Oracle Corporation (ORCL) Earnings Call Transcript & Summary

I'm just a little blown away how big this has all gotten here. I feel like I'm way high this time. If those of you here last year, recall that it was definitely a small room, and it's much bigger this time. Our tenants in this event right here is up 70%. The show itself was way up. And I just have a few slides because I know that as much as you like hearing from me, there's more interesting things coming here. So you've seen it everywhere. AI changes everything. And it's true. It's in the signage, it's in the presentations, it's everywhere, except for this. So kidding aside, I'm not going to read out the whole slide, but it's important to remember here that we will be making forward-looking statements. These statements do come with maturity risks and uncertainties. So please refer to our filings 10-K and 10-Q with the SEC for more information about those. You'll also see us using non-GAAP financial measures. A couple of the presenters today will be using those. So just as a reminder, that they're intended to be used in conjunction with GAAP measures, and those can be found in our financial statements, of course. And then a number of our execs will be talking about -- for informational purposes about where we're going from a technology and service standpoint. And just a reminder, look, it's not a commitment to deliver any material code or functionality that were being discussed today. So you're going to see these slides, each of the presenters will have some or all of these slides. They will just make reference that, as Ken mentioned earlier, here's the slides Ken referred to, this is what they're talking about are these 3 slides. So let's talk about the agenda. We're going to start with Clay in a moment here. And then he will be obviously be talking about OCI and Oracle Infrastructure; followed by Mike, who will take a discussion first initially on the application business and then Mike will then continue on with a deeper dive into Oracle -- he'll be talking about AI and database and then Doug will come up with the financial outlook, and then we'll do a Q&A. Larry, Mike and Clay, do Q&A. And then so we should end up somewhere around 2:00 this afternoon. So with that in mind, let me get out of the way and let's bring up Clay.

Thank you, Ken. Ken still has 1 minute and 37 seconds, which I was really depending on. So okay, now my timers -- okay, now I'm late. I don't know what to do -- okay, thank you all for coming. I really appreciate it. To give you just kind of a quick overview of how I'm going to go. I'm going to talk for what is going to feel like far too long. We'll then have a short section where Mark Hero will come up and talk for another section of what feels like quite some time and then I'll have a bit more details on some financials, and then we'll hand it over to Mike. So we've got a few minutes. At the end of this, there's a few things that I want you to walk away with. I want you to understand the overall growth trajectory of the OCI business. Obviously, we've got myself, we've got Mike, we've got Seema, we've got a lot of people. Larry's coming. We're talking about a huge number of our businesses. I'm going to focus right now on the cloud infrastructure business. I'll be talking about the different segments that make up that business. And when I talk about these segments, obviously, there are -- the ways in which we think about them, about how they grow, about who the customers are about the products that they want, how they consume. So we'll explain that to you. I'll explain what's driving the growth in each of these segments and also why those customers are choosing to invest more and more with Oracle. The main goal is at the end of this, I think you'll understand why we're so excited about where OCI is at and the great growth prospects that come ahead for OCI. Okay. So these are the slides that Ken talked about. I have to say that you can read them. Those are the slides, and one more, and one more. Now we're -- okay, now we're done. So I mentioned different segments. So this very simple equation of enterprise plus distributed cloud plus cloud natives, plus AI infrastructure equals hyper growth. And I'll go into each of these segments specifically, and I'll talk about why we break our business up into those segments. The thing to get at a high level, though, is that all of these actually today have very good growth rates, but they're actually accelerating, right? And a big part of the reason why these different segments are all accelerating is because they're symbiotic. And I'll try to take a minute here to explain what I mean by that. Let's say that you have a customer that shows up. And they're an enterprise customer, the traditional customers that you think of when you think of Oracle, right? They have a long history with us, they're a database customer, they also have some of our applications, they might move those things to our cloud. As they do that, one of the things that they want is they want obviously the great functionality that we provide as Oracle, they also want other ISVs. Maybe they want some security products, maybe they want some new advanced networking features. Well, as Oracle, we don't provide all of that, but we have a great partnerships. I'll talk about some of those and maybe you have an ISV that shows up. And well, they're much more of a cloud-native company. And those -- the demand from those enterprise customers brings those ISVs to our platform, right? And those ISVs join, and they can drive more and more overall compute and storage and networking consumption growth, and they're happy because they provide services that our enterprise customers consume. The same thing is true for our distributed cloud customers. The fact that we actually offer not just the public cloud that we have today, but the fact that we go out and we offer our dedicated regions in our alloy business, that actually becomes an accelerator because our ISVs want that reach. So the fact that we have individual customers say, in Japan that are building their own clouds that they operate and sell to their customers, our -- that also drives things like cloud natives. When our AI infrastructure customers show up, they typically start by consuming raw infrastructure, but they quickly move on up the stack. They start consuming more of our compute storage and networking products. They also then move on to start doing things like consuming our applications. So as you look through these segments, we're experiencing this accelerating growth across all of them because as each segment on its own starts to grow, it actually helps the others grow faster. Okay. So let's take a look at the enterprise segment. So I think you already understand what we call enterprises. Here's a great set of logos across the screen. But as I said, traditional companies, most companies have some Oracle, whether it be from database or applications, our middleware, our industry applications. Those customers are very pleased when they run on top of OCI. So you see the current growth rate, right, 33% year-over-year growth for this segment from Q1 of FY '25 to Q1 of FY '26. However, contrast that with more than 1,500% growth rate in our multi-cloud database business. So the way to think about this is, up until very recently, you could only get the best of our data platform on 1 cloud, which was OCI. And we like that cloud, we think it's a great cloud. But we actually have extremely popular data platform services. As of today, you can now get that data platform on all of the cloud providers. And that's why we see this very rapidly growing, right, multi-cloud database business. A bit more about why there was a margin range here. So when I talk about 65% to 80% as a range for gross margin. The reason for that is actually largely due to the mixture of services that those customers are consuming. So as you can imagine, some customers end up being very, say, networking heavy. And networking, we're -- we think our networking is quite good, and we have a lot of IP in it, but not nearly as much IP in our networking stack as we do in, say, our database services. And so obviously, based on the differentiation we have in our services, we price them at different margin profiles and so there's a range of this based on the workloads that a customer brings to us. And then I briefly want to talk about when I say contract to scale. What I mean by this is, there's a process that enterprises go through when choosing a cloud. Typically, they do a POC, they try things out for a while. They then move forward. There's a contracting process. And then once that's done, there's also now an implementation phase, right? It takes -- people don't move their most mission-critical workloads overnight, right? And so as we see -- the reason I bring up this contract to scale is that we see it move -- the process of moving from initial product launch over to pipeline and then actually to committed contracts and then showing up as revenue, that process takes a certain amount of time for these types of customers, given the criticality of the workloads that they're bringing to OCI. So a little bit about why they're choosing OCI. Well, the most obvious reason is because it's a combination of our ecosystem, right? We have cloud infrastructure, but we also have the world's best database with Oracle, autonomous AI database. We also have amazing applications, both horizontal and vertical, and we'll hear more about that in a few minutes. But even beyond Oracle workloads, OCI is the best platform for enterprise workloads. We built OCI to make it very seamless for you to take an existing enterprise application and move that to our platform. That's very different than building a platform that was designed only for new applications to be written in the cloud. We've been talking a lot about AI this week. We've been talking a lot about the AI data platform that brings together the great work we're doing with our GenAI models, the work that we're doing with our new AI database and the work that we're doing with our new agent service, all packaged together as part of our AI data platform. That being available in all of the clouds makes it very easy for customers to pick Oracle as a place to put their data. And then, of course, enterprises do care about performance and they care about price and OCI is by far the best performance at the lowest price. Okay, so here's an example of a specific customer, NASDAQ. A few years ago, they actually moved Cat Cloud over to OCI and runs exclusively on there. And obviously, far before that, they've been a database customer for a long time. But then more recently, they adopted Oracle database at AWS, which allows them to then bring their cloud to their different environments and be able to rely on X data across all of their different cloud ecosystem. Okay, so let's now take a minute to talk about our distributed cloud. So when I talk about distributed cloud here, I really mean our dedicated regions and our alloys. So dedicated region is, you can get the entire OCI environment as of yesterday in just 3 racks and you can put that in your own data center. A great example for that is someone like a Vodafone who bought 6 of our dedicated regions to take our full cloud experience and run it right next to their network on-premise. The other part of this business that we talk about is our alloy. So as an example, take someone like an NRI that bought dedicated region to begin with, but they're a technology company in Japan. They take our cloud, they add to it their differentiated IP, and they actually can go out and operate in a sovereign way and provide that to their customers in Japan. So as you can see, it's a very good growth rate, right, growing 77% year-over-year already with an average deal size of $67 million. Now here, right, gross margin is in a range between 40% and 60%. And it's not because these things are just fundamentally less profitable, it goes back to what I said about the enterprise sector, which is it's all based on the mix of services that those people are consuming. So when you have, say, a dedicated region that's almost exclusively used for database, the margin ends up being quite high. But a lot of our dedicated region and alloy customers, they're consuming a lot of essential infrastructure services, and those have a lower margin profile in general. And then when I talk here about how there's 60-plus dedicated and alloy regions globally, we have a very large pipeline of this. And look, when someone shows up and they want a dedicated region, there's a time period where -- there's a time for us to get it to them, there's a time for them to put in the data center, this time to ramp it up. But what we've seen is we're planting a lot of these seeds, and as you can see here from this growth rate, this part of our business is growing very rapidly, and the customers keep coming back for more. Okay. Why are these people picking our distributed cloud? It's really quite simple. Rather than talking about why our cloud solves their -- technologically, their needs are simple. They're typically in a regulated industry, they need sovereign or they have things that they either don't or can't move to the public cloud and they want the benefits of the cloud right next to it. Our distributed cloud offering is actually the only offering that solves these problems, right? If you go to anywhere else, they have a subset of the cloud, you have to pay a lot of money upfront. With us, you can get it in a very small form factor. You sign a universal credits commitment that allows you flexibility on what services you're going to consume. We can deploy, like I said, in a small footprint very, very rapidly, and no one else offers a turnkey solution. The fact that, as Oracle, we both are in infrastructure and applications, that's something no one else does. And what that actually enables us to do with alloy is we could not build alloy and enable someone else to actually operate and run a cloud, if we didn't have all the great work that Steve and his team had done with Fusion. So when we talk about alloys, it's not just OCI, it's OCI plus HCM plus our ERP system, plus our coding system plus Service Cloud. That way, we can actually provide a turnkey solution. And then, of course, another part of the reason why people like our dedicated regions, and you'll hear Mark talk about this with, I think, some customer examples later, is that when they buy our dedicated region or they buy Alloy, it doesn't just our infrastructure. you can get a dedicated region and then you can also run our differentiated applications, both horizontal and vertical apps on that same infrastructure wherever you need it. Okay. So here's an example of previously known as EDASlot. So they started a few years ago with a dedicated region. They then expanded to have a second dedicated region for disaster recovery. They then expanded by adding GPUs to those dedicated regions. And then most recently, they expanded again by contracting for an Alloy so they can serve sovereign workloads in the UAE. That's an example of how these customers start small, right? We make them extremely happy and then they continue to grow and scale with us. And as we continue to add more and more of these customers, like I said, this business starts small, but the customers are seeing so much value, they all keep growing and growing and growing. Okay. All right, cloud natives. So when we talk about cloud native, the way I think about this is these are typically workloads and customers that have relatively few number of applications that consume a lot of infrastructure, right? ISVs typically fall into this business, but companies that have workloads that are very focused on, how do you rapidly scale up and scale down and take advantage of all of the benefits of the cloud. So this segment of our business is growing very rapidly as well, with 49% year-over-year growth from Q1 FY '25 to Q1 FY '26 with an average deal size of almost $100 million, right? Again, gross margin range of 40% to 60%. A large part of the reason for this comes down to both the service mix that they choose, but also the size and the scale of their business. So as you can imagine, this group can range from relatively big fish to very, very, very large fish, what you might call whales. And as part of that, with that size expectation, you come different expectations around overall pricing. One thing to think about here, though, is that these customers tend to ramp faster. Typically, these customers might have some of their own on-premise, they have a platform that they can move very quickly. Zoom was an example. I don't know if anyone remembers, but COVID hit and Zoom actually moved from their existing infrastructure as well as in other clouds, they were up and running on OCI in 9 days. And so these are applications that you can ramp very quickly when there's motivation to do so. And so when these customers find out about us, they tend to be very large and they scale very fast. Okay. So why are these customers choosing OCI? Well, primarily, it's about best price and performance. And the reason it matters so much to these customers is that typically, if you were to take their IT spend, typically for, say, smaller customers, the largest expense for them is labor costs. And their overall infrastructure spend tends to be a tiny portion. For these types of customers, by far and away, the biggest expense for them is the actual infrastructure. And that's why having the best price performance and being the most secure matters to them. We also work with these customers through a combination of engineering partnerships. They -- and Mark will explain a bit more about this in a minute. It's not enough that we just say, "Hey, here's what's on the truck." All of them, given their scale and their unique business, they might have some features they need, they might have some customizations they want of different hardware we haven't considered before. And the fact that is Oracle, we can actually go work with them, figure out how to implement all of that in a standardized way, right? Because what's important when we do this is that we don't fork our cloud, where there's like an Uber cloud, which is different than a TikTok cloud, which is different than an open AI cloud. We need to have 1 cloud that everyone can use, and it's our job to go in and put those features and functionality into the base product. So there's really important reasons why those customers are choosing us. It's really about those deep engineering relationships. Okay. So an example of one of these companies is Cybereason. As I mentioned, they picked us because of our focus on performance, efficiency and security. Cybereason is a security company that focuses on endpoint protection. They have an amazing platform. They tested our platform, and they've quickly migrated a significant chunk of their infrastructure from GCP over to OCI. The reason for that is that moving to OCI save them more than 40% on their overall infrastructure spend, which directly goes to their bottom line given the type of business that they have and the scale at which they're growing. Okay. So now I want to talk a bit more about AI infrastructure. So when I talk about AI infrastructure, AI means many different things. Here, I'm really talking about companies that want accelerators for either doing training or reasoning, both fit into this category. We have a lot of these customers, right? If you take a look here, when we say there's more than 700 of these customers consuming on our platform, that doesn't mean there's 700 customers using AI, just to be clear, right? You hear Mike talk about it. We have thousands and thousands of customers using AI on our platform. This is people who show up and go, "No, I want some type of accelerated computing, whether it be an NVIDIA GPU or an AMD GPU or as we're rolling out different accelerators, any type of accelerated hardware, that's all we're counting for you to fit into this category. This, as you can imagine, is growing very rapidly, right? 117% year-over-year growth and overall annualized consumed revenue. The margin is different. And the reason for the margin range here is less to do with product mix because in general, these customers actually are pretty well defined on which types of products they want. A lot of it comes down to the location and how efficient we can be. So as an example, as we continue to be more and more efficient, as we can go in and design better networks, as we can optimize our data center build-out, as we can do things like power capping, that then allows us to have a higher margin. And in some cases, the other part of the reason for the margin range is, obviously, just like I talked about with cloud natives, there's customers of different sizes, right? Typically, these customers are relatively large to begin with, although there's a lot of very small ones, but they can scale extremely high, as I think all of you know. Okay. So why they're picking OCI? Well, part of it is, is that we move very quickly. We move quickly when things are stood up, but we also move even more quickly before things exist. So our ability to rapidly deliver the latest and the accelerators. Networking matters a lot, right? Fundamentally, when people are buying a cluster, like yes, the actual accelerator matters, but all of these workloads are operating in a clustered fashion. That's true across training, especially, but even for inferencing, all of these inferencing is happening at a cluster level. And then the fact that we actually have extremely efficient data center designs that allow us to optimize our power usage, which then translates into more -- either more power available for them or lower costs. And then it's not enough to just be really good at compute and networking. Storage is also critically important because these clusters are doing something with data, whether that be for training runs or for inferencing, they need access to a huge amount of context for these models to be valuable. All right. So this is a slide of an example deal. I'm going to walk you through all of the different details. So as an example, let's imagine that somebody shows up and they want to buy approximately 1 gigawatt of GPU accelerators, right? And they sign a contract for 6 years, which would be 6 years at $10 billion a year. It's a $60 billion TCV. Okay. Now here, I've broken the costs we have into 2 sections. One is what I'm calling land, data center and power. That's obviously the buildings, it's the actual power generation, it's the people cost to run that portion of the business, right? That ends up being about 35% of the cost to deliver that service. Now if you add up compute, networking and storage, right, all the things that you put inside of that data center, that ends up being about 65% of the cost. Okay, so when -- say this deal is contracted and you have to build a data center from scratch. Well, we actually align through the contracts that we do and the way in which we align our overall delivery, we don't pay for land, data center and power until it's actually delivered to us, okay? So let's say, it takes you a year to build a data center. During that year, we're not paying for anything. Now there's a point at which and that's highlighted in this kind of left section before year 1 here, where the data center is working, and it's our job as Oracle to put all of the compute storage and networking inside of it, all right? During that time period, there's a cost, where we're paying for something, but we're not making revenue yet, right? And as we can reduce that time, if I can go from 3 months to 2 months to 1 month, the amount of time that I'm paying for something that I'm not able to then provide to a customer, it goes way down, right? So in this example of this example deal of $60 billion, I've written down here, okay, a 35% margin deal, right in the middle of that 30% to 40% margin range. But the thing to understand is when I talk about this 35% margin, it doesn't just include each year after it's running. That 35% includes, right, the fact that there's a startup cost upfront. Now obviously, if you can imagine, if you were to put a demarcation line before year 1 starts, we have an expense with no compensating revenue yet. And that can be happening across many different sites, right? We're building not one of these things. We're building 10, 20 of them at a time. And based on the delivery schedule, right, and our ability to ramp and put the capacity in there, you've got a waterfall across many of these different pieces of infrastructure where some are now starting up, some are now giving you a ton of cash flow off. And part of the job that we do is we're constantly laying those things together to understand how our overall business works. So key takeaways from this slide. We spend a lot of our energy aligning our revenue and our expenses. The ramp-up time for this stuff has cost, but it's minimal, and it's something that we're continually optimizing. And the expense of that ramp-up is included in the gross margin. So when I talk about, hey, here's the gross margin range of one of these customers. We don't go in and assume, right, that, I don't know, some -- you're not accounting for these ramp-up expenses, the gross margin includes the assumptions around those ramp-up expenses. Okay. Next. I've been talking to a lot of different customers and investors and reporters and analysts about, okay, how do we actually go out and we accelerate and scale this build out? Fundamentally, there is no problem. It's really just an opportunity for us to grow faster, right? It's not -- there is no issue. There's just opportunities the way I see it. So when I think about what a huge amount of my time and energy goes into, it's about how do we actually enable ourselves and the industry to grow faster, given the unprecedented demand we're seeing for infrastructure like this. And the way that we're growing faster is that we're actually pulling together. The entire industry is coming together across data center providers, energy providers, hardware providers and capital providers. This is already happening all day, every day, right? You see constant announcements of new business models being created of different ways to go out and minimize risk and grow this business faster. That's actually what's enabling the current growth. And I think you're going to continue to see from us and others, new models created to actually help grow this business faster and faster. And so to understand the result of what happens when all these partnerships come together, let's take a look at the video to see what's actually possible. [Presentation]

Okay. So what I'm going to do now is I'm going to kind of just walk you through a little -- a few pictures about the video that we just saw to give you some more context. So this is a site in Abilene, Texas. It's 1,100 acres across 8 total buildings. So what you're looking at right here that were kind of quad thing with this thing in the middle, that's actually what we call 1 building. I know to me, it kind of looks like 5 buildings, but it turns out that apparently, we just don't have enough numbers, we couldn't get to that. Each of those individual pieces or data halls that are all part of 1 building. In this location, there are more than 6,400 construction workers on site every day. And once we're complete with this campus, we provide nearly 1,700 jobs, direct jobs and even more indirect jobs. So on site, we have both grid power, and there's an existing substation as well as a new 1 gigawatt substation that's going in. But in addition to that, there's also 300 megawatts of gas turbines that are installed to provide power to some of the initial buildings and also as partial backup power for the site. Okay, so we started building this in May, May of 2024 and the entire campus will be completed in the middle of next year. The workloads for the customer for this site, which is OpenAI in the beginning, went live less than a year after the construction started. Each of these buildings is actually mechanically isolated. They're all networked together. So in that giant campus, all of these buildings are actually through a massive set of interconnections, both within the buildings as well as through very secure fiber between them that actually allows the entire cluster to function together as 1 giant supercluster. And once this is finalized, as of right now, this will be the largest supercomputer that's ever constructed. Okay. I don't know how many of you are familiar with the details of data centers, but liquid cooling has something that's existed for a long time, but it's really gone from 0 to 100 very, very quickly. Liquid cooling, so these big pipes are actually the cooling liquid that then go in to cool these accelerators. Sometimes people ask about, well, what about the water? Does this use a lot of water? The reality is no. This water is in a closed loop system. The actual annual consumption for each one of those buildings that you saw there, which is quite large and is 100 megawatts of power, actually consumes less water per year than a single-family home in the same region. So obviously, you have a lot of water when you first started up, you have to fill up the swimming pool, but the -- because it's not evaporating all the time, it's a closed loop system, okay? So an example of a customer that is choosing us for AI infrastructure. This is Modal Labs, they provide a developer platform for AI, ML and inferencing. They enable customers to easily fine-tune and do batch processing for AI workloads. They choose us because we have amazing infrastructure. We have the best bare metal compute available and they really appreciate the price performance advantages that we have, combined with our excellent networking. And then what I want to do now is take a few minutes. I'm going to bring Mark Hura up to the stage, and he's going to talk a little bit about some of our customers and why they are choosing OCI across all these different segments. And at the end, I'll give a little bit more details on where we see OCI going over the next few years. Mark?

Okay. I did -- my job is really very easy. Hi, Mark.

Hi, Clay.

We have mini segments of businesses.

We do.

Let's talk about enterprise. Why are enterprise customers choosing OCI and give us some examples of what it's like working with us.

Yes. So you gave some examples before in terms of why enterprise customers choose Oracle. And that has been our bread and butter for so many years around customers that have deployed Oracle applications, Oracle databases throughout their entire businesses. But then when you think about OCI and how we've defined and developed and deployed a differentiated cloud that serves many different types of workloads. And for enterprises, we think about 3 different major categories. One is enterprise customers that have been utilizing Oracle applications and databases that serve either our applications, third-party applications, custom-built applications or large-scale enterprise data warehouses. And those are customers that take advantage of the price performance and capability that we have in OCI, and I'll give some examples there. The second category is that customers that are looking to exit their data centers and their workloads. And what we have found is that many other clouds requiring you to redefine and build those applications in a new way to fit onto their cloud, so it becomes a constraint for our customers that are looking to take advantage of what a true cloud can do for their business. And then the third is really bringing pure infrastructure, compute storage networking capabilities to enterprise customers that are taking advantage of raw infrastructure capabilities. Those are 3 distinct areas of why we're winning, how we're winning and how we're engaging with our enterprise customers all around the world. And if you think about that first category, an example such as Emerson. They look to build and move their entire Oracle EBS infrastructure, while they're migrating to Fusion, but in doing so, they brought the enterprise data warehouse in all of their Oracle databases to our cloud. In doing so, they also, what's unique and what we have found, all of the boundary applications that support that supply chain, that financial system all come into OCI, allowing them to exit their data center, get the performance capability while they start to transform to Fusion at the same time. And so it's a great example of how we support our customers, bringing together the power of the capability of OCI, the performance for them to be able to improve the performance and the speed for their end users around the applications that they're serving. Clariti is another example, where they were living under the constraint of another provider telling them they had to replatform before they move to the cloud. And the cloud that you designed, Clay, which is BareMetal from the core, right, with off-box virtualization and flexible compute for our customers to be able to pick up the VMs that they have and exit that data center without having to replatform, allowing them to focus things that are most important for them around delivering services to their customers. And when we think about just bringing pure infrastructure to our customers in the enterprise, we have so many examples, but one that may be different and unique is like Goldman Sachs, right? Taking advantage of bringing high-performance computing capability to do risk analysis in their business. And we weren't their original provider, but we are a critical provider of services to them now where they just -- they had to take an opportunity to look at what it meant in OCI and how we are executing and delivering compute capabilities for them and how they were used to doing it before. And the results were staggering in terms of the price performance and capability that they were able to get and it doesn't have any Oracle databases that they're running in that environment. So there's multiple examples of native VMs and entire data center capabilities, Oracle applications and our entire data platform capabilities for customers or pure infrastructure around enterprises.

Okay. Well, Mark, I agree with you, great examples. I also kind of explained a bit about our distributed cloud customers, and I used a few examples already. But I think there's probably an example that is more surprising and also a bit different than the traditional telco or MSP-type workload here. Tell us more about some distributed cloud customers.

So I mean, I think there's an obvious point, which is sovereign security, highly regulated industries, banking, health care, utilities that can take advantage of a distributed cloud, bringing it to them in terms of the industries that they're serving. But what's also unique about our distributed cloud, it is our full cloud, which means our customers can take advantage of running our full suite of capabilities. Meta, as an example. Before we get into thinking about infrastructure at scale in training and inferencing, Meta is a user of Fusion, where we've deployed a dedicated cloud at Meta's facilities where they run Fusion to run their back office of their business. And so we have the capability not only to bring infrastructure to our customers, but the ability to run our full suite of industry applications as well as data layers for our customers. So it's a different example, it's a unique example that our customers can take advantage of wherever they deploy.

Okay. So we've got enterprise, distributed cloud. What about cloud natives?

Cloud natives, I mean cloud native has been a wonderful spot to truly bring the capability of a next-gen cloud to the industry. And you brought up an example around Cybereason. What we have found is these patterns of success that have continued in a variety of different industry segments for cloud natives. Cybereason is one example, but many security companies are running on OCI, right, whether it's Palo Alto, CrowdStrike, Commvault, Sentinel, Fortinet and on and on and on. And there's a reason why. As you mentioned before, there are certain types of workloads where we perform at a higher level than where the industry is. And whether that's the networking requirements from a security company of how they're protecting others or whether it's the compute intensity that they're leveraging to scale up, scale down to run their businesses efficiently to protect what they're providing to their users. And it's a great example of thinking around what wait a second, if the security companies are running on OCI, is the most performing the most secure cloud out there? Well, that makes a lot of sense. In addition to that, we see a lot of ISVs that come to OCI as well. It's an opportunity where we become a critical part of their cost of goods that they provide. And the technical differentiation that we have and the partnership that we bring to our ISVs is unmatched, where we are working with them to optimize the workloads for OCI to allow them to focus on delivering the services that they bring to their customers so that we are a critical part of their success, but most importantly is it's a highly engineered engagement with the ISVs and it's unmatched in terms of the performance.

Okay. So I also had -- I had Peter from OpenAI on stage from yesterday. He was very complementary to your team. And then we just talked about Modal. Give everybody here a little bit more perspective on why we're doing it so well in AI infrastructure and why those customers are choosing us?

Yes. AI infrastructure, and when we really started in this space many years ago, I recall a distinct conversation when we were working with NVIDIA actually, around NVIDIA needing to run their workloads in a cloud and engaging directly with Jensen and what he said is, "Wow, I'd never thought I would see this, but Oracle, you are fast, you are highly technical, right, and you are incredibly capable from an engineering perspective." And he said, never lose that advantage. It wasn't necessarily something that always aligned in terms of what he thought in the past. And for us, it's something that we just continue to operate it with all of the customers that are taking advantage of our AI infrastructure that we're building. Our teams are incredibly fast. We know what we're really good at. We're highly technical and then we provide a white glove service for our customers that is a 24/7 engagement. And what we constantly hear over and over again is that we are the best to work with, and we're the easiest in terms of operating. And what happens is this is a pretty tight community. And it spreads very quickly. So is the early stage companies that we started with, they were telling our story to others, and it's just continued to snowball from there. And obviously, we have the largest of the largest AI companies from running infrastructure in the world today. And so again, highly technical. We don't waste anybody's time. We know what we're really good at and the speed is unmatched.

No. Mark, thank you. I think it's great summary of why people are choosing OCI. Really appreciate it. Thank you.

Thank you. All right.

All right. So this is the long-range plan that we provided on our September 9 earnings call, just a few -- well, I guess it was last month technically. The thing to understand about this is that when I talk about this business, all these different segments are included in this. So this projection about future revenue includes all of these business segments that I'm talking about. The other thing I want you to understand is that when we talk to you about these types of projections, there are projections that we believe in. We have very real reasons across each of these segments that we're across all of these plans that add up together to provide this kind of projection. The other thing to understand a little bit, and I'll go into it a bit more in a second, is that for some segments of our business, there really are actually supply constrained, not demand constrained, specifically AI infrastructure. And I'll talk about what that means and why you're seeing the changes from us that we're making in the industry. So to give you some context here. In 30 days during this quarter, 30 contiguous days, we contracted for $65 billion of additional commitment across infrastructure contracts. Now across those, it was across 7 different contracts from 4 different customers. None of those customers are OpenAI because I know some people are questioning sometimes, hey, is it just OpenAI? The reality is we think OpenAI is a great customer, but we have many customers. One of these customers is Meta. And as I share these numbers with you, this is not all of our contracts. This is not saying, hey, this is -- we didn't add up all of the deals. This is literally 7 deals, 4 customers, all of them other than open AI, and it shows the diversity of our customers and the size of their interest in our business. Okay. So as I mentioned before, most people are assuming that we are demand constrained, but we're not. We're actually supply constrained. And the thing to understand here is that a large part of what I spend my time doing is securing what I consider to be good supply. I'll give you an example of an e-mail I received recently. There was a very nice person who lives in Nebraska, who has a corn field. They said that they think that would be a really good place to put an AI data center. You're probably laughing, but I received. So there's -- you cannot imagine the amount of reach outs that come my way in the way of my team. It's actually quite a hard job to filter through all of the opportunities to understand which ones bring together the energy that you need, the hard work, the capacity that you need, the land that you need and the capital that you need. Our job is to put together all of those pieces. And only then does that result in supply. And so what we find is that when we actually have good answers around supply in the reasonable time frame, customers then contract very quickly. So when we talk about the business that we're doing, it's not that I have this infinite supply over here of options, and I spend all of my time going to customers asking, "Hey, would you possibly want this?" Instead, the customers have come to us saying, we would like a lot of stuff. And our job is to go find actual good supply that we can accelerate and deliver on the time line they need. That is what we do all the time. And once we have that done, suddenly, the customers contract very quickly, and we're contracting with all the different suppliers to bring together these pieces, so we can execute as quickly as possible. So as part of that, we are updating our long-range plan between now and our fiscal year '30. Again, the reason we're updating this is exactly for what I showed you 2 slides ago. As we find ourselves able to execute our supply constraints, as we actually can go in, we have customers that want that. And that's part of the reason to, and you see the change in these numbers, that it's a little bit easier for us to find supply not this year or next year, but in subsequent years. So as we're able to find that supply, customers contract for it, we see immense demand and then we go about delivering that to customers. Okay, the other thing I want people to understand is that I talked about these different segments. And obviously, the numbers we just talked about was the all up number for OCI. But we're also extremely focused on our AI database and our AI data platform business. We are very confident in our ability to grow this very rapidly. Now there's a few reasons for that. One of them is, up until recently, as I mentioned before, there was only one cloud selling our AI database and our AI data platform, and now we've transitioned to all of them. The other reason we're very confident in this projection, you'll hear more about that from Larry later today is the huge investments that we're making into AI and to building out our AI data platform only makes the previous investments we have and our customers have made into our database that much more valuable. So as we go through this next period over a few years, we're rapidly accelerating our cloud database and our AI data platform business. And this is a -- it's amazing for us to see, right, a business that is such a high margin, that's so valuable to our customers grow so very quickly. So with that, I'm out of time, and I'm going to hand it over, I think to Mike, and he's going to talk to you about applications. Thank you very much.

Please welcome to the stage, Mike Sicilia, Steve Miranda and Mark Hura.

Okay. Hello again. So we're going to talk about applications here. As a reminder, we'll do so under Safe Harbor. We will talk about a road map, we will talk about our future product direction. So you've heard us say throughout the event here that AI changes everything. And I think it's really important to understand is that, for all the reasons Clay just described, for everything that we're doing in OCI, our ability to deliver AI to our customers embedded inside of our application stack and the ability to have just an unbelievable time to value, we think is unmatched. We've got -- I think we've gotten to a point, Steve, Steve and I have been discussing this in the week, is that in our applications suite across the board, when you think about our fusion applications, our front-of-the-house applications like CX, our industry applications, we're at the point where there isn't a difference between the AI version and the non-AI version. It just doesn't exist. And I'll -- we'll go through some examples of some of those. We're seeing, particularly in health care, we just went generally available with a new EHR. It's all AI. You can't choose not to consume AI to actually run the application. Now if we didn't make all the investments we've made, if Clay and Mark were out there attracting all these wonderful models to OCI, we would not be able to deliver that as a packaged service. And that's one of the things that I think when we see in the application market, when people say, well, maybe the -- there's not an ROI on this stuff. Actually, I think it's because they're trying to stitch too many things together, they've got too many vendors involved, and they get right back into this old mantra of a very large implementation, a very large time of materials implementations, which frankly, customers are losing patient for. That's not at all what we're doing. We're actually winning more customers because we're shifting our focus to outcomes. We're winning retail customers in Germany. We're displacing them from SAP because we have an all-in platform of Fusion merchandising plus our retail applications. Our customers are achieving more. Our customers in health care, and I'll go through this example again, it went live with our AI agents. Within 3 weeks, the ROI in terms of return on time spent with the system was 50%, right? It wasn't small. It was by half, an order of magnitude by half. And the fact that we're delivering that as a service has been really just, we think, game-changing. And along the way, as we rely on all of the OCI tech, we're also doing quite a bit with the II data platform. And to make a long story short, good things happen all the data is in one spot. And that has expanded our ecosystem such that we're not just talking about automating entire industries, but actually automating how those industries communicate with other industries, and we'll go through some of those examples in just a bit. But to dig in, Steve, if you could maybe take us through all the great things in Fusion that we've got with AI to start us off.

I'm going to do just a couple of the great things in Fusion, Mike. Thanks. So first off, let me just give you update on our customers. So I think you'll find and hopefully, you've seen this week, just by walking around, we have a who's who of the top customers, the top brands globally, by industry, by what I'll call heritage, meaning people have moved off of PeopleSoft, JD Edwards, Siebel, SAP and a variety of other third-party applications. I used to show the slide really to show some credibility of Fusion and how Fusion is real. I think now it's unquestioned that Fusion is a market leader in terms of cloud-based applications. The reason why I show this slide now is twofold. First off, the growing trend of what's happening is customers are going to Phase II, III and IV of their projects. They started an ERP, they're adding HCM. They started in ERP and supply chain, they're adding CX. They're starting in ERP supply chain and they're adding industry applications or they have industry applications and they're adding others. So it's a tremendous leverage to us. But more than that, the referenceability in the case studies we have in the customer. So just this morning, I was talking to a major airline who owns a variety of subsystems who have dozens of ERP systems. And what they want to do is they want to consolidate, but they are essentially a corporate headquarters with federated businesses and they don't want to -- while they want to bring this system together, they still want to have different lines of business. I was able to quickly give them FedEx did that. Except FedEx centralized it all, very similar industry to the airline into a shared service. But they said, well, we don't want to share service. No problem. That's exactly what DWP and the U.K. government did, a bunch of government entities together but had federated ownership. And as it turns out, this airline had already met with DWP at this conference, shared their use case and are going to now connect to them offline and follow that same program. So this is not just a customer base that's buying more products. This is a customer base that's talking to each other and helping us expand and helping others expand and succeed. It's just a tremendous, tremendous success story for all of these customers, including AI, and including Oracle AI. Now safe harbor aside, and Mike said, we're going to talk about futures. Everything I'm going to talk about here in Fusion is not futures, it's today. So as 1 example of AI being today, we start first, as always, at Oracle, our own use case. And we have, as you guys all know, a world-class finance organization, and we talk a lot about our efficiency of close and our speed of close and reporting. Well, we just made that much better. Our internal finance group has rolled out our ledger agent. They're rolling out our payment agent. They're using AI to take an already extremely efficient group and fast group and make it more efficient. We've implemented agents across HR even in functions, we never really had like HR benefits, goals and functions using AI to improve there. And then for our support business, handling the support tickets are all of our external customers now have AI agents, both on the front end to deflect SRs and questions and for our assistance on the back end so that our support agents can better find the answers and service our customers. We've just rolled out those agents already receiving faster time to resolution, less people intervention, meaning less cost to us and more accuracy of the resolution, and it's resulting in greater customer satisfaction on the SR surveys. So we're Safra's mantra, we're paying less, and we're doing a better job with it through the AI agents. And those are just 3 examples at Oracle. Now when we announced -- what I talked about at this conference last year was that we would have 100 AI agents in Fusion, we actually have 600 AI agents, 400-plus in Fusion, 200-plus in the industry verticals, but that is a massive understatement into AI because what we've built is an AI ecosystem across Fusion. One are the agents that we built internal to Fusion. Second, however, is we have an agent studio that allows our customers to modify what we give them in agents, to build their own agents, to integrate with third parties. And then at this conference, we just announced an agent marketplace. The agent marketplace allowing you to extend and expand them, and we have over 2 dozen partners part of the marketplace each who've contributed already about half a dozen agents together. So the 600 are agents that we've built that does not count agents that our customers are building through the agent studio, that does not count what our partners have built.

Steven, I think what's really interesting about that, as we discussed, is that you have kind of this age-old question in procurement cycles of build by partner. I think your answer is, well, how about all 3 right? You can build your own agents, you can buy partner agents or you can buy our agents, but you're doing it in the safety and security of a single platform, right? It's not -- it's still not this idea of stitching a bunch of things together and bringing a bunch of bespoke...

Well, I think that, that last but is a significant difference because in theory, could you use a third-party agentic framework and build agents on top of Fusion? Absolutely. However, when you build huge agents on top of the agent platform or use ours, the context is there. So it knows Mike Sicilia, it knows your role within Oracle, it knows what you can see and what you can't see...

I had no access. I tried to...

Now you have all access. But let me take a different example. When you're Steve Miranda, you have somewhat more limited access. So it knows -- but the benefits agent example. So if I log into Oracle, we have the benefit agent, right? I want to ask you a question. I'm traveling in Europe, I want a prescription. Is it covered or not? I have a -- someone on my team has a leave of absence. They want to extend it. Do they need to extend COBRA? There's all sorts of benefits questions. Well, the agent native to Fusion, not only can our customers configure them all the ways you see here, but it's embedded contextual and secure. So it knows a person's role, it knows our HR status, so it knows their tenure. It knows what's health care plan they've selected. It knows what country they're in. So it knows exactly the benefits policy on which to answer the questions. If you had a bespoke or build your own, you could certainly do that. But if you did it totally outside of the Oracle Eagle system, you have to code in the security, you have to code in the context and you have to keep that updated always for every agent you do. It's a much different time to value, to your point, and that's where we're seeing a difference in our customers adopting this quickly and moving forward. And by the way, we now have over 32,000 people, and actually, I think this is -- I know this is already dated, I think it's almost 40,000 at this point, certified on our agent studio. This is Oracle, internal Oracle consulting. These are our partners. To show you a list of the partners. I talked about those 2 dozen agents studio. These are partners large and small, that have already been certified and already delivered, again, at least half a dozen agents each that have been validated by our development team to the agent marketplace. Every one of these agents is available to every 1 of our customers at no additional cost. So these are agents that are contributed to make the implementations easier, make industry functionality easier, make things better across the board. And I said it was a massive understatement. So not only are the 600 that we built, doesn't count what our customers built doesn't count the marketplace. We had a hackathon on Monday at this event. Mike and I were just talking about it, Mike got a chance to poke his head into it. We had a little bit over 165 different partners and customers is all sitting around a table in rooms like this. So last year for perspective, I promised 100 agents in Fusion. On Monday, not from Oracle, partners and customers in hackaton, they built 109 agents, which will go into the marketplace. So when you talk about the speed and progress across the applications and the speed and progress of consuming AI and consuming all the great things that Clay talked about, that's just a perfect example. Okay, so let's take a look about industries.

Okay. Thanks, Steve. All right. So today already, in our industry applications or whether they're edge applications or they're more sophisticated applications like core banking, we have 2,400 customers that are already leveraging Oracle AI, embedded Oracle AI inside these applications today. They are live across a large variety of industries. And again, as Steve mentioned, we have over 200 AI agents and features and agents just in our industry portfolio live today. We'll eclipse that, and I go through the numbers here in just a bit, very, very quickly in the coming months in terms of total agents to market. And our view, and we've heard a lot of feedback that there aren't applications in the future, really just a collection of agents and what do you think about your stance there? And our view is, well, you're exactly right. We're actually doing that thing exactly. Our applications stack, whether they're industry applications, Fusion applications, are quickly becoming a collection of AI agents, and our ability and our time to market is second to none. It's not just interesting that we have 2,400 customers live or 200 new AI agents. But if you go back 18 months, both of those numbers were 0, 0. We had 0 AI agents live in our industry applications, and we had 0 industry customers live on any industry AI. 18 months later, we're at 2,400 in very sophisticated, highly regulated industries like health care, so far, the longest go-live we've had with an AI agent has been 3 weeks. That's been the longest. The shortest has been in a matter of days. The amount of professional services dollars spent by the grand total of every customer, there are over 250 customers just in health care alone live on the AI agents, is 0, 0 dollars, self-directed, self-implemented, the amount of dollars spent on training collectively by all of those customers is 0, 0 training, work side of the box. And keep in mind, these things are working in highly sophisticated clinical applications. These are in -- running inpatient rooms with no training.

Yes. I think just 1 point, your point about the SaaS going to change or what it is, especially in industry applications, but even take the finance example, Maria Smith's team at Oracle, they're their role isn't to use our user interface and type invoices into the system, their role is to report our earnings to pay our suppliers, to invoice our customers to collect. Our IP was not our UI to do that. We take great pride in our IP. Our IP is how efficiently we organize that and give data to our customers. What we've done with these agents, the 600-plus is we've allowed customers to do that more efficiently. That's what we've always done. And now we have that much, much more accelerated way.

Yes. And of course, Steve, it's all much easier when you're a custodian above the data, you need to build these agents, right? And the fact that all of this runs on the Oracle AI data platform makes this engineering cycle, this innovation cycle just so much faster and the ability to roll this out as a quarterly update, it's -- we really -- we're quite excited by our customers' ability to update this. So as you know, we operate in many different industries. And I'll go through a couple of examples here of agents that I think are really going to be very popular. The first is the embedded AI agent for retail. So this is an intelligent inventory agent. It's designed specifically to help customers bring together data from CRM for merchandising, from Fusion inventory, from Oracle Analytics Cloud from Oracle Xstore, which is the point-of-sale system that we have from our procurement and order management systems and in Fusion. The agent is not just a point of intelligence. It's not just helping retailers decide which product should I put on the shelf -- and what is the impact of my forecast demanding, my planning buys and moving goods around, selling across channels, my omnichannel strategy, all that, of course, is an output of the agent. The intelligence is built into the agent to figure that out. But it's also a very interesting point of integration. If you think about how much money organizations spend today on integrating applications across the stacks and agents do it mostly for free. I mean the ability to traverse data from multiple sources across multiple systems. And by the way, the CX, the merchandising, the inventory, the analytics, the Xstore, procurement, order management, it doesn't -- they don't necessarily have to be all Oracle systems. That's not how the agents are designed. The agents are designed to pull from multiple systems and put together points of intelligence to give, in this case, the retailer very high visibility, so they can flag potential inventory issues and translate complex data into very clear transparent recognitions. I'm sure those of you who have covered or understand retail, inventory management is one of the costliest and most difficult things and if they can avoid liquidation of inventory, it's real money back. So a lot going on in retail. I did mention a bit about health care. It's important to know that we've got dozens of AI agents live across our health ecosystem today any more planned. We're looking at chart review care navigation, clinical decision support, patient risk predictions, preventative care and many more. In fact, our next-generation AI EHR is now generally available. It is generally available and it is also approved by the regulators. This is also another interesting AI story. We've got customers running this in beta. Seema is going to talk a little bit more about this as we go forward and why we're so excited about this. And the feedback has been absolutely tremendous. Remember that these customers are coming from a -- literally a Windows 95 citrus experience to agentic AI. That's the leap of technology that we see in health care. And the feedback, it's not just a great business, and it's not just going to be a great growth engine for applications, but it's actually an emotional piece as well. I mean, the feedback that we get from providers and patients is this changes my life. It changes the way I practice medicine because you've actually given me a tool that helps me not burdens me. And we've got a lot of competition in that market. But if you break it down and you think about AI as powering EHR, the 1 question I ask our competitors as well as see how many fuel cell power plants are you building on site right now? Because you're not doing that, probably not going to have as good of a chance to be closely provisioned to a large language model and apply reasoning models and all the things you actually need to work -- to make this work at scale to automate an entire hospital. Takes usually the regulatory cycle for approval for electronic health record software today is about 2 to 3 years. So it takes about 2 to 3 years to get through a regulatory cycle, and they get the self-fulfilling prophecy because the cycle is so long for approval, people end up running very old technology for a very long period of time because the pain of switching is very high. We were able to get through the full regulatory cycle in the United States for this in 6 months. The reason we got through the regulatory so quickly is because we actually used AI to generate a lot of the documentation that we cross-checkExadata that we have at baseline, we make sure it's right, we make sure it's accurate. But it's actually saving us a tremendous amount of time and putting us in a position to be able to bring these products to market, particularly in heavily regulated industries like health care and banking and utilities, where there's a very large burden of documentation to actually bring products to market because in health care, you've got federal government regulations, but you also have state-specific regulations. California is different than Texas, for example, in terms of some of the things you need to do for Medicaid compliance. We're actually able to automate all of that process across our industry applications and bring these products to market. And in such, we think that regulators are actually happier with that as well. They're actually happier to receive more timely information, they're actually happier to reduce the burden on them in terms of having to review an incredibly complex set of documentation. I'll switch gears now to banking, which is a really, really interesting business. So Steve, I mean, I know the Fusion applications are a very popular choice among big banks, the core system, the HCM systems. And the banks have largely had an appetite to move that to the cloud over the years. Now when we talk about core banking systems, for lack of a better way to put it, the stuff that moves money around inside the bank, there's still a lot of mainframe, still a lot of There's still a lot of very big iron running these applications. And we are definitely -- if you look at core banking applications move to the cloud, let alone move to AI, okay? Just about just think about just the first step. You're in single-digit percentages in the terms of that, that has been moved to the cloud. But as we said, this week, AI changes everything. So the future of what we're building in helping banks move away from large on-premises bespoke applications is with an agentic approach. And we see this as a tremendous opportunity to unlock our core banking installed base and actually to win new customers in the shift to the cloud. Again, very, very difficult in a highly regulated, very sophisticated application space like core banking, unless you're doing all of it. It's going to be very difficult for competitors to give in, we think, with piecemeal applications and stitch stuff together to make a AI solution. So just in the next year at most, just in the next just in the core banking space, we'll have 125 agents live. And I want to go through some of those here in just in the next year. Steve, last year, made a prediction about 100, we ended up with 400. I think we have a good chance of being in a very similar situation here where we're even surprised as to how fast we can go. So these are across a wide variety of banking and insurance spaces. They're going to include things for human interactions, we'll go through some of those a bit, domain agents and actually those are really focused on improving accuracy and processing of time. Our agents and features are designed to automate the entire bank or the insurance We're talking about corporate banking, retail banking, revenue management, billing, insurance policy administration and just got so many terrific things coming out. So I'm going to start with one of them, which is a huge pain point. Again, I'm not going to go all though 125. I'm just going to pick 2 in the interest of time here, but financial crimes and compliance. So this is a huge pain point for the banks as we talk to them. So many of you may know, and your colleagues for those of you that are with the big banks, spend an incredible amount of time it's very cost it's very time consuming to investigate financial crimes. Unfortunately, financial crimes are not going away. And selling it last year, I said the firm spent $155 billion in 2024, just on the investigation process. Tier 1 banks have thousands of people, staffing investigative teams and are spending hundreds of millions of dollars in a year leveraging AI, we can improve accuracy and drastically reduce the investigation time. So if you took a look at these 3 representative banks, your names but these banks in the chart look how much they've disclosed that they're actually spending on this. We believe that our agentic approach, and we've been beta testing this with the banks, can save up to 60% of the work. Just in Phase 1 of the agent, we should be able to knock out 60% of the work. So I want to take a look at some of it here. So this is the output of a case that's already fully being investigated by our financial crimes AI agent. And with the agent -- without the agent, the investigator would have spent hours, days, weeks, opening this case, digging through the details, understanding the parties, their professions and whether they had prior activity with the bank. With the AI investigator, the initial groundwork it's already done. Before the investigator even opens the case, it's done. And we can immediately see -- hopefully, you can see on the slide that this individual is, in this case, a retired nurse on a fixed income and that she's had prior cases where suspicious wires were sent to unknown parties and they were blocked. Now the AI investigator is going to give the investigator a total overview of the case. It's showing patterns, it's showing activities and typologies, and things like funnel behavior, repeated wire deposits, withdrawals to high-risk jurisdictions and then I have the ability to see all of that over time. So the investigator runs through this, decides and whether or not they agree with AI with the findings. And if they want to dig deeper, they're certainly able to dig deeper manually, but the interface is going to show them all the relevant risk factors and it provides a very clear narrative of what was observed versus what wasn't observed. And if the investigator agrees with the AI agent, they can finalize decision. If not, they can intervene. System is going to guide them through all the various views so they can quickly see what happened and why. And the UI is built for the investigator to feel confident about decision. This is just exactly the same way our AI works in health care, not replacing the doctor, not taking away from the provider, but actually showing the provider, in this case, showing the investigator exactly how the decisions were built, exactly where they came from and the investigator is always in the loop. So the person still signs off some in this, but if you could do your job 60% faster as a result of this, I think we'd all be happy. So this is a really powerful shift. We think it's a perfect application of AI today in large banks. This is all generally available and really much easier for our customers to absorb because we're supplying as a service here, the whole stack. OCI is the database, it's the core banking applications, it's the analytics, the generative AI data platform, large language model integration, which wrote all the narratives here. In this case, again, if you're the custodian of all the data, it's so much easier to bring these type of agents to bank very, very quickly. All right. So I'll show you another one of the -- #2 of the 125 is our retail banking agent. So we're going to use AI to improve both customer and bank experience and streamline a very critical business process. So we're nearly in the process of providing a personalized service to an individual customer inside a bank would be quite complex. I mean banks like all businesses want to provide as much personalized service as they possibly can. But with AI, we think we can actually help the banks get to that more personalized consumer experience when they want to position exact services like loans or even before the customers may know they need them. So we've got a video here that we're going to play to show this agent. [Presentation]

Okay. A 123 more of those agents -- such agents are planned for the next year here. So we'll have a lot more to say about banking. Again, it's an industry where I think AI is just going to be -- agentic applications are going to be a tremendous wedge to move a very large, very dated infrastructure base, both applications and infrastructure as well to our cloud and AI solutions. So you see some of the stats across other industries. Again, I won't go through all the industries. I spoke about the clinical AI piece for hospitals. In hospitality, we released an upsell AI agent, which helps hoteliers understand where the upsell will, maybe what's the sweet spot for engaging with customers, how many weeks prior to check-in, what are the services these people like and how do we understand how to position. Just in year 1 of rolling it out, which was actually just a small bit of our installed base for hotels, hoteliers generate $350 million in upsell. That's what they reported back to us in the upsell wheel from this AI agent. I spoke about the financial crimes in AI investigator. We just rolled out a brand new agent for energy optimization in our Opower stack and very early days, but we got an immediate feedback from one of our very large energy customers that save them $2 million instantly and reduced calls to their call center. So this story goes on and on and on across the board. What's really interesting about the banks, too, if I go back to that before I ask Mark for his comments, is the whole stack advantage, I think, is really interesting in banks. We're having conversations not just about how do you take all these very old customers bespoke applications and how you take everything that's bolted on to them and how do we use AIs and unlock mechanisms to move them. But what are you going to do with all your infrastructure? And because of our dedicated region in OCI, we're actually having an entire stack conversation with these customers. And we think it's different than our competitors. And you've heard us say many times before that we have the widest portfolio of applications of technology services of cloud services, GPU, CPU on the market. And that's been something that we've all been -- we've all been quite proud of that we've been able to be so good in those industries. One of the things that sometimes is a little suboptimal in that market is that you have a lot of sales people. You have a lot of people who are calling on customers in many different pieces. And then the on-premise stays and even in early cloud days, network because a lot of the buying was by pillar. But now we're seeing a shift in customer buying patterns. And we're seeing customers, as we position the whole stack, really wanting to talk about outcomes and less about all the parts that make up the outcome. In other words, we're selling and positioning to the customer and outcome. We're selling a 70% reduction in financial crimes. We're selling a 49% reduction in paperwork for healthcare. And in order to do that, we made a lot of changes about -- with our go-to-market strategy. And I think that those changes are just as important as everything we've done in the product stack so that we can accurately communicate that to customers and engage with customers at the senior level. So Mark, I'm going to ask you to kind of walk us through what we've done to address this whole stack advantage.

Perfect. Thanks, Mike. I think AI changes everything. It changes how we develop products. It changes how we deliver products. It changes how our customers consume our AI agents embedded in our applications and our AI database and the capability that exists. It also changes how we go to market. It's given us a unique opportunity to really transform how we engage our customers to bring the power of our entire portfolio to the industries and the customers that we serve all around the world. There's 3 key things that I want to make sure that we all can walk away with is that we are making Oracle easier to understand, easier to do business with. We have simplified and unified our teams of how we go to market and how we bring the capabilities of the industry suites of applications that have embedded agents and the ability to build and extend on our agent platforms, the ability to deploy our database anywhere and allow our customers to take advantage of the AI database that has AI native capabilities in our data platform or take advantage of that infrastructure. And we've done that in a way that allows our customers to work with us in a more seamless fashion. In addition to that, as Mike was describing, the capabilities that come across the entire platform, when we work with our customers around bringing our infrastructure, our data and our applications together, that one Oracle advantage, when we do that at scale, the opportunity is incredible. For our customers to be able to consume and bring outcomes that deliver real value, where they're not focused on integrating solutions, they're not focused on deploying differentiated capabilities, they're not worrying about where their data is moving and how is it secured or not. When we bring that entire value, we do truly bring an advantage to our customers to focus on the things that matter most to them, which is the services that they are providing to their customers or their employees. We also -- it also gives us a unique advantage to help our customers accelerate on their AI transformations with the capabilities that we have throughout the portfolio. So let me start with the simpler unified approach to the go-to-market. At the top level, it's bringing the Oracle brand to our customers. It's Oracle to our customer, it's Oracle to governments, and in some cases, it's Oracle to countries around how we bring the full capability. And that may be in certain instances, bringing our cloud to the customer with the full stack capabilities that exist. If we really think about our commercial teams, and AI didn't allow us to say we're going to reduce the commercial teams, it allowed us to deploy our resources so that we actually have more sellers in front of our customers that are bringing meaningful solutions for them. In our applications teams, we bring together our industry suites and our fusion suites together for our customers. If we're going into a retailer, we bring the entire suite of merchandising, right, capabilities around inventory management, the back office around financial consolidation, close, human capital. But also, we have the ability to bring other solutions, a retailer that may be online but also stores that are deployed, not only in the United States, but all over the world. Our engineering and construction platform because they need to build out those capabilities and need project management resources. We bring some capabilities around financing solutions, loan and lease applications that these customers need. Also, they may have a call center. Well, guess what, our communications business has call center capabilities to allow that customer to take advantage of the AI inherent capabilities. So when we bring 1 resource to our customer, around our full application suite. They can take advantage of the integrated capabilities that exist across that entire platform, which is a unique differentiation that there is no other competitor in the industry that has that capability. In addition to that, when we think about our AI data platform, and you'll hear from Larry later around the innovation around native AI capabilities in the Oracle AI database, and we announced and launched the AI data platform to allow our customers to take all that enterprise data. Our applications are data generators. They bring immense value around incremental data that is generated around a customer buying something around an employee and how they work. Well, that data needs to go somewhere. And in that data platform, the ability to bring the latest large language models to inference off of that data, where that customer wants that data to be securely in that environment allows us to help customers transform. Well, in the past, when customers bought on-premise, we had a person that was selling to them on-premise, we had a person that was providing them and selling them hardware, we had a person that was trying to sell cloud databases and capabilities. Well, no longer it's one person that brings the entirety of our data platform to our customer. but giving them choice where they want to run, database anywhere, on-premises, in OCI, in our multi-cloud partner capabilities, but also cloud at their facility as well. And in our infrastructure business, it's not just good enough to bring those Oracle workloads to OCI. We've developed a differentiated cloud. We've gone from a disruptor in the industry, right, from an underdog to a disruptor and bringing compute storage network capabilities at a differentiated level to our customers allows us to be the destination cloud for many customers that have critical capabilities around high networking, high compute-intensive workloads. But also we become the destination Cloud for training and inferencing, where we now have the ability to bring those models to the customers wherever they choose to securely and inference off of their private data, but again, the opportunity to bring the entire stack to our customers is differentiated that no one else has the capabilities of this entire stack. We've never truly taken advantage of bringing it all together, and we are now. And this is an opportunity for us to continue to execute that scale across the globe of how we engage with every customer across every industry that we serve. But when we bring the 1 Oracle to our customers, we have incredible scale and advantage. Not only do our customers benefit from the integrated solutions and the capabilities, but Oracle also benefits. Let me give you some examples. If our customer base, if you can see here, 55% of the customers own a single product pillar. Let me explain that. And let's just call that 1x of spend. That may mean they own our database capability or they may have a Fusion application, they may have ERP or they may have HCM. They may be an infrastructure user and not amongst the other products. But that doesn't mean they're using all the products in that pillar. They spend 1x with Oracle. Well, as customers move down the path and have 2 pillars of those 3, where they may actually be using our database on OCI or they may be using database with a cloud at customer. There may be a Fusion user that's using OCI for integration capabilities to other applications. When they use 2 of those pillars, they spend 8x more than the 55% of that customer base. As you move down the path and go to 3, where they're utilizing HCM and our data platform with a cloud customer as an example, they spend 25x more the average base. And that doesn't mean they're using everything out of the suite. It doesn't mean they're using the retail solution with merchandising, and warehouse management and fusion across HCM and ERP and CX and supply chain. It just may mean they're using one of those products. But truly, as you go up the scale where customers are truly taking advantage of the entire suite, where utility is taking advantage of our operational solutions and capabilities that cut across their customer care and meter data management, they're operating the grid, they're taking advantage of our energy efficiency solutions, managing their financial capabilities, their customer experience, their human capital running it on OCI and taking advantage of our data platform across their business. 2% of our customers in that category, 150x on average. That's on average. That means customers are spending 300, 400, 500 times as well when they truly take advantage of the entire suite. That is why we have changed our go-to-market to simplify how we're engaging with our customers and executing. And it's not only how we're engaging, but we're also transforming how we're allowing them to consume our products. We started this with OCI, and we have a universal credit because customers don't know exactly what they're going to use out of the products, they may flex in their compute, they may need more network, they may have some different types of storage. Well, we just announced our multi-cloud credit capability for our customers to deploy their database anywhere, it's one price, no matter where they choose to deploy. And we're going to continue to modernize and do the same with our applications for our customers and make it easier to consume and move up the stack for them to get more value out of our solutions. And there's just some examples...

Sorry, I think, Mark, just to clarify, too, the customers are actually spending less. They're spending more with Oracle. Because what Mike talked about, the single stack advantage and all the engineering and putting all the pieces together, instead of customers having to stitch it together and spending the money between that, they get 2 components, 3 components and more that market share.

And it's not -- and in any one of these categories on a stand-alone product basis, Oracle is still right, rated as a top product and capability in that area. So bringing it together does not sacrifice the customers' capabilities, but it truly takes advantage of all of the integration that exists across the platform, where customers can blend together different solutions and take advantage of capabilities across industries and whether that's in financial services, along with our Fusion platform and our data platform. It allows them to spend their time, energy and effort focusing on the things that matter most of them around their transformation, around their AI deployments and not worrying about integrations, not worrying about patching, not worrying about continuing to make sure things are going to work together. What cloud is it in? Where is it in? How does that need to work? We're allowing them to take advantage of the capabilities and the work that we are doing. And here's just a smattering of examples that you see here, which it doesn't cover every industry that we're in, but you could see, it covers a variety of industries, whether it's in the energy industry and utilities, transportation, logistics, health care, financial services hospitality, communications, high tech. It cuts across every 1 of these industries. And whether customers choose to start with the entire platform like Exelon, and Mike, you had the CEO of Exelon with you on stage on Monday; or it's AtlantiCare that's focused on deploying our latest AI-enabled health applications to have a data strategy running on OCI, where they're focused on providing outcomes to patients and not trying to worry about securing all of their applications and managing that infrastructure. Or whether it's maybe federal, that's deploying the SaaS core banking solutions as well as our Fusion suite, but their CEO talked about the need to have a foundation of data across their entire business running at an OCI or Avis. Avis has deployed Fusion across their platform. They recently brought their entire data stack from another proprietary environment into the Oracle AI database, running on OCI, 1 trillion rows of data. Now they're deploying AI agents embedded infusion and extending those with the agent platform. And in a very short period of time, realize the importance of how they can quickly tap in to that trillion rows of data that they have in the Oracle AI database to deliver outcomes for their operations their employees and their customers to transform their experience, all running on OCI. And that is the Oracle Advantage. When our customers take advantage of every layer of the stack and whether that's an OCI, where we've gone from an underdog to disruptor, where we train and inference on the large language models in the world, having the most secure, scalable and deployable performance platform in the industry to our AI data platform where we have the AI database with AI capabilities native to allow our customers to transform all of their data to be enterprise ready for AI capabilities and our most complete suite of industry applications. It is a simpler, easier approach to the marketplace. It is truly differentiated that no one else has to allow us to accelerate our growth, more importantly, is to help our customers transform on their AI journey, it truly is an unprecedented time, and AI changes everything of how we go to market as well.

Mark, a quick question. Early days in rolling this out, what's the feedback been from our customers? Steve, I know you mentioned you heard some of it as well in terms of our new approach to customer engagement.

The feedback has been amazing. The conversation quickly transforms to I didn't know that you had all of those capabilities because it wasn't necessarily brought to me in the past because there was so many different people now I can see where you've developed, how you develop more importantly, how you can help me drive outcomes. It's amazing to see our customers, even many of the customers that were here speaking this week learn so much more about what we have to offer and by doing it in a much simpler approach. And it's -- we're seeing our pipeline grow. We're seeing customers choose more of the full suite of our capabilities, and they're selecting to run it on our infrastructure as well.

Perfect. Mark, Steve, thank you very much.

And the feedback of simplicity, too. So yes, that's great.

Great. Thanks very much, guys.

Thank you.

Thank you.

Okay. So we're going to take a deeper dive into health and then also at the end, talk about what health has to do with our banking business, which I think is quite interesting. So to talk more about health care, I'm pleased to welcome Executive Vice President and General Manager of Oracle Health globally, Seema Verma. Seema, welcome. Thank you.

So Seema, health care continues to be front page news everywhere in the world, usually not for great reasons. You have an interesting perspective as the former administrator of the largest payer in the world, which is the United States government for health care. And for several years now, you've been engaging with both our commercial customers and our government customers around the world. What's going on? What's top of mind for everybody in terms of the challenges and problems with costs and all the other things we're reading about?

Yes. So I've been in health care for a long time, but I think that this particular period, I think, is becoming more challenging, and let me just start with some high-level issues that I don't think we contemplated not in this country or anywhere in the world. First that starts with just the aging population and people are living longer, which is a good thing. But we really haven't prepared the system. We don't have the workforce. So that's becoming very challenging. And I think most of us would say, hey, it's hard to get a doctor's appointment, so just getting into the system. And then the other part of it is we're seeing more disease. We look at in terms of mental health, we're seeing cancer happening in younger ages. So the system in and of itself is very stressed. At the same time, we're seeing costs just continue to rise. And for governments, this governments around the world, this is just becoming unsustainable. So if we look at health care costs for states, an estate budget. Medicaid is actually their largest budget item. For the federal government, it's 1/3 of the federal budget, and these are entitlement programs. costs are continuing to go up. And I think there's a fatigue from government around how do we solve these problems. And if you look at the history, just in the United States, the history of health care legislation and how government has tried to solve these problems as they've thrown money at the problem, right? So they've expanded programs. They've added more services. We think about the Part D program, the prescription drug program, adding more people to our existing entitlement programs. I think we're getting to a point kind of a tipping point of fatigue with how the system operates. And I think for the very first time, which you saw with the legislation that passed with a Big Beautiful Bill, we said, this is kind of a pushback, right? So for -- in a long time, we've been adding -- and this is the first time the sort of a push back where I think the government is saying there's fatigue. If we look at -- in the U.K., the same thing with the NHS, right, so we can't continue to sustain these programs. And so we think about what's going on and what is the problem here? Like what is the underlying problem? And there's lots of reasons of what's driving cost. But one of the things that I think we could all agree on is that there's a lot of inefficiency in the system. And that inefficiency kind of gets tied back to we have a lot of people. This is a very people-oriented business, doing a lot of work. And a lot of that work is very manual, it's very redundant. So there's -- and if we go back to sort of say, why is it manual, why is it redundant? I would venture to say a lot of this is a data problem. And health care is inefficient because the data that we have is not -- sometimes it's too much, sometimes it's too little, and we don't have data at the right place and the right time. So I'll give you a couple of examples, like prior authorizations, right? So in this country, you want to get a service, you need to have this -- the insurance company agree that this is an appropriate service and the back and forth that goes on, it's just a lot of time, a lot of energy. And so we're spending about 25% to 30% of our health care dollars just on administrative costs. So it's just be getting to a point where it's unsustainable. This is passed, I think we're hearing that premiums are at an all-time high in the United States, one of the largest increases. So for all of the money that government has put into it, we're still seeing poor health outcomes. We're seeing more disease and the system is just becoming more and more unsustainable.

Well, lots of problems.

Yes, yes.

Long list of problems. But as I say, you can't fix the problem unless you understand the problem. So maybe you can give us a little bit of inspiration and dive into how you think AI both in health and life sciences, I mean, I think because both have some similar challenges and why you're so excited in the work that you've been leading for our team around AI? Why you think it helps solve these problems?

Yes. So that is kind of a grim view of what's going on in health care. But I'll tell you what I couldn't be more excited from where we are in health care, and that is the opportunity to use AI to help solve some of these challenges. I think we're at a point now where government can't do it, and they really do need the private sector. So we think about great moments in history, right? So we think about what shape's history? A lot of it is government, a lot of it is war, but it's also technology, and technology has an opportunity to really reshape the future here for health. And I can't think of a better industry that can really benefit from AI because AI can help. We talk about a lot of the people problem, the data problem and AI can really come in and help with a lot of those tasks, right, with the agentic AI, but even just the understanding of the data and having that real-time data and not just a ton of data, but understanding what it means, right? So one of the issues, if we think about it in terms of health care costs are drug costs, right? So expensive because it takes -- it's very difficult to conduct a clinical trial, and we have, what, 1% to 2% of the population even participates in clinical trials, getting people, finding people, matching them to a trial is an enormous task, it costs the system a lot of money. And that's why drugs cost so much money. There's a ton of money in development. But what if we could use AI to match patients, right? So the doctor knows -- you look at the patient, the doctor knows right away this patient qualifies for a clinical trial, and we can easily enroll the person in a clinical trial. Same thing with prior authorization, the doctor decides they want to prescribe a certain test or a medication, that whole process can be completely automated. So I think there's this incredible opportunity to automate a lot of the manual work. and also to make sense of the data that we have and bring intelligence to the bedside.

Yes. No, I certainly share your enthusiasm. I mean I've had the great honor of being with you in many of these discussions with governments throughout the world as well as large health care -- well, large health care organizations and frankly, small health care organizations, too, in the United States. And I think you're right. I think there's -- at first, there was some skepticism around AI, as there always is in clinical settings. But now just with the proof points that we've delivered and the things that we've delivered even in early days, the line of people lined up to consume the AI is longer than I think that we could align this form more quickly than we could have ever imagined. And maybe you could talk specifically about some of the products that we're bringing to market and again, why we think they're so to have such a dramatic impact on this huge inefficiency and cost problem in health care.

Right. I think you're exactly right though, right? People -- I think in the health care system, they're getting to a point where they understand that the problems that they have are not going to be solved by government anymore, right? There's not going to be this big bolus of cash that solves the problem and that they need to become more efficient. And there's a lot of enthusiasm around AI and what AI can do. I think there's also recognition that the technology that they've had in the health care system isn't going to cut it, right? You can't take a 1990s database and think that you can bolt-on AI and that it's going to work well. So the industry has been so excited about all of the applications that we're bringing to the market. In our recent health care conference, we had the CEO of the Mayo Clinic, the CEO of the Cleveland Clinic and just a lot of enthusiasm about what we're building.

By the way, they're our competitors, to biggest. Some of our competitors the biggest...

That's exactly right. I think that our competitors' customers are starting to understand the power, the value of AI and that they need a platform that can allow them to use AI to build their own agents, to bring in other agents. And the current products that are out there in the market are not quite cutting it, and they're not able to do it. I think the other thing that I really appreciate, and we can talk about the products in a second. But I think that our approach in Oracle's approach to AI and how we are using AI in the last -- just in the last few months here has become very apparent that what we're bringing to the market is way superior. So you can probably tell them more about how we're building the AI and how that's better.

Yes. Well, we're going to show a demo here maybe in just a minute.But as I said earlier, you've got to have the data in one spot to actually have to have any kind of an AI strategy, let alone in the clinical setting. And what you said here was really important. I think it's important to underscore that there actually isn't more money to put into the health care system. This is true globally. I mean governments and payers are running out of money to put into the health care system. At the same time, we need to deliver higher-quality services to our customers and the providers need to deliver higher quality, better outcome services, better outcome-oriented services to their patients. But they got to take a bunch of money out of the system at the same time. It's an incredible challenge. There really is no practical solution to that except for AI. I mean we're hundreds of thousands of people short in terms of clinical providers just in the United States. We're not going to manufacture with the birth rate where it is. I don't think we're going to manufacture enough people very, very quickly here to solve that problem. So we've been thinking long and hard about how do we solve this problem? How do we deliver better outcomes and how do we actually take cost out? How do we help our health care customers spend less at the same time? And that's an important part of what we do. So we started -- I'm going to show a couple of examples, both the patient example as well as the provider example. So this is a patient example. So here I am as a patient, and I just got my cholesterol report. And what's the first thing you do when you get a lab in isolation, you get your doctor, you get a test, it comes back. First thing you do is paste it into some search engine or pace it into GPT or some other large language model and say, what does this actually mean? And that's the first mistake, because the first mistake is you're only pacing that particular lab and that particular metric in isolation and none of your longitudinal record. So the lab result, while it may not be normal, might still be okay clinically when you consider everything else you have going on and you deliver -- you consider your factors over time. So what we're doing is we're actually putting patients at the -- and this is another problem. This has gotten even worse because -- and a lot of states have these prompt notification laws where you actually get your test results sometimes before your doctor gets, which can be a little scary, right? Because if you're not a doctor, you know how to read this, it's a little scary. And then we got the situation where people are going outpacing this stuff. And what happens is that the doctor's call centers are completely overwhelmed as a result, they're completely overwhelmed. So when we're actually doing this, and this is based on our integration, we announced this at our health conference recent in Orlando. This is based upon coming back to what Clay said, it all starts with OCI. We didn't have OCI and we didn't have the large language models actually running in very close proximity to our health applications, we wouldn't be able to do this. But when you ask a question and you receive a diagnosis, we're actually considering your entire longitudinal health record. We're considering all everything about you, and we're giving the providers guidelines as to what they're comfortable and not comfortable communicating to customers, and it's all in plain English. None of it is in medical jargon. So you can see that with these embedded engagement tools, the patient portal really becomes a basic hub for health care services. It's a trusted partner in care because it's automated using large language models. It's automated using arbitral augmented generation by taking your longitudinal health record and making the searches contextual, but it also has all the guardrails of the clinician in it so that we're not going to actually get into a situation where we're going to talk to it, it's all completely AI-powered. And you can use -- you can see here, I started the type, I started to talk, and I'm asking very simple questions. How does this compare to my last test? Questions in English, not questions about LDL versus HDL versus VLDL and all these other things. And what does this metric mean and what does that metric mean? Just speak in English. This is very helpful for patients, but it also saves the providers a ton of time and a ton of money in answering questions, direct questions from consumers in isolation. As I move forward, the integrated clinical assistant is also going to guide the patient on the content that matters most. So you get this, first, you want to figure out what's wrong, if anything at all. The next question you had, what should I tell my doctor? What am I supposed to ask? You ask a very simple question. What should I ask my doctor? And the generative AI is going to help the customer understand and help the patient in this understand and actually make recommendations to say, this is how you should communicate with your doctor. These are the questions you said ask. Particularly helpful sometimes for patients who -- you have a situation where you go into a provider and the provider was 10 or 15 minutes and you get out, you say, I forgot to ask that. I meant to ask this, I meant to ask that. So all of this is done before the visit. And then actually drafts a note and said, would you like me to draft a note to the doctor, so you don't forget about this when you go into the provider. When you go into the patient -- when you go into your next appointment, the provider has all of your questions ahead of time, has all of your contextual information and patients are in a state of far less worry because they've got this whole process automated. Again, we rolled that out. And without all the infrastructure and without the GPU infrastructure and the large language models running on top of it, we wouldn't be able to deliver that whole thing as a service.

Yes. And I think it's an important piece, right? So we're looking at this portal, and let me kind of go up one level higher, right, which is to say the engagement by us bringing these tools together by bringing AI directly to the patient, right? What we're going to see with our portal is engagement in a level that we don't normally see with most portals. Today, most of us have 4 or 5 portals on our phone because our health care data is in many different places. So the point that you made about bringing together the longitudinal record, that is not happening today. We have our data in all these different places. So by Oracle bringing it all together, and then also bringing AI. What we expect to see in our patient portal is a high level of engagement. And then we don't want to just keep that to Oracle and our providers' customers, but we want to open that up as well. So we're going to allow the health care ecosystem to be able to connect directly with our portal as well. So we sort of see in the future that this portal becomes this is your go-to place for all of your health care information. You should be able to connect it with wearables, different devices, maybe new, maybe weight watchers, whatever that is, that you want to connect your health care data with. So it kind of becomes a one-stop shop for the customer. And we think we've been talking to a lot of the other health care companies out there, and they want to partner with us because they want to be able to have a place where they know patients are going to be engaging. A lot of -- one of the biggest challenges for health care providers is that patient engagement. So I think that's the power of the portal that we're bringing.

Yes. It's been stunning to see the engagement before and after engagement with us with of AI technology. We're rolling out the same thing for providers. So if we flip the script now, and you're into your doctor's office, and 1 of the first questions that doctors say, "Well, what can I help you with today? You sort of feel like saying, "Well, aren't you really supposed to know? I mean, you kind of know that before we show up here?" And oftentimes, the doctor has the same problem. There's an overwhelming amount of information. They can't read everybody's chart. They can't read everything and they actually want to have that same consumer-like experience with the application. They want to ask a very simple question. What should I know about William before I walk into the room. And that's the simple question that they ask. And now they get a summary here of the patient's last visit, things that may be happening with social [indiscernible] not just medical records, but everything we know about that patient, briefly summarize, so the doctor walks in to a far more engaging interaction with this customer. And this is a preview of -- well, this is actually generally available. Now this is our new electronic health record. And you can see, just like we saw in the financial crimes investigation agent, where you have these time lines over time that at any given point, I can click on this and see what's happened with this patient over time in a very easy-to-use screen. This is all runs on tablets. It runs on mobile phones. So the form factor automatically resize itself. And again, it's all built with AI built in. There is not a non-AI version of this. The AI is the underlying mechanism that powers the entire EHR. We just went through, as I said, went through regulatory approval. And you're going to see the same benefit that the consumer gets, the doctor gets the same as well. doctor wants to know about A1c trending over time. They have the same problem where you got to stack a PDFs and a stack of papers that are very difficult to get longitudinal information about, and therefore, very difficult to have an intelligent contextually aware conversation with patients. All of this is now available. And if you'd like, as a provider, all the screens are really not necessary because all of it can run in the background with a listener, and that's how most of our providers have actually chosen to update the new technology. You simply place a listener in the room, the listener can be a mobile device. And the whole thing is automated. The whole thing is automated. The whole conversation between patient and provider is automated, the orders, the labs, the summaries, the patient discharge notes. And just like our financial crimes investigation AI agent at the end, the doctor still has to sign off on it. It's just a -- it's orders of magnitude different experience for patients and providers with health care systems, and we're just hugely excited about it.

Yes. I think it's a game changer for the industry, right? So let me just dig in a little bit deeper. You talked about the patient summary, right? And that's a short summary. But think about what it's like for the doctor, let's say, they're dealing with a 70-year old patient, right? There's multiple comorbidities. We think about the veterans administration that we're working with and they're going to extend the Oracle EHR across their entire enterprise. And this is really important because you can think about the complexity, and they're having to go through sometimes thousands of pages of health care information. And a lot of times things get missed or we're not addressing something. We don't -- the data is there, but the physician may not see it, it's a very short period of time. So the ability to have that summary is a game changer. And like you said, the SDOH, all of those different things. And the automation, I think the other piece that's important is the autonomous coding, right? So the other thing on the screen there is that it'll recommend which codes. And this is an important piece because if we think about it for the health care industry, we're spending 25% to 30% on administrative costs, a lot of that is around billing. And the cost to collect for providers is about 5%. So all of that money, just to be able to get the bill paid, and we're automating that entire process. So if we think about it, the whole billing process, at least in the United States, was built off a paper system, right? And then when we went to digital recorders, we just basically took that process and digitized this process. But with AI, we don't need to do all those things. We don't need all of the different middlemen to be able to get this claim paid, right? We can actually do all of that with AI. And so our EHR sort of starts as, it's almost a misnomer to call it an electronic health record because it's really a system of intelligence. And it goes way beyond just the patient-doctor interaction. We started there. But we're trying to address all the problems across the entire ecosystem. So that autonomous coding piece is really solving a lot of the friction between payers and providers. Not only are we going to try to make claims payment easier, but even just the prior authorization, all of those things that happen between the payers and providers, we're introducing those solutions. And it's ironic that it's actually the payers that are coming to us and they're saying, look, after the big change event, there's a lot of concern about security and so being able to have the Oracle Cloud in the middle of this, helping with those transactions is really important to the industry, it's safer, it's more secure and also we can use AI to automate a lot of those processes. So that's 1 piece on the payer side. And then there's a whole other piece with clinical trials, right? So if we think about it, most clinical trials and a lot of research is only happening a big, large academic centers. So if you're lucky to go to one of those, you may get connected with a clinical trial. If not, you never know what's going to happen. And so what we're able to do with AI is we're able to do that matching inside the clinical trials. But where we're going is to be able to use that EHR not to actually do the clinical trial right inside the electronic medical record. Today, with clinical trials, what's happening is that they have to go to an entirely different system. So they actually physically move the data from the electronic health record into the electronic data capture for the clinical trial. So you can imagine a lot of the back and forth work and the retyping and you got to have somebody to check in all the information. So our electronic health record is not only addressing, it's actually providing payer solutions and also the ability to help clinical trials and research on that end of the spectrum. So it's doing more than just helping the doctor and the patient, it's a much broader view.

Yes. Well, that's terrific. Thank you for giving us hope and for sharing the solutions because I really do agree that bringing this whole consumer-like experience to health care, both for patients and providers is going to be game changing. And obviously, your passion and knowledge in the space has been a big driver in helping us get to this point. Something else interesting happened while we were bringing all this data together for health care providers. And that is we actually started to realize that there was more utility in the data in the collection of data on the AI data platform that we've used the power of these health care systems that just for the providers themselves. But actually, as we become intimately close to these providers, we realized, particularly in the United States, that a lot of providers, a lot of health care providers have cash flow problems. And for those that are publicly traded, when they report out, they report out their cash flow in days -- report out their available cash in days, not weeks, months, years, but days. That's how much cash they have on hand. And they've got to make decisions around payroll, ICU -- equipment for the ICU. These are real decisions that very big health care systems have to make. We looked at the data that we had. And we thought this might be -- and we asked questions about how do you finance these systems, how do you -- banks don't really know a whole lot about what we do, they don't know about a receivable flow. And it's not that they had a receivables problem, but they actually just have a cash flow problem. It's not that they're not going to get the money, but when they're going to get the money is an acute problem, no pun intended for health care systems. So we see the same kind of transactional flow happening across the board in our whole applications. You can see some of the stats there. And -- but we particularly leaned into a couple of industries, and we're going to talk about a few of them here, where the traditional financing models are very complex. And the financing models really are complex because the banks, the financer this situation does not have access to real-time data and the data that they do have access to has very little qualitative information about it. Very little qualitative information associated with. So we, through the course of conversations with the health care providers, construction companies, we're going to talk about a few different examples here and the banks, we actually found out that the AI data platform is a wonderful vehicle for banks to learn a lot more about their customers and for customers that have a much better relationship with their banks. So we're going to talk about embedded applications that benefit -- why it benefits the banks and also why it benefits the business or, in this case, the health care provider. So with that, I'm very pleased and honored to have Lia, who is the Managing Director of Global Head of Payments and Embedded Finance at JPMorgan; and Geoff, the Managing Director and Head of Global Lending for Trade and Supply Chain Finance at Bank of America to come talk with us more about this exciting platform. So thank you, Lia. Thank you, Geoff, for being here. I gave a very brief example, a very brief introduction to embedded finance and a little bit about health care, but I know it's -- there's more than the health care. Geoff, maybe just go down a little, I'll start with you. Could you give us a little bit of a preview of why this is so exciting and what we're actually doing here together to better serve our collective customers?

Yes. So Mike, as you alluded to before, so if you think about health care providers, they are our clients as well. And for years, they've had working capital issues and they come to their bank and say, look, we've got a working capital issue. We look at the model and we say, okay, it's not a payer problem. There's no shortage of customers. And what you realize quickly is what you have is just a working capital problem or a cash flow problem. And if you think about the way that banks help our clients solve these things, each industry becomes kind of a little bit different because it's got its own characteristics. And we think about health care. Health care, the receivables can be hard. So the banks can do certain things, certainly of credit lines. You can, in some cases, hospitals can access the municipal bond market. But some ways that we traditionally help to free up cash flow and accelerate working capital are difficult for the banks because we don't have what we need in terms of like, okay, who's going to pay this and when and how much. And for years, we chased that data, right? And Seema talked a little bit earlier about the data that's being followed to get a better health outcome, well, we at the banks, we're sort of chasing that same data. Somebody could think, well, what is the bank care about a certain procedure and how much of that gets paid and when. And the reason that's important to us is because if we've got this receivable and we're trying to accelerate cash, we need to know what's going to be paid and when. And it became clear to us as we went through this process. And Mike, you alluded to this earlier that some of the data that you had aggregated, consolidated, and then we're very good at being connected to was exactly the same data that we would need to provide additional working capital options. And as we thought about that, we said, okay, well, if we could do that, then we could layer in some of the AI predictive models on top of that to be able to say, "Okay, well, now we think we know with a bunch of procedures that happened what the expected payment will be and when, right? And any of you that have experience with health care know that the original invoiced amount has very little relationship with what's actually paid at the end of the day. So we needed a way to get from point A to point B. And it became clear to us also as we saw that the availability of data that was coming in that Oracle had access to, not only that but also you were very good at layering on that AI at the end of the process to say, we're going to give you a predictive model that tells you what's expected to be paid. And that solves our big problem, which is we don't know how to get from point A to point B in terms of what's invoiced and what's paid. And by doing this, we're able to help accelerate cash.

Thank you. Your perspective, Lia.

Yes. First, great to be here. So at JPMorgan, we are partnering with Oracle to embed payment services and financial services directly into the Oracle ecosystem. So that in Oracle solution like industry applications, Oracle can provide those payment services and relevant financial services to their clients directly in a very seamless, integrated and scalable way. And I think for the banks, in my view, is a very meaningful evolution of how financial services are accessed and distributed, right, really through a platform approach. And by doing so, I think there's tremendous benefits to the -- our clients and to Oracle's clients as well, right? I think of it as a very streamlined automated workflow so that the end clients have ultimate data and visibility and transparency of where the payments are. And then to Jeff's point, also the data plays a huge role here in delivering those benefits, especially in our strategic partnership, right? As I think about at JPMorgan, we -- in the Payments division, right, we processed about more than $10 trillion of payments every single day. So we have a tremendous amount of data around who pays whom and when and how and where and in which currency. And that, coupled with the enormous amount of data in the Oracle industry applications with that partnership, we know a lot about every single client. And I think that is a very powerful way to serve our clients to have that intelligence to serve our clients.

Yes. That's very helpful. Thank you. Geoff, when we started -- first started talking about this partnership, you gave me some stats about how much money was sitting on the sidelines in each one of these industries waiting to invest. But the problem was that the people willing to invest didn't know enough about the industry and couldn't get enough of the qualitative metrics to feel comfortable about this investment. So this just changes that, we think. But ultimately, how does it benefit you as a bank? What's the ultimate benefit to the bank? How does it change your unit economics? How does it change your investing philosophy?

So we can kind of look at it along 2 vectors. So the first is we've got clients who are looking for better working capital management, so we can help them with that, right? I think that's the first thing, right? As we serve our clients, and we think that's a good thing because as you both have noted, you've got hospitals in the United States alone that are struggling financially, right? And it's not because of lack of customers, it's not because of lack of high-quality payers. It's for free cash flow and working capital reasons often. So we can help. The second thing is part of our business is connecting our clients to investment options, right? As we think about this, we think about the opportunity to take some of what is out there, predictable receivables in a way that hadn't been there before with high-quality payers, we can translate that into investment opportunities for the capital markets. Now I can tell you the capital markets have, to your point, Mike, been waiting on the sidelines for great investment options that are always looking for things that are all over the risk profile. And for us, to have ways to be able to wrap things like receivables and say to them, look, not only do we have this investment option for you, but it's coming from a bank in a regulated environment with a risk management profile under a regulated environment. And we've got it, and we're putting it out into the market, that means something to an investor, right? It means, okay, so there's been work that has been done behind the scenes to get us to the point where we think we have an investable asset. And that work, again, I think the reality was 5 or 6 years ago, there just wasn't enough out there. We were chasing data in a 1,000 different ways. And people underestimate, I think, what I always call mechanical side of that. We can do credit analysis pretty easily. But what we can't always do is say, like, how will the mechanical parts of this? Where is the receivable being created? Where is it housed? How will it get from wherever it sits to the bank systems so that we can then do something with it and wrap it and send it to the market, all of that stuff is impactful to an investor, right? A smart investor looks at all of that and says, look, I don't want to take my chances that this receivable becomes something else in the course of its lifetime. And I think as we track just historical data, performance data, all of the data that becomes available, all of that starts to become solved, and then we've got investable assets. So we've got on one side of it, we're helping our clients be better payers and on the other side of it, we're creating investable assets for our clients.

So are these -- the investment products that you can create as a result, are these products that you previously couldn't create or you had to create with a different rating or different risk profile associated to them?

It's a really good question. So we're in the business of pricing risk, right? So part of that is, as I said, the credit risk, which is easy, but the other part of it is just as important, and that's -- people underestimate that side of it. And I think the reality is if the receivables were really hard the banks just kind of sat on the sidelines. So as you think about just some industries like health care, like construction, where there was uncertainty of payment from a bank's perspective because remember, by the time the data got to us, we only had access to small portions of it, right? So all of it just looked like it was very unreliable. So in the context of a regulated risk managed bank environment, there just wasn't a way we could get comfortable with the predictability of outcomes. So for us, we put -- park that part of our toolkit and then we moved on to what we do, right, create a facility or a municipal bond market or whatever it might have been. But that's the difference between what we had before and...

Yes. I think as we've talked, Geoff, it's been quite interesting to understand the bank's philosophy and all this is it's not that -- and we're going to talk about construction and retail and more than health care in a second. It's not that the bank necessarily is interested that Medicare is going to reimburse the provider for a hip replacement, but what you're more interested in is to make sure that, that person doesn't get readmitted to the ER in the next 30 days with an infection as a result of that because that actually changes the reimbursable for Medicare in a value-based arrangement. That's the level of data that you need real-time. And obviously, for 1 patient easy, millions, hundreds of millions of patients, we'd have to scale that over time, very difficult to do without an AI data platform, kind of constantly monitoring and scanning for anything that could change the risk profile as well.

And this is to your point, people might think to themselves, well, would a bank be interested in that? We're very interested in that because it's predictability of outcome. It's the predictability of us getting repaid and an investor getting repaid. So it's very important to us.

Might just going to add 1 more thing, right, before we lead health care, right, is that the government in and of itself is now moving more towards those value-based payments, more capitated payments where they're not -- providers are not getting paid on more real-time. It's more after a year if you've taken risk. And so for providers, the need for cash flow is even -- has really increased dramatically. So I know our providers are very happy about this solution as well.

Yes. So many -- we could go on for a very long -- but just on health care alone, there's just so many interesting dynamics. And Lia, I'm going to shift gears for a little bit. Retail, restaurants, construction in all of these other industries where we're managing all these transactions, either at the point of sale or payment or money changing hands using the Oracle platform technologies today across so many different industries. How do you see the different needs in embedded payments across these different verticals industries, how do you see that evolving? And why is the automation so important to you?

Yes. No, great question. I think across all the industry verticals, I think there are common themes no matter which industry vertical, we want the payments to go out in a very secure way. I think we want to know or Oracle and the clients want to know the transparency where is my money. If something goes wrong, they want to know where the payment is stuck, right? So I think there are foundational basic needs of security, transparency, right, and the optimization of the payments. Those are universal. Then to your point, Mike, for different industry verticals, they're nuances. We talked a lot about health care. Seema mentioned, is a very complicated ecosystem with payers and providers and regulations and HIPAA compliance. So that really implies a lot of the payments handling, right, for data privacy and whatnot. You think about another vertical consumer retail, you mentioned is really fast evolving into already omnichannel in person, in-store experiences with in-app purchases with online, like how do we deliver that payment experience way. And today, we talked a lot about AI and people talk about agentic commerce. And in the future, maybe all the agents will do all the purchasing and whatnot on your behalf. Then we need to think about fraud and authentication and liability shift and all those very interesting future business model or current business models that are evolving very rapidly I think with the power of data combined in our partnership and with the AI models on top of the data, that's where what I mean earlier, like the payment intelligence, right? How do we deliver that insight into Oracle clients so that they know the fraud prevention, they know how to position cash, how to really mobilize all the transactions to add value to the ecosystem.

Yes. And so in some ways, this is automated embedding into the applications it becomes another distribution channel for the bank -- compliant distribution channel. We know a lot about the customers, we can automate KYC, all the things that need to be done. We've got that data. All of that data available as part of our industry applications just becomes a wonderful distribution channel and frankly, far easier for consumers to consume because it's just built -- it's built into everything that we do.

Totally? It's like a prebuilt enterprise-grade or banking and the payments core that's already compliant and extensible. So I think that is really where we're headed in cocreating this model.

We appreciate it. So I'll ask you both the same question. We're not the only technology vendor in the world. Why partner with us, why Oracle? What's special about this relationship between Oracle and the banks?

Look, you go with what works, right? And so there is as I said, we spend a lot of time chasing data to try and sort of predict outcomes, right? As I said, we're in the business of pricing risk and predictability is a big part of what we do. And I can tell you, we've burned a lot of calories on this in the banking world trying to sort of figure out how we can get from point A to point B in certain industry verticals. One of the things about, as I said earlier, when you -- when each sort of financing opportunity grows up in a certain industry, it becomes bespoke to that industry to a large extent. And it's hard to move from one to the other, right? You have to have a level of expertise in each industry. And what we realized, I think about Oracle is you're doing a lot of the really hard work. We don't always need to know when we talk about health care, like following data to get to a better health outcome is harder than what it is that we need to do. We're just sort of looking for -- at the end of the day, we need predictability of payment. That's a part of it, but it's not the hardest part of it, right? So you guys are doing the hardest work, but a lot of that is stuff that's really, really useful to us. And what we found is it doesn't look that different in health care as it does in construction as it does in retail. So now not only have we been able to solve and unlock some of these industry verticals. We've actually been able to migrate from vertical to vertical because we realize like predictability of outcome in an Oracle built model often looks the same across industries, but it's the same usefulness to us.

I think it's a terrific point, Geoff. We're not building a financing vehicle that is specific to an industry. We're building a platform that has industry context build into it, it's going to give you the data that you need, but actually scales across all the industries that we serve, right?

And that's not what we expected at first, right? We expected to kind of stay industry by industry. And it was only after going down this road a bit that we realized, this is more scalable and valuable than we thought it was, which I think is going to be a really good outcome.

You're right. I think for us, the partnership with Oracle is really critical. In terms of we share a lot of common values, right? I think Mike you talked about in your keynote just at the beginning of the conference, right, scalability, security, full stack advantage. I mean those are things that we look at our JPMorgan's platform, we share the same value. And then we think by partnering with Oracle, that's a force multiplier, right? With the scale is just going to be that much more even more global, more scale, more extensible. And the security is really a critical, critical component. I think you also mentioned, right, whenever you add in a new component into the system, there's risk by integrating the 2 global platforms in a very digital right API-driven way, in a modular way, I think that creates this tremendous platform and infrastructure layer to serve our clients even better and more seamlessly.

Yes. Well, that's much appreciated. And we do resonate with the shared value. So I visited both of your organizations in person many times now and even -- but on the trading floors. And I can tell you that the passion is very contagious for this access to real-time data. So much appreciate it. And to be clear, we're going to market together right now in construction in restaurants and health care and certainly plan so much more to come. So I'll finish where it started is that good things happen when the data is all in 1 place. And our ability to aggregate operational data, both qualitative and quantitative data and to deliver that as a service to you so that you can deliver that as a service back to our collective customers. We can't thank you enough for being a part of that. We can't thank you enough for pushing us and inspiring us and helping us to find this product, and I'm so excited about what's to come. Thank you, everyone.

Thank you, guys.

Thank you.

We will now take a short break. Lunch is located next door. Our programming will resume momentarily. [Break]

Please welcome to the stage, Larry Ellison.

Hi, everybody. Let's see. Okay. So everyone is very excited about AI. It does extraordinary things. In fact, my son and I were just consulting it on a legal matter this morning because there was -- anyway, there was an interesting dispute between a couple of investment bankers as to certain rules about how much stock foreign corporations could own under certain circumstances. And quickly ran off and asked a multimodal AI model and got an answer almost immediately. They're quite extraordinary. They certainly know all the laws in the U.K., all the laws in the United States, the rules of the New York Stock Exchange, the SEC, all of that is trained on all of that data. And all that data is publicly available. What is less common and what people want to do, but really can't do very easily right now is use their AI models, ChatGPT, Grok, Llama, Anthropic, all of them, to reason not on publicly available data where the models have been trained on the Internet, all of the Internet, but private data, the private trading records of an investment bank on Wall Street or the private genomic data of a genetic engineering company. that's analyzing genomes. And genomes, it's an enormous amount of data, and they'll do gene sequencing on plants. I'm actually going to -- I'm going to show it in a minute because I think it's such an interesting example. We're working on a lot of plant genetics, and I'll describe a couple of the projects we have ongoing. But people don't realize how big plant genomes are. The human genome is right around 3 billion base pairs, actually a little short of 3 billion base pairs. The wheat genome is 15 billion base pairs, 15 billion base pairs because wheat has been around a lot longer than human beings, and wheat has been evolving over time. And when you sequence wheat, you get this massive information. And by the way, there are all these different varieties. I didn't know this a year ago, 2 years ago. You got all these different varieties that we grown all over the world based on variations in soil and variations in climate. And they all do things slightly differently. They do photosynthesis slightly differently. The genomes are different. And what you want to do, if you want to optimize wheat, if you want to increase yield or make it drought resistant, you want to really look around at all the varieties of wheat and analyze and understand the genomes of wheat. And you've spent a lot of money to sequence all those wheat plants. And that's your proprietary information. And your business is to produce this new variety of wheat that is going to be drought-resistant, higher yield, and that's your business, and you don't want to share that information with other people. How do you do that? How do you do that? Oracle ran a project inside. First thing we did, we took all of our customer data, all of our proprietary customer data, and we have a lot of customer information. We have a lot of people using Oracle in the cloud every day where we keep track of what they're doing. We're curious about what features of Fusion applications they use more frequently, what features they tend not to use as frequently, what features require -- what features are not so easy to use. People tend to make mistakes in that. So we monitor all of this, and we find someone's making a bunch of mistakes using a particular feature, there's probably -- the feature is not easy enough to use, and we want that insight, and we want to go ahead and fix it. But how do you get -- but back to the basic problem, the basic problem that has not been generally solved is how do you take these fabulous reasoning models and allow them to reason on your private data, whether it's plant genomics or customer usage of -- in the cloud and what features are easy and what features are error prone. How do you take your private data, make that available to AI models while keeping that private data private? And can we make that easy to do? Because everybody, everybody wants to do that. And we've been working on this problem for some time. And we call it -- and we have a new version of the Oracle database called the Oracle AI database. And we didn't name it the AI database just because AI is fashionable, as I said the other day. We did it because the AI -- our new database has a lot of new features to solve this problem, to solve this exact problem, solve this one problem. How do you make all of your private data accessible to AI models for reasoning while keeping that data private without compromising data privacy in any way. And we've done that, and we call it the AI database. And by the way, just the latest version of the Oracle database. This is not an all-new database. This is not an AI database in the sense of pine is where just -- it's just a vector database for AI. By the way, we added vectors to our database. But it is still the full Oracle database that has all the Oracle security features, all the Oracle high-performance features, all the recovery fees, all of that. But we've added all of this AI capability to the Oracle database. So it's highly secure, highly reliable, highly scalable, very fast. And -- but it makes your private data easily accessible by the AI models, okay? And again, we also decided that if we're going to do that, if we're going to make the data -- we actually should have -- make it easy in the Oracle Cloud for users to pick the AI -- their preferred AI model. We use a variety of AI models. I mean, again, Anthropic tends to be pretty good at code generation. So if you're doing programming, Anthropic is pretty good at that. ChatGPT is a phenomenal legal expert. I can just go into it. These models are somewhat different. And depending on the application, you might use one model or you might use a different model. So we decided to make all of the popular AI models available inside of our cloud. So Oracle, if you go to OCI, you can get ChatGPT 5.0. You can get Google Gemini. You can get xAI Grok 4 Heavy. You can get the latest versions of Llama from Meta. You can -- and because the Oracle AI database, to be an AI database, to do reasoning, Oracle doesn't do the reasoning. You need the model on top of Oracle to do the reasoning. So when you configure the Oracle AI data platform, you pick one of these models and we then give you a private version of that model sitting on top of the Oracle database with your private data in it. And so it's all there. You just configure the model you want. I mean you basically click on the name of the model you want, we configure it and put it on top of your database. And then the Oracle database will make all of the data, all of your private data that you authorize, all of your private data that you authorize, it will make it all available to the AI model. How does it do that? Well, it uses a technology called RAG, Retrieval Augmented Generation. It simply allows the AI model to read the database. There's a server, there's an MCP server. The AI models are designed to be able to go and read anything on the Internet that's publicly available, but they also can read private data, whether it's in a file system or in a database, in different kinds of databases, it can do all of that. It can go out and look at that data, read it and actually understand it. So that's what we did. I'm going to go into a little bit of detail on how it works. What we added to the Oracle database, obviously, we had RAG capability. But right now, the Oracle database can vectorize all of your data in the database. They can vectorize text, it can vectorize images, it can vectorize videos. And what AI models understand the format of data that AI models understand are vectors. And for example, a very famous AI search is vectorize a particular movie and then show me movies that are similar in content to that movie. And that's a vector search. Find me a movie where the vector is similar to the other movie or other movies somebody else watched. They use it for recommendation engines. But also for gene sequencing, for gene sequencing, there are genetics -- there are gene sequences to do with photosynthesis. So you find a set of sequence gene sequences that are doing photosynthesis and you say, find me all in all the other genomes that I have, find me the gene sequences that are involved with photosynthesis. And we can find that part of the genome, searching through the 15 billion base pairs, we can immediately zoom in on that part of the genome. And then you can say, show me the differences between how this plant does photosynthesis and how this other plant does photosynthesis. And you do that with something called vector search. And there's a whole bunch of vector mathematics that has vector spaces and vector distances and all of that. But that's what we do. That's what we've done to make this an AI databases is allow you to vectorize all of your data, all the different types of data. Once you've done that, if you look at what we were able to do as the first project doing this inside of Oracle, the first of our private data that we decided to make available to an AI multimodal model was our customer data because I'm not sure for us, there's anything more valuable than our customer data. The -- and then we started asking questions once we vectorize our customer data. And for example, you'll see what I mean by valuable, what Oracle customers -- this is my second group under the second bullet -- first line under the second bullet. What Oracle customers are likely to buy another Oracle product in the next 6 months. We'd like to know who they are. And we'd like to know second line, what Oracle product are they most likely to buy? And those are the kinds of questions you can ask using a reasoning model. And you get -- and it tells you. And then you can ask it, but you can also have agents associated with this. It doesn't just have to be ask a one-step question. You can actually ask us, okay, well, let's send e-mail to all of those prospective buyers and let's show them the 3 best Oracle references in your -- for you, in other words, in your industry, in your country that are bought the same product and used it successfully. So as you look at this, I mean, you would build -- if you were a company building CRM software or sometimes called CX software, customer engagement software, the ability to enable your customers to ask these kinds of questions to build an application -- CX application suite where you could do this kind of AI marketing, if you will, AI reasoning on top of the customer data and qualify leads and then pursue leads with agents. You qualify leads through the reasoning process and then through the agent process, you would go ahead and pursue those leads. That's how you go about building the next generation of CX applications, and that's what we're doing. And we do all of that while maintaining the strict privacy of your customer data or your genomic data or whatever and medical genomic data is very, very sensitive. So you have to keep it private. So the Oracle Database security model, which we've worked on for decades, is what we rely on to keep this private because the vectors are inside the Oracle database. And we use a security model, again, that we've been working on for a long period of time to make this -- keep your data private while making it accessible to AI models. Next slide. I'll press my clicker button okay. Great. Okay. So as we're building the next generation of CX products, we're not -- we are the owner of Java. The Supreme Court might disagree with that, but -- and Google probably would, too, because they won the case. But we bought Sun who developed Java. We are the primary maintainer of Java, and it is the world's most popular programming language this day. However, in the age of AI, you can use something like Anthropic or ChatGPT or Grok to write code. And you can just declare your intent what -- in other words, say what you want the program to do and the AI will generate the step-by-step process to do it. So it won't be conventional coding. It's called vibe coding, an interesting term, which is just, I guess, field vibe, very modern term for coding. But it's really just what do you want the program to do. You just tell me what you want the program to do and don't worry, I'll generate it. Now we have been doing this for a while, doing code generation for a while. And there's a big debate inside of Oracle, and there are people on both sides of the debate. I'll tell you what slide I'm on, which is programming in English. In other words, declaring what you want the program to do in English. English is a notoriously imprecise way to communicate. It's not like mathematics. There's a lot of ambiguity in English. To make English perfectly clear, perfectly precise is very difficult. So we think you could have an alternative declarative programming language, which was designed to declare intent of what a program should do. You could create that language with great -- and then with great position, you could generate the code. Now the jury is out. People are programming -- declaring intent in English and generating code and people are declaring intent in specialized languages to declare intent designed to be precise and designed specifically for code generation. I'm having done both, I'm a great believer in a more specialized language. I think once you learn the specialized language, you're much more productive in generating code. And our experience is -- I mean, these are huge improvements in productivity, 10x productivity if you generate the code rather than writing the code. One of the reasons we felt we could take on something like Cerner, and we knew we were going to have to rewrite all the Cerner code. We have to rewrite it all. And it took decades for Cerner to write that code. And we thought we could rewrite it all in a few years because we weren't going to rewrite it. We were going to generate it using AI. And that's what we've done and built very complicated and interesting agents using AI. And in fact, in the future, what our computer programs? Computer programs are a collection of agents connected by workflow. That's what they'll be. Okay. This is a big leap from code generation and making your private data accessible to AI models. This is a greenhouse that we have developed. It's version 3 of a greenhouse that we're developing. Actually, it's Danny Hillis' team. Oracle made an investment in what's called Applied inventions. Danny Hillis, by the way, invented thinking machines when he was undergraduate in MIT. Actually, Danny and I used to be competitors. I was working on a computer out of -- with the Caltech team doing something called nCube, which was massively parallel computing. It was parallel computing. It was, if you will, the precursor to NVIDIA and vector processing in computing. It was doing a lot of calculations in parallel, which obviously is very important now. Unfortunately, as Danny and I discussed because we both failed, nCube failed and thinking machines failed, we both lost a lot of money and a lot of time on this. We learned a lot. But we were about -- I was going to say 20 years too early, but it was more than that. I don't want to talk about it. I'm going to do the calculations. But anyway, Danny is now -- we bought his company. And one of the things that we're working on is -- and he's really responsible for, amongst other things, robotics inside of Oracle because robotics is a very special case of AI. The leading AI model for robotics is very easy to figure out. It's owned by Elon Musk, but it's not Grok. It's the AI model he built at Tesla. And the first popular common robots in the world other than -- I mean, yes, there are robots, there are general purpose robots. Yes, there are robots that assemble PCs and laptops and desktop PCs and a lot of electronics and iPhones and all of that. There are tons of special purpose robots. But the first, what I would call kind of general purpose robot is the self-driving robot that Tesla has created. And then Elon is working on his second group of robots, which are humanoid robots. So it's going from 4-wheel robots to 2-legged, 2-armed robots, general purpose robots using full AI model, real-time AI models. And we're doing -- we're not creating the AI models from scratch. We're using those models to automate a variety of things. And this is a greenhouse, by the way, that has no people in it. That yellow thing over there, they're very large. The reason there are no people in it, there are a few reasons is one is the atmosphere inside of this greenhouse. By the way, there is no structure. This is -- this building is held up by air pressure. So there's positive air pressure inside of the building, and that holds up the roof, which is ETFE, which is a kind of plastic that lets through -- it's the most transparent material in the world, let's through more light than any other material, more light than glass or other forms of plastic. And if you talk to Danny and said, what do greenhouses do? Well, basically, they convert sunlight and CO2 into food. And we don't let people in here because people can contaminate the plants and people aren't going to like all the CO2 and all the humidity we have in here. And the robots will move the plants from the growing area into the harvesting area. And AI decides when you harvest the plants, there are cameras, AI decides we can grow different crops in here. the only greenhouse you can have lots of different crops inside the same greenhouse because it's completely computerized and the nutrition if you're growing strawberries is very different than the nutrition when you're growing lettuce. The heating requirements, we heat the plants from the bottom. We don't heat the whole building. Anyway, I'm not going to give you all the details. But each area is carefully climate controlled, atmospherically controlled. The hydroponic nutrition is all computer controlled. And it's designed to produce food at a much higher quality and a much lower cost than currently other forms of indoor growing. By the way, when you grow indoors, you use 90% less water, which is truly incredible. By the way, this is also a habitat for Mars. So if you think about an interesting use for a greenhouse, I don't think it's a huge market. I don't think our marsh market is going to be gigantic. It's not in our numbers, by the way. None of the Martian consumption of our technology is in any of our numbers. That's all upside. So the -- what does the greenhouse do? The greenhouse grows food. And if you're going to have people living on Mars, you need to grow food. But it also converts CO2 into oxygen, doesn't it? So if you're going to have people living on Mars, you're going to have to -- they're going to have to breathe and you can either ship the oxygen from earth to Mars or you can create the oxygen from CO2 on Mars. And the people can create the CO2 that the plants will consume. So this is kind of a combination. It's intent, by the way, our first market, we really have focused on earth. But just notice that this thing would be a perfect habitat for Mars or other places you want to go. As I say, it's really -- it's not in our business plan. This is what the whole building looks like from the outside. The green areas are the harvesting areas. The robots will move the plants into the harvesting and packaging areas. And again, there are no people in the growing areas at all. It's kind of interesting. As long as we're talking about plants, I want to give you an example of the kind of things we're doing with our database to make it work better with AI. And in addition to being able to vectorize all of the data, which makes the data easy for AI to consume, we've also created special data types in -- let me get my verb tense correct, are in the process of creating special data types for DNA. And say DNA search of -- by the way, that is wrong and it's my fault. It says the wheat genome is 15 million base pairs. It's 15 billion base pairs. -- human being 3 billion base pairs. And it's got all of the historic genes, all the genes that became obsolete during the hundreds of millions of years of evolution of wheat on the planet Earth, just a grass, what on the planet Earth is recorded in that genome. That's why it's so large. We don't delete when genes suddenly stop being used, they don't get deleted. So there's a whole history of how the wheat evolved. That's very interesting that you capture when you gene sequence what or your gene sequence plants or animals for that matter, you capture their evolutionary history. And the things we're doing -- another team is doing, not Danny's team, is looking at wheat photosynthesis. And if you can improve wheat photosynthesis, and they have, they have to improve what photosynthesis using AI. They've changed the gene -- they've used CRISPR-Cas9, a gene editing technology to improve photosynthesis. When you improve photosynthesis, you convert more CO2 and more sunlight into food. So the yield per acre of this suite we created, they created. I didn't have much to do with -- I didn't have much to do with it. A actually has 20% more yield per acre. So we're producing more food in the same space. Another thing that we're looking at -- and that's already working today. So the increased yield through using AI to figure out how to improve photosynthesis. And once we figure out how to do it, you use CRISPR/Cas9 to go ahead and do it and actually do the gene edits. The thing we're looking at right now is not just converting CO2 into food, but also converting CO2 into calcium carbonate. I'm sure all of you will be fascinated by the fact that the reefs, Coral reefs are made up of calcium carbonate. The plants that live in coral reefs actually set they actually secrete calcium carbonate, their own skeleton. Well, we have skeletons too, right? They also have a lot of calcium in them. The -- we are now engineering a version of wheat that will convert CO2 out of the atmosphere into calcium carbonate, an inert version of CO2. Therefore, you can take vast -- you can have varieties of wheat that take vast amounts of CO2 out of the atmosphere and deposit it in the ground as little tiny microscopic pieces of sand, calcium carbonate sand. And you can manage the level of CO2 in the atmosphere pretty much to whatever you want it to be at no cost at no cost. And the world right now is working on this problem of figuring out what to do with CO2 in the atmosphere, and they have all these interesting ideas, which -- including getting rid of all fossil fuels, which is very difficult. And -- but there are other ways to tackle the problem that might be much easier and much more cost effective. And then, by the way, if you look at the markets, you can actually figure out if you're a farmer, you can use satellite imagery, AI and satellite imagery to look at your we fields and figure out exactly how much CO2 you're taking out of the atmosphere and then you can get carbon credits. You can trade it for carbon credits, okay? And kind of an interesting approach to -- initially a much simpler approach to managing CO2 in the atmosphere than what we're currently doing. Another one -- another interesting example that we're working on, again, with plant genomics using DNA data types, using vector search for using all these AI capabilities. It's -- right now, we fertilize almost all the plants that we fertilize in North America and Europe throughout -- we fertilize them with nitrogen fertilizer, huge amounts of nitrogen fertilizer because -- even though there's tons of nitrogen in the atmosphere, nitrogen is the most common element in our atmosphere. We breathe it in. We don't use it, but we breathe it in every time we take a breath. But some plants, soybeans, for example, actually get their nitrogen directly from the atmosphere. Well, we can engineer corn or wheat or other plants to get their nitrogen from the atmosphere so we don't need fertilizer. And fertilizers does incredible environmental damage during rains, it runs off into rivers, a bunch of nitrogen runs into the rivers into lakes. You get these big blooms of plants in the lake and in the rivers. And plus a lot of farms can't afford to buy fertilizer. Therefore, their yields are half what the farm yield should be. Well, we can fix that. We can fix that by having versions of corn, version of these grains that fix the nitrogen from the atmosphere. So that's my -- that's my presentation on what we're doing with the Oracle database. The primary thing, make it easy for people to use AI models on top of their private data while keeping it private. And then we have all of these advanced technologies that we're adding right now so that as -- if you are in the business of genomics, and by the way, that's every pathology department, that's all of medicine is in the business of genomics. All of agriculture is in the business of genomics. These 2 enormous businesses. You have to fully -- your database have to fully understand DNA and have operators that can operate on these enormous data types, these enormous genomic data types. That's what we've been doing. That's what we're currently doing. I think the -- I think, again, fully ignoring Mars, there's enormous upside to our database business over the next 5 years. We think it's going to be one of our fastest-growing businesses. And we don't think -- and we're happy to talk about this later when we -- in Q&A. We don't think we have a lot of competitors left in the database business. It's fascinating. Our primary competitors in database, let's say maybe Databricks, you could say, is new or it's kind of the newest one, Snowflake. They don't even do transactions. They're query-only systems. There aren't a lot of new database technologies being invested in. We're kind of the only game in town. We think that gives us an enormous opportunity to increase our franchise in the database business over the next 5 years and the dawn of the AI era if we can merge our database technology with the latest AI technology. Thank you very much.

Please welcome to the stage Doug Kehring.

I think Larry used the magic word, upside. So I'll be presenting our updated financial outlook, which I know I think everyone has been anxiously waiting to hear. But let's start off with the usual exciting stuff that Ken went over earlier. It's funny now that they're asking me to certify the financials. This actually means a lot to me. So please pay attention. Okay. This one reminds you about our forward-looking statements. And this one is reminding you that we'll be using some non-GAAP financial measures in my presentation. Okay. You've heard the strategy throughout the day from Larry, Clay, Mike and the rest of our management team. We have an unbelievably strong and deep set of enterprise technologies for both cloud and AI. And we have the vision, leadership and experience to execute this. It's now time to see how all of this impacts our financials. Building on our long-term expectations can be boiled down to a few simple steps. First, we work extremely hard to turn the customer momentum we are seeing as evidenced by the amazing enthusiasm you probably -- you've seen here at AI World into an accelerating RPO backlog. Second, we then deploy our operational expertise to provide capacity to customers that help us turn that backlog into accelerating revenue growth. Third, we leverage this growing footprint, scale and the utilization rates of our data centers to turn this larger revenue into profit growth. And the result is that we are raising our long-term financial outlook again. I'm going to quickly walk you through the process of how we arrived at these new figures. The best way to think about Oracle these days is as a hypergrowth company. Our remaining performance obligations, or RPO, is the clearest indicator of the revenue that's about to come. As we announced on Q1 earnings, we highlighted 2 things on this topic. First, that our RPO balance now exceeds $455 billion, up 359% year-over-year. And second, that we expect RPO to likely exceed $500 billion. In fact, already through the first 1.5 months of Q2, we've signed several additional large contracts, as Clay mentioned, during his presentation, which put us over the $0.5 trillion mark. The demand we are seeing is really hard to comprehend. To put it in perspective, our RPO balance is up nearly 10x since fiscal year 2022. Clearly, the customer demand is strong, but it's also enduring. The biggest impediment to growth right now isn't so much finding customer opportunities, but rather executing on these opportunities by converting that demand into revenue as soon as possible. As our data center operations engine has revved up and we become more experienced at it, we are bringing on capacity faster and faster and with more efficiency, as Clay discussed earlier. The ramping has already started, as you've seen over the last couple of years, with cloud growing as a percentage of total revenue from 20% in FY '20 to 44% in FY '25. As cloud crosses the 50% mark as a percentage of our total revenue, the revenue growth rate is further accelerating as evidenced by the forecasted 16% growth rate for fiscal year '26. To put this growth rate in perspective, the last time Oracle grew this fast organically was over 15 years ago. As well, when you look at the expected revenue growth rates over the next 12 months, for companies in the S&P 500 with more than $50 billion of revenue, there are less than 5 companies growing faster than Oracle, and we aren't even close to seeing the peak growth rate yet. As our revenue begins to accelerate, so does our operating income growth. The reason is that our pricing discipline, coupled with scale efficiencies enable us to gain significant profit leverage as our revenue grows. As the utilization rate of each data center increases, they contribute more profits. And the rest of our operating expenses have grown much more slowly than revenue, also helping further our profit growth. Now before I turn to the updated financial outlook, I wanted to revisit the figures that we presented at last year's Financial Analyst Meeting. As you may recall, we announced last year our expectation to reach over $100 billion in total revenue by FY '29, nearly double the revenue from FY '25, while simultaneously accelerating our profit growth. But that was last year. As we dive into this year's outlook, I want to start by explaining what is guiding us as we work to deliver the financial outlook that I'm about to share. First, every customer is put through the prospect of the lens of both a revenue opportunity and a profit opportunity. I've read a lot of stories that are speculating that Oracle is chasing revenue for revenue's sake. But let's be crystal clear. We only pursue opportunities where we have a clear line of sight to attractive margins that reward us for our intellectual property and the activity we bring to customers. Second, as we work to build capacity, we are pursuing a range of financing options to support our growth. We pay careful attention to our cash flow, our debt ratings, our debt capacity and the various funding mechanisms that are at our disposal. These all factor into how we strategically grow revenue and profits faster. Third, we are working diligently to constantly match our expenses as our revenue ramps in our data centers. This operational discipline is critical to be able to deliver profits to our shareholders faster. And fourth, our overall cost focus on every aspect of the company will help us deliver higher profits from our revenue base. That has not changed, and it will not change. And finally, if all of this works in harmony as we expect, the result is superior investor returns for our shareholders. So here goes. Clay showed you earlier our updated infrastructure revenue targets, which are even higher than what we shared on our Q1 earnings call. Building on that, along with the RPO backlog that we've already signed, the customer opportunity pipeline and the strength of our competitive differentiators, we see much more revenue upside in the next 5 years than just a year ago. Our updated revenue target is to reach $225 billion by fiscal year 2030. This represents a CAGR of over 31% over the next 5 years. Our pipeline is very deep, and we could see more large-scale opportunities signed over the next 12 months, which could change this forecast and outlook further. And in terms of profit growth, we forecast reaching $21 of EPS by fiscal year 2030. This represents a CAGR of 28% over the next 5 years, consistent with my comments that revenue and profits are symbiotic. These figures are stunning with both revenue and EPS growing nearly 4x over the next 5 years. Now before I turn it back to Larry, Clay and Mike for Q&A, it's important to note that these figures are as of this moment in time. If we see additional demand that enables us to grow revenue and profits faster, we will accelerate near-term investments in order to capture additional market share. As Larry recently said, "AI is a much bigger deal than the industrial revolution, electricity and everything that has come before. We are extremely well positioned for this opportunity, and we will pursue more growth so long as it fits our profitability expectations. Thank you.

Please welcome back to the stage, Larry Ellison, Clay Magouyrk, and Mike Sicilia.

Give me a moment to recover from those numbers. Okay. Oh my God, this is nuts. Okay. No questions. Thank you all very much.

Jackson Ader at KeyBanc Capital Markets. Yes, this is great. All right. Let's start with those numbers. I guess, first, I'm curious, Clay, when you gave the gigawatt illustrative example, right, like we're going to make $60 billion, it's going to cost $39 billion, something like that. Is that purely illustrative? Is that an average? And then your largest customers, whether it's Meta, OpenAI or what have you, are they even close to that type of gross margin? Or do they come in below that?

Sure. So first, it is illustrative. But the reason why it's more illustrative than exact details is because a gigawatt is changing very quickly, right? Is it a gigawatt of H200? Is it a gigawatt of GB 200? Is it a gigawatt of GB300? Are we talking about MI355? What's the mixture of the number of GPUs to the amount of storage compared to the amount of general purpose compute? So obviously, it doesn't sound like a big difference, but that can be plus or minus 10% to 20% of revenue both ways. In terms of the margin profile, no, it's very illustrative of even the very largest customers. So we are we are very committed, like when I gave you that range of margin, it wasn't like this is the margin and there's an asterisk and this is only for like the customers that aren't driving all of the revenue growth. That would be counterproductive for me, it'd be counterproductive for you. That absolutely is illustrative of even the very biggest deals that we're doing. I feel like you're like confused.

Microphone back. No. Okay. No, that is very helpful. And then I'm curious, Larry, maybe this makes more sense for you. Just if we think -- and I'll stand up again, I'm sorry. If we think about what AI can do to your internal operations, we're looking at $21 in EPS in 5 years. What does that imply your internal usage of AI does for your operating expense in that kind of time frame?

Yes. I think -- I mean we've underestimated how the positive impact of AI for internal use. I don't think we've -- but I'll let the guys comment on that. But I don't think -- we do have estimates. I mean, clearly, our program is going to be more productive. We're going to produce more product. We're going to be more ambitious. We're going to write more programs. We're going from industry suites to, if you will, entire ecosystems where you look at the health care ecosystem, it's hospitals plus government regulators, plus pharma companies, individual patients, it's entire ecosystems. And we'll be able to do that. So a lot of the productivity will be -- we'll have more comprehensive suites of software. But I would still say we've not fully accounted for the scale of the productivity gains, but I'll let these guys respond to that because they actually run the business.

Yes. A couple of examples, I think, that are relevant. Just internally, I shared earlier in the health care section. usually, these regulatory cycles take 2 to 3 years to get through approval. We got through a regulatory cycle in 6 months because we're able to automate all of the documentation required for the regulatory. Now it doesn't really displace any people, but allows us to get to market far more quickly and capture revenue much earlier in the cycle than we would have had. To Larry's point, we start to look at certain functions across the company in our engineering space. And we do have some internal estimates that we've been going through. And frankly, they keep changing for the better. They actually keep getting better. cogeneration, QA, support ticketing and support tickets. I mean, all these operations are large global scale operations. At this point, we're really focused on productivity enhancements. How can we make people more productive so that we don't have to bring on a lot more labor to get the same thing done. Early days in terms of where we'll go in terms of the labor force in general. But I would say we're certainly optimistic that particularly for -- at least for industry development, I can tell you very specifically, that we hit a baseline in cost that scales dramatically, and we don't need to add additional cost from a labor perspective to get not just the same amount done, but actually about twice or 3x as much done. That's the kind of scale factor that we're looking at.

Let me add one more thing. We really can't just look internally if how AI is going to change Oracle and make us more productive. It's also going to change our customers. It's going to change the FDA. What happens if the FDA can get through clinical trials in half the time they be used to. What happens if pharma companies can design drugs in half the time and 1/4 of the cost than they used to do it. It's not just us changing internally. The entire ecosystem called the Planet Earth is going to start becoming more efficient. And it's -- there are going to be these incredible effects on the entire economy. We will be a much more prosperous and wealthier world because of artificial intelligence, because of robotics, because of drug design, because of government agencies and regulators making use of these technologies. As they say, AI changes everything. It's going to make us much more efficient across the board.

Rishi Jaluria, RBC. Really appreciate the session. A lot of great detail and obviously, amazing to see these sort of numbers. One question that I think a lot of us have been kind of weighing on the architecture side is -- and maybe it's an overly simplistic way, but what goes into as we think about the balance of power shifting from training to inferencing, fine-tuning, reasoning, et cetera, how easy is it to repurpose that architecture and really eke out the most efficiency out of all that you're building? Maybe if you could walk us through, that would be helpful.

Sure. Well, look, in my keynote yesterday, Peter from OpenAI, I think, did a good job of addressing that exact question. So for an even better answer, we can send you the video link. But one of the things he described is that especially for these large model providers, -- it's critically important that they have flexible architecture. And the reason for that is that there's this concept that, hey, models get trained, then they get copied off somewhere and then reasoning happens. But actually, models are constantly being updated. And also, as a provider, you need the flexibility to shift back and forth, right? You're doing research on new models. You're doing training updates on existing models, and you also have customer demand. Let's say that you're a provider and you have a viral event where suddenly you get a huge amount of demand, you need the ability to say, okay, let's not do that training run and use that for inferencing. But -- so obviously, it's better if the infrastructure is flexible. Well, it turns out you can build it flexibly, and that's exactly what we're doing, right? So you build it for the maximum requirements, which is really the highest kind of demand training workloads. And if you do it well, it's a huge amount of effort around optimizing the power of the data center design, optimizing the networking design, then fundamentally, that infrastructure is both very capable of doing the reasoning as well as it's also very cost effective at doing the reasoning.

Great. Tyler Radke from Citi. Congrats again to you, Clay and Mike on the well-deserved promotions. Clay, I wanted to go back to one of the slides from your opening presentation around the AI database forecast. I think you're expecting $20 billion of revenue in FY '30. And I was just wondering if you could kind of unpack the -- or stack rank the key drivers of that? Is that kind of the traditional database migrations to the cloud? And then what is it going to kind of take for the AI start-ups that are buying your infrastructure to start using your databases?

Well, I don't know if you saw it, but I signed this up for $20 billion.

I saw it.

Well, honestly, I say, Larry, why don't you start? I think it's actually a great question of why are you so excited about people adopting our AI data platform?

Yes. I think -- again, I think there's a lot of -- it's an incredible number. The $20 billion is a pretty solid growth rate over the next 5 years. That said, we think everyone is going to want to do reasoning on top of -- by the way, reasoning and inference -- I'm using reasoning and inferencing the same way. And inferencing is kind of the -- AI models do more than inferencing. They do deduction, they do inferencing, they do rules, they do mathematical calculations. So reasoning might be is a little more modern word than inferencing, but it's applied AI. It's actually using the models to reason. I don't know who's not going to do that. Now the question is how quickly can we deliver it, transfer the technology to our customers. Again, it's the same -- it's not going to be a demand problem, that is for sure. It's going to be how quickly can we get -- transfer this technology to our customers and help them be successful and get started using this technology. Now the good news is there are a lot of our customers -- well, customers are very familiar with the Oracle database. And it's not that they have to relearn the Oracle database. They just have to take advantage of these new features of the Oracle database. And we've got great distribution of the Oracle database right now. You can get it -- obviously, you can get it in Azure. You can get -- by the way, that alone, just multi-cloud alone is going to drive a huge amount of adoption. Remember, it wasn't long ago. The only cloud you could get the Oracle database in was OCI. And OCI is a great cloud, but not everyone is in OCI. -- so the fact that we're building all of these data centers, we started with Azure, and we have quite a few data centers in Azure. And if you look at the numbers, if you unpack our numbers, that 1000% plus growth rate we have in multi-cloud is being driven by Azure, who were the first ones that signed up from multi-cloud, and we got the data centers all built and running for them. We're not anywhere near at scale at Google, who came second and then Amazon who came third. So just scaling out those data centers, just the normal migration, what you asked for the normal migration that's going to be enabled because of multi-cloud is going to, I think, easily get us to $20 billion. I'm not trying -- I don't want to raise those numbers up any at all. But Clay said at 20, I'll take it. But I think we get -- I think multi-cloud alone might get us there. Well, multi-cloud alone gets us there, what about the use of -- it is a vector database for AI. It's very hard to extrapolate because we have no points. We really don't know how big that's going to be, but I think it's going to be all of our -- it's going to be everybody.

I think it's not just an accelerated business on its own as a platform service. But keep in mind, it's the same platform that we build all of our applications on top of as well. So the $20 billion is just the revenue that we'll collect from that platform service alone. We're also very optimistic that this continues to propel our application growth because all the Agentic AI agents that we spoke about earlier today with Steve and Mark and Sima, all of that's built on top of the AI platform as well. And if not for that, we wouldn't be able to go as fast and grow our applications business as quickly as we are, too. So it all sort of works -- it all works together.

Yes. I think that's so interesting with what Mike is saying because you said who are the first users of the AI data platform? It will be our internal application groups or our first users. And one of the advantages I've always thought Oracle had, and you could also say one of the disadvantages Oracle had, and I'll come back to that, is that we do -- we do applied technology. We do applications. We use our tech, and we also build the tech. We build data centers. We train models. We build databases. We build all of this -- we build code generators. We do all this stuff to enable the creation of applications and then we actually create the applications. None of the other 3 big cloud vendors do that. They are primarily tech platforms. They are not -- they don't build large-scale applications. We get all of these insights from building these large-scale applications where Mike's team will say that, gee, it would really be nice if I could do this more easily. And not that these guys ever -- not that anyone ever asked for more around here. But the database guys get asked for additional features, more ease of use, more automation and backups. That's how we got so much better at the tech. We had the -- our captive -- very large-scale captive applications team that was putting demands on the tech and helping guide some of the new features that we developed. And as we -- so as we build the AI data platform, we also test the AI data platform on our own applications. And then what our customers get is something we've already used successfully rather than them being the guinea pigs very, very early on. So -- and then we constantly -- then it's continuous improvement. We constantly make it better because we improve it, we use it, we get insights, we improve it again, all of this other stuff. So yes, the database is now inextricably linked to our AI strategy and inextricably linked to our application strategy. So all the pieces at Oracle now are fitting together. The -- why some people thought it was negative? People said Oracle is trying to do too much, right? Oracle really needs to focus either on tech and spin off the application business. I remember hearing that a long time ago or they should just do applications and not try to do because no one can do both. Well, so far, we're the only one doing both, but one is more than 0. So we think it's working out very well for us.

This is Patrick Colville from Scotiabank. Great to be here and really exciting time to be part of the Oracle story. I've got one for Clay, please, and one for Larry. So Clay, I think one of the big standout announcements this week from AI World was the AMD partnership. So my question is, I guess, what was the logic there for that AMD partnership? And then also, what does it mean for Oracle's NVIDIA partnership? Because that's been just tremendous for both firms. And then, Larry, if I may, love the long-term targets. One of the questions we get from investors is what happens beyond fiscal '30? And I guess the reason we get asked...

I missed the funny part. What was -- we couldn't hear it up here.

He said what's beyond fiscal '30? So if you could just make up like a '31 projection and '32, he would love it. It'd be...

Yes. Well, wait til we talk about 40 man. That's going to be awesome. I'm looking forward to that.

So do these AI labs, do the Tier 1 AI labs beyond the midterm start in-sourcing infrastructure? Or do they lean even more heavily on neo clouds and can Oracle accelerate share gains beyond the midterm?

Sure. Okay. Well, let's start with the question. So look, I'm also very excited about the AMD announcement. And you said, okay, so what does it mean for AMD? What does it mean for NVIDIA? What does it mean for us? Well, first, look, -- we have an amazing relationship with NVIDIA, right? NVIDIA, I think everyone would agree, did a really good job of getting this industry started. And we -- as a tech industry and as AI, we wouldn't be where we are today without the work that NVIDIA did both on the accelerator side and the networking side. Now I'm glad that AMD is doing a good job because I think that we don't have a shortage of demand. We have a shortage of supply. And so the reality, I think the biggest thing that I find myself having conversations with people across all areas is they're using a scarcity mindset in a world that doesn't have that problem. So for me, it's like, oh, well, if AMD does well, does that mean that NVIDIA is not going to do well? Or I look at it like imagine there was infinite AI demand. What if we had options and choices that allowed us to scale even faster, right? So our AMD partnership is amazing. We have -- the reason we're doing it with them is they've been a great partner across our CPU business, our networking business. They make a good product, and our customers want it. So it's just good for all of us. But then you asked kind of the second question, and I'll let you finish, Larry, the -- you can make it think longer about 2031, 2032. The reality is that I think that there's this perspective that these AI companies are only coming to us because they're temporarily out of luck and they don't want to be doing that. We do a really good job, and we do a good job that actually complements and supplements what these people are doing. If you go -- I would advise you to go talk to these people. When I -- you saw Peter -- go watch my keynote from yesterday with Peter on stage, he doesn't need more problems. He's got a lot of work to do to be able to give the infrastructure he needs to all of his researchers and all the demand of this technology. Everyone wants help. So I don't think that there's a shift at 2031 or 2032, where suddenly people go, "Oh, there's no more AI infrastructure business for companies like Oracle. I think it's more interesting to figure out what -- like we just talked about how we have so much unexpected demand from this inferencing and reasoning and how that then goes out and makes everyone's lives better. Those are going to need computers, too. So no, I don't see like a step shift. The reason we give the forecast out is we can only see so far.

I think I'm going to let Mike talk about the future because I think the future -- to understand what the world is going to look like, you have to understand the automation in the different -- how the world is going to change. This is back, okay, Oracle using AI, how is that going to impact our financials using it internally. Well, let's look at our medical business, and then I'm going to let Mike go into detail about that. We need to work with the FDA to make it more efficient to a drug that works to get it approved more quickly. We need to do a better job of delivering health care all over the world with modular -- working with companies building modular hospitals all over the world. And I know in some countries, health care is considered a human right. Other countries, health care is hardly existent. -- as our -- we have the chance to democratize a lot of these technologies, make governments more efficient. Sometimes governments are -- big corporations might not be very optimally efficient. AI is going to help that. Governments will be more efficient. Poor countries will get food supplies, they will get energy supplies. They will get hospitals. And -- but they'll get this next generation of smart hospitals. Mike, maybe you can go into, again, that the world -- how we benefit as the world gets better.

Well, I think what does it look like beyond 2030? I think we're rapidly heading towards self-implementing self-learning, self-healing systems. You can certainly get your head around what that means for the GDP -- if you just look at, to Larry's point, something like health care and hospitals, and I'll expand on that in a second. What does it mean in terms of Oracle? It's hard to say. I can't imagine that there's any -- anything negative to say about it because I like our chances of being able to deliver that full system ecosystem. But in terms of -- let's just talk about clinical trials for a second and compare and contrast. COVID-19 was not that long ago. We were involved as the technology provider and for basically every COVID-19 vaccine and every therapeutic that was coming to market. And here's how it worked. we were shoulder to shoulder with our customers. And what that meant was we had people that would help gather the documentation at the pharma, and they literally wrote buses, caravans of buses to Washington, D.C. with stacks of paper. The bus was half filled with people and half filled with paper. People had to read the paper on the way to make sure that it was complete. And that's how it worked in COVID-19. It's exactly how it worked from the most sophisticated pharma to the start-up biotech pharmas, all of whom were involved in trying to create vaccines and therapeutics. Now fast forward to now, and we're working with regulators across the world, not just the United States to actually accept electronic documentation, accept electronic documentation so that we can take proof points, we can take efficacy points, safety points from these clinical trials, and we can get drugs to market far more faster. What that means is they're actually cheaper because there's a lot of time and money that's wasted in very long latency processes around documentation, around reading around all this stuff. The next phase of it is, and we're not that far away from this, is that how do we have clinical trials that are reliant on just real-world data. Right now, the gold standard for clinical trials is double-blind, placebo-controlled trials. And there's not a bad thing. That's not a bad thing. It's led us to some wonderful therapeutics and some wonderful vaccines along the way. But what if you could capture data in real time from everybody who's taking a particular pharmaceutical. Not only do you change how fast you get to market, but you actually have a clinical trial that never ends because right now, clinical trials are thought of as a project. It's a project and it ends. And once the project ends, we actually stop tracking those people. We actually don't know what the counterindications are of those people when they start to take other medications or they start to develop other preexisting conditions down the road. I think that, that's an example of not just changing the way an industry works, but actually benefiting humanity in a way that is really just -- it's hard to put a monetary value on what that means just yet. But I would tell you that we are very, very quickly approaching the spot where the technology is not the barrier to make any of that happen. But there's one key. You have to have all the data in one spot. You have to have an AI data platform. to actually make that work. You've got to be able to get all that data very quickly. You've got to be able to collect it from multiple endpoints in real time, and I like our chances to do that. That's just one example, Ari, one industry. I can go on about all 22, but...

No, the fact that what you've done, I mean, the real -- it impacted us. The fact that hospitals suddenly have access, they're connected to the bank, the banks. And we've connected hospitals to banks. Now the banks can look at if you will, a bunch of receivables and see are those receivables likely to -- is the payer likely to pay the insurance company likely to pay the hospital? And can I safely make a loan to the hospital. So again, these ecosystems as they get connected, and the liquidity you're providing to the medical business, maybe just a couple of minutes on that. And I love the example -- there's an Oracle example that did directly affect our quarter. Absolutely. It's worth mention...

Can I give that? I give Yes, you got it. All right. So we had the banks here earlier. We had JPMorgan at Bank of America here earlier, and they went into quite a bit of detail about the ecosystem.

So I think what's -- to your point, Larry, we're not just talking about automating the health care system. We're actually talking about creating the ecosystem that didn't exist before. That ecosystem is the automated real-time autonomous connection between the banks and the providers. We had -- as we mentioned, lots of very big hospital systems have cash flow problems, not necessarily a long-term receivables problem, but a point-in-time problem, which is they don't have the cash to pay certain bills, including sometimes our bills at any given point, money that they owe us. So we actually connected one of these banks that was up here today with the hospital system. And based upon the real-time debt -- based upon the real-time access to the receivables position, the quality of the payers, all the things I talked about earlier, how many readmissions we have for hip infection and all that stuff, based upon all that data, which they never had access to, they actually created a municipal bond offering for that particular hospital. And actually, we're able to, for the first time, rate it -- actually rate it. So one of the hard problems that banks have is exactly rate the debt because it's a little bit of a crystal ball to say, what's the timing? What's the repayment factor and what's the risk profile associated? That customer in the quarter was able to pay a bill that they owed to us. Now fortunately, what's more important, they were able to pay the doctors and nurses and everybody else that was in there. But as a side effect, they were actually able to pay us a bill that was, shall we say, rather late. So these type of ecosystems benefit everybody and the bank actually got a customer that they didn't have before. So that's the kind of situation that works out in terms of these automated...

Yes. I mean the fact -- the liquidity that information is able to provide that the banks can use AI to consume that to come up with bond ratings to decide to make -- take what the interest rate should be on the receivable, figuring all of that out. With all the information, you know the receivables safer, you can charge a lower interest rate because it's more likely to be paid. I showed you a picture of greenhouses for the food business, but we got really interesting modular hospitals that, again, we're not doing these, but partners are doing them. And the modular people of the modular hospitals are coming to us and saying, we'd like to incorporate all of the Oracle technology into these modular hospitals, which we will be building all over the world. And people all over the world, it's a huge -- health care is a huge market. A lot of people can't afford it right now. As the world gets wealthier, more people will get good health care. The global economy is going to get much bigger, help us make our numbers.

Siti Panigrahi from Mizuho. Larry, I want to ask you about enterprise application software. How is that going to evolve in this AI world? Means do you expect this to be rewritten like we see in prior architectural safe like client server, 3-tier cloud applications were rewritten. Do you think in this agenting world, enterprise applications have to be rewritten again? If so, how Oracle is positioned?

Yes. We're constantly rewriting our applications. Steve, I remember I get this every 3 months, we come up with a new version of our applications, which is just unbelievable. If you think about what it used to be like and SAP customers would upgrade every 20 years even if they didn't need to. Well, actually, that sometimes they wouldn't upgrade in 20 years. They just -- 30 is fine because it's -- you know what some of those SAP implementations in excess of $1 billion to put in SAP. You don't want to do that every 3 months. So we give we're continuously rewriting our applications. Michael?

We have 600 AI agents live right now across our portfolio of applications, and we have 2,400 customers that are already consuming those AI agents. That's across the industry applications and the Fusion applications. As I mentioned earlier, in banking alone, within the next year, we'll have an additional 126 agents live, just in banking, not counting everything else that we do. And by the way, if history is any teacher for us in the AI world, history is not that long ago, Steve, right here on this stage last year predicted that we would create 100 new Fusion over the last 12 months, we actually created 400 and had 400 go live. So that's the pace and scale that we're moving. Will applications become a collection of AI agents? Yes, and that's exactly what we're doing. That's exactly what we're doing. And certainly, again, at the expense of repeating myself, much easier to do when you've got the underlying infrastructure and the AI data platform on top of it, and we are the custodian of the world's most valuable data. So our agents are incredibly rich, and they're very easy to adopt. They're just part of what we do in our quarterly release. There's not a special AI agent release vehicle and a special implementation plan. You just take it out of the box and it works.

Larry, you've historically seen value in software companies when the market has been skeptical and you've never been afraid to price software for the value it delivers. Now we're in a time when the market is questioning the terminal value of application software and is worried about the seat-based model. Do you think AI will shift value away from the application layer to other layers? And how do you think software should be priced?

Interesting question. The do I think the seat-based model works? Yes. I mean I think it's a combination. There really are 2 models for pricing and applications right now. There's kind of CPU consumption. I mean how much -- and then there's -- so there's the -- on the supply side, how much CPU did you use? And then maybe storage, but primarily CPU, how much CPU, GPU did you use on the supply side? And then on the consumption side, how many different people used it are the 2 models. And I think we'll continue oscillating between those 2 models. I think for certain kinds of AI, very complex reasoning, I think we're going to go by the supply side model, you're going to pay for GPU usage. For some of the more agentic things, automatically fill out my expense reports for me, something as mundane in that is that. Give me a couple of choices for a doctor's appointment is like in the NHS in the U.K. Give me show me the soonest appointment I can get and show me with my preferred doctor, how soon I can see my friend to decide between those or you figure out and decide between those and schedule it for me. That might be down to -- still be down to how many individuals are using it versus what the costs are of actually supplying the service. And I think, again, we'll be -- we've been constantly adjusting that. this is nothing new. We've been adjusting between these 2 models for many, many years. What I don't think makes sense, by the way, is people saying, well, here's my application, and here's the really cool AI stuff you buy separately. I wouldn't even know how to build an application like that. If you don't buy the AI stuff, your application won't work at all. I mean it is your application. So I don't understand the separate charge stuff where -- because the AI is going to be so dominant. I don't -- I think that's a transitory model. That model will disappear from most of the application companies who are using it right now during the introduction of AI. We're more in the middle of this converting our applications to AI, where they don't work without the AI parts.

It's John DiFucci from Guggenheim. And Larry, thanks for the answer. That was actually my question. The last part, too, I really appreciate. But -- another question I had was AI changes everything. In Oracle, some of the stuff you showed us today is changing the world of health care and maybe agriculture. I mean these are things that Oracle has always run a business for profit. But I see something more here like to me anyways, having covered Oracle for 26 years, it's Oracle sort of doing good for the world, not that you haven't before, technology itself does it. But I guess going back to running a business for profit because there's a lot to do with Cerner. You guys -- you acknowledge you have to rewrite everything and you're doing that. But when should we expect something like Cerner, which is huge, to get to the profit margins we'd expect for a software company, if ever? And how should we think about that?

Well, I'll give you a very high-level answer because I work at a very high level space. He's the guy who builds those applications. I'll let him go into the detail. But I think we will have largely finished the rewrite of all of Cerner next year, everything will be new. And we will have a comprehensive -- and it's not just Cerner that we rewrote. We're writing agents for payers. We're writing agents for clinical trials and all of these other things. So it's going to be much bigger than Cerner ever was. Cerner automated hospitals and clinics. This is automating the entire health care ecosystem down to individual patients and individual doctors, individual nurses. The code will be in place next year. And I really don't understand how a small company can compete with what we're doing in health care. But Mike...

So in terms of doing good for the world, look, you're absolutely right. I mean, I'll tell you that there's nothing more inspiring and there's nothing that will tug at your hard strings more than walking around a VA hospital and looking and working with people, which we do every day, who are caring for those who serve our country in the United States. And that's a mission for us that will continue to be a mission and where we will deliver for all of those people and actually all hospitals worldwide. So you're absolutely right. There is something here that's a mission, and we've got people rallied around that -- in terms of thinking about how the business evolves profitably, I don't think we should stop and just think about Cerner. I think we should think about the entire ecosystem of what we built. We had customers 2 of Epic, our primary competitor in the EHR market. Two of their largest customers in the world, the CEOs were on stage with Sima just 3 weeks ago at our Orlando conference, and they were talking about the AI data platform. We're not just talking about the EHR. We're talking about all of it. And it's going to take all of it to fix the -- at least the American health care system. The American health care system, there's no more money to go into the American system. As we said earlier, we actually have -- to be successful, we have to take money out of the American health care system, but I think we can make more money doing that because the only way to take money out of the American health care system is to be able to automate the entire process. Just the EHR is not enough. It's got to be supply chain. It's got to be HR. It's got to be finance. It's got to be the banking relationship. It's all of that. And when you look at our Fusion business, health care is actually one of our most popular businesses for HCM and ERP applications. In fact, we're doing wonderfully well in the health care business with our Fusion business. When you look outside the United States, we are the largest provider and our growth profiles are excellent outside the United States in just a pure EHR market before you even count all the rest of it. We just won a deal yesterday in the Middle East that was signed here, displaced SAP with Fusion. -- with our Oracle Health EHR, with OCI for all their bespoke workloads. People are buying all of it. So we no longer think about the Cerner margin, so to speak. In the first couple of years of acquisition, obviously, we keep those things separate. But we don't think about it as Cerner anymore. We think about it as the Oracle Health ecosystem. And when you put that all together, we're actually already operating at a clip, which is not diluting the company, not dilutive to the company margin. And I think to the point that Larry made, I just don't see how the competition is going to keep up with what we're doing because I go all the way back to sort of first principles with -- and this is what I said to some analysts at our health conference -- how many fuel cell power plants is Epic building? That's the first question I asked, right? How many large language models are -- generative AI models are positioned on their cloud stack? Second question I asked. Then I stopped and I said, you get the point, right? Unless you're going to do all of it, you're actually not going to do that. And I think the health care system in the United States is tiring of point solutions. They're tiring a very large system integrator. They don't have the money. just don't have the money. The only answer is complete system automation. And for those reasons, I think going to be just fine, if not wonderful in health care. And to your most important point, going to do well for the world in the process.

Let me just add one thing that I've said several times before, that is what is our model for all of this? My model, at least in my head is Must'slaw. I don't think Elon ever created Must'slaw. I mean he just -- he didn't write it down, but he just did it. And if you look at Tesla, how did you -- he built -- had to build an electric car ecosystem. He couldn't -- the problem was not building an electric car. I mean if you build an electric car, you've got to be able to recharge that car in Sweden and Norway and Vietnam and all these other places. How do you build a global charging system? How do you do that? How do you build -- building electric car is really easy. Rivian can do it. A lot of people can do it. I can build an electric car in my basement. But how do you build 10 million of them, 20 million of them a year. How do you build those factories? The largest building ever built in human history was in Austin, Texas is the Tesla factory filled with robot. You got to build robots. The robots he's building, the Optimus robots, the first inspiration, the first use of those Optimus robots are in Tesla factories. He had to create the back of the model Y is one piece of steel. It was -- it's lighter. So you had to create all new stamping machines for doing this, different machines, different factory automation, supply chain, battery technology, battery science had to do the whole ecosystem for an electric car to change transportation. but he did it, and it works pretty well. Those cars are rather inexpensive considering they have these incredible computers and drive them and he had to solve one of the hardest AI problems of all time, which is self-driving and real-time AI. So you take what he did at Tesla, and that's how at least we look at the ecosystem for health care. It's not simply a hospital or a clinic. It's the entire the patients, how do patients make appointments? How do payers do payers decide to authorize that hip replacement or not? What's the process they go through. How does a regulator approve a new drug? How does a pharma design a new drug? That's the entire -- the health ecosystem, by the way, a good deal more complicated than and larger and a bigger opportunity than the electric car market, even if the electric cars are fancy electric cars and fully robotic. So the way we're approaching these problems, the way Mike's teams are approaching these problems is to look at automating the entire ecosystem. We -- our HR teams changed when we bought Cerner because we now had to train doctors, train nurses, schedule people differently. It was partially -- they were partially gig economy, partially hospital employees, supply chain, inventory, all the shipping, keeping track of inventory in hospitals unbelievably complicated. I'm going to go into all of the details, but we have to design RFID tags and RFI readers. There's security in hospital systems. It's doing the entire ecosystem. If you do the entire ecosystem, you get a much better result, and it's a much, much bigger opportunity.

I just wanted to say congrats to Mike and Clay. Obviously, you guys are going to be great partners with Larry, but it's the only time I'll say this, but I can kind of miss Safra a little bit.

She's here.

Larry, Mike Clay, unless you want to keep going, this will be the last question. But if you want to keep going, they'll stay here all day with you.

Except to these guys. I've got nothing to do. I'm retired. -- actually not true. It's really not just Safra was just shaking your hand. No, no, no. No, no. Okay. So you get to retire, I don't. Okay. Something is very wrong with that. That doesn't mean -- I know you're not retired. But you're sitting there and somehow I'm still hitting here. Okay. All right. I'll figure it out in time. I think you guys want to keep going a little bit up to you guys.

Awesome. I'm over here, Larry, Brad Zelnick with Deutsche Bank to your left. I actually have 2 questions. Larry, my first one, I've been coming here for many years. And every year, I look forward to seeing you and the team every year, I get a year older, and you seem to stay the same age.

If it were only true.

My first question, if you have 1 or 2 tips for how we all stay young, I would love to hear it. My second question for you and for Clay.

I'll send you the research papers.

Okay. As we think about Stargate, can you talk about how strategic AI is to governments around the world and how Oracle is working with them hand-in-hand to make it all a reality?

Yes. Well, I mean, I think if you imagine for a minute that you were a government, and let's imagine that what we've all been saying is at least mostly true, you would want to make sure that you have that technology available inside your country into your citizens. Otherwise, I think in the same way that people have -- for the past few hundred years, well, how do I manage my food supply? How do I manage my energy generation? How do I make sure I have the right telecommunications, right? AI is technology that we think is even more valuable than that. You need to have access to it. And so we're in constant conversations with different governments in multiple different areas. I think one aspect of it is around how do you make sure that your citizens are getting access to the AI, right? And that's about bringing access to those models to those different geographies. There's the conversation about, well, how do we get things like sovereign AI. Part of the way in which we solve that, if you think when I was talking about our distributed cloud situation, we have a set of technologies that enable us to actually deploy regions globally to enable them for individual customers, but also for local sovereign operators. So as an example, the one I used earlier about EA, they not only have a sovereign alloy that they're using to serve the needs of the UAE government, they also have access to the latest and greatest GPUs inside that environment. So I think it's critically important for all of these governments to have that technology, not just from an infrastructure perspective, but then also making sure that -- because if you don't have the infrastructure, how can you suddenly then deploy the latest and greatest EHR on top that makes use of all that AI. And as Larry said, we don't have a non-AI version anymore. Now specifically to the question about Stargate, I think lots and lots of countries are very interested in how do we get that sovereign AI. And we have good relationship with OpenAI as kind of, I think, due to our flexibility and our speed of being that partner of choice to go out and then deploy local sovereign AI infrastructure facilities for them. And so there was UAE Stargate. There's things that we're working on right now in Africa. I just had a conversation with a customer yesterday out of Latin America that's very interested in the same thing. And part of what I think makes us a great partner in those sovereign AI conversations is that we can scale up and we can scale down. Not all countries need a 500-megawatt OpenAI deployment. Some of them might be very well served by 5 megawatts. Well, we can do that, and we can do it quickly. And our relationship with the top leading model providers actually works to our advantage because then we can go deploy the infrastructure and then suddenly the AI is available, both in terms of sovereignty as well as in local secure access. So I think Oracle is extremely well poised across all the different layers of the stack for sovereign AI and enabling all the world's citizens to get access to that technology in a secure and controlled way.

Alex Zukin with Wolfe Research. Thank you for an amazing day, truly unbelievable numbers. I have maybe just a quick 3-part question, which is...

Only 3 parts?

A quick 3-part question?

Is there a prequel?

I have a really fast 9-part question. Sorry, I enjoying myself up here.

As we look at the pacing of reasoning versus training, when does that inflection point happen? Like in 2030, what does that workload ratio look like? Because it's important for margins. Interesting. You talk about demand and supply. How do we think about the pacing of CapEx to facilitate that? And then any back story on how you're able to kind of over jump or jump over some of these other hyperscalers to win and become the preferred vendor for some of the model companies as they scale beyond what we all thought was possible in a given time scale.

Those questions have almost nothing to do with each. I feel like -- would you like us of ice cream on the salad with some bread. I've already forgot. Did you remember the first part of the question? I'll start with the back -- the last part, why are people picking us? At least I remember that part of the question, I can answer it quickly. Look, the reason that customers are picking us is for the reasons that we say customers are picking us and for the same reasons that those customers also stand up and tell people like you why they're picking us. And like I said, Peter from OpenAI was on stage yesterday explaining exactly why they pick us. It's because we are extremely fast at getting things done. Everyone has excess needs. If you can be somebody that has an answer that says, okay, you need capacity. If you go 2 years, 2 years, 2 years, 8 months, okay. I like 8 months better than 2 years. Okay. If if you have unique requirements where you show up and you're like, "Hey, I have custom requirements around some of my networking or the way in which I want to power cap this infrastructure, I want to codesign it with you or perhaps I want to deploy it in a location that might have lower cost, okay, no, no, no, yes. Okay? So if someone is saying yes to the things that you need and they're delivering very, very rapidly, and they're extremely high performance and very cost efficient, that's why people are picking us. And that's why it's not just one customer, it's all of the customers, both very, very large customers and small customers, right? When I showed the details of that 700-plus AI infrastructure customers, that's why they're all choosing OCI. Now not 100% of the market, like we do definitely have competition. But as we continue to execute and as I think Peter said yesterday, this community is very small. As this information gets out there more and more, all of those people are coming to us with more and more capacity bands. Now you're going to repeat the other 2 sections because I already forgot. Larry, do you want to talk about training versus reasoning and how you think it changes?

Well, everyone is going to do reasoning and very few people are going to do training. So it will definitely cross over. I think I wish -- I think it's a great -- and actually, it's quite a fabulous question because I don't think we have enough data points yet to be able to figure out when it's going to cross over. I think what's going to help is the AI -- believe it or not, I think it has a lot to do with how good our data platform is. If our AI data platform is as good as we think it is, it's going to help people get to inferencing/applied AI/reasoning, all the same thing, using the models as opposed to training the models, which is, again, everybody. So we have to make it easy for everybody to do on their own private data. 5 years from now, will we still be spending more money on training than on reasoning? I don't know. Anyone -- Clay, do you want to take a shot at that?

Well, I'm going to have a complicated answer, and then Mike is going to have a useful answer. I think people also -- I think it's an interesting question. I think it's also a question that's unanswerable. So look, if you think about what training does, we take together all of the information that humans are willing to share and then we train something on it. Eventually, you run out of data, okay? Well, how does data get created? Well, people use reasoning to create data and they write it down. If you actually look at where AI models are going, much of the data that they're going to be training on is actually data that they're doing their own reasoning on. So I think this question, if you actually look at what I think in 5 years, what most training of AI models looks like, it looks like AI models thinking -- and then AI models using the results of that and going, oh, is this better or not? If you look at how RLHF, right, reinforcement learning with human feedback works today in these AI models, what's happening is you train a model and then you give the results out to human beings who score it and then you feed that back into the training data. Guess what happens as the models get really smart. You don't pay a bunch of people to score the outputs. You have models score the output. So I think that this -- that's kind of what I think Peter yesterday was trying to say was like, hey, you think of these as different things. They are the same thing. And in the same way that we don't think of our brain is like, well, are we using our reasoning function right now? Are we learning? You're constantly reasoning. And as you're reasoning, you're also learning. It's going to be this iterative cycle.

Let me reframe the question. So I think it's really simple -- in a way much -- forgive me, I think simpler to understand. When will AI -- the people creating AI models be taking in more money than they're spending?

Well, I think that actually depends less on how much money they're taking in because that's growing very, very rapidly. I think it's when do we reach diminishing returns on spending extra money, how it makes it more valuable. And if I had the answer to that, we would do a different company.

Yes, even the simpler version of the question is still hard -- it's the same question and still hard we create still hard to answer. I think the problem I have, and I agree with everything Clay said, it gets very blurry because every time you ask AI a question, it's learning something. It's training itself. So what is training? What is inferencing/reasoning? -- how fast -- how fast does -- let's just pick one example, OpenAI or Grok, how long are they in hypergrowth mode where they're spending money faster than their revenue because that's very common in the early -- obviously, railroads obviously spend a lot of money. When does it cross over? When do passenger tickets exceed laying of track? I don't think we have quite enough data yet on inferencing and the speed of instruments and growth. Though one of the most interesting calls we ever got was, hey, do you guys have any capacity? -- where? Anywhere? Well, how much are you looking for? All of it. You want to buy all the capacity we're not using everywhere in the world? Yes. Okay. I've never heard that one before, but I haven't heard a lot of this stuff before. This is very strange. I remember I called Safra, but someone just called and said, what is going on. I mean it's not like I was trying to sell anything. They called us. I just wanted to buy everything we had. So I don't think we have quite enough data. I think we'll understand it a lot better about -- believe it or not a year from now. I mean it sounds funny, but I think Mike?

It's really a philosophical multipart question. It's a great question. How does synthetic data feed into this? Is it really retraining? Or is that inferencing? And then the next frontier or 2 is how does private data fit into all this? And do we take some of that scale and create very rapid small language models, too for enterprises. I think that's going to continue to evolve over the next, I would say, 2 years, but probably not even at the rate we're moving. We're going to have clarity on that. But I think either way, we're going to be in a good position to serve those markets.

I'm going to say this is the last question for me.

You guys keep going. That's fine. If he leaves, I'm leaving. We work together as a group, okay? You can't just walk off and leave me and Mike out here.

Watch me.

All right. And then Matt is getting us off the hook as well. So thank you all very much.

Are we done?

You are done.

All right. Thank you.

Please welcome to the stage, Ken Bond.

All right. Okay. Thank you all very much. That concludes the show. Thank you very much for coming out. Really appreciate you taking the time out. Look forward to it. If you have any questions, just follow up with us. Take care. Safe travels home. By-bye.