Last month, I published More Of The Latest Thoughts From American Technology Companies On AI (2025 Q4). In it, I shared commentary in earnings conference calls for the fourth quarter of 2025, from the leaders of technology companies that I follow or have a vested interest in, on the topic of AI and how the technology could impact their industry and the business world writ large.

A few more technology companies I’m watching hosted earnings conference calls for 2025’s fourth quarter after I prepared the article. The leaders of these companies also had insights on AI that I think would be useful to share. This is an ongoing series. For the older commentary:

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

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

Coupang (NYSE: CPNG)

Coupang’s management is not concerned about disintermediation by AI because they believe that consumers will still want to shop where they can find the best combination of selection, service, and savings; management thinks there’s tremendous potential for AI to amplify the value Coupang brings

[Question] AI seems to be destroying a lot of things and agentic AI impact on e-commerce has been hardly debated in recent periods. So I was hoping you can talk about how you view platforms such as Coupang will not be somehow disintermediated by some chatbot or AI agent somewhere from somebody else.

[Answer] Ultimately, we believe customers care about selection, service and savings. And they’ll shop where they can find the best combination of all three. And as I mentioned in the call earlier, we’re a business that involves not only technology and software, but it’s not just a business made of electrons, but we’re really — we have real retail real infrastructure and people to move physical inventory. There’s tremendous potential for AI to amplify the value that we deliver across all 3 of the pillars that we strive to improve, selection, service and savings. And we believe AI will be a powerful means of us trying to — of us doing those jobs better over time, delivering the best experience at the lowest cost, and we intend to make a strong effort in the coming years to capture those opportunities.

MercadoLibre (NASDAQ: MELI)

MercadoLibre’s management introduced an AI-enhanced search experience in the marketplace business in 2025 Q4; the new search experience expands product discovery in a persoanlised way; the new search experience includes an AI assistant that can help users refine broad searches; management has introduced a Seller Assistant in the marketplace business to scale onboarding and support for sellers; Seller Assistant helps sellers improve listing quality, create videos of their products, and handle customer queries; management wants to embed AI across the marketplace to improve discovery, increase relevance and conversion and deepen engagement; the Seller Assistant in MercadoLibre’s marketplace already advises 20% of the company’s GMV

In Q4’25, we introduced an AI-enhanced search experience in Argentina that uses insights from individual buyer behavior to expand product discovery from a single search term – for example, a search for “ball” will show tennis balls for tennis players, and footballs for football players. It may also show specific brands or premium / value products, depending on our knowledge of the buyer from their extensive search and transaction history. An interactive assistant can refine broad searches, such as “smartphone”, into more personalized results by guiding users through key product attributes.

On the supply side, our Seller Assistant is helping us scale onboarding and support. It accelerates sellers’ progression to higher reputation tiers, improves listing quality through targeted recommendations, creates short-format videos from a single product photo, and handles inquiries that were previously managed by customer service teams.

These initiatives are early steps in a broader effort to embed AI across the marketplace to improve discovery, increase relevance and conversion and deepen engagement…

…I think it’s worth highlighting the fact that we have a seller assistant today running in our platform, basically 20% of our GMV is somehow advised by our assistant.

Improvements in MercadoLibre’s advertising technology are driving higher adoption and spend; AI tools in MercadoLibre’s advertising business are supporting account managers for large advertisers, and are engaging directly with long-tail sellers

In Q4’25 we launched tools such as “budget orchestrator” and one-click campaigns, which performed well during peak season. In parallel, AI tools are supporting account managers for big brands and top sellers, while engaging directly with long-tail sellers to stimulate demand. We also launched our DSP for advertisers in China, which contributed to growth in Q4’25, and should support monetization of our growing CBT business.

MercadoLibre’s management launched the Mercado Pago AI Assistant in October 2025; the Mercado Pago AI Assistant handled more than 9 million conversations in 2025 Q4 and resolved 87% of these conversations without human intervention; management plans to expand the Mercado Pago AI Assistant’s capabilities in the coming months to handle more use cases; management sees potential to use the Mercado Pago AI Assistant for cross-selling fintech products in the future

In October, we launched the Mercado Pago AI Assistant, and early results are encouraging. In Q4’25, the Assistant handled more than 9mn conversations, with nearly 90% resolved without human intervention. There are dozens of use cases, including general inquiries, making transfers and paying bills, and in the coming months, we plan to expand the Assistant’s capabilities to make it increasingly proactive…

…Our Mercado Pago AI assistant is solving 87% of interactions without the need of human support…

…So far, we have been mostly dealing with these interactions that are initiated by users and the vast majority of them are responded by the agent without any kind of human intervention. But I would say, so far, we have not yet started using the agent for cross-sell, but it’s something that we will start doing. Given that you are in a conversation, you can, for example, tell the consumer that she has a credit offer or a credit card offer and the benefits of the credit card. We are not doing that yet, but we believe the opportunity there is significant and the system will become more proactive. And beyond cross-sell, it will also become more proactive in terms of acting like a personal banker. So helping you, I don’t know, allocate your portfolio or make the recommendations of what kind of credit is better for you.

AI tools are helping MercadoLibre’s Merchant Acquiring business’s sales teams by identifying new customers and deepening relationships with existing customers; the Merchant Acquiring business has 25% FX-neutral TPV growth in Brazil in 2025 Q4; the Merchant Acquiring business has 50% FX-neutral TPV growth in Mexico in 2025 Q4; the Merchant Acquiring business’s base of active POS (point of sales) is nearly equal to all of the incumbents combined

AI tools are improving the effectiveness of our sales teams by helping identify new customers and deepen relationships with existing merchants. In Brazil, this has supported higher TPV per merchant and shortened payback periods. This contributed to strong FX-neutral Acquiring TPV growth of 25% YoY in Brazil in Q4’25. In Mexico, growth is being driven by onboarding long-tail and SMB merchants, many of whom are accepting digital payments for the first time. Momentum remains strong with FX-neutral Acquiring TPV growing 50% YoY in Q4’25. As adoption continues to rise, our installed base of active POS devices is approaching that of all incumbents combined.

MercadoLibre’s management thinks MercadoLibre has the best features for agentic commerce and these features go beyond merely searching for an item; management’s focus with agentic commerce is on developing MercadoLibre’s own agentic experience inside the company’s marketplace; management believes MercadoLibre has the first-party data to create the best search, recommendation, and discovery engines for an agentic experience; management thinks the emergence of agentic commerce will mean an even faster transition from offline to online retail; management thinks MercadoLibre is well-positioned to capture advertising revenue from agentic commerce because it is the go-to place for online shopping; management thinks MercadoLibre’s advertising revenue also stands to benefit from agentic commerce activity that happens outside of MercadoLibre because the company has a unique set of data, customer knowledge, and attribution capabilities; management thinks there are many unknown aspects of agentic commerce today such as what hardware and AI models consumers will use, but there are also known aspects, such as what consumers value; management is cognisant of the risk of disintermediation of the MercadoLibre platform when it comes to agentic commerce, but they are confident that the company is coming from a position of strength

Let me start with the idea of Agentic commerce and how that will play out for us and potentially disintermediating, which is something that I’ve been asked over and over. So I think it’s still a bit early in the game, but we don’t think that solving one part of the value chain will actually change the rules of the game, meaning that we still think that the key is to provide the best end-to-end experience for our customer. So we know that searching for an item is one important task but reading reviews, making sure the package arrives on time, offering the widest selection, having the best prices, the best financing, preventing fraud, having the best customer support and so on are also key parts of the end-to-end job on — that we need to solve and that drive the decisions on where buyers will end up buying…

…Where we’re putting most of our efforts is in developing our own agentic experience inside MercadoLibre. We think and we are convinced that we have the first-party data to create the best search, best recommendation, best discovery engine on which we can personalize and lay over the agentic experience that the new technology drives…

…If you believe that there is a world of agentic commerce, that could mean that retail will move even faster from the offline to the online world. So all this to say that I do think that we are well-positioned to actually capturing ad revenues in the future because we still think that MercadoLibre will be go-to place for demand to do shopping online…

…What happens with all the agentic commerce that will occur outside of MercadoLibre because for sure, we will not have 100% market share. And we think that, that also represents an incremental opportunity for many, right? So today, we are providing with our tech stack advertising services to third parties, we do that with Google Ad Manager, with Disney. We do that with Roku with HBO Max. And the reason behind that is that we have a unique set of data, customer knowledge, attribution capabilities that we think are very hard to match…

…[Question] Essentially, how these independent agent systems could introduce new forms of the intermediation and engage clients directly, right, leading to potential changes in — the most obvious 1 we can think of and discuss a lot is the dollar full of advertising. So I really want to hear how you view these risks and how you’re approaching them strategically.

[Answer] There are things that we know and there are things that we don’t know. So we don’t know which hardware people will use in 10 years to buy. We don’t know whether the winning model will be X Y or Z and so on. We do know that consumers do value or do look for the best end-to-end experience. We do know — and that means not only searching for products, but also getting products fast, having the wider selection, pricing, the best financing alternatives, post-purchase support and so on. We also know there’s a technology today that can dramatically improve the product discovery process. And for that reason, we are putting all of our efforts and deploying lots of engineers in building our own agents and our own shopping assistant within MercadoLibre. It’s early to know what will happen with other shopping assistant. I take your point that it might present a risk. I understand where you’re coming from. But we are confident that we are playing this 1 from a position of strength that we have the relationship with consumers. We have a brand that Latin America loved. We have information and data about past purchases that allow us to offer them a great shopping assistant. And we are betting and putting our efforts on what we can control, which is building the best assistant possible

MongoDB (NASDAQ: MDB)

AI is not yet a material driver of MongoDB’s results, but management is encouraged by the growth in customers leveraging the company’s AI capabilities; the number of customers using Vector Search doubled year-on-year in 2025 Q4 (FY2026 Q4); the number of customers using Voyage embedding models has doubled since February 2025; management is seeing customers expand their use of MongoDB as a strategic data platform for both foundational and next-generation AI workloads; management thinks AI and agentic applications require memory, state, and high-quality retrieval capabilities, and these are all native to MongoDB’s OLTP (online transaction processing) platform without the need for ETL (extract, transform, load) or bolt-on systems

While AI is not yet a material driver to our results, we are encouraged by the growth we are seeing with customers leveraging our AI capabilities. The number of customers leveraging Vector Search has nearly doubled year-over-year, and the number of customers using Voyage embedding models has also doubled since the acquisition last February. This growth is across a diverse range of customers, AI natives, digital natives and large enterprises…

…Large enterprises are increasingly standardizing on MongoDB to power a wide spectrum of portals, including both core mission-critical applications and emerging agentic AI applications. Rather than treating AI as a stand-alone initiative, many are expanding their use of us as a strategic data platform that supports both foundational workloads and their next generation of intelligent applications…

…MongoDB is increasingly recognized as the architectural foundation powering innovation for frontier model companies, leading digital natives expanding into AI and AI native organization scaling globally. The database layer has endured through multiple technology shifts over the past 60 years, and it is even more critical in this AI shift. AI and agentic applications require memory, state and high-quality retrieval capabilities native to our modern OLTP [online transaction po platform, which powers real-time applications, without ETL odd bolt-on systems through integrated search, vector search and embeddings. In this platform shift, OLTP is the high ground and MongoDB’s purpose built to win.

MongoDB signed a $90 million deal with a large tech company for the tech company to expand both core and AI workloads on Atlas

We signed several large deals in the quarter, including an approximately $90 million transaction with a large tech company that plans to expand both core and AI workloads on Atlas.

Axon Networks, a global leader in telecom network management, is using Enterprise Advanced to power its operator-as-a-service platform which delivers a real-time digital twin and API-first architecture; Axon Networks’ operator-as-a-service platform is AI-first; management is hopeful that the Enterprise Advanced business can accelerate in the future; management is seeing a trend of companies wanting to keep critical data on-premise because of issues related to AI for mission-critical applications

Axon Networks, a global leader in telecom network management, serving 32 telcos and over 90 million homes and enterprises selected EA as the foundation for its operator-as-a-service platform. This platform delivers a real-time digital twin and API-first architecture designed to handle massive data peaks and high-volume time series workloads. EA provides the flexibility to run across mission-critical environments including hyperscalers and bare metal, along with the enterprise-grade security and operational tooling required to support Axon’s AI-first autonomous networking platform at scale…

…We are actually investing in EA to bring it to parity to Atlas. So certainly, our expectation and hope is that we continue to grow that and can even accelerate it in the future…

…Over a large set of very important customers that is definitely the trend that I’m speaking from our customers is, number one, that because of a variety of issues related to also AI that for mission-critical application, there is this trend I’m seeing where they do want to keep their critical data estates on-prem. And this is not just only in financial services, we are seeing that in health care and other verticals like government. But when I was in Europe and even in Asia, I’m also seeing there that there is a preference for those industries to also use MongoDB potentially with EA and only certain workloads in the cloud

Indian vibe coding startup, Emergent Labs, selected Atlas over PostgresSQL for agentic coding workloads; Atlas is helping Emergent Labs power 6 million applications across 190 countries

Emergent Labs, a leading AI white coding platform in India that just crossed $100 million run rate, selected Atlas over PostgresSQL to power AI agents that build production-ready applications from natural language prompts. They power nearly 6 million applications built across 190 countries and handle applications that averaged 35,000 lines of core with some reaching 300,000, all made possible with Atlas’ flexible document architecture and reliable scale.

AI startup Eleven Labs is using Atlas Search and Vector Search to power the long-term memory and knowledge base of their agents, and to deliver highly personalized interactions in real time, globally

We are also fueling innovation at AI-native customer Eleven Labs, which is redefining conversational AI with its new enterprise agentic platform. Eleven Labs selected Atlas to power the critical long-term and knowledge base for their autonomous agents. By leveraging Atlas Search and Vector search, they enable their agents to retain complex context and deliver highly personalized interactions in real time and at global scale. Supporting the rapid expansion to $330 million of ARR and $11 billion valuation.

Adobe recently expanded its long-term commitment with MongoDB; Adobe now uses Atlas Vector Search to power its agentic experiences, and will soon include Voyage embeddings; Adobe also uses Enterprise Advanced

A marquee example of the platform in action is Adobe, which expanded its strategic partnership and long-term commitment with us to accelerate AI-driven innovation. MongoDB now underpins a range of Adobe’s key initiatives, including Agentic experiences powered by Atlas Vector Search and soon Voyage embeddings. Adobe leverages Atlas to manage large fleets and always on database deployments at global scale, while also continuing to partner with us for support of self-managed business-critical workloads on EA, highlighting our ability to operate seamlessly across both cloud and on-prem environments.

MongoDB’s management does not expect AI native companies to contribute much to MongoDB’s revenue in 2026 (FY2027)

In terms of AI, we remain optimistic regarding our opportunity and are seeing encouraging trends with a number of AI native. While this subset of customers has significant potential — many of them remain early in their MongoDB journey and are not yet meaningful drivers of revenue…

MongoDB’s management has found that the key to win in an era where agents are going to be spinning up databases and not humans, is to get agents to love MongoDB as much as human-developers to love MongoDB; this view of management is validated by an AI-native customer of MongoDB that chose to build on the company’s database; management thinks that the way to get agents to love MongoDB is to ensure MongoDB has all the right integrations in place; a key focus area for management is to build MongoDB’s database in the way that would make agents love MongoDB; management has an ambitious roadmap, spanning 2026 (FY2027), to build MongoDB’s database for agents

[Question] How is your product and go-to-market strategy changing, if at all, ahead of the growing reality that agents are going to be the things that are spinning up most databases and not humans in the future.

[Answer] I have a very simple philosophy here. And the philosophy also was validated by one of the AI native companies that has completely built on MongoDB. They had many choices in many clouds and they chose MongoDB. And my initial intuition was the same as you outlined, is that MongoDB’s success over the last many years since the company was founded in 2007 was that builders or developers love MongoDB. And if that’s the premise, there was a lot of work done in the product to ensure that it’s a very natural way, flexible way while keeping the business agile as in the database agile so that it can move with the business.

We want to do the exactly same thing for agents. Agents also need to love MongoDB. That requires us to ensure that we have all the right integration with the right places, whether it’s MCP or whether we are looking at making sure that our APIs, in how you manage how we auto scale, how we ought to perform during the peaks and valleys. All of that truly needs to be autonomous and driven by machines. And that requires absolutely the focus from the engineering team that how would machines look at this if they want to provision an additional node or if they want to manage cluster because of resiliency across multiple clouds. So that will be the North Star for us that our agents will love MongoDB as much as today, human developers love MongoDB…

…We do have ambitious road map, of course. Today, we are already leveraged by some of the AI-native companies and some of them I outlined this time and also last time. And we are learning a lot from them. So we have ambitious road map in terms of truly machine friendly APIs or making sure that our protocol integration across a variety of protocols that machines demand and how do we Auto Scale, Auto chart. All of that will be throughout this coming year.

MongoDB has high-profile AI startup Anthropic as a customer, but MongoDB does not have any customer-concentration among AI natives; management thinks AI natives choose MongoDB for performance, scale, and security; management is seeing some AI natives make their initial database decisions without considering the database’s ability to scale; reads and writes are important with AI applications, and MongoDB is able to scale both for reads and writes; MongoDB can scale reliably with any AI native’s growth

[Question] Great to hear about Anthrapic as a customer at the MDB local event I’d love to hear how you think about the opportunity for Mongo to grow within large AI natives from here. And there’s also mention at the event that Agentic workflows require heavier storage and memory requirements. Love to hear why you think MDP architecturally is that suited for these growing types of AI use cases.

[Answer] The entire cohort, AI natives, frontier model companies, others, many of them choose MongoDB for performance, scale, security and other things. And I would say that the good news here from my standpoint is that we are not concentrated in any one customer when it comes to AI native cohort. So that’s number one. And as they scale, we will scale with them, but we are not concentrated. Even when I look at the growth as a percent of total, we were not concentrated…

…People are making initially database decisions in these AI native companies without realizing that they will run into scale issues or potentially, there was one of the choices that people could have gone with as an AI native company’s founders, had a massive security concern over the weekend where a couple of governments block them from being used. So what I find is that truly enterprise-class database that can scale, and when I say scale specifically, as for these AI native companies as their weekly active users or monthly active users continue to grow, like the example we had with Emergent or Eleven Labs and so on, they find that MongoDB scales better with them. Write performance as well as query performance really matters, and us being a native JSON with search, vector search, and embedding in one rather than multiple moving pieces — if I have to just simplify that, that is the strength because it’s an integrated platform that scales both for read and rights that as you scale your AI native company, they can rely that MongoDB will scale with them.

MongoDB’s management is seeing large enterprises from many different industries still wanting to pursue the modernisation of their technology stack; the enterprises that want to modernise want to shift to MongoDB, but they cannot do it all with AI tools and still require MongoDB’s help, that’s why MongoDB’s management still sees a huge opportunity with modernisation tooling

I was talking to a large financial institution in the U.K. And the Head of Transformation, she told me that, Hey, CJ, I have 50% of real estate that I want to modernize, I know that some of the AI tools can get me to some level, but I really, really need your health and your team’s help to make sure that for this mission-critical applications, we take help from MongoDB to help us land once you prove this out for the first workload, a very critical workload that is moving to MongoDB. The same thing happened, Alex, with a large customer in Spain when I was there a couple of weeks ago, this individuals said, “Hey, we are relying on MongoDBs, as we are modernizing. This is extremely critical workload, once you do that, we are going to open up the aperture and I know that AI will help us modernize, but we still need your help because the destination we want is absolutely MongoDB. So what I’m seeing is the feedback is the modernization and the need for modernization is still very much relevant in the high end of the enterprise, whether it’s a health care company, financial services or even government for that matter or health care.

Number two, they know that AI tools can help you to some extent, but they definitely want to get there on a modern database to get AI ready where they won’t help from MongoDB to be on MongoDB. And then the last thing I would say is that even with some of the use cases, they try it and they’re like, hey, sometimes this is too hard to assure the reliability, security and all of those things for the application we build.

So I consider this as an opportunity in early stages, and this is definitely a top-down work that we have to do as MongoDB with the CTO and Head of Transformation, but the opportunity still exists and is massive.

MongoDB’s management is seeing that Fortune 500 companies across nearly all verticals are not scaling their agentic workloads into production right now; management thinks that it’s only a matter of time before enterprises scaling their agentic workloads into production

I would tell you it’s not if but when…

…I ask them that simple question, where are you on your Agentic workloads? And I’m talking about Fortune 500, okay, or big retail companies, health care companies pick one and ask them — where are you on agentic workloads? And are they really scaling? And the answer is still not yet. Yes, they have done a few productive productivity types of apps internally, but nothing of scale that is customer-facing, even including with a large retailer on agent commerce and so on. So my first thing is, 1 day, it is going to hit in a positive way. where you will have agents making a meaningful difference to the growth of our customers for either new product lines or existing product lines. We are not seeing that today in the large enterprises across pretty much most of the verticals that we speak to because as you know, MongoDB is across every vertical.

Nu Holdings (NYSE: NU)

Nu Holdings’ foundational AI model, nuFormer, is now in production for credit decisioning in Brazil; management is testing nuFormer in other use cases; management wants to expand nuFormer to more lending in Brazil and to credit cards in Mexico; Nu Holdings’ credit operations see a significant lift when using nuFormer

Our foundation model, nuFormer is now in production for credit decisioning in Brazil and in testing across additional use cases…

…We will expand nuFormer to lending in Brazil and credit cards in Mexico and continue putting AI directly into customers’ hands, moving closer to our long-term vision of an AI-powered personal banker in every customer’s pockets…

…We’ve discussed a few times over the past year, the significant lift that we’re seeing when we’re using our own foundation model on credit

Nu Holdings’ management’s long-term vision for AI is to have each customer have an AI-powered personal banker that they can access through their smartphones

We will expand nuFormer to lending in Brazil and credit cards in Mexico and continue putting AI directly into customers’ hands, moving closer to our long-term vision of an AI-powered personal banker in every customer’s pockets.

Nu Holdings’ management sees AI as both an opportunity and a risk for Nu Holdings, but sees AI as more of an opportunity; management thinks there is one common denominator across every technology transformation and that is businesses that simply move bits from Point A to Point B gets hurt the fastest; in financial services, management thinks that the movement of money from one point to another has the higher risk of being disrupted by AI and that providing credit is the most sustainable activity; management thinks Nu Holdings is well-protected against AI-disruption because of its strength in credit and the proprietary data on credit it has; management thinks that AI will significantly enhance many aspects of a bank’s business; management thinks that Nu Holdings is well positioned to take advantage of AI to grow revenue and reduce costs

[Question] Do you see a risk that Nu could be disrupted by AI? Or do you see Nu as a potential winner in this transformation?

[Answer] It is both a challenge and has potential for disruption as well as significant opportunity. Net-net, we think it’s more opportunity than challenge for us…

…I think there is one specific trend or one common denominator across every technology transformation. And this goes all the way to even the internet era, which is any business model that relies on simply moving bits from point A to point B, where you’re effectively a broker tends to be hurt the quickest because one of the things that technology does is remove a lot of that friction in those processes. So I think to — some of the commentary that has been around in the market about financial services is, I think businesses in financial services that are simply moving money from one point to another point, will have the higher risk of potential disruption. You need to be able to add more value than that. And I think from that angle, we think — we have always believed that credit, specifically, credit revenue is actually the most sustainable type of revenue in financial services because of the capital intensity, the regulatory nature of it, the balance sheet aspect and the proprietariness of the data where AI plays a role and ultimately allows you to make a better decision on that. So I think from one angle, there is potential for challenging around the business model, but I think we’re very well positioned given the way we are set up in the strength around credit that we have…

…I think every single company really might benefit from that, where every function that you do, especially as a bank from customer service to compliance to regulatory to AML will be significantly enhanced or being significantly enhanced through AI…

…When you think about the fact that 95% of the world’s financial services profits are still concentrated in incumbent banks that still have significantly larger cost structures. Means that we’re very well positioned to take advantage of AI as a technology enabler for revenue and cost and ultimately be one of the winners in this technology shift.

In 2025, Nu Holdings’ management deployed new AI technologies for credit underwriting to increase credit limits in Brazil, under CLIP (Credit Limit Increase Policy); the full benefits of CLIP, especially in driving net income growth for Nu Holdings, has not appeared yet, because there are a few stages for CLIP’s effects to flow through, although the early signs are very promising; management thinks CLIP will continue growing credit limits for Nu Holdings in 2026 and beyond; management wants to deploy CLIP beyond Brazil; management wants to use the predictive AI technologies behind CLIP and apply it to other areas of Nu Holdings’ business

This was a year in which we have deployed this new technologies and approach to credit underwriting very successfully so far in allowing our customers to increase kind of their credit limits, especially in Brazil so far. And the best way for me to kind of illustrate the magnitude of this increase is Jorge, maybe, refer you to explanatory note, #32 of our financial statements in which we are then starting to provide what I call the unused credit limits. And you can see that unused credit limits went from about $18 billion to $29 billion. So an increase of about $11 billion, which accounts for about 60% increase in unused credit limits. It’s a big one. And I think it wouldn’t be possible for us to do so if we hadn’t be leveraging kind of the entirety of the predictive AI credit underwriting tools that have been kind of developed by us over the past now 18 to 24 months.

Have we seen all of those benefits translated into net income? The answer is no, not yet. So usually, I think at least I see kind of credit limits increases playing out in three steps. First, you have to offer the additional credit limits, then the credit limit translates into purchase volume. And then you have to see whether the purchase volume will then translate into IBB, We are starting to see the first step, Jorge, which is in the fourth quarter of 2025, our market share in purchase volume in Brazil has gone up by about 50 basis points. It was the biggest market share gain that we’ve seen in Nubank over the past 10 to 11 quarters. There’s two more to come, and then we still have to see kind of all of those purchase volumes reflecting into IBB.

Even though 2025 was, I think, a big sign of the magnitude of this ability to increase CLIP, I don’t think it will stop there. You will continue to see this kind of unfolding in new models and new improvements throughout 2027 — 2026, 2027 and onwards. And I would also say that the advent of the predictive AI technology will not stop at CLIP Brazil, right? It will be and is being exported to CLIP Mexico, CLIP Colombia, and then we’re going to go acquisition Brazil, acquisition in Mexico, what you’re going to go to fraud. It’s going to go to deposits, pricing and designs.

NVIDIA (NASDAQ: NVDA)

NVIDIA’s management is seeing continued strong demand for demand for Blackwell; NVIDIA’s Data Center revenue again had very strong growth in 2025 Q4 (FY2026 Q4), driven primarily by chips from the Blackwell family; there are currently 9 GW (gigawatts) of Blackwell systems deployed; management expects sequential revenue growth throughout 2026, exceeding what was included in the $500 billion Blackwell and Rubin revenue opportunity management shared last year

Demand for our Blackwell architecture, extreme co-designed at data center scale continues to strengthen as inference deployments grow in addition to training…

…Q4 data center revenue of $62 billion increased 75% year-over-year and 22% sequentially, driven primarily by sustained strength in Blackwell and the Blackwell Ultra ramp…

…Nearly a year has passed since the release of our Grace Blackwell NVL72 systems. Today, nearly 9 gigawatts of infrastructure on Blackwell are deployed and consumed by the major cloud service providers, hyperscalers, AI model makers and enterprises…

…We look ahead, we expect sequential revenue growth throughout calendar 2026, exceeding what was included in the $500 billion Blackwell and Rubin revenue opportunity we shared last year. We believe we have inventory and supply commitments in place to address future demand, including shipments extending into calendar 2027.

NVIDIA’s management is seeing the continued transition to accelerated computing and the infusion of AI across the hyperscalers’ workloads; management thinks the company’s hyperscaler customers are producing evidence of strong ROI; Meta Platforms’ GEM model drove a 3.5% increase in ad clicks on Facebook and 1% gain in conversations on Instagram

The transition to accelerated computing and the infusion of AI across existing hyperscale workloads continue to fuel our growth…

…Strong evidence of ROI as hyperscalers upgrade massive traditional workloads to generative AI, including search, ad generation and content recommender systems is encouraging our largest customers to accelerate their capital spending. For example, at Meta, advancements in their GEM model drove a 3.5% increase in ad clicks on Facebook and more than 1% gain in conversations on Instagram, translating into meaningful revenue growth.

NVIDIA’s management is seeing agentic and physical AI starting to drive the company’s business; NVIDIA’s management recently introduced Alpamayo, the world’s first open portfolio of reasoning Vision Language Action models; Alpamayo enables vehicles to think; the Mercedes-Benz CLA will be the first passenger car featuring Alpamayo; physical AI contributed $6 billion to NVIDIA’s revenue in 2025 (FY2026); management is seeing robotaxi rides grow exponentially; management thinks robotaxi vehicles will scale to millions of vehicles over the next 10 years, driving demand for orders of magnitude more compute from NVIDIA; management continues to advance robotics development; NVIDIA recently announced new partnerships to bring NVIDIA AI infrastructure Omniverse digital twins, World Models and CUDA-X libraries to millions of researchers, designers and engineers; OpenAI’s GPT-5.3-Codex agentic system was trained with, and will run inference on, NVIDIA’s systems; management thinks Anthropic’s Claude Cowork agent has ushered in the ChatGPT-moment for agentic AI; management is certain that agentic AI has reached an inflection point and the tokens generated are productive for users and profitable for the cloud service providers; in agentic AI, inference equates to revenue; all of NVIDIA’s engineers are using agentic coding tools

Agentic and physical AI applications built on increasingly smarter and multimodal models are beginning to drive our financial performance…

…At CES, we introduced Alpamayo, the world’s first open portfolio of reasoning Vision Language Action models, simulation blueprints and data sets, enabling vehicles that can think. The first passenger car featuring Alpamyo built on NVIDIA DRIVE, will be on the road soon in the new Mercedes-Benz CLA.

Physical AI is here having already contributed north of $6 billion in NVIDIA revenue in fiscal year 2026. Robotaxi rides are growing exponentially with commercial fleets from Waymo, Tesla, Uber, WeRide and Zoox, and many others are expected to scale from thousands of vehicles in 2025 to millions over the next decade, creating a market poised to generate hundreds of billions of dollars of revenue. This expansion will demand orders of magnitude more compute with every major OEM and service provider developing on NVIDIA’s platform.

We continue to advance robotics development. With the new NVIDIA Cosmos and Isaac Group, open models, frameworks and NVIDIA’s powered robots and autonomous machines for leading companies, including Boston Dynamics, Caterpillar, Franka Robotics, LG Electronics and NEURA Robotics. To accelerate industrial physical AI adoption, we also announced new expanding partnerships with Dassault Systemes, Siemens and Synopsys to bring NVIDIA AI infrastructure Omniverse digital twins, World Models and CUDA-X libraries to millions of researchers, designers and engineers building the world’s industries…

…We recently celebrated OpenAI’s launch of GPT-5.3-Codex trained with and inferencing on Grace Blackwell NVLink 72 systems. GPT-5.3-Codex can take on long running tasks that involve research, tool use and complex execution…

…Anthropic’s Claude Cowork agent platform is revolutionary and has opened up floodgates for enterprise AI adoption. Between Claude Cowork and OpenClaw, Anthropic’s Claude Cowork agent platform compute demand is skyrocketing and ChatGPT moment of agentic AI has arrived…

…I am certain that at this point with the productive use of Codex and Claude Code and the excitement around Claude Cowork and just the incredible enthusiasm about OpenClaw and the enterprise versions of them. All of the enterprise ISVs who are now working on agentic systems on top of their tools platforms. I am certain at this point that we are at the inflection point, we’ve reached the inflection point and we’re generating profitable tokens that are productive for customers and profitable for the cloud service providers…

…It’s really important to realize that inference equals revenues now for our customers because agents are generating so many tokens, and the results are so effective. When the agents are coding, it’s off generating thousands, tens of thousands, hundreds of thousands because they’re running for minutes to hours. And so these systems, these agentic systems are spawning-off different agents, working as a team. The number of tokens that are being generated is really, really gone exponential. And so we need to inference at a much higher speed. And when you’re inferencing at a much higher speed and each one of those tokens are dollarized, it directly translates into revenues. And so inference equals — inference performance equals revenues for our customers…

…Coding is obviously supported by agentic systems now, and all of our coders here at NVIDIA Corporation are using systems—either Claude Code or OpenAI Codex—enormously, and oftentimes both, and Cursor, oftentimes all three, depends on the use case. But they have agents and co-designed partners, engineering partners, to help them solve problems.

NVIDIA’s data center revenues have grown 13x since the introduction of ChatGPT; management is seeing NVIDIA’s business broaden beyond chat bots, driven by a few forces, namely, (1) a fundamental platform shift from classical machine learning to generative AI from the hyperscalers, (2) skyrocketing adoption of agentic systems, and (3) the growth of sovereign AI

We have now scaled our data center business by nearly 13x since the emergence of ChatGPT in fiscal 2023…

…Our demand profile is broad, diverse and expanding beyond just chatbots. First, there is a fundamental platform shift from classical machine learning to generative AI. Strong evidence of ROI as hyperscalers upgrade massive traditional workloads to generative AI, including search, ad generation and content recommender systems is encouraging our largest customers to accelerate their capital spending…

…Frontier agentic systems have reached an inflection point. Claude Code, Claude Cowork and OpenAI Codex have achieved useful intelligence. Adoption is skyrocketing and tokens are profitable, driving extreme urgency to scale up compute. Compute directly translate to intelligence and revenue growth…

…Every country will build and operate some parts of its AI infrastructure, just like with electricity and Internet today. In fiscal year 2026, our sovereign AI business more than tripled year-over-year and over $30 billion, driven primarily by customers based in Canada, France, the Netherlands, Singapore and the U.K. Over the long run, we expect our sovereign opportunity to grow at least in line with the AI infrastructure market as countries spend on AI proportional to their GDP.

Research firm SemiAnalysis recently declared NVIDIA as the Inference King; NVIDIA’s latest generation Blackwell system, GB300 NVL72, has 50x performance per watt and 35x lower cost per token compared to the Hopper systems for inference; optimisation of CUDA software has helped the GB300 NVL72 to perform 5x better on inference compared to just 4 months ago; management sees NVIDIA has having the lowest cost per token for inference; management thinks data centers using NVIDIA systems will generate the highest revenues; NVIDIA’s next generation of chips, the Rubin family, was recently unveiled at CES; the latest Rubin family of chips consists of 6 different chips; the Rubin chips can train MOE (mixture of experts) models and reduce inference; management has shipped the first Rubin systems to customers; management expects Rubin to have better resiliency and serviceability compared to Blackwell; management expects every cloud provider to deploy Rubin

SemiAnalysis declared NVIDIA, Inference King, as recent results from InferenceX reinforced our inference leadership with GB300 NVL72, achieving up to 50x performance per watt and 35x lower cost per token compared with Hopper, and continuous optimization of CUDA software helped deliver up to 5x better performance on GB200 NVL72 just within 4 months. NVIDIA produces the lowest cost per token and data centers running on NVIDIA generate the highest revenues…

…We unveiled the Rubin platform last month at CES, comprised of 6 new chips, the Vera CPU, Rubin GPU, NVLink 6 Switch, ConnectX-9, SuperNIC, BlueField-4 DPUs and Spectrum-6 Ethernet Switch. The platform will train MOE models with 1/4th number of GPUs and reduce inference token costs by up to 10x compared to Blackwell. We shipped our first Vera Rubin samples to customers earlier this week, and we remain on track to commence production shipments in the second half of the year. Based on its modular cable-free tray design, Rubin will deliver improved resiliency and serviceability relative to Blackwell. We expect every cloud model builder to deploy Vera Rubin.

NVIDIA’s management will use a $20 billion R&D budget, and the company’s strong system-design capabilities, to deliver X-factor performance leaps per watt for each new generation of AI chip systems

Our pace of innovation, particularly at our scale is unmatched, fueled by an annual R&D budget approaching $20 billion and our ability to extreme co-design across compute and networking across chips, systems, algorithms and softwares, we intend to deliver X factor leaps in performance per watt every generation and extend our leadership position over the long term.

NVIDIA’s management is seeing even older generations of the company’s AI chips being sold out in the cloud; NVIDIA’s older generation of chips continue to work well because all of the company’s GPUs are compatible, so the ongoing optimisation of NVIDIA’s software stack also benefits the older generation of chips

With NVIDIA infrastructure in high demand, even Hopper and much of the 6-year-old Ampere-based products are sold out in the cloud…

…All of our GPUs are architecturally compatible, which means that when I’m working on optimizing models today for Blackwell, all of that work and all of that dedication to optimizing software stacks and new models also benefit Hopper and also benefit Ampere. It’s the reason why A100 continues to feel fresh and continues to stay performant years after we’ve deployed it into the world. Architecture compatibility allows us to do that. Architecture compatibility allows us to do that. It allows us to invest enormously in software engineering and optimization, knowing that our entire installed base in the cloud, on-prem, everywhere from generations of architectures and GPUs will all benefit.

NVIDIA’s networking revenue had very strong sequential as well as year-on-year growth in 2025 Q4 (FY2026 Q4) (networking revenue was $8.2 billion in 2025 Q3), driven by strong demand across NVLink, Spectrum-X Ethernet, and InfiniBand on a sequential basis, and by strong demand for NVLink 72 scale-up switches on a year-on-year basis; management thinks NVLink scale-up fabric has revolutionised computing; management recently announced that NVLink will be able to integrate with custom silicon from AWS (Amazon Web Services); management is seeing strong momentum with Spectrum-X Ethernet; NVIDIA’s networking revenue exceeded $31 billion in 2025 (FY2026), up more than 10x compared to (2020) FY2021; NVIDIA is now the largest networking company in the world, and is also now, or soon, the largest Ethernet networking company in the world; management has built an Ethernet capability that is powered by AI; NVLink 72 was really hard to develop

Networking, a cornerstone of our data center scale infrastructure offering, was a standout this quarter, generating $11 billion in revenue, up more than 3.5x year-over-year. Demand for our scale-up and scale-out technologies reached record levels, both growing double digits sequentially, driven by strong adoption of NVLink, Spectrum-X Ethernet and InfiniBand. On a year-over-year basis, growth was driven primarily by NVLink 72 scale-up switches as Grace Blackwell systems accounted for roughly 2/3 of data center revenue in the quarter.

NVLink scale-up fabric has revolutionized computing and demonstrates the power of extreme co-design across all of the chips of the supercomputer and the full stack. In Q4, we announced that we will enable AWS with NVLink to integrate with their custom silicon.

Momentum is strong with our Spectrum-X Ethernet scale up and scale across networking as customers work to unify distributed data centers into integrated gigascale AI factories.

For the full year, our networking business exceeded $31 billion in revenue, up more than 10x compared to fiscal 2021, the year we acquired Mellanox…

…We’re also now the largest networking company in the world and if you look at Ethernet, we came into the Ethernet market about a couple of years ago into Ethernet switching. And I think that we’re probably the largest Ethernet networking company in the world today and surely will be soon…

…We created an Ethernet capability that extends Ethernet with artificial intelligence, a way of processing in the data center, and we’re incredibly good at that…

…NVLink 72 has enabled us to deliver generationally 50x more performance per watt. It’s just an incredible leap. And it’s sensible. NVLink 72 is a great invention. It was hard to do. The creation of the switching technology, disaggregating the switches, building the system racks, all of that, we did it all in plain sight and everybody knew how hard it was for us to do. And — but the results are incredible.

NVIDIA’s management is seeing the company’s major customers (the hyperscalers) significantly increase their AI-related capex; the hyperscalers make up 50% of NVIDIA’s data center revenue; management thinks the hyperscalers’ revenues and cash flow will grow and this will generate more demand for NVIDIA’s systems because compute equals revenue; management is certain that agentic AI has reached an inflection point and the tokens generated are productive for users and profitable for the cloud service providers; management thinks inference tokens per watt translates directly into revenue for the CSPs (cloud services providers); management thinks all of NVIDIA’s major customers understand that without investing in compute, there can be no revenue growth

Analyst expectations for 2026 CapEx across the top 5 cloud providers and hyperscalers who collectively account for a little over 50% of our data center revenue are up nearly $120 billion since the start of the year and approaching $700 billion…

…[Question] When you look at your top cloud customers, cloud CapEx close to $700 billion this year, many investors are concerned that it would be harder for this level to grow into next year. And for several of them, their cash flow generation capability is also getting compressed. So I know you’re very confident about your road map, right, and your purchase commitments and whatnot, but how confident are you about your customers’ ability to continue to grow their CapEx?

[Answer] I am confident in their cash flow growing. And the reason for that is very simple. We have now seen the inflection of agentic AI and the usefulness of agents across the world and enterprises everywhere. You’re seeing incredible compute demand because of it. In this new world of AI, compute is revenues. Without compute, there’s no way to generate tokens. Without tokens, there’s no way to grow revenues. So in this new world of AI, compute equals revenues…

…For the data centers, inference tokens per watt translates directly to the revenues of CSPs. And the reason for that is because everybody is power limited. And so I mean, no matter how many data centers you have, each data center, 100 megawatts or 1 gigawatt, has power limits. So the architecture that has the best performance per watt translates because each token, each — the performance tokens per watt, each token is dollarized. Tokens per watt translates to dollars per watt, which translates in a gigawatt directly to revenues…

…Without investing capacity today, without investing in compute, there cannot be revenue growth. And that, I think everybody understands.

NVIDIA is yet to generate revenue from China and management does not know if the company’s AI chips will ever be allowed into China; management thinks China’s AI companies could disrupt the structure of the global AI industry over the long term

While small amounts of H200 products for China-based customers were approved by the U.S. government, we have yet to generate any revenue. And we do not know whether any imports will be allowed into China. Our competitors in China bolstered by recent IPOs are making progress and have the potential to disrupt the structure of the global AI industry over the long term. To sustain its leadership position in AI compute, America must engage every developer and be the platform for choice for every commercial business, including those in China.

There was strong growth in NVIDIA’s gaming segment in 2025 Q4 (FY2026 Q4) driven by the AI-capabilities of the company’s gaming systems; management thinks the memory supply for NVIDIA’s gaming systems is very tight

Gaming revenue of $3.7 billion increased 47% year-on-year, driven by strong Blackwell demand and improved supply. GeForce RTX is the leading platform for PC gamers, creators and developers. In Q4, we added several new technologies and advancements, including DLSS 4.5, which uses AI to bring game visuals to a new level. G-SYNC Pulsar, bringing incredible clear graphics even in motion, and 35% faster LLM inference across leading AIPC frameworks…

…As much as we would love to have additional more supply, we do believe for a couple of quarters, it is going to be very tight. If things improve by the end of the year, there is an opportunity to think about what that is from a year-over-year growth. But it’s still too early for us to know at this time, and we’ll get back to you as soon as we can.

NVIDIA’s management expects tight supply for its advanced chip systems to persist

While we expect tightness in the supply for our advanced architectures to persist, we remain confident in our ability to capitalize on the growth opportunity ahead with our scale, expansive supply chain and the long-standing partnerships continuing to serve us well.

NVIDIA’s management is working on a partnership agreement with OpenAI and are thrilled with it

We continue to work with OpenAI toward a partnership agreement and believe we are close. We are thrilled with our ongoing partnership with OpenAI, a once-in-a-generation company, we’ve had the pleasure of partnering with since their first days.

NVIDIA’s management recently announced that Meta will be deploying Blackwells and Rubins, and NVIDIA’s networking systems, for training and inference

Meta Superintelligence Labs is scaling up at lightning speed. Last week, we announced that Meta is deploying millions of Blackwells and Rubin GPUs, NVIDIA CPUs and Spectrum-X Ethernet for training and inference.

High-profile AI startup Anthropic recently announced a partnership with NVIDIA, and will run training and inference workloads on NVIDIA’s systems

This quarter, we announced a partnership with Anthropic, and a $10 billion investment in their company. Anthropic will train an inference on Grace Blackwell and Vera Rubin systems.

NVIDIA’s management recently entered into a non-exclusive licensing agreement with Groq for low latency inference technology (as part of the agreement, Groq’s top leaders have joined NVIDIA); management intends to extend NVIDIA’s chip architecture with Groq’s technologies as an accelerator

We recently entered into a nonexclusive licensing agreement with Groq for its low latency inference technology and welcome the team of brilliant engineers to NVIDIA. As we did with Mellanox, we will extend NVIDIA’s architecture with Groq’s innovations to enable new levels of AI infrastructure performance and value…

…What we’ll do with Groq is you’ll come to see GTC, but what we’ll do is we’ll extend our architecture with Groq as an accelerator in very much the way that we extended NVIDIA’s architecture with Mellanox.

NVIDIA has made strategic investments across its ecosystem because management thinks the investments will help to expand and deepen NVIDIA’s reach into its ecosystem

[Question] You talked about some of the strategic investments that you’ve made into Anthropic and potentially OpenAI, CoreWeave as well but also partners, Intel, Nokia, Synopsys. You’re clearly at the center of everything. Can you talk about the role of those investments?

[Answer] We used to be largely a computing platform on GPUs, but now we’re computing AI infrastructure company, and we have computing platforms on, well, every aspect of that. And everything from computing to AI models to networking, to our DPU, all of that has computing stacks on top of it. And as I mentioned before, whether it’s in enterprise or in manufacturing, industrial or science or robotics, each one of these ecosystems have different stacks. And we want to make sure that we continue to invest into our ecosystem. So our investments are focused very squarely, strategically on expanding and deepening our ecosystem reach.

NVIDIA’s management thinks the implementation of the dilate architecture should be delayed for as long as possible

Everybody should want to extend, push out dilate as long as they can. And the reason for that is because every time you cross a dilate , you have a dilate , you have to cross an interface. Every time you cross an interface, you add latency, you add power unnecessarily. We’re not allergic to dilate . We use dielets already, but we try to use dielets only when we absolutely have no choice but to do so. And so we — if you look at the Grace Blackwell architecture and the Rubin architecture, we use 2 giant reticle-limited dies and we abut them, and that reduces the amount of architecture crossing. The dilate [ tax ] shows up in the architecture effectiveness of the competitors.

The strategy of NVIDIA’s management is to deliver an entire AI infrastructure in each year

Our strategy is to deliver an entire AI infrastructure every single year.

NVIDIA’s management thinks the economics for data centers in space is currently poor, but will get better over time; management thinks the heat-dissipation methods used on Earth will be different from those used in space; NVIDIA’s Hopper is already the world’s first GPU in space; management thinks one of the best use cases of GPUs in space is for imaging; management sees very interesting applications for AI in space

[Question] I’d like to ask about space data centers, which some of your customers are considering. How feasible do you think that is and what kind of horizon? And what do the economics look like today? And how do you think that could evolve over time?

[Answer] The economics are poor today, but it’s going to improve over time. As you know, the way that space works is radically different than how it works down here. There’s an abundance of energy, but solar panels are large, but there’s plenty of space in space. The heat dissipation, it’s cold in space. However, there’s no airflow. And so the only way to dissipate heat is through conduction and the radiators that you need to create are fairly large. Liquid cooling is obviously out of the question because it’s kind of — it’s heavy and freezes. And so the methods that we use here on earth are a little different than the way we would do it in space. But there are many different computing problems that really wants to be done in space. And so NVIDIA is already the world’s first GPU in space, Hopper is in space.

And one of the best use cases of GPUs in space is imaging, to be able to image at extremely high resolutions using, of course, optics and artificial intelligence. And to be able to do that computation of reprojection of different angles and be able to up res and do noise reduction and just be able to see, be able to image at very large, very high resolutions, extremely large scales and very, very fast. It’s hard to do that by sending petabytes and petabytes of imaging data back here on earth and doing that work. It’s easier just to do it out in space. And then ignore all of the data collected and processed until you see something interesting. And so artificial intelligence in space will have very good, very interesting applications.

All 1.5 million AI models on Hugging Face run on NVIDIA’s CUDA software

There’s 1.5 million AI models on Hugging Face, all of it runs on NVIDIA CUDA.

NVIDIA’s management has designed CPUs for AI data centers that are very different from the CPUs designed by other companies; NVIDIA’s CPU is the only CPU that supports LPDDR5, and it is designed to have very high data processing capabilities; in agentic AI, the tools used by agents often run in CPUs only; NVIDIA’s Vera CPU was designed to be an excellent CPU for the post-training process of an AI model; some use cases in AI require a lot of CPUs; at the current phase of development of AI technologies, really fast, single-threaded CPUs are required; NVIDIA’s Grace and Vera CPUs are both great at single-threaded performance, but Vera is much better

At the highest level, we made fundamentally different architecture decisions about our CPUs compared to the rest of the world’s CPUs. It’s the only data center CPU that supports LPDDR5. It is designed to be focused on very high data processing capabilities. And the reason for that is because most of the computing problems that we’re interested in are data-driven, artificial intelligence being one. And the single-threaded performance and its ratio with bandwidth is just off the charts.

And we made those architectural decisions because in the entire phase, the different phases of AI from data processing, before you even do training, you have to do data processing. So you have data processing, pre-training and in post-training now, the AIs are learning how to use tools. And the usage of tools, many of those tools run in CPU-only environments or they run in CPU with GPU-accelerated environments. And Vera was designed to be an excellent CPU for post-training. And so some of the use cases in the entire pipeline of artificial intelligence includes using a lot of CPUs. We love CPUs as well as GPUs. And when you accelerate the algorithms to the limit as we have, Amdahl’s Law would suggest that you need really, really fast single-threaded CPUs, and that’s the reason why we built Grace to be extraordinary to be great at single-threaded performance, and Vera is off the charts better than that.

Salesforce (NYSE: CRM)

This is not the first SaaSpocalypse Salesforce’s management has been through; management thinks a SaaSquatch will be eating the SaaSpocalypse because the SaaS companies will be getting a lot better by also providing AaaS (agents-as-a-service)

This is not our first SaaSpocalypse, we have been through many SaaSpocalypses. I remember the horrible SaaS pockets of 2020 when not only the software industry was doing, but we were all dying. But we made it through that. And now everyone is back, doing great — so we’re so grateful to make it through that, and we’re going to make it through this at as well…

…If there is a SaaS polyps, I think it might be eaten by the SaaS watch because there are a lot of companies using a lot of SaaS because SaaS just got a lot better with agents-as-a-service.

In Agentforce’s 1st 15 months, Salesforce has closed 29,000 deals, up 50% sequentially; customers in production with Agentforce is up 50% sequentially in 2025 Q4 (FY2026 Q4); Agentforce and Data 360 reached nearly $2.9 billion in ARR (annual recurring revenue) in 2025 Q4 (FY2026 Q4), up 200% year-on-year (was $1.4 billion in 2025 Q3, up 114% year-on-year) including Informatica; more than 75% of Salesforce’s top 100 wins in 2025 Q4 (FY2026 Q4) had both Agentforce and Data 360; management thinks Agentforce has the potentially to be similar in size as Salesforce’s current software business; Agentforce ARR reached $800 million in ARR in 2025 Q4 (FY2026 Q4) up 169% year-on-year (was $540 million in 2025 Q3, up 330%); Salesforce’s most premium SKUs related to Agentforce saw new bookings triple sequentially in 2025 Q4 (FY2026 Q4); more than 60% of Agentforce and Data 360 bookings in 2025 Q4 (FY2026 Q4) came from existing customers expanding their commitments; all of Salesforce’s top 10 wins in 2025 Q4 (FY2026 Q4) included Agentforce and Data 360; Informatica was in 6 of Salesforce’s top 10 wins; management thinks Agentforce brings incremental value to Salesforce’s software

We’re seeing incredible demand for agent force. In its first 15 months, we closed 29,000 deals, up 50% quarter-over-quarter. Customers in production have increased as well, nearly 50% in Q4…

…Our Agentforce and Data 360 ARR, including Informatica, now exceeds $2.9 billion. I heard ARR doesn’t matter anymore. But in case it does, we have $2.9 billion, up 200% year-over-year. More than 75% of our top 100 wins in Q4 included both agent force and Data 360…

…I can’t tell you when the — an agent force is like about an $800 million business now. So I can’t tell you exactly when Agentforce will be a $46 billion or $30 billion. But it has the potential to go just like… 46×3 is 120 plus 18…

…Agent force and Data 360 ARR inclusive of Informatica Cloud ARR reached $2.9 billion. That’s up over 200% year-over-year. This includes Informatica Cloud ARR of $1.1 billion and Agentforce ARR of approximately $800 million, which is up 169% year-over-year. New bookings for Agentforce 1 Edition and Agentforce For Apps or as we call it, A4X, our most premium SKUs nearly tripled quarter-over-quarter…

…In the quarter, more than 60% of Agentforce and Data 360 bookings came from existing customers expanding their commitments…

…Every single 1 of our top 10 wins included Agentforce, Data, sales, service, platform and analytics. Our newest addition to our portfolio Informatica, landed in 6 of those top 10 wins, proving it is a critical component of us building the data foundation for the Agentic enterprise…

…What we see is now with Agentforce with the system that you laid out, the system with the agents, et cetera, we’re just seeing incremental value to our software.

Salesforce’s management launched Agentforce for life sciences in 2025 (FY2026) and it has won many global pharma companies, including existing customers of Veeva Systems

We built an amazing new life sciences product this year. Agentforce for life sciences and since we launched so many of the global pharma companies, and I’ve met with so many of the CEOs myself, they’re leaving Veeva, the purgatory of Veeva, including AstraZeneca, Novartis, Takeda and of course, Albert at Pfizer, they’re all saying that they are going to Salesforce Life Sciences, which is a product that has apps and agents. And this is amazing. They are the most regulated businesses in the world. and they’re choosing Salesforce.

When a Slack customer turns on Slack Bot, the bot will be able to look at the customer’s data and understand the customer’s business and provide advice and support; Salesforce is able to bring all the LLMs (large language models) from the AI labs into Data 360 to activate AI agents, and have Slack be the Salesforce layer that engages with, manages, and orchestrates AI agents; 90% of Forbes’ top 50 AI companies are using Salesforce and Slack; Slack handles 1 billion messages a day, and they are all about work; Slack Bot is able to orchestrate other agents; management thinks Slack Bot has the #1 AI ecosystem in the world with its partner marketplace having more than 350 AI apps and agents; the high-profile AI start-up Anthropic runs its business and products on Slack; management thinks every AI company runs their businesses on Slack; management sees the UI (user interface) of apps changing in the AI era because of the combination of humans and agents working together, and Slack is the best place to get work done between humans and agents with this changed-UI environment; management thinks Slack might be the most important piece of data Salesforce possess in the AI era

Customers tell me that they want to basically kind of get to that next level. And the way to do that is by including this context, the ability for the AI, the data to know you. No better example of that than Slack Bot immediately as you turn it on you’re a Slack customer, it looks at all your slack. It looks at your DMs, it looks through Salesforce. It looks through Google. It looks even that Microsoft teams as hard as that is for some agents to go and do, but we’ve told them how to do it. And then it says, I understand your business, and I can give you help, advice, support…

…We love all of our children equally and down below here, whether it’s anthropic or open AI or Mistral or Lama all of them, and there’s more coming. They’re amazing. World models are coming. They’re amazing. They’re all down below here, and we’re using them. And then, of course, we bring them into Data 360, and that lets you harmonize your data, integrate your data and federate that means connect into other data sources throughout your company and grab it. Other data repositories, you might be using Snowflake or data bricks you might be using big query or anything, even IBM mainframes and you can bring it into Data 360, you activate your data and then it comes up into your apps. So if you’re using the service app, and you want to have an experience like help that salesforce.com for your company. Now the service app has that Agentic capability, the data is coming up — and it comes up to the next level to agent force and you can build your agents, train your agents, put the guardrails in your agents, give them voice. They can talk now, they’re talking. And then all of a sudden, you can even manage and orchestrate and collaborate from Slack. So this is our architecture…

…Agentforce has the tooling to build, to manage, to orchestrate the agents, to make them talk, to give them determinism, to give them the capabilities if they want. And then we have the engagement layer to deliver agentic enterprises, where work happens in Slack across our apps…

…Nearly 90% of Forbes top 50 AI companies, Forbes top 50 AI companies, use Salesforce and Slack…

…Slack is hosting 1 billion messages a day. And remember, every one of them is about getting work done…

…Slack Bot can access all of those messages as well as your files, your calendar, your sales force, your Google, your Microsoft teams you’re this year that Slack bot goes around, pulls it all together, — and then it knows your business. So then it’s able to orchestrate with other agents. It has an incredible partner marketplace, really the #1 AI ecosystem in the world and has more than 350 AI apps and agents already. There is no other AI ecosystem like it…

…We love Anthropic. We love Dario, Daniella. I tweeted about what they did yesterday, incredible demo. Just yesterday, Dario demonstrated how he is doing something amazing with Salesforce in the enterprise. Every single one of their demos, whether it was for HR, engineering investment banking, started and ending in Slack, pretty awesome…

…Anthropic runs its whole global operation on Salesforce and Slack. I think actually every AI company does…

…Everybody through the past few years has been so enamored with the model, of course, it’s this brand new thing, this intelligence layer that we never had but also the data. But what’s really happening around us is the apps are changing. — the UI is changing, as Miguel is alluding to. And that’s really what we’re seeing because these old apps of these point-and-click buttons, those were designed for human beings to interact with. But what happens when you have human beings and agents in the same place. Right? Suddenly, a lot of those interactions, those UI paradigms kind of get thrown away. You don’t need all of this complex UI anymore. And that’s what makes Slacks powerful, and I think that’s what Anthropic knows. I think that’s what we saw in their demos yesterday. — right? You kind of like process the work. But ultimately, it’s coming — that work is getting done because some person or some agent is asking for it, and then you need to give it back to that person or that agent. And where do you do that? You do that in Slack. And that’s what makes Slack Bot so unbelievably powerful is you never have to leave. And of course, it’s powered by Claude. We love our partners of Anthropic but it knows all of the context of your business, not just the context of your systems of records as we think about it, but all of the conversations happening inside of Slack and has access to all of that and the knowledge that it gains from that truly unmatched. It might be our most important piece of data that we have.

Salesforce’s management thinks companies will be deploying hundreds or thousand different types of agents, and many of them will be from Salesforce; management thinks the deployed agents will need a home, and the home is Salesforce

We already know now, our customers aren’t going to deploy just 1 agent. There’s going to be many agents, many capabilities. the ability to automate many different types of work, and they’re going to deploy hundreds or thousands. Many are going to be from us…

…But these agents can’t work in isolation. — like it, each one of them needs to okay. So that home is Salesforce. And they are calling us through the MCP server or maybe even just through one of our core platforms, and the more agents that our company deploys us or anyone else, the more essential our platform becomes.

Salesforce’s management sees the company as one of the largest consumers of tokens in the world with 19 trillion tokens consumed to-date; management has introduced a new metric, Agentic Work Unit (AWU) that measures how much work agents have performed; Salesforce has delivered 2.4 billion AWUs to-date and 771 million AWUs in 2025 Q4 (FY2026 Q4), up 57% sequentially; AWUs came about because management wanted to really look at the ratio of tokens consumed to effective work produced

We are 1 of the largest consumers of tokens in the world to date, now over 19 trillion tokens. So we continue to show you that because — we want you to see that we’re actually doing what we say. I know that there’s been some enterprise software companies who say they’re doing agents or they’re doing AI, but then they’re not showing up in the token rankings from the language model companies…

…Today, we’re introducing an additional metric. The Agenticwork unit created by our very own Patrick Stokes sitting here at the table. — the AWU not to be confused with our customer, AWS. And AWU represents one unit of AI work, a genetic work unit. We’re rolling this out to see how you like it actually here in earnings. It’s a record updated, workflow triggered, decision made, MCP called. And to date, AI agents on the Salesforce platform delivered 2.4 billion agenetic work units. That is where AI isn’t just thinking or calling things, it’s getting work done, transactions, and in Q4 alone, we delivered about 771 million of them…

…When we started looking at that across our customers, we can start to see, okay, our top 10 customers are consuming this many tokens. We know how many tokens sales force is consuming internally. But it begs the question, well, is it — are they doing anything? Are they working? Are they providing any value? Or is it just input and output of intelligence, right? So you can ask it a question, it can write you a poem, but that’s not really all that valuable in the enterprise world, what’s valuable is creating a document for you or updating a record or helping us right here at this table, we all use Slack bot to prepare our notes here, our customer stories, we’re all preparing that with Slack bottom. So what we did is we said, what if we could count those individual work units. And then what if we could look at those work units relative to the tokens, and we said, “Oh, there’s a relationship between the 2. We can start to see a ratio of tokens being consumed and work coming out…

…The tokens are kind of a leading indicator, but the work unit we think is a much more valuable indicator in terms of where the value is actually coming from for our customers and for our own transformation into an agentic enterprise.

Salesforce’s service organisation did well in 2025 (FY2026) because it used its own Service Cloud with an omnichannel supervisor deployed with Agentforce; Salesforce’s sales organisation did well in 2025 Q4 (FY2026 Q4) because it deployed multiple agents; Salesforce used Agentforce to call back 50,000 customers in 2025 Q4 (FY2026 Q4) that they did not call back

Our service is so much better this year because we’re using our new Service Cloud with our omnichannel supervisor deployed with agent force. Our sales, Miguel just hit record sales numbers, you can see them. We’ve never sold or had so much ACV in our history in the fourth quarter because not only does he have 15,000 account executives. But he has all these agents who are out there doing this amazing work…

…Because like believe there’s $20 million, $30 million. We don’t even know, maybe 100 million people we didn’t call back in the last 26 years. But Miguel called back 50,000 people with agents last week that we would not have gotten to. Even though he’s got all these reps, he still doesn’t have the ability to call everybody back.

Salesforce has invested a total of $330 million in Anthropic, for about 1% of the company and management wishes they had invested more; management thinks the AI models could become platforms in the future, but the reality of today is that software companies are needed to get humans and agents to work together to deliver the desired outcomes of organisations; management thinks that SaaS is needed to convert the raw intelligence of AI models into reliable, accurate enterprise work, and Salesforce is in a great position because it is the system of work and system of agency, and it is already proven in 4,000 production customers

We’re so thrilled of our relationship with Dario and I think we just put another $100 million into the new round. We’re up about $330 million into Anthropic invested. It is almost about 1% of Anthropic. And believe me, I wish we had invested a lot more, John. I don’t know why we didn’t do more…

…Could those models themselves become platforms? So could Open AI then also be a platform? Could Anthropic be a platform, can Gemini be a platform, can DeepSeek be a platform, can Mistral be a platform, can Lama be its own platform? So that in the way that we have Windows and Mac, or HTML, or different things as platforms where applications all of a sudden appear, will all of a sudden, an application come in within one of those platforms and then use some of those services? Absolutely. Those could be new platforms, there will also be other new platforms. I have a platform right here as well – iOS. There are many platforms.

And our job as a software company is to help our customers to create success and to take that and help them connect with their customers in a whole new way. So we’ll deliver our products, our capabilities, our value proposition with our customer relationships, of course, we have over 150,000, I think, customers on our core, 1 million on Slack. We have 15,000 sales reps who are out there. Their job is to work with customers to help architect their future success with these ideas.

And our primary vision though, today, because this, in the current reality, this is about humans and agents working together. And these customers, like you saw today with Wyndham, with SharkNinja, even SaaStr, even Salesforce. Our job is to take what’s available today and make it successful. And that isn’t where those platforms are today, as you know. And in your business, you have — you work for an amazing company. Keith works for an amazing company. And these large banks where we are providing a lot of automation for the sales professionals, the service professionals. There is a lot to do, to not only automate those call centers, those contact centers, the sales forces, the employees with Slack, but then to also then unleash the agents in a way that is compliant, that is secure, that is available, that is scalable, that is reliable, that is able to operate in hand in hand…

…SaaS is more important than ever. In the world of LLMs, I mean, we are so happy that this raw intelligence exist, but to convert raw intelligence into reliable, accurate scalable enterprise work, you need a solver infrastructure like the one that Marc described with our 4 layers system of context, the system of work, this is our big differentiator… We are the systems of work. We have the system of agency, very sophisticated. Some companies are building it, whatever, but we have the best because we are proven in 4,000 production customers, 23,000 total customers. Nobody has that at the scale and the complexity because our agents are connected to the data, able to trigger actions, and then we have the system engagement, which is Slack.

Consumer products company SharkNinja used Salesforce to build a guided shopping agent in 8 weeks right before the holiday season; the shopping agent brought tremendous value to consumers; SharkNinja launched with Salesforce in 2025 Q4 (FY2026 Q4) and Salesforce agents have already participated in 0.25 million consumer engagements; the Salesforce agents have helped SharkNinja provide a better service experience for customers while lowering customer service costs

[SharkNinja executive] We set up with you and your team, a guided shopping agent in 8 weeks right before the holiday season. I was nervous about it as I went to my team and I said, we’re putting this in place in October. There’s generally kind of a cutoff in our business where after October 1, you don’t really do anything. And we launched this in 8 weeks, and it brought tremendous value to the consumer. I mean, it helped them with researching and buying and troubleshooting really all in one seamless conversation. So it was a great success for us this holiday season…

…[SharkNinja executive] Since we launched Salesforce in Q4, I mean, agents have participated in 0.25 million consumer engagements during that period of time… We put so many products out into the market and sometimes that many products creates complexity for the consumer. And so whether they’re calling about a service issue or a troubleshooting issue or where is my order issue, it’s allowed our customer service agents to focus on really the really challenging issues, and it’s freed up an enormous amount of time for them — it’s a win for the consumer because the consumer is getting their questions answered quickly, they’re not waiting. And it’s a win for us because it’s driving down cost. And it’s, in the end, just having a better service experience.

Hotel company Wyndham deployed Agentforce a year ago and now has 5,000 agent deployments across its 8,300 hotels; Agentforce is a crucial part of Wyndham’s agentic platform and Wyndham is starting to roll out the agentic experience internationally; Wyndham has used Salesforce’s products to build a single source of truth about each customer, called Wyndham Guest 360; Wyndham Guest 360 is a key enabler of Wyndham’s agentic experience; Wyndham’s management thinks agents are (1) saving significant labour costs in Wyndham’s operations and (2) driving higher revenue; before Wyndham was integrated with Salesforce, Wyndham had to spend time gathering basic information about every guest; Wyndham saw a 200 basis point increase in direct bookings from AI voice agent conversions; Wyndham’s guest satisfaction scores are up 400 basis points because of its agentic experience

[Wyndham executive] When you think about just how far we’ve come in the last year, today, we have over 5,000 deployments of agent force across our over 8,300 hotels. It is a huge, huge part of our Agentic platform, and we are really just getting started. We’re starting to roll out to Canada and internationally.

But with Salesforce tools like MuleSoft and Data 360, we have built a single source of truth, unified all of our guests reservation information and data, all of their loyalty information and all of their CRM data so that all of our agents now are operating with the same trusted and real-time guests and hotel information, which they weren’t before. We’re calling it Wyndham Guest 360. It is a key enabler for our agent foundry. And it is delighting in better guest experiences, improving those experiences and building on increased loyalty engagement.

But most importantly, Mark, you’ve talked a lot about labor, which is agenetic, — it is taking millions of dollars of labor costs from our small business owners in the front office out of their operation and it is driving millions of dollars of increased revenue for these franchisees…

…Before our integration with you all, our agents had to spend time gathering basic guest information on who Marc Benioff was before he checked in tonight. And that was not easily at their fingertips or even worse, asking Marc for his information that we should have had — and our agents now have encyclopedic knowledge. Think about it of all of your guests history, all of your booking behavior, all of your loyalty status because we tied it all together, giving us an ability to answer any question imaginable that any guests like you might have before you check in tonight before you stay. In moments, not minutes, and we’re booking you into your preferred room based on our knowledge, our guest, salesforce knowledge of your past day history. We are successfully working now. I hope to upsell you a suite upgrade if we haven’t already an early check-in Sounds like you’re getting in at a late checkout tomorrow if you’d like one. I don’t know if you’re bringing — if you have pets, but if you were, those agents would be selling you a pet or an F&B. This is all being done autonomously, which small business owners and operators would not have had time to do before.

We have been working so hard. It is generating so much money. We’re seeing faster average speeds of answer. 0 hold times. I’ve heard you talk a lot about why no customer should wait. And that’s why we’re doing it. we’re receiving and we’re moving more importantly, millions and millions of dollars, as I said, in the front office, but we’re generating millions of dollars of increased ancillary revenues to these small business owners. It’s not costing anything…

…We’re also seeing, which is really, really important, a 200 basis point increase in direct bookings. — from AI voice agents and AI voice agent conversion versus having to get those bookings through expensive third-party online travel agencies. That is increasing guest satisfaction. Our guest satisfaction scores are up 400 basis points, they’ve never been higher. And this customer experience that we’ve created is more efficient. Again, humans with agents driving customer success, we’re agent first, and we’re very proud of it.

The management of SaaS community builder company SaaStr thinks agents-as-a-service is good for Salesforce; SaaStr used Agentforce to close $2.7 million in contracts, and $3.5 million more are in the pipeline; management of SaaStr thinks complex agentic work was simply not possible a year ago because of hallucinations but the situation has changed; with the help of Agentforce, SaaStr recently called back 3,000 customers that it previously failed to do so; SaaStr’s management thinks Agentforce has the potentially to be similar in size as Salesforce’s current software business

[Salesforce executive] Now here you are as agents as a Service as well. You have your vision there now as well. So I guess once a visionary, always a visionary, — but give us your vision then. Where are we going? Because you’ve heard about the SaaSpocalypse. And you know that this isn’t our first SaaSpocalypse. We’ve had a few of them. But now where are we going over the next couple of years?

[SaaStr executive] I think this is good for Salesforce, but I think we’re underestimating how powerful these agents are. I think Look, for most people, AI is confusing, the media is confusing, what the hell is going on. Let me simplify this. I was just looking at our numbers on agent force this morning. So far, and again, we’re a small organization. We went from 15 humans to 2.5 and 20 agents, okay? That’s a lot of change. But an Agentforce alone, as a tiny organization, we closed $2.7 million. That’s not the army contract you got, but that’s a lot for us, $2.7 million with an agent, and we have $3.5 million more in the pipeline…

…[SaaStr executive] Not only was this not really possible a year ago — and this is this — a year — the problem — all of us, we were using ChatGPT in the early days. It was all hallucinations. It was hard to believe this stuff would work even 18 months ago, wasn’t it? It was hard to believe, but everything got okay last summer, and then at the end of the year, it got great. And there’s reasons that Salesforce has got great, but to be nerdy, even Anthropic, your customer, when they rolled out these 4-dot models, up to 4, 5 for B2B stuff like we do, it wasn’t a little bit better. It was like jaw-droppingly better. The hallucinations will be worse than a human mixes and the productivity side…

…[SaaStr executive] We did 3,000 with agent force. And for 1 — I was just looking at a couple of examples. We closed a $250,000 customer this week. But the first 1 with agent force was Freshworks. You know Freshworks. They do support and a bunch of other stuff. — but they’ve changed. Gaurish isn’t the CEO anymore. The marketing teams turned over. We don’t know anybody. The agent found the right person and close the deal. That’s sort of magical. That wouldn’t have really been possible without agents they are…

…[SaaStr executive] I think Agent force — and I’m not being infectious. I think it will be $150 billion at the table because I think the value is about 3x the software.

Salesforce’s management thinks token prices are going to decrease over time and commoditise; management thinks Salesforce’s gross margin will not be affected even with all the agentic work Salesforce is doing

Tokens, those prices, we’re working with our various partners, those are going to start to go down over time and commoditize…

…Short term, we don’t see gross margins getting worse. fairly neutral, long time. We’re doing everything in conjunction with our FY ’27 framework and our overall operating margin improvement to continue to get efficiencies in gross margin and operating margin.

Salesforce’s management has 3 ways of monetising AI, which are (1) upgrading seats to premium SKUs, (2) giving customers access to seats that they couldn’t get before because the agentic experience provides good ROI to customers, and (3) sale of flex credits

We have found the formula to monetize AI. There are 3 ways Three ways, distinct ways, and the main ones that we are using to monetize AI. Number one is our large installed base of 100 millions of seats, we are upgrading to our premium SKUs that contain already embedded AI and unlimited access to Agentic for employee use cases. Number one…

…The second way to monetize. this is very peculiar because now our apps are Agentforce Sales, Agentforce Service, all of them are agentic. So now the ROI that companies generate by implementing our apps has increased. So now we have access to new seats that before companies couldn’t afford to roll out sales force or any of our apps.

And the third way is for customer facing agent use cases, agents, we sell through the credits, flex credits. And companies, if you look at the bookings of Agentforce in Q4, 50% were credits, flex credits, fuel and 50% were higher SKUs.

The Trade Desk (NASDAQ: TTD)

Nearly 100% of Trade Desk’s clients are running through Kokai today (was 85% in 2025 Q3); management thinks Kokai is the most advanced AI-powered advertising-buying platform for the open internet; Kokai has enhanced every unique function in the advertising valuation process with AI; Kokai is an upgrade over Solimar in every aspect; Cheerios used retail data and Kokai to achieve 88% more conversions and 7x higher CPA (cost per acquisition); Deal Desk is a new innovation in Kokai that allows advertisers to centralise their deal creation, management, and analysis; prior to Deal Desk, advertisers have increasingly sought one-to-one deals, which has led to inefficiency in the supply chain; Deal Desk uses AI to forecast the performance of a deal; early results of Deal Desk are promising, with Deal Desk deals meaningfully outperforming legacy deals; more suppliers are signing up with Deal Desk, and 2 of the biggest SSPs (supply side platforms) in Germany recently announced an integration with Deal Desk; IKEA used Kokai to achieve a 17% reduction in CPA; Best Western used Kokai and achieved an 89% improvement in incremental reach

Almost 100% of our clients are running through Kokai today. We think Kokai is the most advanced AI-fueled buying platform ever pointed at the open internet. Kokai broke advertising into the basic elements of an advertising campaign and enabled every unique function in the valuation process to be enhanced with AI. From identity probabilities to valuing impressions to predicting performance to forecasting spend to predicting the right clearing price to detecting auction manipulation or even fraud to generating creatives to supply path optimizations or to even surfacing insights that could once easily be buried in a mountain of data. Kokai and AI enhanced and upgraded nearly every part of Solimar…

…Cheerios ran a display campaign in the U.K. recently using retail data for audience targeting on Kokai. They saw 88% more conversions and 7x better CPA…

…I want to share one more innovation built on Kokai, and that’s Deal Desk. Complexity has brought many advertisers to seek out one-to-one deals as a means of simplifying supply chains, much like they used to in a nondigital world. But in that process, some buyers have inadvertently given up buy-side decisioning power, especially in CTV. They have also given rise to inefficient supply chains or inadvertent oxygen to some bad players that a more efficient supply chain would not allow for. Deals can be a way to leverage size and get a better deal, but measuring the deal’s outcomes becomes very important. It is easier to do a bad deal than ever, especially when pursuing cheap cost. Historically, 90% of deal IDs never scaled, either because they were set up poorly, hard to troubleshoot or simply didn’t perform. Deal Desk centralizes the way buyers create, manage and analyze their deals. It uses AI to forecast how a deal is likely to perform relative to the open market and then highlights where things may go off track. Early results are encouraging. So far, deals that are set up and managed through Deal Desk are performing meaningfully better than those managed the legacy way. More suppliers are signing up for Deal Desk every week. Deal Desk is in early stages, but it is rolling out around the world. Most recently, the 2 biggest SSPs in Germany announced that they are integrating with it…

…IKEA, for example, is using Kokai to get a more intelligent perspective on how their ads perform across all channels. Thanks to Kokai’s AI-fueled omnichannel optimization, they saw cost per acquisition decrease by 17%, while also gaining valuable new insights on the effectiveness of different channel activations at different stages of the customer journey…

…Best Western saw their booking rate double when using Kokai to target live sports opportunities, thanks to an 89% improvement in incremental reach with Kokai.

Every engineer at Trade Desk is using AI coding tools; management has injected AI tools across Trade Desk, resulting in higher productivity

The most obvious of AI’s features is that it is a productivity enhancer. As one example, every engineer at TTD is using AI tools to write and/or test code. We’ve injected AI tools across the company and productivity is going up.

Trade Desk’s management thinks the company’s business will benefit more from AI than any of its competitors; Trade Desk’s scaled competitors are selling their owned and operated (O&O) inventory, whereas Trade Desk does not, and this aligns Trade Desk’s interests with ad buyers; management thinks the buying platform with the most objectivity and the most trust is the one most likely to win; Trade Desk is trying to make millions of complicated decisions every second and AI can help with this

We think our business model is more conducive and will benefit more from AI than any of our competitors. Every scaled competitor we have is first and foremost, selling their owned and operated inventory, O&O. We don’t have O&O. We have aligned our interest with buyers, and that is even more valuable in the AI-fueled ecosystem. AI makes it easier to make better decisions for advertisers and match the best ad opportunities. Valuable data like advertisers’ first-party data is way more valuable in an AI world. Retail data is more valuable in an AI world. The buying platform with the most objectivity and the most trust is the one most likely to create the most scale and win the most market share. At The Trade Desk, we have built the industry’s most advanced, trusted and objective data set, which is based on factors like these, 20 million ad opportunities every second, each with thousands of data variables and each valued objectively.

Our clients’ valuable first-party data, which they trust us with, that we will never jeopardize. The industry’s most scaled data marketplace, including most of the world’s leading retailers, close integrations with thousands of suppliers and publishers across channels. In short, we are trying to make millions of complicated decisions every second based on massive data sets. This assignment can obviously be enhanced with AI…

…I don’t think there’s any company in our industry that is better positioned to take advantage of advances in AI.

Trade Desk’s management thinks that platforms with scaled, unique, data, and that are trusted, are in a great position to leverage AI, including agentic AI

There is an emerging narrative that AI will compress software value or disintermediate platforms altogether. That might be true for some SaaS businesses, especially those that deal in generic process or low-grade data. However, for platforms that have earned the trust of their clients and partners and have amassed data that is scaled, unique, refined and actionable, they are in the perfect position to leverage advances in AI to add more value…

…We are convinced that Agentic AI will ultimately accrete the most value to companies that already have deep customer trust that have scaled, refined and objective data sets and that prioritize objectivity, not by companies with limited data hoping an AI framework becomes their business model.

Trade Desk’s management recently introduced Audience Unlimited, a new data marketplace; management thinks Audience Unlimited will benefit the entire digital advertising ecosystem in the AI-era; management thinks 3rd-party data and retail data have been massively underutilised since the advent of programmatic advertising because of a lack of price discovery of the data; Audience Unlimited helps advertisers use the most relevant data for a given campaign at an all-in cost; Audience Unlimited was not possible to build before the arrival of agentic AI; management has already seen very positive results with early adopters of Audience Unlimited; the roll out of Audience Unlimited will enable Trade Desk’s partners to use more agentic AI; management sees data as being more powerful when it can be used in AI instead of in a simple algorithm

Audience Unlimited is one of our biggest innovations ever. This will change the usage and value of the data marketplace for both buyers and sellers, and we think that agencies, advertisers, data providers and retailers will all benefit from this innovation, and it is essential in this new AI-fueled world. There has been massive underutilization to third-party data and retail data, in particular, since the advent of programmatic about 20 years ago. I have argued that the data marketplace is anemic for one primary reason. There is no price discovery for data. The cost has really been complicated for marketers. So generally, they don’t use it. We can see though that the value is obvious, especially leveraging AI. And using a flat cost structure, Audience Unlimited helps advertisers use a wider range of the most relevant data to any given campaign for an all-in cost, where value and impact is clearly understood. This innovation wasn’t possible before advances in AI, particularly Agentic AI in this case, which allows us to surface the right data segment at the right moment. Of course, Audience Unlimited is completely optional. Clients can use it or continue to buy third-party data a-la-carte. We are already seeing very positive results with early adopters, and I’m excited for more advertisers to get access as this year progresses…

…The Audience Unlimited rollout is part of a much bigger effort to reform measurement and enable our partners to use more Agentic as well…

…In this AI-fueled world, the third-party data ecosystem that we power using things like Audience Unlimited and other things, all these innovations are meant to make it easier to bring data onto the platform and make it more powerful in an AI-fueled world. It just inherently is more powerful when you can use it in AI instead of a simple algo or a basic bid factor.

Trade Desk’s management thinks agentic AI is the best thing to happen to programmatic advertising because it makes it easier to make decisions in a very complex environment

Agentic AI, I believe, will be the best thing that ever happened to programmatic advertising. And it’s because it makes decisioning in a very complicated environment easier. And when I say easier, I don’t mean that the nature of the market is getting less complex. I mean that the power of man and machine together can reason through this really complicated decision that is in front of an advertiser, which is should I buy this ad that literally you’re deciding in milliseconds. So Agentic is just an amazing tool to use in that environment.

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