially and tripled year-on-year in 2026 Q2 (FY2027 Q2); management is seeing regional cloud surging everywhere, because of the fungibility and strong economic characteristics of NVIDIA compute; neoclouds are surging everywhere; management has introduced a revenue-sharing structure for neoclouds, where NVIDIA provides a minimum revenue guarantee to give lenders confidence in neocloud projects, in exchange for a portion of neoclouds’ revenues above that floor; NVIDIA is not making loans for the neocloud projects under the revenue-sharing structure; NVIDIA gets paid twice under the revenue-share structure; management thinks the revenue-share structure expands NVIDIA’s addressable market and has the potential to deliver billions in revenue; management expects the AICE segment’s computing needs to be larger over time than the whole of global cloud computing today
Hyperscale revenue of $49 billion grew 13% sequentially, driven by sustained strength in Blackwell…
…ACIE revenue of $40 billion increased 25% sequentially and 138% year-over-year. Growth was driven by neocloud capacity additions to meet the rising demand from enterprises, AI startups, and sovereigns, as well as hyperscalers purchasing capacity to supplement their own build-outs…
…Hyperscalers will remain a major growth driver, but non-hyperscaler growth, our ACIE segment spanning sovereign regional neoclouds, enterprise edge, and air gap data centers will represent roughly half of our data center business…
…In sovereign AI, our business, primarily through the regional neoclouds, grew 35% sequentially and more than tripled year-over-year in Q2…
…Because NVIDIA Compute is productive, fungible, rentable, and durable, regional cloud interest is surging around the world. We helped CoreWeave, Nebius, and Nscale build entire infrastructure businesses, and neoclouds are emerging everywhere. Firebird in Armenia, Cassava Technologies across Africa, GMI Cloud in Taiwan, Yotta and Neysa in India, Firmus in Australia, YTL AI Cloud in Malaysia, pairing local land, power, and operating expertise with our platform. Last month, we announced a partnership with Noetra, Japan’s national AI company, to build an NVIDIA DSX AI factory that will create open models to power AI agents, digital twins, robotics, and physical AI applications. South Korea’s LG and Hyundai Motor Group are partnering with NVIDIA to build and scale AI. In Europe, a record 35 new NVIDIA-powered AI supercomputers were unveiled to advance industry and scientific breakthroughs.
Neoclouds are seeing strong demand pipelines for many diverse offtakers. Rather than allocating their entire capacity to a single long-term offtake guarantee that lenders typically require to finance a data center independently, we have introduced a revenue-sharing structure. NVIDIA provides a take or pay commitment on a portion of the facility’s capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project, and in exchange, we share in a portion of the neocloud’s revenue earned above that floor. Independent capital still underwrites every deal on its own merits. We’re not making loans. In this model, we get paid twice, once on the hardware sale and again through the share of rental revenue, a highly reoccurring stream layered on top of a one-time equipment purchase. Over time, this model can expand our addressable market and create reoccurring usage-linked revenue stream alongside our core platform revenue, with the potential to drive billions in revenue over the medium to long term…
…There is sovereign AI, there are regional AIs, there are neoclouds, there are AI startups at enterprises where we are seeing, which represents about half of our business, and that is growing 100% a year. That part of the world’s computing is likely to be larger over time than even what we are currently experiencing in the cloud.
Half of NVIDIA’s Data Center revenue comes from hyperscalers, and the other half comes from ACIE (AI Clouds, Industrial, and Enterprise) customers, including sovereigns; ACIE customers grew 31% sequentially in 2026 Q1 (FY2027 Q1), with AI Cloud revenue tripling year-on-year; the number of partner data centers in the AI Cloud business exceeding 10 megawatts is now over 80, up nearly 100% year-on-year; Sovereign revenue was up 80% year-on-year in 2026 Q1 (FY2027 Q1); NVIDIA’s AI systems are now in nearly 40 countries
NVIDIA’s management sees a backlog of $2 trillion in the cloud industry; management expects hypercaler capex to be nearly $800 billion in 2026 and $1.3 trillion in 2027
With cloud industry backlog now greater than $2 trillion, CapEx by the top five hyperscalers is expected to reach nearly $800 billion in 2026 and $1.3 trillion in 2027.
NVIDIA and Amazon’s AWS recently expanded their partnership, whereby AWS will be deploying an additional 2 million GPUs, and Vera CPUs, from 2026 Q3 (FY2027 Q3) through to 2028 Q2 (FY2029 Q2); AWS will be serving NVIDIA’s Nemotron family of open source models; Amazon will be adopting NVIDIA’s full physical AI stack for its warehouse robots
Today, we are delighted to announce an expansion of our partnership with AWS. Building on its already vast installed base of NVIDIA Compute, AWS is deploying an additional 2 million GPUs starting this quarter through the second quarter of fiscal 2029, along with Vera CPUs, some integrated with Rubin, others standalone. AWS will serve NVIDIA Nemotron family of open models on Amazon Bedrock and SageMaker. Amazon will also adopt our full physical AI stack, Omniverse, Cosmos, Isaac, and Jetson to power its fleet of warehouse robots.
NVIDIA’s management is seeing the company’s neocloud partners bring capacity online faster and at lower cost; management expects neoclouds to have 8 GW of total capacity by end-2026, up from 3 GW at end-2025
Using NVIDIA DSX reference designs, our neocloud partners are bringing capacity online faster and at lower token cost. They are expected to exit the year with 8 GW in total installed capacity, up from approximately 3 GW at the end of 2025.
NVIDIA’s next-generation GPU system, the Vera Rubin, has brought its revenue opportunity to $40 billion per gigawatt; Vera Rubin delivers 30x higher throughput per megawatt, and 35x lower token cost, compared to Grace Blackwell Ultra systems; NVIDIA started shipping Vera Rubin in Aug 2026; management expects Vera Rubin to be the fastest product ramp in NVIDIA’s history; management expects Vera Rubin to be 20% of Data Center revenue in 2026 Q3 (FY2027 Q3); management thinks the revenue opportunity for future generations of GPU systems on a per gigawatt basis should increase materially over time, as the newer GPU systems become ever more productive
Since Hopper, our revenue opportunity has grown from roughly $18 billion per gigawatt to $25 billion with Blackwell, to $40 billion with Vera Rubin…
…Vera Rubin exemplifies this, delivering 30x higher throughput per megawatt and 35x lower token cost relative to Grace Blackwell Ultra. We commenced production shipments of Vera Rubin earlier this month. Having already received purchase orders from every major hyperscaler, AI cloud, and system OEM, we expect Vera Rubin to mark the fastest product ramp in NVIDIA’s history…
…We see Vera Rubin accounting for about 20% of data center revenue in Q3…
…[Question] As we think about the path even beyond Vera Rubin, we think about Vera Rubin Ultra and so on and so forth. Should we really conceptualize $40 billion goes to $60 billion, $80 billion?
[Answer] Is our goal to put as much compute on a plot of land? Is our goal to put more compute into 1 GW or less? Obviously, we would like the speed of light answer. The perfect answer is actually infinity per gigawatt. If we could literally get $1 trillion of compute into 1 GW and one piece of Land, Power, and Shell, it would be a fantastic outcome. The answer is directionally in that direction. We started in the world of general purpose computing during Moore’s Law. We were probably, pick your favorite number, but I am going to go with something like $5 billion, $3 billion per gigawatt of compute with general purpose computing. Then eventually with Hopper, it was $18 billion. Now Grace Blackwell is $25 billion. Next, Vera Rubin is $40 billion, and after that it is going to be higher. That is excellent. That is fantastic for the industry. It is fantastic for customers. So long as the productivity of it continues to grow, the durability and the fungibility continues to grow, then people are happy to invest in assets that generates revenues, generates profits, and helps them recoup their returns so incredibly fast.
NVIDIA’s Spectrum-X Ethernet product grew 2.6x year-on-year in 2026 Q2 (FY2027 Q2), making NVIDIA the largest and fastest-growing network company globally; there are 5 different types of networking systems needed to run AI data centers
Spectrum-X Ethernet, which grew 2.6x on a year-over-year basis, is already helping us become the largest and fastest-growing network company in the world…
…We just mentioned, each gigawatt of technology and NVIDIA’s revenue exposure in the Hopper timeframe with Hopper plus InfiniBand, and now Vera Rubin and CPU and three types of different networking, because it takes that many types of networking to address the entire world’s data center. Not to mention the scale-in security networking and the scale across multi-campus networking. You could argue five different types of networking systems.
NVIDIA’s ethernet networking product, Spectrum X, is now larger than all ethernet peers combined; NVIDIA’s other networking product, Infiniband, grew 4x year-on-year in 2026 Q1 (FY2027 Q1), driven by XDR technology
NVIDIA’s management sees agentic AI driving demand for CPUs; NVIDIA is in full production for its Vera CPU; the Vera CPU completes agentic tasks 1.8x faster, and provides 5x the bandwidth per watt, compared to other CPUs; management expects the Vera CPU to be deployed by all hyperscalers and major neoclouds and AI labs; management continues to see $20 billion in total CPU revenue in 2026 (FY2027); management expects CPU revenue to more than double in 2027 (FY2028)
Rising adoption of agentic AI is driving an acceleration in demand for data center CPUs…
…Today, we are in full production of our next generation Vera CPU. As a standalone product, Vera expands our TAM even further. Vera completes agentic tasks 1.8x faster on the spec benchmark and provides five times the bandwidth per watt than any other data center CPU. We expect Vera to be deployed by every major hyperscaler, neocloud, AI lab, and system OEM, with shipments already underway to our lead partners, including OCI, SpaceX AI, and starting this quarter, AWS. We continue to see demand for approximately $20 billion in total server CPUs. Based on our customer demand and improving supply outlook, our preliminary expectation is for CPU revenue to more than double in fiscal 2028, positioning us as one of the world’s leading server CPU suppliers.
NVIDIA’s rack-scale LPU (language processing unit) system, Groq 3 LPX, is in full production; Groq 3 LPX produces 4x the number of tokens per second against the next best alternative; management expects to ship Groq 3 LPX in volume in 2026 Q3 (FY2027 Q3)
At Hot Chips earlier this week, we announced that Groq 3 LPX, our first rack-scale LPU system, is in full production and already setting records, demonstrating nearly 4x the number of tokens per second against the next best alternative on our Artificial Analysis benchmark. We expect to ship Groq 3 LPX in volume later this quarter to early adopters. Nebius will be the first.
Global VC funding for AI exceeded $400 billion in 2026 H1; 70% of global VC funding is spent on compute; 20 AI startups have exceeded $1 billion in annualised revenue run rate, up from 13 AI startups in 2025 Q4; AI startups specialising in vertical enterprise software grew the fastest
Global VC funding in AI, roughly 70% of which is spent on compute, exceeded $400 billion in the first half of 2026, surpassing the $265 billion raised in all of 2025. Nearly 20 companies, including Cursor, owned by SpaceX, Figma, and Together AI, now exceed $1 billion in annualized run rate revenue, up from 13 companies in Q4 of last year, with vertical enterprise software logging the fastest growth.
Samsung Electronics is using NVIDIA’s solutions to achieve 20x greater performance in computational lithography
Samsung Electronics is using NVIDIA cuLitho to achieve up to 20x greater performance in computational lithography.
NVIDIA’s management sees the company as a neutral partner to sovereign and neoclouds
We don’t own a cloud ourselves. We are a neutral partner to every sovereign and neocloud.
NVIDIA’s management sees extraordinary compute demand from frontier AI labs, but the frontier AI labs’ balance sheets and credit profiles cannot support their compute needs; NVIDIA has invested $50 billion in frontier AI labs; NVIDIA has partnered with 6 of the world’s leading infrastructure capital providers to establish financing platforms that will raise more than $500 billion of 3rd-party capital to fund the compute needs of the frontier AI labs; NVIDIA recently partnered Softbank Energy for a Portsmouth data center that will exclusively host NVIDIA compute for OpenAI, and NVIDIA is providing credit support; OpenAI has existing and planned commitments for 12 GW of NVIDIA compute; NVIDIA has provided credit support for 2 GW of compute for another frontier AI lab that has also secured substantial amounts of NVIDIA compute independently; management does not see the credit support for OpenAI and other frontier AI labs as circular financing, and instead, sees attractive returns from the support with limited risk; management thinks the frontier AI labs are once-in-a-generation companies that will become the largest technology companies in history; NVIDIA’s compute for supporting the frontier AI labs is fungible and can be used by other customers; management expects NVIDIA-supported demand from frontier AI labs to be 25% of NVIDIA’s business in 2027 (FY2028); management sees the frontier labs having skyrocketing sales and fantastic margins
The frontier AI labs have extraordinary demand for training and inference compute, but they are growing faster than what their balance sheets and credit profiles can support. They have rapidly growing customer demand, yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently. In other words, their growth is not limited by their technology or customer demand. It is limited by compute. For these companies, more compute means more intelligence, more users, and more revenue. NVIDIA is needed to help power this flywheel.
First, we have invested nearly $50 billion in the frontier AI labs. This was a meaningful commitment, but it represented a small fraction of our expected free cash flow over the same period. Further, to support the frontier labs infrastructure build-outs, we recently announced partnerships with six of the world’s leading infrastructure capital providers, Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR, to establish financing platforms that will raise over $500 billion of third-party capital. With these partnerships, building on our unique, fungible, and durable computing platform, the AI labs will be able to build and assess AI infrastructure funded by long-term institutional capital at relatively attractive rates.
Last week, we announced that we secured land power shell capacity through our partnership with SoftBank Energy to exclusively host NVIDIA Compute at their Portsmouth campus. The initial deployment, expected to support 4.25 GW of AI factory capacity, will be utilized by OpenAI. Each generation of NVIDIA AI factory systems deployed at PORTS-Pike could represent approximately 1.5 million NVIDIA GPUs, and over 20 years, the site could support multiple upgrade cycles. Here is the essential economic point. The LPS commitment secures a long-lived AI factory site, while the NVIDIA Compute within the data center can be upgraded repeatedly. This project deepens our longstanding partnership with OpenAI. OpenAI has committed to substantial deployments of NVIDIA AI infrastructure through 2030. OpenAI’s existing and planned commitments represent approximately 12 GW of NVIDIA Compute. For another frontier AI lab, we will provide selective credit enhancement for nearly 2 GW of compute. This complements the substantial NVIDIA Compute capacity they have secured independently without NVIDIA’s credit support.
We recognize the scale of this support, and we know some will call this circular financing. We see it differently. We are going through a major computing platform shift, the creation of one of the most important technologies in human history, and these are once-in-a-generation companies. Their technology leadership is proven, and their customer traction and usage are skyrocketing. We expect them to become the largest technology companies in history. We believe these investments, measured against the strength of their demand, the business they create for us, the ecosystem they build on NVIDIA’s platform, and the equity returns on our invested capital will be excellent, and our risk is limited.
The NVIDIA Compute platform is fungible and durable and can be redeployed to support other customers. For context, we expect demand from the AI labs for which we expect to leverage our balance sheet to contribute toward roughly a quarter of our business next year. This remains compute we ship will be consumed by investment-grade customers or those that are backed by one…
…The frontier labs, their sales are skyrocketing. Their margins are fantastic. They are generating profitable tokens. They are only limited by the amount of compute.
NVIDIA earned minimal revenue from China in 2026 Q2 (FY2027 Q2); management does not include China in its outlook
In Q2, we shipped less than 1% of our total data center revenue in Hopper 200 products to customers based in China in accordance with the U.S. government licenses. Current Hopper shipments are dilutive to corporate gross margins, and given ongoing geopolitical uncertainty, there is no China data center compute revenue in our forward outlook.
NVIDIA is extending longer payment terms to customers
Days of sales outstanding increased to 60 days, reflecting extended payment terms for large purchases by certain investment-grade customers to be shipped over multiple quarters.
NVIDIA is experiencing extreme pricing conditions for memory chips; management has decided to absorb memory-related costs, resulting in lower gross margins for NVIDIA; the scarcity in memory chips is driven by the AI build-out; NVIDIA has good relationships with the main global suppliers of memory chips, and is working with them to increase capacity for NVIDIA
GAAP and non-GAAP gross margins were both 75%, largely unchanged from last quarter due to a similar product mix…
…Many of you have expressed concerns regarding our gross margins, as component costs have risen significantly. As you are already aware, we are experiencing extreme pricing conditions in memory. The magnitude of the price increase has exceeded our prior expectations and are headed even higher into next year. As a result, we are resetting expectations today. For Q3, we expect GAAP and non-GAAP gross margins to be 74% ±50 basis points. We expect margins to bottom in Q4 in the 71%-72% range before settling at 72%-73% in fiscal year 2028 as executed price increases take effect in Q1…
…Memory scarcity today is being driven in large part by the AI build-out itself, and unlike a component that simply raises our cost with no offset benefit. Tighter memory supply is a symptom of the same demand surge that is driving our own growth. We have longstanding, deep relationships with all three major memory suppliers, and we are working closely with them to further increase the capacity our roadmap requires.
The amount of compute required by an AI agent is 15-100 times higher than that of a human
The amount of compute necessary for an agent versus a human using it is probably 15 – 100 times, depending on the type of problem you are trying to solve. The amount of compute necessary is just extraordinary.
It’s getting increasingly more difficult to stand-up AI infrastructure; NVIDIA’s management thinks the cost to develop 1 GW AI data center has increased from $30 billion 5 years ago to $60 billion today, but the increase in productivity has also been tremendous; management thinks that the absolute-large sum of capital needed for AI data centers today, coupled with the fact that NVIDIA’s compute can be used across multiple phases of the AI life cycle and models, mean that NVIDIA has an extraordinary advantage; NVIDIA’s management thinks that the return on investment on a $50 billion data center is now less than a year
It is also the case that you can no longer procure technology per se and stand up this infrastructure. You have got to go secure the Land, Power, and Shell, which oftentimes is a couple, two, three years out. All of the rest of the supply chain necessary to align the construction, the power, the cooling, all of the labor that is necessary…
…Each gigawatt of data center increased from, say, $30 billion about five years ago to now $60 billion today. Of course, the productivity’s tremendous. The performance is incredible in comparison. But you are talking about a $60 billion investment. To the extent that you could use it across multiple phases of the AI life cycle, run every single type of model you can imagine running on it, whether it is diffusion or autoregressive or state space or some hybrid version of that, every version of attention mechanism you can think of, small or large models. The investment that you make will be preserved and useful and productive for a lot longer time. I think our advantage in this new world is really quite extraordinary, and it could explain why it is that our growth is actually accelerating…
…I heard the other day that return on investment capital is now less than a year, and we are talking about $50 billion data centers.
NVIDIA’s management sees any procurement of NVIDIA compute by the hyperscalers to be very profitable for the hyperscalers
Back in this hyperscale space, that is growing incredibly too, right? You know that they now have backlogs of $2 trillion. You know that when they stand up NVIDIA Compute, when that happens, their revenues go up, their earnings contribution go up. Compute is profitable, very profitable today, and Compute directly translates into increased revenues.
NVIDIA’s management sees the company’s computing systems as having a different purpose to the inference-specific AI chips that the rest of the AI ecosystem, including the frontier labs, are developing; management is confident that NVIDIA’s computing systems will remain very valuable for, and widely used by, the frontier labs
[Question] A lot of these investments are designed to help the frontier labs, especially OpenAI and Anthropic, but both of them are designing their own custom chips. In fact, OpenAI just in the last few days spoke about Jalapeño and their claims about being better than Blackwell and so forth. So how are you balancing this dynamic where you want to invest a lot in the ecosystem, but part of that ecosystem wants to develop competitive solutions?
[Answer] We’re building something very different. Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform, an entire AI factory platform that spans the entire AI life cycle that you can use in any cloud. It’s in every cloud. You can run anywhere. We’ll help you set it up anywhere. We built something very different. All of the AI services, at some point, are going to want to go around the world, and those data centers won’t necessarily be just built by them. They’re going to run, and I think they’re going to run on NVIDIA all around the world. And of course, I think our technology, I have 100% confidence that our technology will continue to be extraordinary for them and that the economics of using our technology, whether it’s from data processing to training, to post-training, to agentic processing, our technology’s going to be extraordinary for them. They’re going to use it. I’m very confident that they’re going to be customers and partners of ours for a very long time.
NVIDIA’s management sees a need for both open-source and closed models; management sees skyrocketing use for both open-source and closed models; management sees that nearly all open-source models are running on NVIDIA’s computing systems; management sees skyrocketing revenue and great margins for both open-source and closed model providers; management thinks NVIDIA’s position in open-source models is great because of the wide proliferation of CUDA; management thinks open-source models are reaching frontier capabilities, and is vital in cybersecurity; management thinks NVIDIA is the only platform that runs every frontier model; management thinks both open-source and closed models will succeed, and is happy that is the case
The world will need both closed models and open models. Both closed models and open models are skyrocketing in use. I would say nearly all open models run on NVIDIA, and the reason for that is because NVIDIA’s footprint around the world is the highest, and our architecture is the most fungible…
…The frontier labs, their sales are skyrocketing. Their margins are fantastic. They are generating profitable tokens. They are only limited by the amount of compute. That is equally true for open models. Our position in open models is very good because the CUDA ecosystem is literally everywhere.
The open models are also foundational to just about every AI startup and every enterprise company around the world. It is vital to them. The reason for that is because you should rent intelligence, strong intelligence, smart intelligence wherever you can, which is the reason why we rent it, and I encourage my employees to use the cloud service as much as they can. But every major company and surely every country and every startup needs to build their domain-specific, their proprietary AI, their proprietary alpha. The open models reaching frontier levels has made it possible, has enabled them to all do that. One of the areas where frontier models is vital is cybersecurity. You see the number of cybersecurity companies that are enabled by frontier models so that they could have distributed, massively distributed, continuously running autonomous cybersecurity systems to defend. Those companies are emerging…
…I’m fairly certain we’re the only platform that runs every frontier model, whether it’s closed or open. Most of them were built on NVIDIA, so they run great on NVIDIA. So we’re delighted by any model succeeding. So long as models succeed, I’m very happy. Both closed and open models are going to succeed, and they’re both simultaneously driving our sales.
NVIDIA’s management thinks that demand in the AI industry will inflect further upward when recursive self-improvement and AGI (artificial general intelligence) happens with AI models; the inflection will be driven by the proliferation of agents
[Question] There’s recursive self-improvement, which apparently at Anthropic and OpenAI is going very well with AI that improves itself. Even OpenAI said they could hit AGI by the end of this year. With the developments in RSI as well as AGI, what happens to industry demand? Does it inflect further? What does it mean for NVIDIA when those things take place?
[Answer] It’s going to inflect further. Today, the vast majority of AI is prompted by people. I believe that this last month it has crossed. Most AI are now agentic. But in the future, every company will have a whole bunch of agents. We have 40,000 employees, roughly. In the future, we’ll have 400,000 agents, 4 million agents. Those agents are running continuously. They’re running in the background. If you know anybody who builds edge personal AI agents, and they run it on their DGX Spark. I know a lot of people who run it on DGX stations, this incredible workstation that we’ve built, and you can buy it from Dell, and they’re incredible. These AI agents running on a DGX station runs 24/7, because you got stuff for it to do all the time. When the world goes to agentic, fully agentic systems, you are going to have agents running all the time, working with other agents running all the time. Those will be working in the background, improving your company, improving your lives. In a lot of ways, we are kind of recursive at this point. You could argue it is coarse-grained, but every time you run through an agent, it reflects on how it could do a better job next time, and it updates the skill file. The skills document, the markdown, is updated at the end of every single one of them. Next time you run it is going to get better. It is a kind of a loosely coarse grain self-improvement.
NVIDIA’s management thinks AI is now doing useful productive work and generating profitable tokens; management thinks the AI industry will generate more profitable tokens if they had access to more compute
I think the most important thing that matters for the industry is that, one, AI is now doing productive and useful work. Two, AI is generating profitable tokens. Three, if we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we are at.
State of the AI market?
Last year, one of the funnest things to do is just to go figure out where I go for dinner and who I have dinner with, and their stock price doubles the next day.
NVIDIA’s management sees the entire supply chain for the semiconductor industry as being challenged
Our entire supply chain is challenged. Everybody is really running flat out. More capacity is coming online all the time, which is one of the advantages of what’s going to happen this year. It’s not going to come online in an instance in time, but it’s going to come online every day.
Okta (NASDAQ: OKTA)
Okta’s management is seeing the emerging use of AI elevating the importance of identity in a company’s security posture; management thinks securing AI identity is a no-regret investment that all organisations will make, regardless of how the other areas of AI develop
The emerging use of AI by organizations and threat actors alike has further elevated the role identity plays within a company’s security posture. Organizations are accelerating their infrastructure modernization timelines to address this heightened threat environment. We are seeing conversations that begin with securing AI broaden into identity modernization initiatives…
…We don’t have all the answers, but what we do know is that there are some no regrets investments. We know that every customer is going to have to figure out where their agents are. They’re coming from all over the place. They’re going to have to figure out what they can connect to, and they’re going to have to figure out what they can do. No matter what happens at the model layer or the platform layer or the app layer, or if applications build their own agents or they get disrupted with agents, what the companies build themselves, that’s all going to unfold. What model is the best? Is it an open source model? Is it a frontier model, some combination? That’s all going to unfold as it will over the next several years. But the no regrets decision is you have to have this foundation of where are my agents, what can they connect to, and what can they do?
Okta recently acquired Permiso; Permiso detects and resolves threats across human to agentic identities in multi-cloud environments; management will integrate Permiso into Okta’s existing Identity Threat Protection and Identity Security Posture Management solutions; 80% of security breaches are identity-based attacks, but only a small percentage of Okta’s customer base has the most advanced Identity Threat Protection product that is integrated with Permiso; Permiso has 400 native risk detections
We just completed the acquisition of Permiso, a cloud-native identity security platform that detects and mitigates threats across human, non-human, and agentic identities in multi-cloud environments. Permiso Will be integrated into a unified security offering with our existing Identity Threat Protection and Identity Security Posture Management solutions. The combination strengthens Okta’s AI security offerings with enhanced visibility into autonomous agent behaviors and additional runtime controls to ensure secure agentic activity in real time…
…80% of breaches are identity-based attacks. But when you look at our customer base, relatively small percentage have the most advanced Identity Threat Protection product. Identity Threat Protection, we have talked about it for a while, Jonathan. It is very important and very unique, and by the way, very differentiated. None of the other IdPs have this. It not only evaluates session risk at the time of login, but also post-login. Continuously monitors it, looks for risk signals, not only from Okta, but risk signals from the ecosystem, from CrowdStrike and Palo Alto Networks, and takes those all together and can shut down sessions after login. So any company that is running identity without this technology, you are at risk and you are behind, but not everyone has upgraded to it… Permiso, the way to think about Permiso, it is like the next generation of that. So instead of 90 native risk detections, they have 400 native risk detections. So it is a much richer and deeper set of correlative processes and machine learning that can really look at a session deeply across many vectors and many variables and detect risk.
Okta’s management continues to see 3 advantages the company has in securing AI agents for organisations, namely, (1) distribution, (2) product breadth, and (3) neutrality; Okta’s product breadth was a driving force in its recent mult-imillion deal for Okta for AI Agents with a large healthcare company; the large health company was dealing with agentic sprawl, and Okta will give it a single control plane to discover, secure, and govern its agents; Okta will also manage the healthcare company’s entire identity fabric; Okta’s existing distribution scale of being the identity system of record for more than 20,000 customers helped it win a deal for Okta for AI Agents with a global business management consulting firm; Okta’s neutrality helped it win a deal for Okta for AI Agents with a large asset manager; the asset manager had thousands of agents in production from multiple vendors and only Okta’s neutral platform could cover all the agents without vendor lock-in; Okta’s customers see the company as being well-positioned to secure AI agents
Identity is the primary control plane for securing AI, and customers are extending the trusted foundation they already rely on with Okta’s neutral, modern, enterprise-grade identity platform to now cover agents. We continue to build on three unique advantages to help our customers navigate this shift: distribution, product breadth, and neutrality…
…Our product breadth was a key driver in securing a multimillion dollar Okta for AI Agents deal with a Fortune 50 healthcare company. AI was spreading across their organization, and they could not tell where their agents were, what those agents were connected to, and what they could do. Okta will give them a single control plane to discover, secure, and govern those agents, helping them meet strict HIPAA compliance requirements. Okta will manage their entire identity fabric, including agent governance, Privileged Access Management, and identity security, helping to ensure every human, non-human, and agent identity is managed.
Our distribution advantage comes from the reach and trust we’ve built as the identity system of record for more than 20,000 customers. We saw it at work with a global business management consulting firm that was racing to put its own AI agents into production. After considering an in-house build, the firm chose Okta for AI Agents for its single control plane for human and non-human identities, faster deployment, and lower cost of ownership. Okta will carry the agents’ identities through every handoff, binding the agents to the original employee’s delegation with a verifiable record that can satisfy client and regulator requirements.
Our neutrality was critical to an Okta for AI Agents deal with one of the world’s largest asset managers, where AI was rolling out faster than their security team could govern. Thousands of agents from multiple vendors were running in production, creating risks the organization couldn’t consistently see or control. Only Okta’s independent and neutral platform could cover their heterogeneous environment, from employees and devices to AI agents without vendor lock-in. Okta will provide visibility across all agents and enforce least privileged access so every agent gets only what it needs…
…Our place in the ecosystem is super important and super strategic. It is not just me saying that. I think it is in all these customer conversations. I am having many customer conversations. I am flying around meeting these customers that are trying to solve these security challenges in general, but in particular around AI agents. They see us as like the naturally well-positioned to secure this agentic future. We are going after that on all fronts…
Okta for AI Agents lets organisations secure and control every AI agent they deploy; Okta for AI Agents is still too small to show up in Okta’s overall numbers, but management is very optimistic about its future and thinks being the system of record for agents could be the biggest category in cybersecurity; Okta won dozens of Okta for AI Agents deals in 2026 Q2 (FY2027 Q2) and the deals included several million-plus deals; Okta for AI Agents deals continue to be bigger than the average deal size; management thinks the biggest competitor for AI deals is customer-confusion because every vendor is saying they have the answer; management is seeing a big pipeline for Okta for AI Agents and the question now is how fast the pipeline will convert; 81% of the CISOs (chief information security officer) Okta has spoken to are aware of the risks of deploying agents without adequate security; management thinks Microsoft is trying to copy what Okta is doing with Okta for AI Agents; management sees ServiceNow and Salesforce as being more complementary than competitive, with Okta helping ServiceNow build kill switches for AI agents, and helping users log into Salesforce’s Agentforce; management is currently implementing a per-user uplift pricing model for Okta for AI Agents because that’s what customers want; management will change the pricing model for Okta for AI Agents quickly based on customer feedback; there is some consumption limit with Okta for AI Agents’ pricing model; management thinks it’s possible that Okta for AI Agents will become material for Okta by 2028
Customers want to move quickly without compromising on security and control. Okta for AI Agents lets them do both by helping them discover, govern, and protect every agent…
…As exciting as that is, Okta for AI Agents, it is too small to show up in the numbers right now. Going forward, especially over the next couple of years, we are super optimistic. We think this being the system of record for agentic, for agents in the enterprise and being the system of record for agent identity. In the fullness of time, it could be the biggest category of cyber…
…Particularly strong was the 30% of the new bookings were from new products. Okta for AI Agents inside of that bucket, there were dozens of deals in the quarter, including several million-dollar-plus deals, which is super exciting…
…But what I said last time around, the average deal size for AI deals being bigger than the average deal size for the rest of Okta, that still remains the case…
…[Question] It was great to see those early AI security wins. I wanted to touch a little bit more on just competitive dynamics. Could you help us understand that scene right now?
[Answer] The biggest competitor is confusion. We are competing against confusion, so our solution has to be clarity. And customers are confused because there is so much excitement and so much opportunity in AI. It is the natural tendency of every vendor to say, “What we are doing and what we have done in the past is critical to AI. We have the answer. We have the one answer.” And I think that confuses the customer because they have 17 vendor meetings, and every vendor tells them they have the right answer…
…Last quarter, we talked about record pipe. The pipe is even bigger, and there’s more pipeline. The question is how fast it will convert, right? We don’t have four years of history on conversion. When we think about the future, there’s obviously some degree of being prudent about how fast that’s going to convert. But the pipeline’s there…
…81% of the CISOs that we talk to are aware right now that they are exposed with agents deployed in their enterprises where they do not yet have an adequate security platform in place. A very high percentage of our customers know that they have the need…
…[Question] You got Microsoft, Salesforce, ServiceNow. Maybe can you give us some perspective about to what extent do those products overlap and compete with your Okta for AI Agents?
[Answer] I think Microsoft is copying us, which I think they have been for 15 years. I think they’re copying what we’re doing, and they see the value of an agent registry. I think the challenge for them is going to be it’s really hard to be neutral, and it’s really hard to make an agent registry that works as well for Amazon and Google and OpenAI and Anthropic as it does for Azure and Microsoft. But I think they have a similar vision, and it looks at least from their blogs, I don’t know if they have a real product yet, but at least from their blogs, it seems like they are copying us.
Then, I think ServiceNow and Salesforce are like every vendor. I think they’re coming at the problem from their perspective. ServiceNow is coming at it from a very asset management, workflow management perspective, and we found it very valuable to work with them because we can add a lot of value in that environment. We can really help them sever the connections, the trusted connections between agents and the rest of the ecosystem. We’re at that level of detail. We have the tokens, we have the protocols, so that can really help the control tower from ServiceNow actually come to fruition with a kill switch that can actually kill the connections. That’s been a really valuable partnership. Salesforce, similar, it’s like they’re coming from more of the service and support and platform layer to some degree, but guess what? People log in to Agentforce through Okta, and we can help people securely connect Agentforce to everything else in the ecosystem. Because the more Agentforce agents are connected to data across the ecosystem, the better, and we can help with that.
So everyone’s kind of sticking in their own lane, and lucky for us, our lane is perfect for this world. Our lane is people to technology and then technology to multiple different vendors with multiple different plays in the technology space. We’re very good at that…
…[Question] Just given how quickly agentic AI adoption is happening out there, how are you incorporating the number of AI agents in your deal?
[Answer] Our pricing model is per user. If you want to buy Okta for AI agents, it’s an uplift to your per user charge. The product works across different use cases, so it can be login for the user and the agent, it can be passing the agent credential across the whole chain of command, it can be governing the agent, but the pricing is per user, an extension of the per user price. Now, the first thing everyone says is, “Well, that’s crazy. Seats are going away, and you’ve got to charge per agent.” That all may be true, but the way customers are using agents now and the way they want to buy is per user. One of our advantages is we’re super close to the customers, and as I’m sure this is for sure going to evolve, and as we come up with ways that work for the customer and work for Okta, how to package it and price it differently, we’ll iterate quickly and give them what they want…
…[Question] Is there any consumption limit on those per user pricing?
[Answer] In our products, we haven’t done that much, but we’re starting to add that stuff…
…[Question] Can you talk about how much the raise in the outlook was due to AI?
[Answer] Still immaterial. Still very small. We are very early innings. But like we have talked about here, we are excited about the long-term opportunity. So for FY 2027, we do not think it is going to be material, but 2028 and beyond, if things keep going the way that they are going, then we do think that there is a real possibility for this to be material for the business in the long run.
Okta’s management thinks the fragmented AI landscape is creating huge opportunities for Okta
The fragmentation of the AI landscape creates significant opportunities for Okta. As enterprises deploy agents across models, clouds, applications, and infrastructure, they need a neutral identity layer that can secure it all.
Anthropic recently named Okta as the first identity provider for enterprise-managed auth for MCP connectors; enterprise-managed auth is generally available; enterprise-managed auth allows organisations to centrally govern how Anthropic’s Claude connects to applications; Okta’s management recently expanded Cross App Access, Okta’s open standard for securing AI, with more integrations; Okta’s management thinks that cybersecurity in the future will take the entire AI industry to work together to secure; management thinks the addition of Anthropic to Cross App Access is a big step forward for the open standard; Anthropic’s release of enterprise-managed auth is the first time an AI agent has been compatible with Cross App Access; 26 top SaaS vendors are supporting Cross App Access
That’s why we partner with industry leaders, including Anthropic, which this quarter named Okta the first identity provider supporting enterprise-managed auth for MCP connectors. Now generally available, enterprise-managed auth enables IT teams to centrally authorize and govern how Claude connects to enterprise applications.
We also recently expanded our work with AWS, Cisco, OpenAI, Databricks, and Snowflake, alongside more than 25 new Cross App Access integrations, providing a standardized way to govern how AI agents connect to a growing ecosystem of enterprise applications and resources…
…I think longer term is something we are working on as well. I think longer term, the entire industry needs to work better together. The industry right now in security, everyone is coming at the customer saying they have the only answer. They can secure agents. They are going to be the one to do it. The reality is it is going to take us all working together. Okta has been working on this. On these calls, the last five or six calls, we have talked about standards and ecosystem. We made a huge step forward in terms of one of the main standards we have been working on, which is the standard we have talked about called Cross App Access. The huge step forward this time is when the biggest AI agent in the world, Claude, is supporting Cross App Access. They released Enterprise-managed authorization, which is the first time an AI agent has been compatible with this protocol…
…Everyone in the resource side of the equation is starting to support it as well. We announced 26 top SaaS vendors are supporting from a resource perspective this protocol.
Customers are trusting Okta to secure AI because of its track record
They share the concern that you just articulated, which is the various players in the space and the venture-funded companies are moving very rapidly, and it’s difficult for them to have confidence in predicting what the future is going to be. And one of the reasons that they come to Okta and talk to Okta is specifically because we are a proven company. We’ve been solving this problem for 17 years for over 20,000 customers, and we’ve earned the trust of those customers and the partners that we work with to solve these problems.
Okta’s management is seeing the number of agents explode inside organisations
We were talking to a company that ended up being a nice Okta for AI agent deal in the quarter. When the evaluation started, we ran our technology, and we detected 50 instances of a Claude agent in the environment. They were thinking about what they wanted to do, then we came back a few weeks, and there was 1,500 Claude agents in the environment. It’s like 50, two weeks, 1,500. These customers are really tangible, the risk that they’re seeing and the way this is coming into their organization. This is catalyzing some of these deals, this onrush of agents.
Okta’s management recently put the infrastructure in place for consumption-based pricing for Agent SSO because they expect the usage by agents to be quite high
I don’t know if you guys saw the announcement we did about supporting Agent SSO in our base edition across the board, as we did it on Monday. Agents are going to log in way more than people. So in that product, we actually have a cap of Agent SSO that we’re actually not going to enforce right away, but we’re putting the framework and the scaffolding in there to have a consumption-based pricing eventually, because it’s very likely that with agents proliferating and how they behave, that the usage is going to be quite high.
Okta’s management thinks the previous association between Privileged Access Management (PAM) and agentic is wrong
I think that this association between PAM and agentic was overemphasized. I think there was this mindset three years ago that agents needed privileged access, and so PAM was going to be the right place to do agents. I think it’s wrong. I think agents do need privileged access, for sure, but it needs to start on a much broader base. PAM is too narrow. PAM had very small number of users and super locked down environments. Agents, the whole dream of agents is that they’re there for everyone. It doesn’t make sense to start your agent journey and figuring out where the agents are and what they can do and what they connect to. It makes no sense to start it from the most locked down thing sitting next to the Oracle Database on a Sun server.
It makes sense to start it from the broad IdP [Identity Provider], whether it’s customers or whether it’s employees, and then start from there and say, “Hey, how can I take this token that was generated for this user and pass it through with traceability and accountability all the way through the chain it needs to go through?” That’s what we’re seeing in the industry. It’s a much better place to start.
Salesforce (NYSE: CRM)
Salesforce is now partnering with Anthropic to launch Claudeforce; Claudeforce is the 1st time Anthropic has been able to accelerate its go-to-market efforts within Claude; Salesforce and Anthropic are big users of each other’s products; Claudeforce has Claude Cowork layered on top of Salesforce to let users unlock trapped value within Salesforce; Claudeforce delivers intelligence within the software companies are already using to run their businesses; Claudefore is powered by a new AI harness Salesforce built called AI Force (AI Force is also powering a lot of other Salesforce AI products); Salesforce is rolling out Claudeforce to the entire company, and will make the product generally available in September; all Salesforce sellers will be demo-ing Claudeforce; all Salesforce customers will be able to buy Claudeforce via an upgrade to Salesforce’s premium editions; management thinks Claudeforce will be a new wave of demand for Salesforce; Salesforce’s management team is using Claudeforce to build agents that improve its own sales process
This is the number one AI in the world, Anthropic, and the number one CRM, Salesforce, coming together for the first time in an incredibly powerful way to build a new product called Claudeforce…
…This is the first time that we’ve really been able to incredibly accelerate our go-to-market efforts within Claude, and we want that for all the other enterprises…
…We’re big users of Salesforce. Salesforce is big users of Claude Code, of Cowork, of other tools. We’ve put products like Claude Tag in Slack already, which is a part of Salesforce…
…Anthropic builds this amazing model. And now the model has this incredible user interface, Cowork. And when you take Cowork and then you’re able to put it right on top of Salesforce, it’s able to bring the data, the applications, the semantics, the agents themselves and build complete applications, a total user interface to let you get all the value out of Salesforce that’s been trapped. So customers have put hundreds of billions of dollars into Salesforce. There is a huge amount of value that can be unleashed through this combination…
…They want intelligence delivered inside the software they’re already using to run their business. That’s what Salesforce, that’s what Claudeforce is going to do. I show them Claudeforce, I show them reasoning across their Salesforce data and workflows. I can show it right inside Cowork. I also show it right inside Slack. One click to deploy and their path to AI transformation just opens up in front of them…
…This is the best of both worlds. It is the number one AI meeting the number one CRM and putting them together. As I said, when you do that, you get this incredible result, a radically different type of interface on top of Salesforce’s entire platform. It is really powered by this incredible new AI harness that you are going to see at Dreamforce, AI Force, and that harness is able to power not only Claudeforce, but a new version of Slack that you are going to be seeing, as well as Coworker, which is something inside our Lightning interface, and other amazing things as well…
…We are rolling it out to the whole enterprise, to the whole company, and then we are going to make it, Patrick, we are going to make it available to GA in September to everyone at Dreamforce. Every single seller of Salesforce will be demoing this product as one of the surfaces…
…Every customer will be able to buy it. They will have to upgrade to our premium editions, and it is going to be a new wave of demand…
…We build our own agents based on Claude, and that agent inspects, they are our deputy CROs for all our businesses, and they inspect the business, the pipeline. They tell us if there is risk in the month or in the quarter. I do one thing that is, Marc, you would love this. I talk, by the way, I do this on Slack. This is one of the surfaces. I do this in, at the time, Cowork, now is Claudeforce. But I also do it in an app that I build with Claude Code. So depending on the moment, I use one of the surfaces. But I do one thing that is very powerful. Every time that I visit a country, and don’t tell anyone, because I don’t want my team to know. But essentially what I do is, okay, I’m visiting Italy. We have an amazing leader there, Vanessa Fortarezza. She’s our Forza della Natura. I said, “Okay, give me all the open opportunities for the month, then send a Slack to every account executive, copying the whole chain all the way to Vanessa, telling them, ‘Look at the opportunity record, look at everything, make it sound like it’s me.’ I review all the emails, all the Slacks before they go.” It all of a sudden sends 50 very customized Slack messages to the AEs, asking them if I can help, if we can do anything. It’s incredible. The engine is very smart. It proposes already ideas to close the deal. When I visit the country, everybody’s mobilized. Everybody has been working over the weekend. Vanessa is crazy running around, and we close deals faster.
Salesforce’s management thinks fears of a SaaSpocalypse are overblown; Salesforce’s net new AOV (annual order volume) in 2026 Q2 (FY2027 Q2) is the strongest in 4 years; Salesforce’s seats grew in the quarter; Salesforce’s attrition is near its lowest level; Salesforce’s pricing power is intact, with A1E (AgentForce 1 Edition) and A4X (AgentForce For Apps) bookings more than doubling sequentially in 2026 Q2 (FY2027 Q2); Salesforce’s contract length terms improved across all segments; agentic use of Salesforce was up 6x via MCP (Model Context Protocol) calls and Claude calls; 9 of the top 10 AI companies use both Salesforce and Slack, and grew their spend 435% year-on-year in 2026 Q2 (FY2027 Q2); frontier models depend on Salesforce and are not replacing the company’s products; management thinks that the combination of non-deterministic AI models with deterministic software creates value at a level never seen before; management has been pleasantly surprised that AI coding tools are making Salesforce’s product better; customers that start the agentic journey with Salesforce are at double the AOV, with a possibility to 3x-4x the AOV; only 5% of sales workers have upgraded to Salesforce’s higher-end editions and they are paying premiums of 60%-80%
I really want to start here at the beginning and talk about the SaaSpocalypse. I think you can see from the results, net new AOV growth, it’s the strongest in 4 years. Skeptics really said that seats would decline, and also that Agentforce sales and service and Slack, all seats grew year-over-year. Skeptics said customers would leave, but attrition was near its lowest level ever. Skeptics said pricing power would erode and A1E and A4X bookings more than doubled quarter-over-quarter. Contract length terms improved across all segments in new business and renewals…
…Agentic use of the platform, it surged 6x, sixfold, via Model Context Protocol calls and Claude calls…
…Nine of the 10 AI companies, like you just heard from Anthropic, that standardized on Salesforce, use Salesforce and Slack. Their spend is up 435% year over year. Frontier models depend on CRM. They make sure that it works, that it all comes together. They do not replace them…
…This is not the SaaSpocalypse. As I said earlier, we have been hearing about this for the last two quarters, these dire predictions about the end of software and how the models eat everything. But none of them have come true for us, and I do not even understand how they could possibly come true. Look, models you have to remember, models are probabilistic systems. They are non-deterministic systems. But our system, those four layers that we talked about with data and apps and semantics and agents, those are deterministic systems. But when you put those two things together, that is a level of value we have never seen before…
…I think that has been the huge shock for us, that these new next-gen tools, the Cursors, the Replit, the Claudes of the world, make our product better. That all of a sudden it can customize the data, the metadata, set up the preferences, do the administration, do things that maybe you needed a bunch of service folks to do. Now all of a sudden, you’re getting going in a month. Maybe it might have taken you, before, 6 months to a year. All of a sudden now you’re fully automated. Your ability to add new functionality and do new things, this has provided this very strong platform…
…Customers that start the agentic journey with Salesforce, on average, they are already at double the AOV with a perspective of nearly quadrupling, 3 x – 4 x the AOV…
…Only 5% of the knowledge workers that use sales and service have upgraded to the higher-end editions. We get a 60% to 80% premium.
AgentForce ARR reached $1.5 billion in 2026 Q2 (FY2027 Q2), up 240% year-on-year (was $1.2 billion in 2026 Q1, up 205% year-on-year); Agentforce and Data 360 reached nearly $4 billion in ARR (annual recurring revenue) in 2026 Q2 (FY2027 Q2), up 210% year-on-year (was $3.4 billion in 2026 Q1, up 200% year-on-year); AgentForce ARR’s growth was driven by Slackbot, Headless, and AI momentum; Salesforce’s help agent is powered by Agentforce and it has surpassed 5 million conversations, with 64% resolved autonomously; Agentforce bookings grew triple-digits in 2026 Q2 (FY2027 Q2); 50% of the bookings came from customers refilling their consumption tank; Salesforce added 2,000 paying Agentforce customers into production in 2026 Q2 (FY2027 Q2), up 70% sequentially; Salesforce
Agentforce and Data 360 annual recurring revenue (“ARR”) reached nearly $3.9 billion, up over 210% Y/Y. Agentforce ARR exceeded $1.5 billion, up over 240% Y/Y. Effective Q2 FY27, Agentforce ARR includes our AI offerings, Slackbot and Headless 360…
…Agentforce ARR reached $1.5 billion, as you heard, driven by Slackbot and Headless launches and continued AI momentum…
…As customer zero, we are putting Agentforce to work across our own business. Salesforce’s help agent has surpassed 5 million customer conversations with 64% resolved autonomously…
…[Question] I wanted to circle back on the strong Agentforce ARR growth, and in particular, what proportion of current Agentforce is coming from pay production customers rather than pilots? And how has the timeline from pilot to paid deployment and refilling credits changed over the last couple of months?
[Answer] The top line numbers are obviously very impressive, but I like to go a little bit deeper under the hood. When you look just at bookings, not just the cumulative ARR, our bookings, Elizabeth, grew triple digit, so we doubled year on year. That was pretty fantastic. 50% of the bookings came from customers refilling the tank. So they consume, they use the Flex Credits, they want more, they raise their hand, we go there…
We added 2,000 paying customers into production. That is 70% more quarter on quarter.
Many of Salesforce’s customers in Europe have not started on their AI transformations
That’s why I was in Europe. Most of them have not started their AI transformations yet. If you talk to most CEOs, they may say they have a project or they have a failed experiment, or they were told to build a model, but it didn’t work out for them. That is not AI transformation.
Salesforce is helping its customers clean up their data so they can better utilise AI
All of a sudden we’ll go, “Whoa, are you really paying attention to your AI? Because your data quality doesn’t seem to be exactly right.” All that data inside Salesforce now provides hundreds of petabytes of critical context so agents can actually understand your business. We’re doing more to help customers clean up their data, to make sure that it’s well-harmonized, to make sure that they have the right level of data cleansing, building the right data warehouses. Then our apps, well, they can be more valuable than they’ve ever been. Because now they don’t just run your business, they’re running all of your agents as well.
Slack is where AI lives and where human users can work together with AI; there are more than 1 million companies on Slack now; companies are all building agentic products directly onto Slack because that is what their customers want; Slackbot is Salesforce’s fastest adopted AI product; Slackbot has 1 million active users 5 months after launch, up 150% sequentially; Slackbot can read across all of a user’s Slack data; management thinks Slack is the best play to work with, and build with, AI; management recently introduced Slack Code for teams of agents and humans to build together; Slack Code and Slackbot make for a powerful multiplayer IDE (integrated development environment); upgrades to premium Slack editions have tripled since the launch of Slackbot; Slackbot is driving 8.1 million annualised hours of productivity gain for Salesforce employees
When you look at Slack today, it is where work happens, and it is where these AI lives and can do things. Not only is it where human work happens, now it is where you can build together, too, with Slack Code. AI is not trapped in a single player chat window. It is a teammate that works across all your people, finding the right agent, the right action on the flow of work, fueled by all the enterprise context that lives in Salesforce, the conversational context in Slack. Companies like Anthropic, OpenAI, Lovable, Replit, on and on and on, I think there is more than 1 million companies on Slack now, are all building their agentic products directly into Slack because Slack is where their customers want them. Slackbot is our fastest adopted AI product ever. Five months after launch, it has 1 million active users, up 150% quarter-over-quarter…
…It has the ability to read across all of your Slack data, and you are going to have insights into your business you just were not able to have before…
…Slack is not just the best place to work with AI, it is also the best place to build with AI. We just introduced Slack Code, a shared space for teams of agents and humans to build together right where they already work. When you combine Slack Code with Slackbot, it becomes an incredibly powerful multiplayer IDE…
…Upgrades to our premium Slack editions have tripled since we launched Slackbot…
…Slackbot is driving 8.1 million hours of annualized productivity gains for our employees.
Athenahealth used Data 360 to bring all product usage and account data into a single source of truth, and used Agentforce to connect all that data to their AI; employees across every level at Athenahealth can now get instant answers from Salesforce data, without any data going to any model; Salesforce has been building this zero data retention solution in 2023
Let me show you what this looks like for Athenahealth. Every time someone needed a case summary or opportunity insight, they had to ask IT to run a report. They used Data 360 to bring together all the product usage and account data from across their systems to create a single source of truth. Then, with AI Force, this new user interface harness that sits on top of our core systems, they have connected all of it to their AI, complete with the context and the permissions that already lived in Salesforce. Now, employees across every level can get instant answers from real Salesforce data, from real Salesforce metadata, and all of the sharing models and security models. All of this is done with zero data retention. That means no data goes to any model. It all stays in your company. We started engineering that in 2023. We have been perfecting it over the last three years, and we audit it, and we are sure that that data is staying with you. Without ever opening a browser, the usage and the value they are getting with Salesforce is going way up.
Replit’s sales team uses Slack; Replit has tripled its number of Agentforce sales seats; Replit is building custom apps that connect directly to Salesforce data and workflows
Look at that great company, Replit. You saw them in the pre-show. It’s an amazing company. Their sales team lives here in Slack with Salesforce as their source of truth. They’ve tripled the number of Agentforce sales seats. They’re building custom apps that connect directly to Salesforce data and workflows.
Uber’s Uber for Business service has a mountain of inbound leads that its employees were unable to reach; Uber used Agentforce to manage the inbound leads and generated 60% more leads within 2 weeks
Uber for Business. They’ve got 30 onboarding specialists and a mountain of inbound leads nobody was ever going to reach. Their rules, their workflows, every deal that’s ever gone through them. They’ve used Salesforce since the start of Uber, and all of that lived inside Salesforce. Now, this is how they sell. They just pointed Agentforce at the pile, got to work, looked at their whole history of their whole company, all the institutional memory, everything that’s inside Uber. Now six weeks to launch, within two weeks, 60% more leads and converting.
Robinhood put AI inside Slack, and now any employee can use Slackbot to surface past decisions and solve problems
Robinhood is a great example of what is happening. Robinhood needed AI adoption across its whole workforce, not just engineers. Vlad put AI inside Slack, the place where the entire business comes together and connects to Salesforce, Google, Okta, and other systems. Now any employee can use Slackbot to surface past decisions and conversations, solve problems without engineering support.
The CEO of Ohalo tried to vibe-code a CRM software but quickly realised how difficult it was; Ohalo decided to go with Salesforce’s CRM since it was already using Slack; Ohalo has built custom workflows, interfaces, and sales tools that integrates with Salesforce; Ohalo’s CEO realised the best way the company can create value is not to build something that everyone else does; Ohalo used Claude to build an Ask HR tool within Slack instead of building its own communications tool; Ohalo was previously using a CRM from a Salesforce competitor, but spent plenty of effort building custom tools on it without success, whereas it was really easy to build custom tools on Salesforce; Ohalao’s CEO thinks AI is destroying the value of verticalised software, but increasing the value of horizontal software such as Salesforce
[Ohalo CEO] We did a little vibe coding. I did a vibe code to make a CRM product over the weekend. You quickly realize just how much it takes to do maintenance, to do accounts, to do security. There is just much more to it. We realized pretty quickly there are certain, I would say, software applications that are maybe verticalized, where you have to build custom workflows that make sense for your particular business. Then there is platform software, and platform software, we realized pretty quickly, is really what we need to build our entire company around. Salesforce is what we kind of decided to go with on CRM. We are already on Slack. We are not going to go vibe code Slack. We are not going to vibe code CRM.
Then we build custom workflows, custom interfaces, custom prospecting tools at my sales teams. I have got agents that are out scanning public county databases, trying to find where farmers are planting what crops, what their names are, what their contact info is. I have got agents doing all sorts of prospecting. That interface gives my sales team leads. That integrates with Salesforce. They can then go run the standard workflows that are a little bit more standardized across verticals, and we can build all of our custom workflows in parallel and in concert with the Salesforce tool…
…I am not going to create value as a business by doing something that everyone else does. If everyone else is using Microsoft Excel, I am not going to go build Microsoft Excel. If everyone else is using an ERP tool, we integrate with NetSuite, and I know that you guys have an integration with NetSuite. We are not going to go build an ERP tool, and we are not going to go build Slack. We are not going to build communications and messaging. We are not going to build CRM. We are going to build custom interfaces for doing plant breeding. We are going to build custom interfaces for prospecting customers. We’re going to build custom interfaces for selecting the right product for a farmer based on my satellite and radar imagery that shows me what that farmer’s field is going to look like next season, making an estimation, and then making the right product selections for him. The interface to do that for my sales team, for my customers, that’s where we add value. The actual following up with the customer, connecting with the customer record, making that transaction record, storing it, that needs to be done safely, securely. It needs to be done with the right account settings, the right access. That’s what we’re not going to go build, and that’s really where the partnership works…
…Just internally, we use Slack, right? That’s our primary communication messaging tool. In Slack, we’ve got an Ask HR channel now. So we basically used Claude to set up an Ask HR channel that anyone in my company can ask the HR questions that they would normally spend time calling up the HR people saying, “Hey, I got this question. I got this question.” Ask HR has access to all of our internal HR documents, and it knows who you are, whether or not it can access your personal information. It knows standard benefits, policies, and it can just answer all your questions for you through Ask HR. I don’t need to go build a communications tool to do that. I can leverage Slack, and I can leverage Claude to deliver that internally. It’s another good example of kind of the sort of thing where we can build something custom using our internal benefits, our internal HR policies, but then we can leverage the platforms and Claude to deliver that…
…Ben works at my shop, and Ben used Claude, he used Cursor, and he spoke to your Salesforce guys, stood it up. What took us months of going back and forth trying to customize this other CRM tool and build the application and workflows around it that we needed, we were pulling our hair out. We’re like, “Let’s just build it all from scratch.” That’s when you and I talked. Ben used Claude and Cursor, got into Salesforce, and was able to get everything stood up in under a month. I wasn’t sold until we did it. Then I was like, “Okay, I’ll give Marc credit at this point, and maybe we should send him a check and pay for the product.”…
…When we got in there, and Ben was able to do this in under a month. He told me he did the whole thing in under a month…
…I was probably early on a SaaSpocalypse train, as you know, because I know you listen to my show. And I always said, like, I think SaaS was this temporary phenomenon between the founding of the Internet and the start of AI. But I think that it’s a little more nuanced than that. It’s probably this verticalized SaaS where you’re trying to standardize a vertical on a bunch of workflows, which actually destroys value in that vertical because everyone is now doing the same thing in the same way. So no one can differentiate in that vertical. We’ve actually dumped all our vertical software — verticalized software. We make all of our verticalized SaaS in-house. But the horizontal, the platform, Salesforce, standardizing on Slack. This is really where I think we’ve realized that’s not — that’s actually going to get bolstered and it’s more valuable with all the other capabilities we can now build around it. So I’m definitely sold in that sense. I think there is still a SaaSpocalypse, but with a lowercase S rather than the uppercase S, for verticalized tools, where I think AI really allows you to rebuild something that is unique, which creates value for your business in a vertical.
Salesforce has delivered 7.0 billion AWUs (agentic work units) to-date (was 3.8 billion in 2026 Q1); Salesforce delivered 3.2 billion AWUs in 2026 Q2 (FY2027 Q2), up 97% sequentially; Salesforce has 1 customer, a digital platform company, with 45 million AWUs, up 14x in 6 months; the digital platform company has an activation agent that goes to all the merchants on the platform that are not transacting to activate them
7.0 billion Agentic Work Units (“AWUs”) delivered to date across Agentforce and Slack, with 3.2 billion in Q2, growing 97% quarter-over-quarter (“Q/Q”)…
…Let me tell you one story of some of the agents that are working the enterprise that people don’t see. Big digital platform company here in the U.S. 45 million AWUs, it’s one of our largest. Grew 14 x. We started this six months ago. In Q2, the AWUs consumption grew 14 x, 14-fold. And what it does is, they call it the activation agent. They go to all the merchants that are already registered with this platform, but they are not transacting. They’re not getting money from the merchants. And the agent has 1,500 interactions every day, getting these dormant merchants alive, and then kicking, and then generating revenue.
Salesforce’s management thinks companies do not want to manage their own catalogue of models, and instead, will consume AI through packaged software; management thinks customers do not care about the underlying AI models and that the highest level of value in the AI technology stack is the application layer; Salesforce has its own AI models, and actually has been developing models since 2015
They do not want to also then say, “I have all these models.” Which kind of gets to your question, which is like, you think that these customers then are going to all of a sudden set up and maintain and build the human expertise to be able to build and maintain and train models? No. I believe what they are going to do is consume AI through packaged software. Not a huge statement, just they are consuming their AI through packaged software. You are using Slack, you are consuming AI through packaged software. You are using Salesforce, you are consuming AI through packaged software. At some level, when you use Agentforce Coworker, you are consuming AI through packaged software. When you are using Claudeforce, you are certainly using AI through packaged software. I think that’s the right way to think about it.
I think the model, it’s like, do you even know what chip you’re using? Do you really know what data center you’re on? Do you know what router you’re on, what switch, what type of fiber, what cable you’re using, what interconnect you’re using? At some level, we do get abstracted back to how the way the world used to be, which is we operate at a certain level of value. I believe the highest level of value is at the application layer…
…I did not have one customer really say to me, “Tell me, are you going to be on open source or are you going to be on frontier?” I think one way to think about this is, yes, this is a highly dynamic market. There will be frontier models. There’s going to be open source models, and there’s going to be room for all of the above…
…Salesforce also makes a lot of models too, by the way. We have a lot of our own models that run in our platform. Our platform runs on our own custom models. We’ve been writing models since 2015, and you can find them all on Hugging Face. If you go, you’ll see all the models.
Salesforce’s management is learning that customers want many different ways to pay for AI products, such as on a per user or per agent basis, on a consumption basis, on a usage basis, or on an outcome basis; Salesforce has been flexible with its pricing models for AI products, and management thinks this is a driver of the company’s large deals; management wants to move beyond just outcomes and wants to share in the economics when it generates more revenue for customers
Customers want to buy and want to price in different ways. This is something I have learned really aggressively recently. Some are still buying by user and by agent, and that is important for them. Some of them want it by consumption, and that is important to them. Some of them are just basic usage customers, and they want to pay that way. Some of them even want to pay by outcome, and that outcome could be by a transaction outcome or a business outcome. Because customers want that kind of diversity in pricing, we have created a high level of flexibility, and that, I think, has really expanded our ability to sign very large transactions with our customers…
…We’ve heard so many companies say, “We’re doing outcome pricing. We’re doing this pricing. We’re doing that pricing.” We’re just too big. We have to do it all. We’ve built these new flexible pricing schemes that let our customers choose the price that they want, and we’re going to get more and more into that zone…
…We are moving to not just outcome-based pricing, which is we completed this many phone calls, therefore give us a dollar. We want to be able to say, “No, we improved revenue by this much, so give us $2 because we made you $20, or we made you $40.” I think that that is like the next generation of enterprise software that is more than outcome-based pricing.
Sea Ltd (NYSE: SE)
Shopee’s advertising revenue grew 70% in 2026 Q2, and the take rate increased by more than 90 basis points from a year ago; number of advertisers and their average ad spend increased by 45% and 15% year-on-year, respectively; there are a number of things that helped with the growth of the advertising business, namely, (1) smart voucher, (2) the AI-powered Shopee GMV Max diagnostic tools, (3) the Brand Max feature for audience insights, (4) improvement in the advertising algorithm for matching, powered by the AI-based GR algorithm, and (5) AI-powered personalisation of content; management sees further room to increase the advertising take rate
Ad revenue was up more than 70%, and ad take rate improved by over 90 basis points year-on-year. We continued to make advertising simpler and smarter for sellers. For example, pairing ads with vouchers that are personalized to buyers to increase purchase conversion and improve the efficiency of sellers’ ad spend. Ad adoption and spend continued to improve across our seller base. The number of ad-paying sellers rose around 45%, while average ad spend per seller increased more than 15% year-on-year. Our operational priorities remain consistent, improving price competitiveness, service quality, and our content ecosystem…
…I think there are a few things helping the ad growth. I’m just listing some of the examples. One of the things, smart voucher, which is we combine a personalized voucher from a buyer together with ads, so we enhance the seller’s ad traffic, increasing the purchase conversions. Another example is we have the Shopee GMV Max smart diagnostic tools. So essentially, this AI diagnosis report and tools to help the seller to analyze how can they have better return on the ads. It’s leveraged on the AI capability to analyze the ad performance and drive improvement. We also have an in-depth audience insight for Brand Max. This feature essentially allows more sellers to view the number of shoppers in each stage of their purchase journey. How does the shopper move between stages? This will give them a more robust and algorithm-driven branding solution to capture the buyers better across their life cycles with the seller. On top of that, there’s also quite a fundamental improvement on the algorithm for the ads. Those on how can we match the buyer’s intention to the ad for better. I think that’s where the AI-based algorithm, the GR algorithm helps quite a lot when we come to the matching part. The other part is the content presentation. We’re using fellow AI tools to create better personalized content for the user when they see the ads…
…In the coming quarters, we still see that meaningful potential to increase the ad take rate. Given that many of the tools, many of the algorithms we’re implementing are still in progress, we can see a meaningful optimization potentials, while we are doing more experiments, while we are optimizing algorithms further in the coming quarters.
Sea’s management has improved Monee’s credit risk capabilities through the use of new credit models that are based on the transformer architecture similar to those supporting large language models; the new credit models have lifted approval rates by 10% while maintaining a similar level of risk; management is using more external data sources now to assess new users; management has used AI-built tools to reduce review times of user-submitted income documents by 95% while maintaining accuracy
One key enabler of our credit business growth has been the ongoing advances we have made in our credit risk capabilities. Our latest risk models are pre-trained on a broad set of behavioral and transactional data across our ecosystem using transformer architecture similar to those following today’s large language models. The model learns from the full sequence of a user’s actions over time, capturing richer context around how customers interact with our platform. Recent enhancements to our underwriting models have helped lift approval rates by around 10% when compared to previous models while maintaining a similar level of risk…
…We are also drawing on more external data sources to better assess users who are newer to our ecosystem. For instance, through partnerships with local mobile operators in Indonesia and open finance data in Brazil.
We have also used AI to build tools to efficiently verify a diverse range of user-submitted income documents across markets, languages, and formats. Review time reduced by around 95% while maintaining a very high level of accuracy, letting us respond to credit limit requests from users almost instantly…
Sea’s management is launching an AI assistant for sellers in a number of markets; the AI assistant is essentially a digital key account manager that sellers can talk to; the AI initiatives of Sea for the Shopee business on the buyers side have led to better conversions
We are launching the AI assistant for sellers in quite a few of market. Essentially, instead of the seller talk to a key account manager, the IM, as we call it, there is a digital IM that they can talk to, which can help them to answer many questions or many analysis they want to do with their shops. This is also 24 hours available, of course, compared to key account manager usually are not available 24 hour by 7…
…On the buyer side, we spend a lot of efforts on both helping the apps have better conversions, which reflecting our ad take rate improvement over times, but also just general conversion for our search recommendations. We’ve been rolling out our new GR algorithm, a generative algorithm for recommendation and search, which give us a meaningful improvement on the conversion rate that we observed. We’re also doing follow-up work on AIGC on content. If you look at our platforms, we have a lot more contents can be generated by AI now, which can be used to do a personalized targeting for our buyers to improve the conversion as well.
Tencent (OTC: TCEHY)
Tencent’s management is seeing its existing businesses grow partly because of AI enablement; the growth of the existing businesses provide financial support for Tencent’s AI initiatives; management believes that they can upgrade Weixin for the AI era in a cost-efficient way and accelerate the growth and monetisation of the Weixin ecosystem; management is leveraging Tencent’s Hunyuan model for AI teammate creation and code review in the games business; Tencent’s AI Marketing Plus automated campaign solution was upgraded to better support closed-loop WeChat Minishop and Mini Drama advertisers; management scaled up the parameters of Tencent’s advertising AI recommendation system which led to better advertising conversion rates
Tencent’s existing businesses are growing solidly due to intrinsic modes and AI enablement. As discussed early this year, our modes arise from factors including network effect, depth, and value added along supply chain, IP, low tick rates, regulatory requirements, and private data. In addition to these modes, we’re further deploying AI to boost returns in areas including WeChat, games, and advertising. As a result, our existing businesses provide a very strong financial support for our new AI initiatives…
…As we upgrade Weixin for the AI era, we can do it in a cost-efficient way, and we’re confident that AI will over time accelerate the growth and thus the monetization of the entire Weixin ecosystem, generating attractive return for us…
… In games, we are leveraging Hunyuan for AI teammate creation and code review for games, including our flagship game, Peacekeeper Elite…
… In terms of production, the Delta Force team have integrated AI across multiple workflows, including using data agents for performance analysis and the Hunyuan 3D model for asset generation…
…We upgraded AI Marketing Plus end-to-end execution capabilities to better support closed-loop WeChat Minishop and Mini Drama advertisers. For example, AI Marketing Plus now enables WeChat Minishop owners to automatically select products for promotion, generate product-relevant ad creatives, and then run smart bidding to buy inventory for those creatives. We significantly scaled up the parameters of our advertising AI recommendation system to capture user interest with greater granularity and thus improve ad conversion rates.
Tencent’s new foundation model, Hunyuan 3, has leading cost performance; Hunyuan 3’s production version has substantially better performance than the preview version; Hunyuan 3 had notable improvement in task completion rates and reducing hallucination and errors, because it was trained with feedback loops from Tencent’s product teams; management thinks Hunyuan 3 has strong agentic capabilities and product experience, and thus has advantages for coding, office work, financial modelling, and front-end design; Hunyua 3’s production version has 6x higher average daily token usage compared to the preview version; Hunyuan 3 is consistently among the top 3 models on OpenRouter by token usage; management sees Hunyuan 3 as an important stepping stone for developing frontier models with efficient cost-performance in the future; management has integrated Hunyuan into WorkBuddy, Yuanbao, the games business, and Weixin; the integration of Hunyuan into Tencent’s products is providing valuable feedback for model-training which enables faster model iteration and sustained performance gains; management is accelerating the improvement of the Hunyuan family of models, and is training Hunyuan 4, which is expected to be released later in 2026; management is confident that Hunyuan will reach frontier levels; management wants to develop a frontier model because they think it will help with unit economics, feature innovation, and allow Tencent to capture more value; Hunyuan 3 is a small model but is widely used and has certain characteristics that match or beat much larger models; Hunyuan 3 is focused on use cases rather than beating benchmarks, so it’s more useful than many larger models; management thinks products using Hunyuan 4 will become even more useful once Hunyuan 4 is released; management already has plans for Hunyuan 5; even when Tencent’s models are at frontier levels, management still expects the company to have multiple models for different needs
We’ve made significant progress in constructing a robust foundation, including a substantially improved Hunyuan 3 foundation model with leading cost performance…
…The release of Hunyuan 3’s full production version is very successful, showing a substantial step-up in performance compared to the Hunyuan 3 preview version. Leveraging the feedback loop from product teams to improve the quality and diversity of data used for post-training, and by scaling up reinforcement learning, Hunyuan 3 achieved a notable improvement in task completion rates and meaningful reduction in hallucination and error rates. The improvements in Hunyuan 3’s capabilities are most evident in its agentic capabilities and product experience. The model’s performance step-up across reasoning, agentic, and long contest tasks delivered clear advantages for use cases such as coding, office work, financial modeling, and front-end design…
…The approximately 6x increase in average daily tokens usage of Hunyuan 3 compared to the preview version across all channels during the pay period…
…Hunyuan 3 consistently ranks among the top 3 models globally on OpenRouter based on token usage. Hunyuan 3’s production version has performed well and will serve as a stepping stone toward the Hunyuan family of models, achieving state-of-the-art capabilities in the future while providing users with the cost-performance efficiency that they need today.
We have been integrating Hunyuan into our products, making great impact. For WorkBuddy, Hunyuan can facilitate complex agent workflows with higher task success rates and reduced time to completion. For Yuanbao, Hunyuan delivers leading execution quality in information retrieval, data processing, document workflows, and everyday decision-making. In games, we are leveraging Hunyuan for AI teammate creation and code review for games, including our flagship game, Peacekeeper Elite. In Weixin, we deployed Hunyuan for powering the AI assistant in official accounts and the developer tools for mini-programs. At the same time, product integration is making Hunyuan better by continuously feeding real-world product usage and domain feedback into model training. Our model product co-design approach allows Hunyuan to validate model accuracy and identify and work on edge cases, enabling faster model iteration and sustained performance gains…
…We are accelerating the improvement of our model. We are scaling more powerful reinforcement learning to substantially upgrade models after pre-training is done. We are in the process of upgrading multimodal capabilities. More importantly, we are training a larger parameter model, Hunyuan 4, which we expect to release later this year. By accelerating the technical iteration and pushing the boundaries of model intelligence, we are confident Hunyuan’s capabilities will reach state-of-the-art level…
…The rationale behind investing in our own foundation model is that we can achieve better unit economics, more innovative features, and more exposure to the value of intelligence through co-design across our applications, our model, and our compute infrastructure, especially at this early stage of AI diffusion…
…Hunyuan 3 is a very small model, even in today’s terms, but it’s actually very widely used. I think there are a number of characteristics of Hunyuan 3, which is, it actually has the capability of matching or beating much larger models…
…It’s actually focused on use cases rather than just benchmark beating. As a result, in real life, it has become much more useful than a lot of models of the same size or even bigger size…
…When Hunyuan 4 comes around, the products that would be using Hunyuan 4 would actually become even more powerful and even more useful than what they are today, and that would actually provide a very significant lift for the products that it’s powering. I think that’s the path, and Hunyuan 4 is only another stop. Then we’ll be upgrading to Hunyuan 5. As we continue to progress, we will be approaching SOTA, and at some point in time, we’ll definitely be able to reach SOTA. Once we are there, we would also have a lot of models of different sizes that will be able to solve different kinds of user problems at the different level of model and cost efficiency. At the same time, we have multiple models that can be used for co-design with our different products, and that would help us to make the products feature-rich and help to make the products powerful as well as the speed of execution will be fast.
Tencent’s management believes they will generate a significant return from building AI-native new businesses for the company; management thinks Tencent could earn a significant return from its capital expenditure immediately by renting out compute, but they believe even better long-term returns can be earned by building frontier models and building applications on top of the models; management can actually resell compute at a 30% profit today compared to what Tencent paid for them a few months ago; management thinks the downside for the AI investments are protected; management is currently investing prudently in AI through Tencent’s operating profit; management will step up the investments if they see opportunity to generate outsized returns; the AI-drag on Tencent’s operating profit is also very dynamic in terms of where it its allocated
We believe we will generate significant return in building a large and valuable AI-native new business for Tencent…
…Given the surge in demand and therefore rental pricing for compute, we could recover the depreciation almost immediately by renting the compute out to third parties as many neo cloud businesses are doing. We would then achieve a decent return in an immediate timeframe. However, in reality we are playing a different game or executing a larger strategy in that we are allocating a very substantial proportion of the new compute to building our own models to state-of-the-art status, and also to deploying, popularizing, and bringing our own AI applications to market leadership in China. Our belief is that by providing the superior intelligence that we can achieve through state-of-the-art models, through market-leading AI applications, that superior intelligence, we can then convert into superior economic returns over the longer term, for example, by selling tokens through the WorkBuddy application….
…Today, if we can actually allocate the compute toward leasing on the Tencent Cloud would actually generate a lot more revenue and would generate significant return from the CapEx. As a matter of fact, for the prepayment and for some of the compute orders that we had made just a couple of months ago, today, we can actually sell that at more than 30% profit compared to the price that we paid just a few months ago…
…The AI investments we’re making are mostly in AI infrastructure, and in the worst case, which we do not believe that would happen, we can choose to rent that infrastructure out at cost recovery or even better prices via Tencent Cloud if needed…
…[Question] The new AI product drag rose from roughly RMB 8.8 billion in first quarter to about RMB 10.5 billion this quarter. Can you walk us through how you manage that investment?
[Answer] it is actually very dynamic. I think we would be investing prudently until the point that we actually see breakout opportunity, then we may step up the investment. I think that is essentially the way we look at it, right? It will be a certain percentage of our profit, but if clearly we see that if we step up the pedal, it would actually generate a lot of returns, then we may step the pedal…
…It is also the case that we dynamically reprioritize the spend within the budget or within the envelope. If you look at where the RMB 8 billion in the first quarter flowed in terms of user acquisition spending and so forth, and which products it supported, versus where the RMB 10.5 billion in the second quarter flowed, there was actually a very big change because we identified that WorkBuddy was breaking out. Therefore, we aggressively prioritized WorkBuddy while deprioritizing some of the other products in that new AI product portfolio.
Tencent’s WorkBuddy and CodeBuddy services have breakout user growth and are the clear leading office productivity services in China; WorkBuddy allows users to orchestrate multiple agents for complex work, and can be controlled by Weixin; WorkBuddy has access to over 70,000 skills from Tencent Cloud Skillhub; WorkBuddy has high retention rates and users are willing to pay; WorkBuddy is attracting a growing developer community because Weixin Pay is embedded within, and this enables payouts to developers when their skills are called; management believes there are substantial opportunities to be unlocked in the productivity market; management believes Tencent’s AI productivity products will have attractive economics through growth in paying users and reduction in token costs; management sees WorkBuddy as a new way for Tencent to monetise existing enterprise relationships; management sees WorkBuddy as a new platform which acts as an orchestrator of different models and skills to perform work economically; management sees Tencent’s Hunyuan as one of the models provided by WorkBuddy, but it would not be the only model; management primarily monetises WorkBuddy today through subscriptions, which creates a time lag between cash receipts and revenue; management is seeing a substantial ramp in WorkBuddy cash receipts
Our AI office productivity workspace, WorkBuddy, and coding tool, CodeBuddy, are achieving breakout success in terms of capability and user growth. They are the clear leading office productivity service in China based on monthly interactions.
WorkBuddy serves as a one-stop-shop workspace that orchestrates multiple agents to handle complex work from end to end. Users can remotely control WorkBuddy via Weixin and WeCom, as well as WPZ, and access to over 70,000 skills from Tencent Cloud SkillHub. Besides the rapid user adoption of WorkBuddy, it is also achieving high retention rates and high willingness to pay among users as it directly contribute to users’ productivity. It also attracts growing and more vibrant developer community by embedding skill pay and Weixin Pay inside task flows to enable payouts for developers when their skills are called. This progress supports our view that there are substantial opportunities to be unlocked in the productivity market, including coding and existing office work scenarios…
…Over time, product economics will be attractive as enhanced premium benefits accelerate paying user growth, while we can reduce token costs through agent efficiency, inference efficiency, and model optimization.
Given Tencent applications such as Weixin, WeCom, and Tencent Meeting are already widely used by enterprises, WorkBuddy provides a new way for us to monetize our enterprise relationships…
…[Question] Whether WorkBuddy, in your mind, is a piece of enterprise software that sits next to Tencent Docs, or is this a new platform play that essentially becomes a marketplace for AI in the future?
[Answer] I think it is indeed a new platform. It’s a very flexible workspace for agentic AI. The core purpose is actually it will solve all the productivity needs of office workers and of all kinds of people who engage in their own businesses, right? One-person companies and the like. And below that, there will be a harness which actually helps the users to make use of the capability of different models to solve the agentic problems of the users. And over time, there will be many models serving the users through WorkBuddy. There will be many skills developed over time by all kinds of different developers. And the purpose is actually solving productivity problems, and then the platform itself would make use of all kinds of different tools and models available to do that. Then, of course, we are the orchestrator. So we can actually choose the right model and choose the right skills to help users solve the problems. And we choose that to make sure that the work is done perfectly, but at the same time, it will be done also very economically,..
…But at the same time, if you can actually solve a lot of the user problems, right, and it’s quite effective, then Hunyuan would actually be one of the main models within WorkBuddy, but it would not be the only model…
…The majority of the WorkBuddy spending by users is on subscriptions. And so similar to games and some of our other businesses, there’s a lengthy time lag between the cash receipts coming to us from the users and those cash receipts translating into reported revenue. But we are seeing a substantial ramp in the cash receipts today, and that will translate into reported revenue growth for Tencent Cloud as we move through the year.
Tencent’s management recently released a prototype of Xiaowei, an agentic AI within Weixin that leverages Weixin’s assets and that is powered by a Weixin-customised model; Xiaowei will be rolled out with a phased approach as management upgrades its capabilities over time; when mobile appeared, the QQ ecosystem was magnified by Weixin, and management believes something similar can happen for the Weixin ecosystem with the addition of Xiaowei; management believes Xiaowei will be part of agent-to-agent interactions within the Weixin ecosystem
We recently released a prototype of Xiaowei, which delivers an embedded and context-aware agentic AI experience within Weixin, leveraging Weixin’s social graph, knowledge graph, merchant reach, and payment functionality. Xiaowei is powered by the Weixin customized model, WeLM, built with a focus on user privacy, Weixin-specific use cases, and cost efficiency. Xiaowei can help users navigate and derive insights from Weixin’s diverse content universe in a personalized and efficient manner. Xiaowei can also leverage Weixin’s unique mini-program ecosystem to help users discover products, make purchase decisions, and place orders, laying the groundwork for an agent-to-agent transaction loop…
…Xiaowei will be rolled out to broader user base in a phased approach as we work on several core initiatives to elevate the user experience. These include upgrading Xiaowei’s dialogue, memory, and recommendation capabilities, expanding service and content integrations, scaling our AI infrastructure, and upgrading our harness to support a significantly larger user base…
…If you imagine the time when QQ was a communication and social tool in the PC stage, then when we get into the mobile age, Weixin appears and Weixin essentially, the ecosystem magnified QQ’s value by more than 10x, because it is enabled in the mobile age and it becomes mobile first. When we look at AI, we believe there is another huge opportunity for the Weixin ecosystem to be first enabled by AI, and over time, it will be AI first application and ecosystem. When that happens, users would have a lot of great experiences…
…I think we are envisioning a future in which a lot of users would be executing their instructions and over time transactions via Xiaowei and via agents. In the past, if you think about the Weixin ecosystem is users interacting with content, interacting with mini programs themselves. In the future, if they can actually send a complex instruction to an agent, then an agent can actually start helping the user to execute transactions. A lot of the mini programs, a lot of the merchants would actually also have agents, which over time can interact with the agent of the users.
Within Domestic Games, management has integrated AI into Delta Force
On domestic games, “Delta Force” achieved lifetime high average DAU in the second quarter, driven by the Burst Fest campaign, the game’s first professional esports final, and a global 20 versus 20 tournament. In terms of production, the Delta Force team have integrated AI across multiple workflows, including using data agents for performance analysis and the Hunyuan 3D model for asset generation.
The Marketing Services segment’s revenue was up 22% year-on-year in 2026 Q2, driven by higher eCPM and impressions; Tencent’s AI Marketing Plus automated campaign solution was upgraded to better support closed-loop WeChat Minishop and Mini Drama advertisers; management scaled up the parameters of Tencent’s advertising AI recommendation system which led to better advertising conversion rates
For marketing services, revenue grew 22% year-on-year to RMB 44 billion, driven by higher eCPM and impressions. Most major categories increased their marketing spending with us, including e-commerce, internet services, and local services. We upgraded AI Marketing Plus end-to-end execution capabilities to better support closed-loop WeChat Minishop and Mini Drama advertisers. For example, AI Marketing Plus now enables WeChat Minishop owners to automatically select products for promotion, generate product-relevant ad creatives, and then run smart bidding to buy inventory for those creatives. We significantly scaled up the parameters of our advertising AI recommendation system to capture user interest with greater granularity and thus improve ad conversion rates.
Within the Fintech and Business Services segment, the cloud revenue within the Business Services sub-segment had low-20s percentage year-on-year increase in revenue in 2026 Q2, up from high-teens in 2026 Q1, driven by AI-related demand; Tencent’s international cloud business grew rapidly in 2026 Q2, driven by AI; in the international cloud business, CodeBuddy is enabling Tencent to conduct customer cloud migrations over to Tencent Cloud faster than in the past; token prices in China are low, but token manufacturing costs are also low, so Tencent Cloud’s AI business can have positive gross margin even with low token prices; Tencent Cloud’s new AI businesses have similar gross margins for paying users with the overall Tencent Cloud; management sees the overall pricing environment in China’s cloud market to be easier than in the past, even as memory supply costs have increased
Within business services, while we’re still working through capacity constraints, our cloud revenue growth rate accelerated from high teens percentage year-on-year in the first quarter to low 20s percentage in the second quarter, benefiting from AI-related demand, international expansion, and increased usage and pricing for general cloud services. AI-related demand translated into increased revenue across GPU rental, Model-as-a-Service, and WorkBuddy and CodeBuddy token usage. Our international cloud business expanded rapidly. Using skills developed with CodeBuddy is enabling us to conduct customer cloud migrations over to Tencent Cloud faster than we could in the past, for example, on behalf of a leading telecom company in Indonesia…
…It is true that domestic token prices are low, but the domestic token manufacturing costs are also extremely low. I think much lower than widely perceived or externally estimated. The token business, it can be positive gross margin at these low token prices because the cost is low. If you look at the gross margin for the paying users of WorkBuddy, or you look at the gross margin for our models of service, then the gross margins today are already comparable to the gross margins for Tencent Cloud overall. Of course, WorkBuddy in aggregate has a lower gross margin because there is a proportion of free users whom we are subsidizing to drive market share and market growth. On the paying users, we are generating a pretty good gross margin right now…
…It is true also that the China cloud market is price competitive. But that environment has changed a great deal in the last several months as the input costs, particularly for memory, have gone up. We have been increasing the prices we charge to our customers. We increased prices across the board in May for Tencent Cloud, and beyond those headline price increases, we have also been more substantially reducing discounts. The overall pricing environment in cloud in China is not as difficult as it has been in the past.
Tencent’s operating capital expenditure in 2026 Q2 was up 190% year-on-year and up 66% sequentially because of accelerated investments in AI infrastructure; free cash flow was negative in 2026 Q2, and down year-on-year and sequentially; management now sees Tencent’s capex as having 2 components, one for the existing business, and one for the new AI-native businesses; the upfront investments in compute are needed to get the AI-native businesses kickstarted; the primary use of Tencent’s capex is for training the next generation of Hunyuan models and the secondary use is for inference for the use of Tencent’s 1st party models and 3rd party models; management expects to have sufficient compute capacity toward the end of 2026 and early-2027 to rent out GPUs, but management thinks the production of tokens by WorkBoddy has better economic value; management sees the AI capex as a one-off thing for 2026 and 2027 and does not expect big capex spending by Tencent every year
Operating CapEx was RMB 51.8 billion, up 190% year-on-year or 66% quarter-on-quarter as we accelerated investments in AI infrastructure to support Hunyuan Model enhancements with PAPI and coPAPI inference needs, Huaxin AI initiatives, and development of AI capabilities across our products and services, as well as to meet growing external demand for our cloud services. Non-operating CapEx was RMB 1 billion. Free cash flow was -RMB 13.8 billion, reflecting large AI infrastructure CapEx and AI-related prepayments, as well as seasonally lower games gross receipts. Excluding the prepayments for compute procurement, free cash flow would have been RMB 37.6 billion…
…I would say the CapEx will be divided into two parts too, right? One part is really in relation to our existing business, which you can just like in the past, right, you can just say, oh, this is the free cash flow in which we generate operating cash flow, and there is a CapEx in relation to that. That part of the business still very cash flow generative. Then there is another set of CapEx which is related to the new AI native business, which is essentially a lump sum that we need to invest in order to get our compute for model training, in order to prepare for inference needs, and in order to also order some more for building our AI compute and AI Cloud business…
…The reason we are actually investing in all these compute is that we need that in order to essentially get the business kick-started…
…The immediate primary use case for the CapEx is for training bigger and better Hunyuan models in the coming months… An important secondary use case is providing inference for the use of Hunyuan models as well as DeepSeek and other models behind WorkBuddy…
…Then toward the end of the year and into next year, we will also have sufficient GPU ASIC capacity to step up in terms of Tencent Cloud renting out bare metal GPU or providing Model-as-a-Service. But within those opportunities, renting out GPU Model-as-a-Service and then token production for WorkBuddy, we think that it is token production for WorkBuddy that carries the most enduring economic value to us, and that’s why we’re prioritizing it today…
…When we look at the CapEx that we allocate for building the AI native business, it is more of a sort of a lump sum that we are going to be investing this year and next year. I think one should not assume that it will be sort of new every year, because the model-building part is more of a fixed cost that you actually have to get enough compute, but it will not be every year you have to invest more.
Tencent’s management thinks on-device inference will only happen over the long run, but there’s a high likelihood of it happening based on a study of computing history; but for this scenario to happen, the AI model architecture itself will be important
In terms of on-device inference, I think it would, number one, be happening maybe step by step and it will be only over the long run that most of the inference will be happening on device. But I think at some point in time, it is not hard to imagine some kind of inference will be actually happening on device, and some inference will be happening in the cloud. Over time, as the on-device compute becomes more and more powerful and as the model becomes more and more efficient, you will have more inference happening on people’s devices.
I think that would be going back to the normal state of the computer industry. If you think about the computer industry as well as the smartphone industry, most of the compute, which is CPU, actually happens on device. The cloud actually only is responsible for a small part of the compute. In this initial phase of AI infrastructure, most of the compute, because it has to be very powerful. The problem of getting enough compute on device, getting it cheap enough, and also getting it power-efficient enough has not happened yet. So that is why everything happens on the cloud. There will be a time in which more and more GPU capability will be put into everybody’s phone and computer. When that happens, then more and more inference will be happening on the device, and there will be going back to the time when it is actually the software, it is actually the model that becomes much more important. The return for running models and the return on running applications will be higher because the compute CapEx will be not just borne by the model company, but it will be borne across the ecosystem. I think that would definitely happen at some point in time and we are building and preparing for that.
Veeva Systems (NASDAQ: VEEV)
Veeva’s management thinks Vault AI increases the value of the company’s applications; in August, management released more standard Vault AI agents, expanded existing Vault AI agents, and improved Vault AI’s ability to develop custom agents
Vault AI increases the value of our core applications…
…In early August, we crossed a key milestone for Vault AI with the release of additional standard agents, the expansion of existing agents, and more advanced custom agent development capabilities.
Veeva’s management thinks Veeva Falcon helps open a new big market for agentic labor; development of Falcon agents for clinical, regulatory, and safety is progressing quickly; Veeva is working with 5 early adopters of Falcon; management expects early adopters of Falcon to go live in 2026 (FY2027), and for the first top 20 biopharma to go live in 2027 H1 (FY2028 H1); the early adopters of Falcon are sponsors (meaning the entity behind a drug undergoing trials) and management’s current priority with Falcon are the sponsors; management acquired Copli in June 2026 and launched Falcon MLR (Medical, Legal, and Regulatory) for automated review of commercial content; customer demand for Falcon MLR is high; management thinks Falcon MLR can remove at least 70% of manual labour in the MLR review process over the next 5 years; management thinks Falcon is addressing things that are high on biopharmas’ priority lists; management sees very high customer interest in Falcon, with Falcon possibly flying off shelves if it was already available; management sees Veeva’s speed of development of Falcon as the bottleneck; Falcon is a completely new area for Veeva, but fits really well with the company’s existing products; there’s no need for a lengthy or complicated implementation process with Falcon; management thinks Falcon will have a relatively easy selling cycle because the product is not sold to IT departments; the buyers of Falcon are mostly same people Veeva has been selling to for the past decade; management expects Falcon to have similar gross margins to Veeva’s traditional software products because the agents will be working mostly with deterministic software; management thinks Falcon will still be a great business even if AI models do not become cheaper over time because Falcon is pushing a lot of work into the deterministic software layer; the labour Falcon will be replacing will be for both internal and outsourced work, but not outsourced work to CROs (contract research organisations); the pricing model for Falcon is still unclear to management, but it may be enterprise license agreements that escalate over time; in early tests, Falcon outperformed humans
Veeva Falcon represents a big new market for agentic labor…
…Development of Falcon agentic labor for clinical, regulatory, and safety is progressing quickly. We are working with five early adopters to test and refine our agents using real customer data, with more early adopters on the way. We expect to have the first early adopters go live this year and the first top 20 biopharma go live in the first half of next year…
…Our early adopters are with sponsors. Now, we have had some interest with service providers. I say that carefully, interest. They’re interested, but we really haven’t engaged heavily there yet because you have to be focused when you start working with your first customers. Service providers will have similar needs to sponsors, but not the same. We’re focusing on the sponsors first, and I fully expect over time that this will be useful for outsourced service providers. We have to work on the sponsors first…
…In June, we acquired Copli and launched Veeva Falcon MLR, our agentic solution to automate the review of commercial content. Execution is going very well, and customer demand is high. Over the next five years, we believe Falcon MLR can eliminate at least 70% of the manual labor in the MLR review process across the biopharma industry…
…I think Falcon has a lot of interest right now because it’s very clear that that’s high priority. Quick cost savings and compliance and efficiency. That’s high on everybody’s priority. I think there’s a lot of interest in Falcon. We’re the rate limiter right now. We have to get that product ready, start working with the early adopters, but interest in Falcon is very high…
…If we had our early adopters live and successful right now, I don’t want it be hyperbole, but Falcon would be flying off the shelf if that was the case…
…It is a major change for Veeva. Falcon is agentic labor. That’s something different than we’ve done before. We’ve done cloud software, data consulting. Now we have this fourth thing, agentic labor. It is transforming the discussion. There’s two different things you could do with Veeva. You can do some agentic labor, you can do core applications. That was never the case before. The important point is Veeva, it fits very well. It’s a structural advantage for Veeva to both have the agentic labor across multiple areas in life sciences, and have the core applications across multiple of those areas in life sciences…
…One thing to know is there’s not an extensive Falcon implementation. There’s not a data mapping from one system to the other. There’s not a cut-over process. There’s not ETL to do. This implementation, the full value is faster with Falcon…
…it’s actually going to be an easier selling cycle because IT is really not involved in the agentic labor. That’s not something they’re involved in. Because it’s not like that. If you’re selling a solution to safety, this is about the budget of the safety team. It’s really the head of the unit, the business unit, and the head of the operations of that business unit…
…We have not hit the case for Falcon where we’re selling into a buyer that we are not selling into, because we’re always selling into the business side with our business applications…
…These areas where we’re doing Falcon, they were ready, I would say, on the average, 60% of business sell, and those are people that we’ve been selling into for 10 years…
…I don’t really want to make predictions on Falcon because it’s early, but in general, I don’t think we’re going to have a gross margin problem. I think the gross margins will be roughly similar to our software. Here’s why. When we really go deep into Falcon and we have what we call Falcon copies, where we have the real customer data that we’re testing the agents with and developing the agents with, we know what’s going on. More and more of that work goes into the deterministic software…
…Even if that wouldn’t happen [referring to lower prices for AI models], I think Falcon would be a great business because we’re pushing a lot of things into the deterministic layer…
…[Question] Do you believe that the work will be shifting to Falcon is something they were outsourcing to other partners such as CROs?
[Answer] In terms of where the labor is done or where it will be displaced, I think there will be a combination of internal and outsourced, although generally not the CROs…
…[Question] Any incremental color on the pricing of the Falcon products?
[Answer] They want predictability for that because for one thing, they get that predictability when they either hire or outsource labor. It is quite predictable, and it is better for them. It is actually better for us too. W hat gets in the way of that a little bit is, well, Falcon is quite early now. It can do certain things, but it cannot do the things that it will do three years from now. How do you have a fixed price when your capabilities are rapidly improving? I think with some of our customers, we will end up having enterprise license agreements, enterprise subscription agreement for the labor based on the size of their company or their function, but it will probably escalate over time. It will be lower in the beginning when Falcon is less mature. If you want a teenage Falcon, it costs you X, and if you want a Falcon that is 25 years old, it costs you a bit more…
…In some early test runs, it is like, Wow, we tested this against the humans, what the humans did, and Falcon is already better than what the humans did.
Veeva’s business consulting and services business is improving with AI
In business consulting, AI makes process design, organizational structure, and change management more critical than ever. In services, AI-enabled offerings that deeply understand our products increase speed and value and drive customer demand. The innovations in our talent processes we started last year are already showing early signs of success that will lead to long-term excellence. We are excited about the future of AI-enabled consulting and services.
In Commercial Cloud, Ostro is growing rapidly; management is looking to expand Ostro from commercial into medical and other areas; management thinks conversational AI that is 100% compliant is a strategic area; management thinks Ostro can grow far beyond its starting point; Ostro is a recent acquisition by Veeva, and it provides conversational AI for brands to provide patients and doctors with immediate, compliant answers
Ostro is really growing rapidly, both in customer and brand acquisition and in our longer-term product vision as we look to expand Ostro from commercial into medical and other use cases. Precision AI, conversational AI that is 100% compliant, is a very strategic area. We believe Ostro, like Crossix, can grow far beyond its starting point provided we deliver product excellence and customer success for all customers and brands.
In Commercial Cloud, Crossix had a strong quarter in Measurement and Audiences; Crossix has been winning new customers and expanding with top 20 biopharmas; management thinks AI is a tailwind for Crossix
Crossix also delivered another strong quarter of growth in Measurement and Audiences, strengthening our leadership position with new customer wins and top 20 biopharma expansions. AI creates new and effective forms of digital marketing, which creates new opportunities for Measurement and Audiences.
Aspen is Veeva’s new horizontal CRM (customer relationship management) product announced in early-August; Aspen is a CRM platform built for AI; Aspen has a few early adopters; Aspen is on track for availability later in 2026; management has very high aspirations for Aspen and thinks the product is addressing a clear market need; it’s very early days for Aspen; management is treating Aspen as a startup inside Veeva; Veeva is currently investing at a small scale into Aspen; Aspen’s users are currently being charged at $50 per user per month, and it comes with overage fees; management is willing to change Aspen’s pricing model based on customer feedback; management thinks Aspen’s advantages over existing CRM products are (1) price predictability, (2) dependability, (3) scalability, and (4) having a better system, data model, and business logic
In early August, we announced Aspen CRM, our next-generation enterprise CRM platform built for AI…
…We are learning from a few early adopters and remain on track for planned availability later this year for a broader group of early customers.
We are just getting started with Aspen, but I am fully convinced that it addresses a clear market need with disruptive innovation that has real potential for greatness…
… Aspen, I think it is very early…
…This is a startup inside of Veeva. It is moving very rapidly. It is on 90-day plans…
…Your question was about how to size the investment as well. The investment is very small on the Veeva scale. It is not something that Brian, our CFO, notices, really, on the Veeva scale, because you have to keep that very small when you are working with early customers and you are iterating an early product…
…Aspen is taking a different approach that may or may not prove effective. It is to say, well, it is a different approach, a different technical stack, a different approach there, and a different pricing approach. It is just much more simple. You get your productivity, $50 a user a month…
…Then there is, of course, usage overage. Okay, let us say you buy five users. It is $50 a month, and you put a terabyte of data in there for some reason. Well, okay. Well, that is not anything that anybody thought about. There will be overage charges that you will pay monthly on the overage. It is a mix. I would say we are shooting for mostly predictable, because at the end of the day, large businesses would really want mostly predictable. You have to have this escape hatch to say, Yeah, I cannot use unlimited compute, because that does not make sense. Now we are also going to listen to our early customers, and we are a very customer-friendly company, and if there is a better way to do it, we will certainly do that…
…[Question] What are the specific customer problems that you’re aiming to solve with Aspen, versus what some of the existing horizontal CRM platforms struggle with today?
[Answer] One is price. Price being unpredictable, getting out of control. That would be one. The other one would be just dependability of the vendors, that you can really count on the vendor to be on your side. Scalability of the vendor. Sometimes they want something that really works for a small company but can scale up to a very large. Now in the market, you have to pick, like, do I want something that works for a small company, or do I get something that’s too big for me now, but it can scale up? Other things are just like data entry. The existing CRM systems really, if you get into them, okay, they require a heck of a lot of data entry. Most of that with AI doesn’t need to be done anymore. And then I just think there’s this other fundamental thing of better CRM system, and that’s just the details of a fundamentally better data model, better business logic, better just details like how do you handle multi-currency? How do you handle forecasting? How do you handle implementation so that you can get the CRM you want for your company in three months, rather than getting half of what you want in three years?
Veeva’s management does not appear to be willing to squeeze the company’s margins to invest in AI initiatives;
[Question] As we think about you guys ramping on Falcon, ramping on Aspen, ramping on some Vault agents, Vault AI, should we expect maybe an uptick in terms of sales investments, R&D investments?
[Answer] We are very excited about Falcon and the path that it can be on. Y ou have also seen us over time consistently think about both growth and profitability. It is not different entering a new market like Falcon. Maybe the dynamics of the market are very slightly different, but it is the same overall approach that we are taking there. We scale investment as we scale revenue. There is certainly nothing material that I would call out for this fiscal year. It is all factored into the guidance that we have updated for FY 2027.
Veeva’s management thinks the current costs of AI models is not sustainable and that prices will become cheaper over time
We use the non-deterministic models, the anthropic models, et cetera, when we need to. A lot of this value is going into the agent. Then I believe everybody knows that the cost of these models are going to go down, whether they’re with better hardware or open weight models or et cetera. The current cost of the models is not sustainable, not based on what we’re doing, but based on this notion of what software development is doing. Eating up 50% of the tokens in the world and hundreds of billions of dollars. Somebody’s going to build a better mousetrap for that over time, and that’ll compress the prices.
Veeva’s management is seeing customers want to buy AI solutions from existing vendors, and not from new, unproven vendors
[Question] An emerging trend that we’ve been seeing across large healthcare and life sciences organizations within the industry this year is one in which customers want to embrace AI, but they don’t want to take the risk on a new and unproven entrant that offers AI for only a specific point solution or a niche use case, as they don’t have the time to evaluate hundreds of new vendors. It sounds like they’d rather consume AI from the incumbent platform vendors that they’re already deeply embedded with. One, are you getting this sort of same feedback from your customers?
[Answer] Some months ago, when we first introduced Falcon, I had customers come up to me personally, we were at an event and they said, Oh, thank goodness you’re announcing that because that’s I didn’t want to evaluate all these small vendors. ” Now that you have an offering, that helps me not have to go and look at all these small vendors. Yes, it’s absolutely what customers want.
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