Oracle Corporation (ORCL)

Investor Day 2026

← View all transcripts

Oct 28, 2026

Summary

AI is driving a broader growth strategy spanning infrastructure, database, and agentic applications, with faster cloud migrations and usage-based monetization. FY2030 targets include $225 billion+ revenue, 40%+ non-GAAP operating margin, and $21+ non-GAAP EPS.

Wow. Great to see everybody. I personally am very excited about this meeting, as you may know. Let's dive in. Knowing this is my last meeting with you all, my wife asked me over the weekend, she goes, "What are you going to miss most about being at Oracle?" I thought about it. I know what it is. I said, "You know what I'm going to miss most about being at Oracle? Yeah. Safe harbor slides." My friend, the safe harbor slide. Those of you who've been here before, you know that I always like to have just a little bit of fun with the safe harbor slide. Kidding aside, I'm showing these slides because you're not going to see them in the decks later on as we go through the day. It is our safe harbor slide, and in there, really what we're trying to tell you is that we're going to be making some forward-looking statements today. I just want to remind you that these are forward-looking statements, and to be clear, as forward-looking statements, there are risks and uncertainties that go around those statements. If you'd like to know more about those, you'll want to open up any of our 10-Qs, 10-K, look in the risk factor section, and you get a lot more information about that. If we see circumstances change in the future, I just want you to understand that the forward-looking statements that we're making today, we're not obligating ourselves to refresh those at a later point in time. Okay? For Hillary, she will be using a lot of financials. In that presentation, she will be making use of non-GAAP measures. Just want to make sure you're clear that she will be using those, and they're non-GAAP. Okay? Finally, more from the product people and the services people, just understand that today's presentations are just really for informational purposes for you, and they're not any type of commitment to deliver on any material code functionality that we're discussing today. With that, let's talk about the agenda. We're going to start with Mike's going to come on stage shortly. He's back here, ready to go. Mahesh will then come up and talk about infrastructure and database. Chris Leone will come up and talk to you about enterprise applications. Simon will then, excuse me, Simon will follow with industry applications and NetSuite. Mark Hura, I see is here. We've got a couple customers we'll be having come up on stage as well, talk about customers in action, how they're actually using Oracle technology in their businesses. Then we'll take a break. I think that's going to be around 11:00 A.M. So just if you're thinking about when, about then. Coming back from the break, Hillary will come up on stage and give you the CFO perspective, give you some sense of how we're expecting things to play out over the next few years. Then Hillary will have Clay and Mike join her on stage for a Q&A. Okay? Following the event, somewhere around 1:00 P.M.-ish. The break will be about 11:00 A.M. We'll finish around 1:00 P.M., and then there would be box lunches for you in the back where you had breakfast, and you're welcome to stay in this room, if you'd like, up till 2:00 P.M. I know that we've got some companies announcing, if I remember right, Microsoft, ServiceNow, so if you want to work from here from 1:00 to 2:00, you're more than welcome to do so. With that, let's get going, and please, let's roll the video. It was just two years ago that we made the decision to change the name of this conference, to rename it AI World. We also made a claim that AI changes everything. Let's take a look at the highlights from Oracle AI World 2026. This is as big of announcement as we can make. Fusion Claw, 75 agentic applications. Dozens of agents for all the industries in this room. Deep Insight Agent. Oracle AI Security Mesh. Jalapeño. Google ADK. Oracle Deep Trust AI Architecture. NetSuite Next. Semantic Reasoning Engine. Oracle AI Workspace. Oracle Restaurants Operations Command Center. Oracle AI Agent Studio for industry applications. Video Edge. Fusion Setup and Configuration Registry. SuiteSkills. Oracle Media Fabric. Snowflake on OCI. Autonomous Clouds. Oracle App Composer. AI Data Platform Innovation Service offering. You get the registry, and you get the registry, and you get the registry. Trust isn't something we can announce. It's something that we must earn through the problems that we help you solve, from optimizing freight shipments, helping employees through difficult moments, to improving how restaurants run, getting critical patient information into the hands of emergency doctors. The goal is for you to spend less time getting software ready and more time putting it to work. 13,000 customers are not just asking questions. They're getting real work done. The lab's operating at incredible speed. The place to get started is the Oracle AI Marketplace. In the past nine months, we have been building AI, embedding on all of the pages, as many as we can throughout the system, to really take the friction away from employees. If you have the right data, AI is pretty simple. In our ecosystem, Oracle sits at the point of sale, so it is a very strategic position with regards to certain AI use cases. It is really about embedding intelligence, embedding AI within tools that they already use. It simplifies. It makes it more intelligible, which makes it easier for your team. I can just sort of move from one idea to another pretty continuously and without getting blocked. It is like I am managing a factory inside of my computer. AI is letting us reimagine a lot of those workflows, really looking across the entire product sphere and saying, "How does AI help us make a TV series become a movie? A movie can become a video game. We really want to be on the leading edge of AI and where it's going. We want to partner with partners like NetSuite that are really leaning into it. The train is on the tracks. It's moving. It's time to get on board. AI is now helping us discover new science, invent new technology. We're seeing that in some of the most valuable technological problems we have. Look what's possible now. Most cancers will be gone in 5 years, and certainly in 10. This dream Because the AI enabler is becoming a reality. We are also helping farmers make better decisions. We built Oracle Agriculture Intelligence to help governments answer questions about food insecurity. Because of AI, we will all become healthier, wealthier, and hopefully wiser. The question is not whether the old way of doing things is changing. It is. The question is whether you are ready for next. The future of implementations will be software-driven and AI agent-driven. In time, everything's going to be AI. AIUI, it's got an AI user interface. All of it is based on AI. AI should allow you to barely think about the software at all. We're freeing people from system burdens, be they forecasters, freight operators, or healthcare providers. We're giving people more time to be human, and that's the ambition. Welcome to Oracle Investor Day. Please welcome to the stage Mike Sicilia. Good morning. Good morning, everyone. I've had the good privilege of hearing Ken read that safe harbor for 18 years in person, and I'm certainly going to miss it. It's been a timeless classic, like the voice of the monorail at Disney World. It's going to be hard to get used to a new safe harbor read. Anyway, thanks for being with us here today. I hope you've had some time to enjoy both AI World and Las Vegas. We find ourselves at a very interesting time, and throughout our history at Oracle, we've never been afraid to make bold moves, as many of you know who have been covering the company for so long. Five decades ago or so, Larry saw that potential for relational databases to not just change, but fundamentally change how businesses organize, manage, and leverage information. Along the way, we turned that technology into the foundation for modern enterprise computing. We then expanded into applications, infrastructure, cloud. We've made many significant investments, always tried to challenge conventional thinking, always tried to be different and differentiated, and therefore, built the technologies that some of the world's most important businesses and governments depend on every single day. In so doing, we weren't just responding to at any point in time where technology was at that time. We're building for where we believe the technology needs to go. In the age of AI, all of that matters even more. First and foremost, it's going to make it easier for customers to move to modern cloud platforms through the automation of go-live services. We think about the time, effort, and capital that it takes to move large mission-critical systems to modern cloud systems. Anything that we can do to accelerate that has always been appreciated. We can now dramatically accelerate that. That's going to be a fundamental unlock, not only for our growth in the applications business, but also a terrific value to return for our customers and allow them to get time to value far more quickly. Second, once our customers get to the modern platforms, they get the AI built right in. You've heard that, and you'll hear that again from Chris and Simon as we talk about the sheer amount of AI features that we're putting into our application platforms and all of our platforms every single quarter as we release these products. In fact, we believe that AI has the potential to fundamentally change, just like the database fundamentally changed enterprise computing, what is possible for businesses, for people, and for the world by solving problems that have challenged us for generations. You heard in the video Larry talk about curing cancer, which I think is not at all far-fetched. I hear it from our customers, our hospital systems, our life sciences customers. They tell me things like, "I think we have the opportunity to, at a minimum, turn cancer into something that we can live with. It is not a death sentence." But we would not be able to do that without the Oracle technology. So if we can detect cancer years earlier, we can rethink how we produce food, energy, and the things that our world depends on, unlocking really extraordinary new levels of productivity and economic growth. Because of our full stack advantage, we have never been in a better position to deliver that to customers. We find ourselves in a very unique, I think, position to benefit humanity. When we do that, we want to be careful that it does not seem like virtue signaling. These are real deliverable outcomes for customers, food, agriculture, healthcare, that are going to solve problems that previously could not be solved. So think about healthcare, for example, whereas there is a lot of positive sentiment around healthcare AI, and the intersection of clinical research and clinical care, and why the full stack matters. That requires high performance, secured infrastructure with very close provisioning to the AI models. It also requires the aggregation and vectorization of hundreds of thousands of healthcare datasets. Further, it requires purpose-built AI applications that face researchers, patients, and providers. I know of no other company besides Oracle Corporation that is providing every single piece of that. If we think about taking, Larry spoke a lot yesterday about robotic surgery, and think about the complications of taking robotic surgery at scale into the operating room. First and foremost, patient safety is paramount. Secondly, from an opportunity standpoint, it allows you to unlock and potentially perform surgeries that were impossible before. In order to deliver that completely and safely, we need the entire stack. We need scaled GPU infrastructure. We need scaled AI infrastructure. The connection to that infrastructure from the models and all the inferencing and all the referencing that is going to be unique to that particular procedure, and then to that particular patient, requires near zero latency provisioning to those models. You cannot have a gap. We cannot have a timeout. We cannot have a small gap in latency or a small increase in latency. We are in the middle of a very complicated surgery. We need clinical applications. We need vector services. We need IoT edge devices, like the things that we unveiled in the ambulance. You need all of it. Today, that is happening with little stovepipes, piecemeals, pieces that people are gluing together. It is inefficient, it is very costly, it is not exactly secure, and it does not allow us to create a scaled infrastructure that can democratize healthcare. So maybe the top clinical research organizations in the U.S. can afford that. Community hospitals cannot. They cannot do it because they cannot buy it as a service. They cannot take it as a service from a vendor because nobody is supplying that. We have a unique opportunity to do that. Now, I can go industry by industry. I can give lots of similar examples, but the point remains. Our full stack provider provides an outsized and differentiated results, and there's no other scale provider beyond Oracle who's delivering that. If you think about these examples, if any piece of the stack doesn't exist, we can't deliver that as a turnkey service because somebody else has to come in to be able to do that. Somebody else has to come in to connect it. Somebody else has to come in to implement it. That's a tremendous difference. When outcomes matter, and they will in AI, I think we should be in a really good position to deliver. The way I think about it is that AI then is the catalyst that's really activating the full potential of everything that we built. In fact, it's making every part of our portfolio more valuable. We don't think about it just as the next chapter of cloud. It's a fundamental expansion of the opportunity, and we really believe that we're built to lead it. As AI becomes more pervasive, every layer of our stack becomes both an enabler and a beneficiary. Enterprise AI powered by OCI becomes exponentially more useful when it's connected to enterprise data. Oracle Database and AI Data Platform provide a foundation for securely managing and accessing business-critical, mission-critical, highly sensitive data. Oracle Applications already run critical business processes. They understand workflows, roles, permissions, business rules, and industry requirements. These aren't separate AI stories. These are the conditions that we believe will allow AI to thrive. A few recent shifts. As you know, things are evolving very quickly, and there have been a few shifts that I'd like to just briefly discover and how I think our stack is aligned for this moment. The first shift, and you heard Clay cover this in the keynote yesterday, if you had the chance to see it, is around trust and security. As AI moves from answering questions to taking actions, the stakes become much higher. You think about an AI agent making a financial decision, executing a transaction, or accessing sensitive employee information. Those agents need to operate with the same security, the same permissions, the same business rules that govern the enterprise. An AI agent going rogue is not an unfortunate side effect. In many businesses and many missions, it's simply not acceptable. It's not something that we can accept. Our integrated stack helps secure that experience. It helps keep the agents on the rails. Since we've spent many decades protecting the world's most critical enterprise data, we engineer security into the infrastructure, protect the data at the database level, and enforce the business rules and permissions with our applications. These are the governance rules that are already in place to run mission-critical systems. We're embedding AI right into those rules to keep it on the rails, to keep it safe, to keep it secure. Rather than relying on a bunch of separate systems to establish those controls, we have designed them to all work together. As enterprises give AI more responsibility, I think that becomes increasingly important. Okay, another thing that's happening in shifts is the way that end users, customers are interacting with technology. Increasingly, people are starting their work with AI. They are asking questions, they are making decisions, getting things done through intelligent assistants and agents, not necessarily what we would call traditional user interfaces. Those agents need a very powerful foundation. They need to access the right data. They need to understand the business processes. They need to know exactly what actions they are authorized to take, and they need the infrastructure to connect all of that very efficiently. Our stack also powers that experience. For example, in NetSuite Next, we announced this week, AI is the UI. Workflows are aggregated by agents operating right within the system, not outside. AI is the UI. Fusion Claw makes AI the UI, not just for a single app, but across many applications, be they Oracle apps or be they somebody else's apps. It does not matter. AI is the UI. Fusion Cloud is the orchestration layer and AI UI to enable these outcomes. This means that AI can become the UI for our customers for business outcomes regardless of the underlying systems. It does not matter. AI becomes the trusted interface and the partner for getting work done faster and more efficiently. That is quite unique to Oracle's full stack offering. In fact, again, if we are not delivering the full stack, these problems get much harder to solve. The third major shift is moving from what I will loosely call AI experimentation to now real AI outcomes. Customers do not just want to see tokens being consumed. In fact, there is a lot of pushback on tokenmaxxing. They want really AI to provide gains in productivity, they want a lower cost, they want faster decisions, they want more efficient operations. They want to achieve the expected ROI. It is about moving from reactive information to helping businesses proactively take action. The full stack, we think, makes a tremendous difference in delivering on this benefit and producing financial results. Think about example of a customer in a mining operation where a freight train is hauling tons of thousands of material and there is a brake failure. We have IoT sensors that detect that brake failure. At that moment, those milliseconds matter because the immediate safety response needs to happen locally, but through specialized control systems that act in real-time on the edge. What is the rest of the story? How quickly can the train be repaired? Do we have the necessary parts? How do we adjust production? How do we minimize downtime? That is where an integrated architecture creates tremendous value. Infrastructure data, applications, and AI all working together to connect the immediate response with the broader business context. The same principles apply whether you are managing a complex supply chain or supporting critical healthcare operations, or rebalancing your workplace. It does not really matter. It is not just about reducing technical latency, it is about accelerating the path from intelligence to action. Again, we are uniquely positioned to do all of this with optimized performance across our entire platform. Our customer base is leveraging AI. We are seeing AI adoption accelerate with very tangible benefits for customers. As an example, I will give you some recent stats on Fusion AI adoption. In Q1 of this fiscal year, so we are in Q2 now, in Q1 of this fiscal year, customers leveraged our embedded Fusion AI capabilities more than 150 million times, with usage growing 42% just from June to August. So 150 million times customers made AI calls in our Fusion Applications just in the quarter. In September of this year, AI usage across Fusion consumed nearly 500 billion tokens, just in September. Month-over-month, it is almost a 23% increase, 22.7% increase to be exact, month-over-month increase from August to September. I did not know that that was possible, right? I had to double-check with Chris, but 500 billion tokens were consumed in Fusion Applications in one month alone. Somebody is using AI out there at scale, because that is a lot of tokens to consume. Our products, the number of agents that we have deployed in our products have grown from over 1,000 in May to 3,500 in September. By the way, that is in the same year, in the same calendar year. We had 1,000 agents live in May, we have 3,500 live in September in our Fusion Application stack. Stunning progress and stunning consumption of AI functionality. We see major brands like DHL, Avis, Orange, and ADT all expanding their use of Fusion Applications and AI. We expect adoption trends to significantly accelerate with our existing cloud customers, given many of the technology deployments that we have unveiled here this week at AI World. We are also expecting further adoption as our on-premises customers can now move far more easily to the cloud by leveraging the AI tool sets that we announced this week that automate migrations and decrease the cost and time to implement. I actually think, and we spent a lot of time internally discussing this is just as important as all of the features, all of the AI features that we are putting into all of our applications. Time to value, time to live, automating migrations of on-premises customers. By the way, they do not have to be Oracle customers. They can be competitor customers. What we are unveiling here to automate migrations to the Fusion stack, to the industry stack, to the NetSuite stack, does not have to be an Oracle workload. This is for everybody to consume. That has been a big headwind. When you had early adopters that moved to the cloud, there were a lot of folks that got on board, and then you get into a little bit of a stall period because the time, effort, and energy to make that happen becomes sometimes not palatable for certain organizations. I think that goes away. I expect that our large remaining base of on-premises customers that have been eagerly awaiting these developments are going to start to adopt this technology quite quickly. All right. Then as a benefit, as a reminder, when we move customers from on-premises to cloud, we see a multiple increase in revenue. It is a good business for us to be in. Cloud is a very good business for us to be in. It is good for our customers because we put them on a modern platform, we deliver AI out of the box, and obviously from a cybersecurity standpoint, we put them in a much safer spot. I also think that in addition to our applications business, we are poised to capture market share across our entire technology stack from top to bottom. That is a bit about customer outcomes. I want to also talk about taking good care of our shareholders as well, because that is just as important for us and it is something that we think about. As we think about our investments, we think about differentiated growth. We are different on purpose. We offer a full stack, and we think that creates a tremendous advantage. We're committed to strong profitability and strong free cash flows, and we think very respectable EPS results as well. We're also committed to disciplined capital allocation, understanding that we're making the most efficient use of our capital to create the most amount of return for both our customers and our shareholders simultaneously. These are things that we are spending a lot of time internally as we think about investments and we think about building things. We're making sure that everything that we do internally aligns to these three pillars to create and accelerate value for our shareholders. It is extremely important for everybody at Oracle, and we all know that this is the mission. Hillary is going to share a lot more detail on our targets and our commitments after you hear a little bit more about some of our business investments that we're making. I'm sure you won't want to miss that session as you'll see those targets and commitments lining up to these three pillars. We wanted to share that. Incredibly important to us. We're very excited about what we're building. We're very excited about what we're delivering for our customers. We're also very excited for our shareholders because we think that what we're building here is going to create fantastic returns for all of our investors. Thanks again for being here. Thanks for covering us. Thanks for always pushing to make us better and giving us all of your feedback. I really hope that you enjoy the rest of the day, and I'll see you back up on stage a little bit later. Thank you. AI is creating an extraordinary opportunity for Oracle, and we're moving aggressively to meet customer demand by rapidly adding AI cloud capacity now and into the future. Oracle brings more than a decade of cloud services and data center experience to this expansion, combining the speed this opportunity demands with a proven discipline to deliver. Across multiple sites, Oracle teams work with experienced contractors and specialist partners to design and build for each location's requirements. Our partners bring expertise in construction, permitting, power, and cooling. Oracle brings it all together with the computing systems and cloud operations each facility will support. We build in phases, putting completed sections into service while construction continues. Advancing sites in parallel reduces dependence on any single schedule, while lessons from each phase improve the next. Large projects encounter challenges. Experienced teams and backup plans help us respond quickly, manage risk, and keep work moving. Oracle works with communities throughout the build-out, addressing local needs, creating jobs, opportunities for suppliers, and tax revenue that support community services. We're committed to paying our own way on energy, funding the infrastructure our projects require so those costs aren't passed to local ratepayers. Our new AI data centers use closed-loop, non-evaporative cooling that recirculates water after the initial fill. We use reclaimed or non-potable water where feasible to reduce pressure on drinking water supplies. We start with AI development environments from our processor partners and full rack systems, the technology our customers want and need for their AI workloads. Then we add Oracle's high-performance networking, cloud software, and management expertise to make that computing power easy to use and easy to manage at highly competitive prices. Customers can focus on building models, launching AI services, and growing their businesses. Oracle manages the infrastructure underneath. Every phase we bring online expands our ability to serve a market with enormous demand. With leading technology, experienced partners, and a disciplined approach to delivery, Oracle is moving decisively to seize the AI opportunity and make it a powerful engine of long-term growth. Please welcome to the stage Mahesh Thyagarajan. All right. Good morning, everyone. Cool. What I'm going to talk about today is it's very clear that the next phase of AI is going to be about who gets faster access to compute, and how can customers generate the most optimized TCO for the outcomes that they actually want to drive. A lot of my session today is going to be about popping the hood, talking about the engine. If you are some of the people who don't like infrastructure, you're probably going to have a very bad time. Let's dive right in. As you know, this is data. This is public data that's already available. This is the last six quarters of sequential growth that OCI has actually shown. What I'm going to do today is explain the set of things that we've actually done over the last year and why we believe this is accelerating, why we believe we've scaled this out enormously. Third, how fungibility works at the infrastructure layer across multiple customers. Lastly, talk about the durability and the differentiation on what we're building. I want to start from the bottom of the stack. Let's start with power. When you think about the power and the challenge with it is first moving to locations where you can actually get access to energy resources. The first challenge about moving to a place where you need access means that you need to build your cloud right next to that place. In Oracle, at OCI, we have built a foundational differentiator of building our regions in less than 30 days as soon as the power is available and the data center is available. That gives us an edge, an opportunity, an advantage to go to places where the power exists. Second, you go to places where there is energy, but you need to build the power and energy plants. A lot of what we have done is first, we were the first hyperscaler at Abilene to decide to use gas turbine-based power generation as a behind-the-meter solution for our backup power. This was in 2024. We are talking two years ago. Second, when we picked our next gigawatt-scale data center, once again in Texas, where there is a lot of natural gas resources, we selected VoltaGrid, which is actually a reciprocating engine-based solution. Third, when we went to New Mexico and we looked at feedback from the community, we actually pivoted and evolved to building a gigawatt-scale fuel cell solution. When you think about all of these different solutions, people inherently, the AI workload has different set of challenges. It is easy to say, "Hey, we need to go build all of these solutions and put it together." It does not actually work. You have to understand the challenge that comes with it. First, when you think about AI workloads, they operate as one giant AI supercomputer, as one computer. This means when something breaks or the workload is stopped, the power drops. Then when it ramps back up, it ramps back up pretty fast. The challenge of handling this across various different power sources is a unique challenge, but that is where Oracle differentiates itself. We are in the business of accelerating the delivery of our compute, and so we have done tremendous amount of engineering work with our partners and many different solutions around existing technologies that exist, like UPSs, to make sure this power comes online and it is ready for consumption. It does not mean that we are building all of our data centers with fuel cells or other solutions. We do connect with the grid. But we have a very simple principle in terms of how we connect to the grid, where we say the way we consume the power from the grid should look identical to there being 1,000 homes. That requires a tremendous amount of engineering and building technology that ensures that the grid interface is presented in a very smart way. I am going to take a simple example. When you think about that load ramped up and down, we have what I call as like the UPS systems that exist to handle sort of these swings in any data center. What we did is we went to those UPS systems, we understood the profile of an AI load, tweaked the software and the firmware that runs on these UPS systems to essentially protect and present an interface to the grid. When this load ramps up and down, it does not actually see it. The great part about that technology is it does not apply just only to the grid, but it applies to the gas turbines, it applies to the fuel cells. A lot of this is foundational technology that we innovate, even on something as simple as power. You build all of these solutions, but then how do you know it is actually going to work? We do not take that for granted. What you are seeing is a real 10-megawatt AI power load testing simulator that is right now deployed and testing our reciprocating engine solution at Shackelford, Texas. This is a solution that we ensure whenever we deliver that power, it actually comes out and is ready to start running compute immediately. We cannot do this without partners. We have a ton of partners, both from grid providers and the energy solution providers like VoltaGrid and Bloom Energy, to bring all of this online on time and accelerate it. Another interesting opportunity or a challenge is working and figuring out how do we explain the responsible data center to the communities. We have a five-pronged approach in terms of what we do across our data centers, and we explain it to the community. First, we create good paying jobs. Two, we prove with facts. For example, at Abilene, we have actually taken local talent, converted them over 200 people who have not gone, were just from high school or community colleges, and employed them at Abilene. That story resonates with the various communities that we are a part of. You saw a closed loop cooling video. We employ innovation and technology to use closed loop liquid cooling systems, not just for the data center, in many cases like the fuel cells, even for the fuel cells. That ensures we are not using the water, which actually helps in our community strategy. Third, we invest and engage in the community. Lastly, we actually drive accountability. One of the projects that we announced is about the transparency initiative, where we are transparently going to share the usage of air, water, heat, light, and everything on a public dashboard because we are very confident about our technology. We talked about power and what we are doing to bring power online, stable power, reliable power online for AI workloads. Next, I want to talk about what we do in the data center side of the house. For me, it is a few things. One, when you think about an entire construction project and the set of things that you do to build a house is one task. The second task is go inside the house and actually remodeling it the way you want it. One of the fundamental things that we did in the last two years is to not think of them as two projects, but think of them as one project. We found the hardest path and complete parallelization of doing both of these activities together. Second thing that we did is to ensure all of our data centers are modular, so you can actually repeatably stamp all of those designs across multiple different data centers, so you are not taking a longer time to do supply chain, longer time to finish the designs or get it approved. The third thing that we do is we design those data centers to be flexible independent of the compute architectures that we are going to deploy. As you heard, I will talk a little bit about it, we have many different types of hardware platforms that have come online, but we may not know at the time of the decision-making what hardware platform is going to be there. So our data center designs are innovative to be very, very flexible. The last thing that I want to remind is that these data centers are phased. When you think about a large data center, it is not like magically one gigawatt of data center show up. I wish it did. It would be amazing. I do not have a job. But it does move in very small phases over a 1.5, 2-year period of time. I just want you guys to understand that. With that, I am going to show you some of the modular work that we did in one of our data centers. Can we play the video? In Abilene, Texas, a coordinated construction process brings together the power, cooling, and computing infrastructure that makes AI at scale possible. Outside each building, power distribution centers, chillers, and transformers provide the essential systems for delivering electricity and managing the heat generated by computing. Inside, preassembled mechanical and electrical systems move into dedicated rooms. Planned access routes allow these large equipment modules to be positioned in sequence, keeping the way clear for the work that follows. This coordinated approach puts supporting infrastructure in place before the computing equipment arrives. Each stage builds on the last, preparing the building for the systems that will power and connect its data halls. Water piping, fiber networks, and cabling connect equipment throughout the building, linking its essential systems. The computing equipment now moves into place. Modules containing server racks enter through planned wall openings and are installed row by row. Power, cooling, and fiber connections bring the data halls together as integrated computing environments. From the building systems to the server rows, a consistent design and coordinated delivery process create the physical foundation for artificial intelligence at scale. All right. What you guys just saw was a ton of prefabricated modular designs that allows us to go airdrop a lot of these things and move them into position, so it enables us to deliver these data centers really fast. The second part of our. Once you get the power and the data center, next thing to think about is the rack, the hardware design, and a lot of that work that needs to happen. But one of the things that we have done over the last year is significantly scale out our supply chain. When I talk about supply chain, everybody start thinking about only the few accelerators or the CPUs, but I'm talking about every small little thing that goes into a rack. Just again, to give you an idea, there's about 6,000 components that go into a single GB300 rack, and it is our job to completely scale out every single one of them across multiple diverse partners for every component. Then you go down the stack at the data center supplier level, at the MEP supplier level. We're completely diversified with many channels that ensures our rack delivery will be on time. The next part of it is manufacturing capacity. I'm going to take one example about driving capacity is one element, but it's about the quality of the racks that come out of it and they getting ready to work in the data center. More recently for Vera Rubin racks, Oracle took over the software that runs in their manufacturing floor to give us that precision and scale that is required so when the racks show up at the data center, it starts working immediately. The other aspect is in the last year, we've doubled the capacity, but at the same time, we've tripled our GPU rack shipments without any delays. Another number that I want to talk about is looking back at Q1 FY 2027, Clay in the earnings call talked about how we delivered about 850 megawatts in a single quarter. But if I pop the hood and look inside, that 850 megawatts delivery in a single quarter was about 38 different projects, and it was happening in about 11 countries globally, and only one of them was our Abilene data center. Just to kind of give you a sense of the scale and the global nature of what we do and how we deliver data centers, that's the magnitude we're talking about. I want to take another data and an example to talk about a sample customer, from customer contract signing to revenue generation. There's a lot of details here, and I'll let you take the data, but the thing that I want to point out is the 21 days. The time is completely optimized in our infrastructure rack delivery and cabling and making sure the customer acceptance goes really, really well. We were able to achieve that in about 21 days from the time that we were able to get into the data center to the time that those racks started generating revenue. You cannot achieve this without the optimization on the network, a tremendous amount of optimization on the rack manufacturing, making sure the racks show up on time, and the quality of those racks, the quality of the power that is going to be on the data center. All of those come together into these 21 days. Just to recap on FY 2027 Q1 numbers. We talked about 850 megawatts. This is roughly 8.5 megawatts of power delivered per day, and that is four times as larger compared to the prior year. All of this work that I talked about essentially enables us to deliver on an accelerated pace. The scale of doing all of this and moving really fast only matters if it works for the customer. At our gigawatt-scale Avalon data center, we have our customer, OpenAI, accepting the capacity in less than 24 hours, and the availability has been over 99%. Let's talk about fungibility. When we think about these AI superclusters, they come in various shapes and forms, and Oracle has the ability and the network designs to achieve this. I want to remind one thing. If you take a single 512 GPU cluster, we were the first in the industry, and we created this for Exadata, by the way, but we were the first in the industry to actually bring security onto our multi-tenant network cluster. A single cluster can be spliced, a 512 GPU cluster can be spliced across 32 customers with complete security, and all of this can be done in a matter of minutes. I want to show you fungibility in action. In the last quarter, we had a customer reallocation where a customer wanted to move to a different GPUs, but new capacity became available. The interesting part about our technology and what we do is that we were able to reallocate that capacity in a matter of hours from customer A to customer B, completely de-risking our ability to actually have capacity that is on the ground and is not used. Just as a reminder, we have a very large AI infrastructure customer base of over 1,000 customers. One of the questions that I've gotten a lot about is, "Hey, you're building a lot of these liquid-cooled data centers, and these accelerator racks are looking different, and then the CPU racks are completely different." We understand the challenge because there are existing data centers that are air-cooled, future data centers are liquid-cooled, so that you have a mix and a combinatorial matrix that is happening on the floor. For us, that fungible hardware and data center design is a foundational principle that ensures our assets are durable over a very long period of time. The only way to get through it is to actually innovate. What we did is we did two things. One, we actually made our CPU racks to be liquid-cooled, our network racks to be liquid-cooled, and we worked with our hardware partners to drive extreme density into these racks where a single CPU rack can draw the power as much as an accelerated rack. That allows me to fungibly place either of these racks in any of the data centers. That's one. The second, what happens to all of these existing air-cooled data centers, Mahesh? Are you going to throw them away? No. What we did is we innovated and invented this concept of a sidecar called OCI L2Arc, which can actually power and cool up to 1 megawatt of a rack, and it sits right next to the GPU racks. In fact, in a data center in the U.S., we actually have over 100 megawatts of these L2 rack deployments that are deployed already and serving customers for accelerators in an air-cooled data center. Let's talk about durability. We've talked at length, and we've been the foundational innovator when it comes to the networking space. We did that for Exadata, as I talked about, in 2018 because we needed to bring RDMA networking directly into our virtual networks. We've been continuing to innovate on that front, either it be intelligent traffic control, be distributed load balancing, better security on our RDMA networks, the highest performance. We've been continuing to innovate. Last year, we also announced the concept of a conversion that gives customers a better price economics and security, where we actually embedded our Zero-Trust Packet Routing protocols directly onto the network. We also introduced better reliability and resiliency where customers can get most out of their GPUs on OCI than any other cloud by innovating a brand-new network type called multi-planar fabrics, where each one of the GPU nodes can completely be available if one of the links flop. We also find innovative ways to shrink the time that packets take to move across our network by introducing fabric accelerators in the path that reduces the amount of time it traverses multiple switches. At AI World, we also brought that RDMA technology to the front end that enables and powers a next generation of AI workloads that accesses storage, that does a bunch of different work around more agent swarm and agentic activities. Second, we also are bringing the technology very close to block storage so you can get very high performance. Lastly, we also released plugins that enables our customers to easily adopt this on our cloud with a new plugin framework. One of the questions that I get constantly asked about is what happens to the CPU business and where it's headed. So I thought it's better to talk about it with numbers and not just tell my story. What you see here is a recent commitment from one of our customers for about 1 million CPUs. 99% of those CPUs are older generation hardware. When I talk about older generation, those are chips and platforms that we launched in years 2017 to 2021. Just as a reminder, we're in 2026. These are contracts that we signed, which are not pay-as-you-go for one hour or two hours. I'm talking about 18 to 48 months of 100% committed consumption. This is our general purpose CPU business. Let's talk about durability. One of the things that you all heard a lot about was our announcements across multiple hardware platforms. As I mentioned at the start of my presentation, we think the next phase of AI is going to be around the fastest access to compute and the most optimized TCO. We are building towards that world, that is why you see a ton of support across all of our hardware partners in our platform, in our cloud. Another innovative way to reduce the capital allocation on our platform is our innovation around bring your own hardware. The easiest way to explain this is what Larry asked Clay 8 years ago, where he is like, "How can you bring Exadata in 4 months onto our cloud?" The good news is we had innovated on our SmartNIC technology and our off-box virtualization that allows us to bring any hardware rack directly and place it on our network with the security properties. We took the technology, we enforced standard designs and industry-standard designs with our customers, and we can virtualize the technology and place those racks in our data centers and give them complete access to all OCI services. You may ask, when you place those services, how does it actually work? The first thing that I want to tell you is that when AI infrastructure customers use OCI, they do not just use the AI accelerators. On average, 13% of the spend of these AI accelerator customers is actually on non-accelerator spend. Second, they use, on average, about 40-plus additional OCI cloud services when they are an AI infrastructure customer for OCI. I am going to jump into data. If you think about even 2 years ago, the amount of data that moves across the internet and the amount of data that is being generated by machines versus humans, 30% of the data was generated by machines. What we are observing is a giant opportunity where AI agents are going to generate exponentially much higher volume of data where this is going to go up to 50% to 80%. The volume of data is going to be so high that it actually creates an opportunity both for customers to create new business models and better outcomes, and for Oracle to actually drive more data workloads onto our cloud. The second thing is that as AI agents take over, the next billion users will not be humans, they will be agents for database. What that opportunity presents itself is, one, how do we actually offer the latest and the greatest technologies for these agentic apps? Second, also, how do you embed security at each layer, not just at the infrastructure layer, but also the database layer? We announced a series of products and capabilities today at AI World, where Oracle's Deep Trust AI Architecture allows you to get those capabilities on your platform. One part of database and data is about storing the information, but that allows you to power applications. You cannot drive meaningful business outcomes without a data platform. Oracle AI Data Platform, what we announced, is the concept of an AI Discovery agent that goes into your environment, reads the application, reads the application code, reads your semantic models, and essentially builds out an ontology of your business. It allows you to generate business context on top, which could be embedded into more meaningful outcomes. That is a very powerful technology. If you connect the amount of data that is going to get generated, the mission-critical data that sits on Oracle databases and how it is going to evolve, and how we can shrink time for our customers to realize that value, we are poised for a bigger opportunity over the next few years. A lot of this AI database technology is not just available on our cloud. It is live, and in this quarter to date, we have about 71 regions, and it is one of our fastest-growing businesses at over 209%. All of these latest and greatest features will be made available, not just in our cloud, but in all clouds. I am going to take one last example to talk about what is actually happening in our multi-cloud space with our customers, where we are seeing in a traditional world, migration, data migration, bringing the database online and bringing it into the cloud was a month-long process or months-long process. What we are seeing with AI and with the great teams that we actually have across the companies, we are seeing adoption move at a much faster rate. For example, Zema Global was a great customer who actually migrated their database onto Azure, Oracle Database@AWS, with no downtime. We also saw another similar example with Conduent, where they were able to accelerate their cloud journey on an Oracle Database really fast. In summary, what you heard today is the amount of work that we are doing on OCI and innovating at every layer to create a durable, scalable, and a forever accelerating business. Thank you. Please welcome to the stage, Chris Leone. All right. That was great. Love it. Love it. All right. Welcome, everyone. All right. No builder vest for this session. I promised Hillary I would put the suit on, so we are suited up. Let's get started. I thought I would start with a little bit of overview of how we fit into the entire stack. I am going to be talking about the application layer. I am going to be talking about Fusion Applications primarily. But wanted to kind of drill down a little bit on what Mike talked about at the beginning, and Mehats kind of brought in a little bit, about where we fit in the overall enterprise AI stack. I am going to be spending the entire session on the application layer, how we have built AI in and not bolted it on. But let me talk about the enterprise foundation and how that truly enables what we are able to deliver at the application level. First, there is a shared set of AI capabilities that all the application teams benefit from. On the OCI side, huge value. This is where we get all of our models from. So whether they are an open weights model like gpt-oss, that is part of what we deliver in Fusion Applications, or they are an enterprise model, we get the endpoints directly from OCI. But it goes beyond that. We get all the guardrails, all the observability, all the explainability, the traceability, all the audit-level capabilities, prompt injection in the guardrails. All of this type of work happens at the OCI level that protects the application layer and makes it auditable for what AI is doing in our applications. Then what Oracle AI Data Platform or the enterprise data platform gives us is the ability to bring data in from Oracle and non-Oracle systems, create agents that cut across the enterprise, and then we can use those agents and that data in at the application level to actually execute work. That is a big part of what we are going to be talking about today, where we are really transitioning the application layer from AI-assisted work, which we have been doing for 18 months, to AI actually doing the work, and doing the work more and more autonomously. I thought, Larry, you had a great analogy, simple analogy. The difference from AI-assisted work to AI doing the work and AI doing the work autonomously is like getting in a car and driving it and getting in a Tesla and having it drive you. It is that big of a leap. That is what AI doing the work and AI doing the work autonomously, that is where we are going. Actually, that is where we are at today. Let me jump in and talk about the announcements. Huge announcements. This is probably the largest set of, certainly the largest set of AI announcements that we have had at any conference. The excitement level from our customers and our partners has truly been amazing. Let me hit on these and then we will go into some details about what they are all about, and we will geek out a little bit and talk a little bit about truly how they are differentiated and the architectural innovations that we have delivered. Let me start with the first announcement, which is we have been delivering agentic applications for a while. They are built around an outcome. I will talk about that in just a second. But we extended those with a whole new technology called Fusion Claw. Fusion Claw is a technology that allows us to do more specialist-type work in the enterprise. When I say specialist-type work, you can think of what a financial analyst in accounting, I know you guys are industry financial analysts, but a financial analyst in accounting, a supply chain planner might do, an inventory specialist might do. They are doing analytical type work, and we will spend a little time on that. But huge value within the enterprise, and Fusion Claw technology allows us to do that work. Second is we announced a new front door, for lack of a better term, entry into Fusion Applications with Fusion Companion. We have a native component to that, which is Fusion Agentic Home, which is our native experience. What Fusion Companion does, it is a universal intelligent layer that cuts across the entire surface area of Fusion Applications. Any user does not have to understand which page they need to go to, which transaction they need to work against, which report they need to look at, which workflow. We go beyond that, too. They do not even need to worry about what agent, what agentic application, or what long-running job they want to run with Fusion Claw. A single universal entry layer. We do all the work behind the scenes. Huge step forward for us from an engagement perspective. Then, a great innovation where now we are delivering Fusion Setup and Configuration Registry. This allows us to deliver a machine-readable set of AI skills to set up and configure Fusion Applications. We will talk about this, but it should reduce the mechanical work of implementing and extending our application from 50%-70%. We will talk about what that means in a little bit more detail. Okay. One of the transitions that we are making, or the extension that we are making, is extending the subscription-based business that we have today. We sell seats, we sell employees, with a usage-based metric. We have rolled this out a few months ago. It is around a concept called AI units, which has tokens involved and has some compute involved, but it is a calculation of work being done in our system. What this allows us to do is drive another layer of revenue across our application tier without necessarily adding more subscription seats. I am going to make four points about what we are doing, and you will see this play out throughout the presentation. At the end, I will summarize those four points so you do not have to write them all down right now. The first one is this transition. We are moving from a, we are adding to our subscription-based revenue, a whole usage-based revenue. The more value, the more work that gets done by AI in our system, as Mike gave some statistics earlier on, the more work that gets done, the more revenue we will see because they are consuming AI units as that work is getting done. That is point one. We do not have to add more seats in order for us to now generate more revenue. We can do more work, and I will talk to you about the type of work we can do. The second part of it, with the innovation that we have delivered around Fusion Claw, we can do different types of work that was traditionally done outside of Fusion Applications, now inside Fusion Applications, and I will talk about that type of work that we can now do that again. The more work we do, the more usage we have, the more revenue we will generate. The third part of this that you will see throughout this session is we have delivered a machine-readable set of skills that allow implementations to go 50%-70% faster. This is going to allow us to move our install base on-premise customers significantly easier and faster, and more consistently because we have best practices built in to the cloud. We are going to get more moving to Fusion, so more subscription revenue that will ultimately also lead to more usage revenue. The third piece is just as important. There is a whole economic side. The way we are delivering AI architecturally allows us to deliver that scale within the enterprise, and it changes the economics. Our margin on the AU component is actually really good. We are not reliant on token and the cost of a token, because part of it is token cost, part of it is compute cost, and when that is work that is getting done. I will explain how that works as we go through the session. Those are the four points for us to drive more revenue through the usage of AI with great economics, and margins. Let us jump into the transition that we have made in enterprise applications. We have transitioned from a system of record. We have extended the system of record into what we call a system of outcome. We have been doing this for a while, and everybody is talking about outcomes now. 18 months ago, we reimagined in the world of AI what our applications should be like. How should we organize applications? We decided, and we came to the conclusion that, look, it would be great if we organized applications around an outcome. An outcome can be pay people accurately on time within policy, or I am going to show you a demonstration later, and it could be get shipments out on time within capacity cost and the right service levels. You can define an outcome, and the application is organized around that outcome. You do not have to go from module to module to module to complete work. An agentic application owns the outcome. It brings together agents that move work forward 24 by 7 to help the user get work done and do the work. That is what moving from systems of record to systems of outcome means. The base layer is our foundation, is the authoritative source, which is Fusion Applications, where all of the controls, the permissions, the policies, the actual state of the transactions you need in order to execute work. That is the change that we have made over the last 18 months, and that is what part of what we have been delivering against. Now, let us bring this into a little bit more detail so you understand what this looks like. As we transition to outcome-based applications, agentic applications, and these agentic applications are designed to move work forward, do more work in the enterprise. The authoritative source is incredibly important because we are going to do more work. We cannot do that work based on an estimation of customer, of supplier, of orders, of inventory. It has to be on the actual state if we are going to commit transactions and move work forward. The operating model becomes relatively simple. The human person running the system is accountable for the outcome. Agentic applications own that outcome. They handle the cognitive load of the process. They move work forward. They know what the next immediate action needs to be in the system, so they can move that work forward. Then it sits on top of the system of record that actually knows the current state of the business, and it can move the transactions forward and commit work via AI and via autonomous AI that we are delivering. This is how we have transitioned going forward. In order to do more work with agentic applications, we needed to go deeper into the type of work we were able to do. If we were going to do more work in the enterprise, we had to take on harder work, what I call specialist work. This is the work that was traditionally done in applications outside, planning applications, reconciliation applications, maybe Excel for ad hoc analysis. That specialist work done by financial analysts or inventory specialists can now be done inside Fusion. The way we can do this is a new technology that we have just announced called Fusion Claw. Fusion Claw allows us to take on long-running work, allows us to do multi-step computational intensive work, allows us to do reconciliation against millions of records, allows us to do simulations, allows us to do optimizations, allows us to do sophisticated high-value work within the enterprise. The second part of this is, this is a huge leap from an architecture perspective. When I say that it is a huge leap from an architecture perspective, we have cracked the economic side of handling extremely large workloads. What I mean by that is when you are doing enterprise AI within Fusion Applications, you are talking about extremely large operational and transactional data sets that you need to do work against. Think of a ledger with a million or 2 million records that you need to go reconcile or you need to go analyze. You might have 1,000 calculations or 100 scenarios that you need to go through. If we applied an LLM or an agent to that type of workload, it would never be efficient enough. It would never be precise enough, it would never be economical to do that. And if you think about it, just the way an agent works, LLM reasons, it looks at a record, it makes a decision, and then it goes back and does the exact same thing and looks at record after record. Just think, when we get to record 992,000 and the context window fills up and it starts making the wrong decisions, that's never going to scale from a precision perspective. From an efficiency perspective, I do not know if it will ever finish, and it's never going to scale from an economic. The cost to do that type of work in the enterprise is never going to be cost-effective. What we did was architecturally, we separated reasoning and planning, which AI does fantastically well, and so we leverage it to do all the planning side, and then we use deterministic compute to do the execution side. What that means is we use AI to understand the problem, to assemble the tools and capabilities and skills and the execution logic, and then we hand that execution logic over to compute, and then we can run it against 1 million transactions, 1,000 scenarios, and do it without touching the token side of the LLM. That's complete compute. So we can handle extremely large workloads, do high-value work, optimization work, reconciliation work at really good economics for enterprise customers. Completely different architecture. We are really the first to deliver this in the enterprise, truly the first to deliver in the enterprise at this scale, and partners, customers are extremely excited about it. I will drill into this just one more thing to bring it home. We did not have to build five different execution runtime engines. We built one. And we can solve thousands of use cases across the enterprise, probably even more. Use cases that we have traditionally solved in the past and whole new use cases that we have not even thought about, or have been able to do in our application layer. So we can do analysis against millions of records against a policy document or a standard operating procedure. We can do reconciliation in the ledger against 1 million or 2 million records, and look at discrepancies in the general ledger and sub-ledger balances and come back with a deep analysis. That type of work traditionally would have been taken out of Fusion, done in a data cloud or in Excel, and then the result would have been put back into Fusion. Now we can keep it all together and do those large AI workloads in one system. And then optimizations. I am going to show you an optimization example in just a second, but we can optimize inventory, we can optimize capacity, we can optimize cost, we can optimize schedules. All of these high-value use cases we can apply to AI and actually do the work. So before I run out of time. And we can execute. The last key piece is we take action. So this is not just like we are recommending moving 20 million inventory positions. We can do and transact that 20 million movement of inventory around the world to get inventory to the right data centers for Mahesh and make sure we have the right equipment there. All that type of work we can do with AI, and then we can execute in the system. Let's go to a demo. Now, I am going to tee this up, but I gave Ken one last one. I usually like to do the demos myself. I did it in the opening keynote, but he was worried that it might not come off, so we did a video. But I finally found someone I trust enough to do the demo for me. Let's take it and go to the demonstration. What you are looking at is our shipment load planning agentic application. The important idea is that this application owns an outcome, getting our shipments out on time while continuously balancing service, capacity, and cost. Rather than waiting for a planner to find problems, it continuously looks across the operation, identifies what needs attention, and moves the work forward. First, it sees demand at risk. It determines that 18 orders can be moved to earlier ship dates to protect that demand. It also understands the trade-off. Protecting the revenue adds $36,000 in transportation cost. Next, it identifies 12 partial loads that can be consolidated into two full truckloads, saving about $12,000 while still meeting delivery commitments. Finally, seven orders are at risk because of a carrier delay. The application compares the alternatives across service and cost, and the planner approves priority haul service to get all seven there on time. That is an agentic application. It understands the outcome, identifies what needs to happen next, evaluates the trade-offs, and continuously moves the process forward. But there is another class of work inside this process that is much harder to automate, building the actual load plan. That is specialist work, and this is what Fusion Claw changes. We hand that specialist work to Fusion Claw and give it the outcome. Generate the optimized load plan for the next eight weeks. Claw works much like an expert planner. It resolves the planning context across orders, shipments, equipment, capacity, and the planning horizon. Then it applies the company's standard operating procedures and planning policies. These are not suggestions to the model. They are deterministic boundaries governing what Claw is allowed to do. For example, we are planning across eight weeks. Orders can be pulled forward within defined limits, but not simply pushed out. We have rules around capacity, equipment, utilization, and when human approval is required. Within those boundaries, Claw reasons through the problem, evaluates the possibilities, and builds the plan. In this run, it evaluates 776 orders and proposes 154 loads. It gets average load utilization to 88.2%, eliminates 16 load moves, and generates approximately $58,000 in transportation savings. It knows where its authority ends. Five loads require planner review. 117 orders are pulled in to reduce partial loads. Once the planner approves the plan, we move to full auto. Claw revalidates the recommendations against the current state, applies the approved changes, and executes the work back into Fusion. Here, 149 loads covering 776 orders are committed back to the plan, including 117 orders pulled forward. Finally, this is critical for enterprise AI. We do not just tell you that the AI finished. We give you an outcome receipt. You can see the outcome achieved, who owned it, the standard operating procedure it followed, the sources it used, the authority it operated under, the transactions it changed, and the complete chain of evidence for what happened. The agentic application owns the outcome. Fusion Claw performs the specialist work required to achieve it, and the outcome receipt proves what happened. That is how we move from systems that record work to systems of outcomes that actually get the work done. All right, nice job. The announcement that we had at this, yeah, it is great. The announcement that we had at this conference, we are delivering 75 agentic applications. Again, outcome-based applications. 25 of those are Claw enabled. We also have a builder component to this, so you can build these agentic application and Claw extensions in our no-code environment completely through natural language, and we have a full pro code environment that you can build in VS Code. We have a full, robust CLI. Builders, if they want to build in VS Code, they can do this, and they can build all of these agentic applications and Fusion Claw extensions. We are now able to do a lot more higher value work in the enterprise. Completely differentiated. Nobody is doing this. Let us go to the next announcement that we have, and I will wrap up here in a few minutes. The next announcement is really how users engage with the system, and so we have dramatically simplified how you engage with Fusion Applications. Companion is our intelligent layer that cuts across the entire surface area of Fusion Applications. We have done a lot of engineering work behind the scenes to make sure it works efficiently, it works securely, the right user has the right permissions, and it works across all the different ways we can get work done, whether it is a bounded transaction, whether it is a workflow, whether it is a report, whether it is an agentic application, whether it is a Fusion Claw long-running process. It does not matter. You just simply describe the work that you want to get done, and we execute that work in the system. Companion travel. When you build this layer, this intelligent layer, it is portable. We have a plugin for OpenAI right now. We can work across different AI clients. More importantly, we have an agentic native home experience, because with Fusion, we have 40, 50 million users that use this system. The majority of our users are not in Codex or Cowork every single day. It is just the reality of, we have a lot of frontline workers. We have a lot of hospitals that we do that run Fusion. A lot of retail runs Fusion. So we need to build an agentic home that still operates on that intelligent layer, simplifies how you engage with the system. But now a frontline worker that wants to update their time card just has to simply describe, "I worked 8 hours. These are the tasks that I did. This is the progress that I worked on." The system will take care of updating the time card. If the user wants to understand why their last four payslips overtime was calculated differently, they can give it and delegate a research assignment or a research project. If a head nurse needs to rerun a schedule because they need to get to 75% or 80% utilization schedule coverage, they can kick off a long-running task in Fusion Claw. They do not have to know anything other than give the task. We handle where that workload gets routed. Again, doing more work, making it easier, it consumes AI units as we are doing this work. It is high-value work and simplifies how you engage with our system. The last announcement, as big as the other ones. This has been one of the most popular ones because essentially, we have created an abstraction layer for setting up and configuring Fusion Applications, dramatically simplifying that task. What this means and what we have delivered, let me just talk about what we delivered. We have delivered an authoritative, machine-readable set of AI skills to set up and configure Fusion Applications. If you are interested, you can hit on the QR code, and there is a public Git repository that has all the 1,200 or so skills that we have published. In there, it has skills across our entire application. What we ship now is the ability to set up and configure our system via software, and we ship best practices in there, we ship product knowledge in there. We have all the dependencies in there, so we make sure this object is set up before this object. We will tell you if you are going to set up or configure an object that is not reversible. Now we do not have to train up every consultant around the globe, every customer around the globe, every single release. We have a set of skills that comes preloaded with all of the best practice setup and configuration already in the system. This is development owned. This is now an engineering-owned product. We will ship this and update it with every release. Now the innovation layer for our partners goes up a level. They do not have to worry about the inner workings of Fusion. The next era of implementation and implementing software will be done completely by software and AI agents. The mechanical work of understanding the inner workings of these complex systems is completely gone. You just need to understand what is the business requirements, what are you trying to set up, and we will take care of that work. What this means from an economic perspective is we can reduce the time to move to Fusion, whether it is on-prem, whether it is a competitor coming over, from 50% to 70%, because that mechanical work is completely eliminated and will be handled by software. This is a huge one for us to move customers faster, get more subscription revenue, and ultimately more usage revenue. Mike talked about the momentum. The momentum has been really, really incredibly positive with Fusion. These are just some of the numbers. 5.7 million agent runs just last quarter. When I talked about the builders building in Fusion, we have over 100,000 builders that have certified to build on our AI Studio platform. Pretty incredible. 7,000 customers, 3,600, 3,700 agents in production, and tens of thousands behind the scenes. This is the final slide. Let me just summarize what this means from more of an economic perspective. The first piece is we can grow our revenue without adding additional seats. The more work that we do in Fusion, the more usage we get from AI, the more revenue we can drive in our application business. The second is we can address new types of workloads that were traditionally done outside of Fusion. We can take on ad hoc analysis, reconciliation. I will give you an example. We have a customer that is a paint customer, and we run process manufacturing. They have a specialty system outside of Fusion that looks at all of the recipes that we store in PLM, and it does cost and paint mixing to get the optimal paint mix for the right cost outside of our system. It is a huge value-added app for them, but they have to move the data out, then move it back in. Fusion Cloud can do it. We have modeled it. Those are the types of things that were traditionally done outside of our system that now we can bring in, and it can be done at the transaction grain. Second is we will get customers to Fusion faster, more subscription revenue, more usage. I told you, we separate reasoning from compute to do work, and that gives us much better margin and economics for the AI units that customers will ultimately consume. That is it. Thank you very much. I will hand it to the next speaker. Oracle is setting the pace for the entire industry when it comes to AI, and especially in healthcare. We have launched our Oracle Life Sciences Data Intelligence solution. This is an open data platform that brings together over 130 million complete medical records with a store of data from external partners that includes claims, genomic, pathogen, and prescription data. We are also launching our new AI clinical trial matching agent. You will see the same strategy in new EHR capabilities for specialties such as oncology and a new pharmacy system. We're bringing AI agents into the full revenue cycle, from clinical documentation through reimbursement and into enterprise finance. The biggest impact in a positive way that we're seeing AI is taking out, reducing the administrative friction for not just the providers, for the members. We are partnering with Oracle to bring precision oncology capabilities into the EHR. I would say that AI is really enabling better decisions faster from the entire value chain, from drug discovery, clinical trials, how we design them, run them, from the interaction with the regulators up to how we bring care to patients. I think AI at scale is really not about technology success. It's really about trust. It's about redesigning the underlying processes and getting the stakeholders to buy in and actually carry out the change. The combination of the AI assisting the human beings, one checking the other, is the best of all possible worlds, is to have a human expert working with the AI expert. If you look worldwide, half of the patients living with a suspected rare disease condition are actually undiagnosed. We can leverage AI to look through the clinical notes and leveraging the structured and unstructured data and be able to surface insights, to surface patterns that a human wouldn't be able to see when they just look at one document after another. I think ultimately, to be able to look at somebody's genomic sequence and know what they're potentially vulnerable, which diseases they may face, and to be able to drive cures and treatments to help them prevent disease, and that would be the holy grail in terms of lowering the cost of care. We have to figure out a way through technology to deliver the data and the insight where the oncologists live. No one has time to take a look at three different portals. It has to be presented inside the EHR at the point of care in real-time as structured information with all the actionable context. I think the nirvana would be for first clinical trial not to answer does this drug work or not, but really answering for which patient and when does it work. I think it's to your point, is not having a patient look for a trial sometimes for years, but rather the clinical trials find the patient. Please welcome to the stage Simon de Montfort Walker. All right. Good morning. I think Ken has actually rebranded me as Simon, which is far cooler, so thank you for that, Ken. I am here to talk to you a little bit about really what we do in industry, but more importantly, how you can tie a string from all of the amazing infrastructure we have talked about, all of the incredible work that Chris's teams are working on, what he has talked about, what we are doing in industries, and really kind of connect that to how we grow the applications business, why it is important for customers, and why we love the economics. So we have this incredible foundation on the infrastructure side. We have an amazing industry foundation in terms of very detailed knowledge of how people work, how the businesses work. We go far further into specific workflows that our customers run than anyone else. These are industry-specific workloads, they are horizontal specific workloads. But ultimately, each part of that reinforces the other. When we bring those capabilities together for customers, typically at an industry level, we create even more value for customers, even more stickiness for ourselves, frankly, even better outcomes. We used to talk a lot about industry outcomes and industry applications a little bit in isolation. They were a separate part of our business. We thought about these kind of very focused applications as something quite different. We have now really switched that up. We talked to you a lot about it last year, about really thinking about the entire business by industry, how Oracle brings to a customer across everything we do, the entire wedding cake, all of these capabilities, and focus them in on really specific issues that our customers have. What it all leads to, though, is we have talked to you a lot about over the last years, hey, this on-prem to cloud move is really important for us. When we take someone from an on-premise application to the cloud, we grow them by 3x. We expand the footprint again when we are able to cross-sell them a second application. It is a huge vector for growth for us, it is one of four I will talk to you about today. It is significant already. The majority of our customers in a lot of industries actually only have one Oracle application. All of the announcements we are talking about, that Chris has talked about, the AI enablement, implementation simplification, adding agents to the application, are all accelerators for those traditional motions that we have talked to you about for a long time. We talked last year about how we have changed our go to market. We are industry first. The teams are aligned. We have got one selling motion. That is complete. But then we are also now combining the industry expertise across the application teams, across our services teams, where we can now serve customer segments in a really differentiated way with differentiated applications that ultimately are additional vectors for growth for Oracle. All of the announcements we have made about implementation accelerate all of those movements. We are tying a much tighter string between the moment of booking to the moment of billing. What is important about the industry side is that the depth we have in industry really extends the value of the portfolio for our customers' businesses, it also creates new opportunities for us. We understand businesses in these two dimensions. The horizontal, where we are doing really deep work managing the business of being in business, then the vertical, where we are managing typically how customers interact with their customers. The technical depth is deeper than anyone in the world has. The depth of functionality we have for specific business challenges in industry, greater than anyone else. When you put those things together and you add AI as the catalyst, it is a really remarkable opportunity for us. We are supporting core functions, we are supporting vertical industry functions, together these things reinforce each other. Ultimately, the value comes when we connect multiple applications for those customers and control much more of their business. Chris talked a lot about, hey, we are doing much more complex work in much more parts of the organization. Really think about it as we are touching work we never had access to before. We're touching work in much broader ways across the organization and accessing a much bigger part of people's businesses. When we add AI to that, it adds a big combination of breadth and depth in terms of what we can do for customers. For us, bringing it together as a broad enterprise-wide capability with industry expertise allows us to access more of a customer's business, do more for them, and add more to the pile. When we talk about on-prem to cloud and then cross-sell for customers, what does it look like? Effectively, what are we doing for customers? Let me give you a couple of examples before we even talk about a lot of the AI enablement. Take MGM Resorts. They're a Fusion Financials customer. We're driving massive increase in efficiency with Fusion for them in terms of how they manage their books. They've just gone live with our OPERA property management system, so we've got 16 properties, 43,000 rooms under management. We're now connecting the guest from reservation to check-in, all the way to the chart of accounts. No one else does that. That entire experience is connected, and then when you add these AI capabilities on top, all of a sudden, we're working across these environments that they've never been able to work across. Same thing in healthcare. Centre for Addiction and Mental Health in Canada, largest mental health teaching hospital in Canada. They've got their footprint of finance, HR, supply chain, and CX, plus Oracle EHR. So now, again, all across their operating footprint, we're connecting workflows, connecting experience for clinicians, for patients, really across their business. Same thing in construction. Construction's a really fascinating one for us. If you think about, in Saudi Arabia, their largest residential construction group uses Fusion and uses Primavera Cloud, which is our construction and engineering application. Ultimately, they're processing payments 50% faster, 50% less effort. So it's a big part of the work, like moving money from the contractor down to the subcontractor. But when you look at construction, you get this massive scale advantage that we have. Our NetSuite colleagues announced their construction package. Think about construction as Oracle's able to serve the owner of a project. They're running Fusion. Maybe they're running a little bit of Primavera. The contractor then is in Primavera, likely in Fusion, managing their financials, managing their people, managing big components of the project. Then the subcontractor is in NetSuite with their project accounting tool. Then we're managing all of the money flow and all of the payments between them. So it's a complete package against that complete project that we can then layer on all of this incredible technology that AI delivers to us. No one else in the world can really do this, and these are incredibly high-value pieces of functionality for customers that drive massive swaths of their business. When we talk about, like, "Hey, why are you guys excited about the applications business? Why are you excited about the growth?" The opportunity and the things we've been talking to you about for a number of years, still present and accelerating. When we move a single customer from one application from on-prem to cloud, we see this 3x-4x bump. But then more importantly, when we take a customer from a single application, let's say, to two, we get another 3x bump. It happens again when we go to the third. All of the tooling we've talked about from an implementation perspective, from an agent perspective accelerates that move, makes it easier, but also draws customers to the move because they want that functionality. If you look at financial services in North America alone, 60% of the customer base has one Oracle application. So 60% are in play for that cross-sell motion. Just in North America across our industries, it's north of a $5 billion opportunity for us. So the traditional motion that we've talked to you about, like, hey, moving customers from on-prem to cloud is massive value for the customer, massive value for us. Still there, growing and accelerating with all of the things we're doing. All of the things that Chris talked about, all of the things we've announced this week just reinforce the value of that move before we've even talked about new agentic applications, doing new things across new parts of their organizations. We've reorganized the global sales teams, Mark talked to you a lot about that last year, so that we can take advantage of it, then AI is the catalyst to accelerate it. Then there's the sort of like, "But wait, there's more" moment. Don't forget about NetSuite. 44,000 customers in the mid-market across every segment, every region, every stage of growth. It's a hugely broad customer base, massively diverse set of industries. We've got massive experience in all of those industries. This week, we announced lots of new AI functionality, agents, apps across NetSuite, but we also announced lots of new industry plays in NetSuite. Faster implementation, SuiteExpress playing into that as well. The mid-market is really crowded. We've typically kind of approached it in a little bit of a piecemeal fashion. We've thought it as an industry application sort of separately from the horizontal application in NetSuite. We've now brought that together, and we have, again, the most complete solution with AI enablement to serve the marketplace. So we're releasing product and attacking the market really aggressively. Here's a bunch of examples. NetSuite integration in healthcare, allowing financial and clinical operations in one platform for smaller providers. Restaurants and hospitality. Oracle Restaurants Operations Command Center, and NetSuite, connecting our point-of-sale system with NetSuite. Interestingly enough, customers that are signing on to that typically are also Fusion customers, interestingly. They're using it for a different purpose. In construction, NetSuite Textura integration, end-to-end payment management. We talked about the value of that sort of network before. Draws in all of the efforts on the construction side into a single operating perimeter so we can understand everything that's going on. Then when we look at things like government. We've got local government edition of SuiteApp. So municipalities and agencies typically underserved. We're doing billing for them, managing payments, and improving their business. But ultimately, it's really about taking what we do across Oracle, taking advantage of it in really fine-grained ways in the mid-market like we do in enterprise, and fueling this cross-sell, which is the same motion in general as we've got in Fusion. So that's sort of like, hey, traditional Oracle application cross-sell, agents, lots of new AI functionality in these applications, combining them together and getting a cross-sell. The beauty of that is that then on top of that, let me bop this forward for you, we're building new agents, new agentic applications on top of that platform. One of the examples we talked about on Monday was in restaurants, we deployed a forward deployed engineer team with one of our customers to think through what they needed to have happen on the restaurant location, to think about improving the business, driving revenue, saving. Restaurants typically have a really big cost issue. It's low margin. It's very complicated. We brought together NetSuite, our point-of-sale solution in the background with a new agent on top of it that's a new product, new revenue, ultimately to help restaurants deliver better outcomes. This particular one is with Burger King Italia. They actually joined us on Monday, and we built an agent that saves them on their delivery reconciliation. It manages inventory, and it does what Chris was talking about. It actually works across organizations that have never worked together before. So their head office, their delivery management companies, their branch manager, their regional manager, and the location manager in a new application that they never had before. As Clay mentioned during his session yesterday, we've accelerated how we're building these. This is not months, guys. This is weeks we built this thing. It's out in the wild, and it's an application that is new and for sale. We're doing it really everywhere. You'll notice, so we have a new class of agentic apps that we've been talking a lot about. This is our industry's Oracle AI Agent Studio. It looks like, tastes like the Fusion version of it because it is the same thing. WYSIWYG, what you see is what you get, development of agents, single pane of glass across Oracle Industry Applications in Fusion that allows you to build your own agents. It's happening really all across the industry teams. We've got the cross-sell, but then we have all of these new applications we're building. The examples this week we've released in utilities, work and asset cloud service. In construction, new command center across projects. Oracle Agriculture Intelligence. In government orchestration service. Financial services, cash management, retail lending, hospitality sales manager, hospitality outdoor, restaurant ops that I just talked to you about, and then retail invoice matching. All new agents, new applications, new revenue sources for us, underneath the surface, bridging together lots of things we've done in the applications historically. The customers are already consuming, but offering them new functionality. It's where the user count on our applications business is growing exponentially. It just happens that most of the users are machines all of a sudden. These are new things for us, and AI is really ultimately the catalyst that makes it all possible. That's prem to cloud, cross-sell into new applications, new applications that are just agents on top of all that, handling new work in a new cross-section of our customers. What about outcomes? We've got outcomes in terms of work, tokens, but what about business outcomes? Last year, we talked to you about embedded finance. We've got an embedded finance business. It's been growing for a while. Right now we do a ton of consumer transactions for people. We are handling ACH and credit card payments in the U.S., U.K., and Canada, restaurants, hotels, healthcare, state and local utilities. We manage millions of transactions, millions soon to be billions of dollars in interactions. Customers love it because it is actually really deeply integrated. They have had these payment services that were separate and apart from their businesses before, and now we have integrated them. It simplifies the customer experience, and ultimately, we deliver typically better pricing for people. That is really just the start. Last year, we had a few of our banking partners come and talk to you about B2B. When we get to B2B, all of a sudden there is a whole series of new transactions and revenue streams for us that are purely outcome-based. Supply chain payments, new wallet products, payroll, cross-border payments, expenses. They are all slight add-ons to things we are already doing, where we are working with our banking partners to create new products that in turn generate new revenue streams, additional margin on things we are already doing for people. This is in flight, and we are working right now on a number of new releases. Ultimately the build really becomes customers on-prem to cloud, your first 3x. Cross-sell gets you the next 3x. All of these new agents, applications as agents, all of these new revenue streams that are outcome-based, where we are doing much more broad work for people. I think we often get this kind of question. It is like, "Hey, how does this stuff all fit together? What is the value of it all being together?" The breadth of what we do is super unique. The more we do up at the top of the diagram, the more embedded value there is for customers, the more we bring it all together and deliver these additional layers of opportunities. Industry is kind of the organizing principle of it because ultimately all of our customers are in an industry. They want applications and things to help them solve problems that are typically foundational in their industry and then move into their head office. As we move to systems of outcome, we are not just managing the data and the transaction, but we are really helping them execute work that they have been executing in different ways before. AI is the accelerator for that. The four big vectors then. We are moving on-premise to cloud, capturing the first 3x. AI is the catalyst. It is accelerating it. We still have a significant opportunity, a significant customer base that is on-prem. We are getting them on the staircase, and we are accelerating with all these announcements and how we accelerate go lives. We are expanding the applications footprint with that cross-sell. We are organized to do it. We have done all of that work. Now we see the results of that. Then we are selling these AI outcomes and these add-ons. New additional revenue on top of what we have already done and ultimately doing more work. I think when you think about us, you probably think, hey, these guys are. Ultimately, the wallet they are after is the IT wallet. The IT budget is their TAM. I think, if you really look at the work that Chris is doing in the application, that we are doing with these new agents, the TAM is really like the entire operating budget of our customers now. It's a massive change for us, given the amount of work we can do across departments, parts of an organization, players in an ecosystem that we could never do before. It's a massive expansion. I want to leave you with a couple of fairly simple concepts. We still have the old vectors. AI accelerates all of them. It then creates additional opportunities. We've aligned the go-to-market to make it happen. All of that is why we're so confident in why this works and why the growth is there. Mike said, AI is the catalyst that's really activating the full potential of everything we've built. It's making everything we do more valuable, more interesting for our customers, and ultimately delivering more for them. It's why we believe we're more competitive. We're much more embedded in our customers. We've got much more durable, high-quality revenue and more value for the end users. That is really ultimately how applications contributes to those pillars that Mike laid out. We're driving differentiated growth in ways that no one else can. The profitability there is incredibly strong. You know that business well. It's not just the next chapter of cloud. It's really a fundamental expansion of the applications opportunity, and Oracle's really the only one that can do it, and we're built to do it. Thank you very much. Have a great rest of the day, and thanks for joining us. Please welcome to the stage Mark Hura, Antonio Parra, and Sanjay Apte. Gentlemen. Great. Well, thank you. You heard more about our updated strategy as well as some releases and key innovations across every layer of our portfolio. The Oracle advantage is when we bring all of those capabilities and innovations together for our customers around the world to help them solve some of their biggest and pressing business challenges and to deliver outcomes for their organizations regardless if they're a large enterprise, small medium business, a cloud-native company in any form or fashion around the world. In order to take advantage of every element that Simon just showed you, the slide, it's infrastructure, it's the AI Database, it's industry suites of applications, as well as the AI Data Platform. When we bring all of that together, there's no other company from a technology perspective that brings all of those capabilities to our customers to deliver modernization and transformation in the era of AI. In order to make that real and for you to experience how our customers are transforming, we have a great opportunity to have a couple of global enterprises here with us today, and I'm excited that Antonio Parra is here with us. He's the Global Business Technology Leader at Grupo Bimbo, as well as Sanjay Apte, who's here with us, who's the Head of Oracle Cloud Services at Vodafone. Thank you both for joining me here today and sharing some of your stories. Thank you. Thank you. Thank you, Mark, for the invitation. Yeah. Great. Antonio, I am going to start with you. First of all, Grupo Bimbo is the world's largest baking company that actually operates in 34 countries. I am going to get, I think, correct me if I am wrong here, 34 countries. 39. 39 countries. You actually serve over 100- Yes countries in terms of where you deliver your services to. You have over 200 brands, 250 brands, I think generally. For people to understand some of those brands here, think about like Thomas' or Entenmann's and- Saralee. Saralee. Takis. My kids' favorite, Takis, is one of them as well. You have been an Oracle customer for quite a period of time, but give us a quick snapshot of Grupo Bimbo today and really the sheer scale and complexity of your business operation. Grupo Bimbo, to be a baking company, is very large, very complex. We serve on a weekly basis over 4 million customers that we go and visit and deliver bread. Bread is one of those products that it is fresh. You have to have a very reactive supply chain. As you were saying, we have 250 brands and almost 10,000 SKUs. The supply chain is very complex, and everything generates information. If you want to be competitive, because we have thousands of competitors everywhere, anybody in a corner in the world can make bread. We are competing against everybody. Of course, there is space for everybody, but if you want to play really as a global company in this industry, you have to have a difference. That difference is around on how you manage, and how you work your information to make you truly global. Yeah. I mean, the sheer complexity, scale, size of how you are operating is enormous, and it is not very different than many large-scale global manufacturers as well in terms of what they are dealing with. How do you simplify that technical complexity by leveraging and partnering with Oracle to drive standardization and business operations to be as efficient as possible? Yeah, Grupo Bimbo today is a result of many acquisitions- across the years, and everybody used to operate on a different platform. They brought in together their own technology, their own way of looking at the business. What we have been able to do with Fusion, particularly for the past eight years, is bring everything together, have one way of looking at things, and then incorporate things as Fusion Data Intelligence. That brings one way to see the business and not have 100 different versions of the truth. That is super important. Then bringing the new AI capabilities, you are able to figure out the true opportunities globally, and particularly when you go into investments. It is super important. A company like ours invests sometimes billions in capital assets. Yeah. The level of information when you look at it globally makes a difference in terms of how, where, and when you are going to be investing. When we talk about Fusion often, people hear Fusion in large scale, they may think ERP, HCM, CX, and yes, you have that, but also it goes beyond that. It goes EPM, it goes SCM, supply chain management and manufacturing, OTM, transportation management, work management, logistics. It is all of these modules that make up in your industry, that is the industry application. What outcomes have you seen by standardizing across that approach? For instance, we started working around year 2000 with prior versions on, not the Fusion, the on-the-edge versions of Oracle. Then we had all sorts of different versions, but everything was around the financial information. Yeah. After implementing Fusion, we have been able to bring on the different supply chain modules, and that is where the value resides. Because when you change the financial part, yes, it is super exciting and very important to change the closing time from 16 days to 3 days. Yes, we love that. But when you come to the supply chain side, you are able to take out of your cost points. Yeah. Those points are hundreds of millions of dollars on an annual basis. That is what really matters at the end of the day, and that changes the business, particularly a business like ours. We are very excited on that side because we are just getting it started on the supply chain side, where most of the significant financial opportunity resides. Yeah, it is exciting to see that transformation that is continued underway, that is delivering real tangible benefits. Sanjay, let us flip over to you at Vodafone. So Vodafone, global telecom and tech company that is in 15 countries, 52 partner markets, serving over 300 million customers in what you do. Vodafone and Oracle have worked together for many years, but that has really transformed strategically around a significant cloud transformation and modernization that Vodafone has gone through. Leveraging our technology in a unique way of how we deliver not only cloud services, but database services and our other capabilities in a flexible deployment option, like our Dedicated Region approach or even our multi-cloud approach. Maybe highlight a little bit of the background on the scale of Vodafone and complexity, but also how your cloud transformation is going with Oracle. Yeah. So, as you mentioned, we are a huge brand spread across Europe and Africa. We have got a number of our technology systems which support millions of customers, and not just in mobile, but also in the fixed network as well. Plus, we have got business, 50 million plus businesses that we support, a huge IoT presence, and also in Africa, things like M-PESA, where 100 million users are supported through our platform. I think our partnership with Oracle has been there for, I would say, a number of years, over 20, 30 years, and it is going really strong. A few years back, I think 5, 10 years back, we had a major challenge where a number of our databases and applications were going end of support, end of life. When you run such a mission-critical business service, you cannot afford to have that sort of risk into the business. That is where Clay's team came up with a huge engineering proposal to say, "Why not you migrate into a dedicated OCI?" The cloud, which is hosted in our data center. Because what we are looking for is something where we have got our control over it and where our applications can uniquely connect to the databases, which is in our hosted regions. We have got one of the biggest dedicated regions. We have got six regions spread across three different countries, so we can offer the resiliency between that. What we have done is, with Gary's organization over the last four, five years, is migrated those databases from on-prem, which is end of life, then transformed them into the cloud, and they are now into the support. We brought the cloud to you in your data center, three regions, redundancy, mission-critical apps, as well as the data into those environments. But in addition to that, you also launched a multi-cloud strategy as well, and now you have the ability to deploy the Oracle Database in any environment and optimize between clouds and take advantage of the Oracle Autonomous AI Database. How is that benefiting Vodafone? Yeah. As you can imagine, we have got quite a complex technology landscape, and one of our main strategies to get out of our data center, so we have got a push into data center consolidation and then moving our applications into public cloud. With that, we work with all the kind of cloud providers, Oracle definitely, but also AWS, Azure, and the GCP. But majority of our applications use the Oracle technologies for the databases. Things like uniqueness of Exadata, which gives us really great performance into when we building up those mission-critical applications. As a part of that, when we are moving those applications into the cloud, all the business was wanted to ensure is that we get the same sort of functionality and the performance when we migrate those into either Azure or an AWS. And that's where we are using Oracle Database@Azure, Oracle Database@AWS. We started that journey few years back now, and we are into that. Yeah. It's great. We give you the ability to deliver the incredible technology that's needed to support these mission-critical applications, but in any consumption model and location that benefits how Vodafone is operating. And it's given you incredible capabilities around autonomous as well. Antonio, back over to you. You're using Oracle Cloud as well to deploy some AI agents within your environments, and we've announced here this week, I think you and I were talking earlier about the amount of announcements that have come out around AI capabilities within our cloud applications. Two things. How do you keep pace with the innovation that we're deploying within the Fusion Suite as well, but also, how is the AI that you've developed on some of the Oracle platforms benefiting Grupo Bimbo? We have to be very selective because there's, I don't know, hundreds of new things to do every week. Yeah. But you can only do so much. Yes. Again, what is going to be making a difference to the bottom line? What is going to be making the difference to the thousands and thousands of people operating daily, making their lives easier? So, on that side, we started extracting information from Oracle, actually in ways Oracle didn't know it could be possible. We partner and we learn together. Then we started using our own AI to make sense of it. Again, millions and millions of points of data on a daily basis. But now, for instance, this week's announcement around Claw becomes something super important. Because working as you announced that it is going to be working, it is going to be simplifying our work significantly. We are going to be getting rid of many of the manual applications that we have to put together and maintain, and then everything is going to be available. Of course, it will have to make sense from a practical and economical perspective, but just those opportunities are going to be making such a difference. We are not in the business of developing AI solutions. If we have a partner, a trusted partner as Oracle to help us develop those and to have them work and evolve in our ecosystem, that is the best way to go. Yeah, that is great. As we see continued benefits across applications, speaking of applications, Sanjay, back over to you. Simon was just talking about our differentiation around industry and focus and communications. Telecommunications is a deep industry that we have focus in. For Vodafone, you are running some of our critical applications that are communication-specific, that run operations within the business. These are, as you said, mission-critical applications that support your 300 million users, your 50 million business users. So running these mission-critical applications, running Oracle AI Database in different formats and infrastructure, how does this all come together around your technology strategy and how you see Oracle as a partner? Yeah. As I said, our mission is to use cloud, and then with that, what comes in the autonomous and the AI facility. So, we use Oracle's application suite, which is tailored for the communications market. So applications like Siebel, which does the customer care and the agents, BRM, which is the billing. So we are using those to transform those into technologies like Kubernetes, so we can then use it into microservices container-based application. It gives us that flexibility, and then things like identity management for the security. So moving into the cloud using OCI, we get those performant, secure, and resilient systems that we can then run our critical business. Yeah. When we think about the end-to-end, back over to you, Antonio. When you think about what you just described earlier, you get financial applications and Fusion, but then when you get into the operational side of this idea of standardizing and simplifying from an operational perspective to be able to see end-to-end operations, how does that improve the data visibility? More importantly, how does that give you the ability to take advantage of AI capabilities going forward? Instead of having the possibilities or, sorry, the need to plan every one of our operations, more than 200 bakeries, more than 2,000 distribution centers on a local basis, we can take a look on a global basis. Yeah. Then we can help the people that were basically just producing reports, now getting into the real decision-making. This is something very important. If you haven't been in the supply chain, you don't get this because everything we've done in the supply chain in the past few years is making reports and trying to get sense of what is needed to survive today. Now we have the chance to go and make a difference for today and for tomorrow. Yeah. That's fantastic. Sanjay, if you could describe your experience in terms of working with Oracle in one word or two words, what would it be? I would say transformational and collaborative. We are able to work so collaboratively over the last few years and modernizing application using the innovations that Oracle is offering. Antonio, how about you? One word, two words, in terms of your experience working with Oracle. Just getting started after 26 years. Right. Fantastic. I just wanted to thank you both for sharing these experiences around these incredibly complex global organizations that are industry-specific, where Oracle is bringing together the power of all of our capabilities to help you transform in this modern era of AI. We are incredibly grateful for the partnerships that we have with you, the trust that you put into the relationship that we have, and we just wanted to thank you so much for being here today. Thank you. Thank you, Mark. Thank you. Thank you. Thank you. We are now going to take a short break, about 15 minutes, and we are going to come back in for the rest of the program for the balance of the day. Thank you. Please welcome to the stage, Hilary Maxson. Hi, everyone. Welcome back to the final sessions of today's Investor Day. In fact, just myself, and then we'll go straight to the Q&A. Great to have everyone joining us here, both in person and also thanks for joining via the webcast. You've heard from Mike and a number of my colleagues here today about the really amazing things that Oracle is doing with technology enhanced by AI to create value. I'm going to put that into further perspective on how it creates value for our shareholders. At Oracle, we are very simply the right company at the right time to take advantage of this wave of intelligence driven by AI. We announced that vision and strategy last year. Now we're a year into the journey and ready to share with you, with confidence, what that looks like from our P&L to our balance sheet between now and 2030. It all starts with differentiated growth. You can already see in our guidance for fiscal 2027 a +34% growth rate year over year. That path continues to 2030, where we expect to be at $225 billion plus in revenues. Of course, profitability and free cash flows are equally important drivers of value creation. We're happy to share with you today our path to free cash flow positive sometime during the second half of our fiscal 2028 and to $30 billion plus in free cash flows in our fiscal 2029. I'll also speak through our margin expectations for the company as we undertake this significant transformation. In terms of disciplined capital allocation, we have done significant work on our funding journey over the past months. I've told you we need an additional up to $20 billion in funding for the remainder of our fiscal 2027. We're announcing that this $20 billion, split evenly between debt and equity, is the final amount needed to fund the remainder of this cycle. Let me dive into the details, starting with differentiated growth. Mike said at the beginning of his presentation that AI is the catalyst that makes every part of our portfolio more valuable. This is translating into a growth opportunity for us that's truly differentiated for a company of our size. In infrastructure, demand remains extremely robust, both for short-term and longer-term capacity. Our RPO at $664 billion is one of the largest in the industry, with more than 90% of that tied to our infrastructure business. In AI infrastructure, we were a first mover in the market, building a significant RPO with a single customer in our fiscal 2026, and we have continued to grow our RPO quarterly since. With those additions, we have diversified our RPO each quarter. In applications, you saw with Chris and Simon the opportunities to continue to grow our subscription revenue with both current and new customers, as well as exciting new opportunities for AI consumption revenue and on-top revenue opportunities like embedded finance. In database, AI is driving more data and usage, while availability through multi-cloud expands our market and accelerates growth. Our portfolio positioning and strategic choices mean our total addressable market is positioned to grow at CAGRs of greater than 20%, and we are positioned to grow far faster than that over the next years. What gives us confidence in that differentiated growth in our portfolio? First, in this slide, we have depicted the flywheel we see accelerating our business today, and we talked a lot about that throughout the sessions already. We have always been a company that drives measurable customer outcomes, and with AI, it becomes even faster to show proof points of strong outcomes that encourage broader adoption across customer portfolios. This, in turn, drives more demand for cloud database, data services, and for compute itself. There are very few companies that can claim this reinforcing flywheel across our portfolio, supporting a path to differentiated growth. Another point giving us confidence is more of our contracted revenues are now tied to projects already under construction. Mahesh shared with you earlier today how we differentiate in infrastructure, including in execution, and we continue to be confident in the plans we have to deliver around 7 gigawatts incremental capacity over the next couple of years. This will put us with a portfolio of around 10 gigawatts at the end of our fiscal 2028. This acceleration in capacity delivery to support contracted RPO leads us to a period of peak CapEx in fiscal 2027 and 2028, with our CapEx stepping down in fiscal 2029. We will continue to grow our business in fiscal 2029 and beyond. In fact, these around 7 gigawatts in projects ramp into strong free cash flow contributors fairly quickly, meaning we will be in a position to both be free cash flow positive again sometime during our second half of our fiscal 2028, and to self-fund the growth of our business starting in fiscal 2029. I will get into some more detail on our cash flow and capital planning in a few minutes. One point to note is our large sites are only driving a part of that contribution. The five large-scale sites we have underway account for around 5 gigawatts in total capacity to be delivered between our fiscal 2026 and mid-fiscal 2029. Each of these sites is plus or minus a few hundred megawatts from 1 gigawatt, and one of those sites, Abilene, is already nearing completion today. Our strong confidence in that path to differentiated growth underpins the revenue commitments we are making today of $225 billion plus in our fiscal 2030. That is a 35% CAGR and an addition of $158 billion plus over four years. As we ramp up our projects, we expect to see reasonably consistent revenue growth across our fiscal 2028 and fiscal 2029. Of course, differentiated growth alone is not enough to drive acceleration in value creation. Profitability, free cash flows, and return on investment are also key drivers. We have a transformation underway at Oracle, not just in technology, but across our business models. The new technology backbone being created with AI infrastructure is driving different growth rates between our different business models over this cycle. It is important to understand that when you think about our profitability over the next years. In our cloud applications or SaaS business, we expect to continue to drive double-digit growth with low capital intensity, very high gross margins, and relatively high operating costs in the form of R&D and sales. Cloud database will have very high growth, well in excess of 50%, and moderate capital intensity. Similar to SaaS, cloud database has very high gross margins and relatively high operating expenses as a percentage of sales. Our infrastructure business is a very different business model with relatively lower gross margins and high capital intensity. However, it also has relatively lower operating expenses as a percentage of sales, particularly when supported by the networking technology differentiation that Oracle has already created and continues to create in its database business. There is a big differential in gross margin between these business models. However, at the level of operating margin, this differential closes quite a bit. All three of our business models contribute strongly to our profitable growth journey. Historically, our non-GAAP operating margin has trended in the lower 40s percent. We show here in fiscal 2026, it was 43%. We do anticipate some near-term margin pressure from mix, as well as the ramp-up of our projects over the next few years. However, with the continued strong contribution from our apps and database businesses and focused cost management, we will be back at a 40% plus margin in fiscal 2030. At the peak of our ramp-up impacts, that is in fiscal 2028 and fiscal 2029, we are not significantly below that. To dive a bit deeper into the contributions from apps and database, there are a couple of points I would like to draw out from these slides. First, on apps. Chris covered the evolution of our pricing models with the addition of outcome-based pricing. We continue the journey made faster by AI to migrate customers to the cloud. That results in, Simon said three, I think it is three to four times, a four times pricing uplift versus on-prem. In database, AI is driving a shift to higher value database services. We continue to scale our multi-cloud business, and AI Data Platform offers another great opportunity to expand our monetization models as customers focus on using intelligence to drive results. We have received a lot of questions about our infrastructure business model, so we thought it would be helpful to provide a 1-gigawatt example for education and modeling purposes. This is roughly reflective of the economics of our next few years of projects, and this is, of course, based on Oracle's business model for data centers. So it assumes we are leasing the shell, the power, et cetera. A few things to call out here. Each of our five large sites is planned at plus or minus a few hundred megawatts from 1 gigawatt. This is a good proxy for each of those sites. One of those sites is close to fully executed, Abilene. We have four sites expected to start first service delivery in calendar 2027. Of course, our financial guidance takes into account up to a couple of quarters delay on any of those. Mahesh highlighted those sites are not built as a single project. In fact, they roll out as a series of data halls and buildings over time. As a result, the lease obligations, CapEx commitments, and revenues ramp up as the build progresses. This means, in the case of a delay, most of the costs are delayed as well. Plus, a lot of the hardware is fungible site by site. We have also given the details on average project-level economics reflective of our current RPO. CapEx at around $37 billion is a bit less than 50% of total contract value. Revenues are around $16 billion per gigawatt annualized, so that is once we ramp up. While our gross margin is at around 35%, operating margin at the project level is around 34% due to that OpEx-light business model I described earlier. This business model generates strong free cash flow fairly quickly as we ramp up. Fully ramped, a 1-gigawatt project will convert 100% of its non-operating profit after tax plus depreciation into cash flows annually. I have spoken about our returns on investment in the high 20s in the past. Here you can see the details of the calculation itself. We have added back depreciation in the numerator because this business has limited maintenance CapEx, aside from new IT equipment, around when we would expect to recontract or later. Please note, effectively, all of our large AI infrastructure contracts are cost plus or similar. So revenues may change, but the margin and ROI will remain. As we demonstrated in our prior slide, this is overall a very attractive business model, albeit capital intense when self-funded. In the model shown on this slide, we pay for all $37 billion of the required CapEx in the first two years of the project. We have been looking at some models to reduce that capital intensity, and we show the directional impacts of those in the next couple of slides. One key lever we are availing of is vendor financing. We have strong partners on the supply side who are willing to support us, and you can see an illustrative example here of a simple 70% five-year vendor financing. This reduces the capital needed from Oracle in the first two years to $18 billion, a 50% decrease. Bring your own hardware is where a customer provides their preferred chipset to us, reducing our capital requirements to a minimal $6 billion or lower. Again, based on this 1-gigawatt example. Of course, with bring your own hardware, there is some impact to the project economics with lower revenues and higher margins. Mahesh talked through the value add to our customers in that scenario. This slide summarizes the simple examples I just walked through. In the case of self-funding and vendor financing, the total level of CapEx and project economics remain roughly the same. What changes is the timing of our capital needs, with vendor financing shifting those payments to be better aligned with our revenue ramp. Bring your own hardware changes the capital intensity of the project itself, driving higher margins and higher returns on investment. I will close this section with a few further points of guidance. Based on the strength of our three business models, including the strong cash Acceleron from our infrastructure projects I described a few minutes ago, we expect to drive greater than $30 billion in free cash flow in fiscal 2029. I mentioned the 40%+ operating margin we expect in fiscal 2030, and we also anticipate $21+ in non-GAAP EPS. Of course, there is a question of recontracting and the necessity for the renewal of GPUs at the end of our various contracts. Mahesh walked through the differentiation we have in terms of technology and how we will continue to drive value for our customers over time. We are building a business for today and tomorrow, and we expect to recontract at similar margin rates or better than what we have today. In the case of renewal of GPUs or other IT equipment, we gave a real-life example in our Q1 where we are selling or renewing four-year GPUs at 20% premiums to their original contract price. We have put that information into the slide here. We think that is a powerful example of the potential upside from the GPUs we have in our portfolio today. We will not count on it, but we do think that these are valuable assets both for today and into the future. Turning now to accelerating value through disciplined capital allocation. Our balance sheet and capital allocation priorities remain clear. First and foremost is to maintain investment-grade credit ratings. That is extremely important to us as a business. Second, to deliver returns to our shareholders in the form of a dividend. Third is to fund the significant business opportunities we continue to have in front of us. I mentioned we have been looking at some different models to reduce the capital intensity of our current build-out as we bring online an incremental 7 gigawatts over two years. I already spoke about the impacts of vendor financing and bring your own hardware. Lastly, as we discussed over the past couple of quarters, we continue to be in a very strong demand environment. Within this environment, customers are willing to make prepayments to preserve the right to receive services from Oracle in the future. Why does this all matter to us? We have been working super hard on sizing the level of final capital needs we can commit to for the remainder of this investment cycle. So between now and 2030, or really between now and when we can be self-funding as a business. This slide reflects how we have gotten comfortable with those calculations. The most important driver of our funding needs is, of course, the remaining CapEx needed to fund the build-out of our projects. This slide is illustrative, so I have simply used the 1 gigawatt example to project our CapEx needs starting in Q2 2027. It may not capture all timing impacts, but directionally it is around correct. After working with our partners and customers with the capital intensity reduction models we have in place, we are confident we will reduce our upfront cash requirements between now and when we will be self-funding by around 50%. And of course, we have the operating cash flows from the business itself. Based on this, we are able to determine and to commit to today the final capital needed to finish our current investment cycle. $20 billion in incremental capital is what we are committing to today to complete the funding of this investment cycle. We will split that evenly between $10 billion equity and $10 billion additional gross debt. Given this amount is quite a bit less than what might have been anticipated, we will look to execute that opportunistically as early as calendar year 2026. I will mention here again that that does not mean we intend to stop growing our business. With the flexible models presented here today, and with our RPO converting into strong free cash flows in the fairly near term, we expect to be able to fund our business for continued differentiated growth in the future. Lastly, a slide on our balance sheet expectations. Based on everything we discussed here today, we expect to improve the strength of our balance sheet in the near future. Following the $10 billion in new debt, we anticipate our gross debt will remain flat over the next couple of years as we refinance in the ordinary course. And we are committing to a less than 3.5x net debt to EBITDA ratio by fiscal 2029. I will finish with the same slide I presented at the beginning. We have solidified our capital requirements for this cycle. We are confident in our path to differentiated growth, to positive free cash flows, strong operating margins, and a strong balance sheet. We look forward to showing you with each coming quarter the stepping stones in that journey. With that, I will pause, and we can shift to Q&A with myself, Clay, and Mike. Okay. Am I on mic? Good. We have Mike, Clay, and Hillary up front. We will start with John. If you want to ask a question, just raise your hand or shoot me a text message. Okay, thank you. Thanks. You guys really left the best for last there, and everybody is waiting for all that. So it was great to see. I think everybody in this room, or most people in this room anyway, and you guys probably do too, realize that the stock of Oracle was not necessarily trading on how you are running the business, but on more tactical things that you actually just addressed. I guess I want to hit one of them anyway in a little more detail. I guess you said, Hillary, and there's a lot of little things, so this is a little thing, I think. But you said that the CapEx in FY 2027 and FY 2028 was going to peak, and you said, "And." So I think a lot of people that I speak to, anyway, believe that that means that FY 2028 will be about equal to FY 2027. But when I think of gross CapEx, at least in my model, when I try to model, and I spend too much time doing that, I guess. But it actually goes up a lot because you have so much new business coming on in the FY 2028 and FY 2029. So how should we think of that for gross CapEx? Because you gave a great bar chart of how much was going to come in from early payments and stuff. It helps a lot. Thank you. Sure. So the way I think Actually, that's one of the reasons we gave that one gigawatt example so that you guys can do a little bit of modeling yourselves a little bit more easily. What I would say is we're ramping up part of those gigawatts this year and probably even more next year. So maybe that can help you a little bit directionally as you model. Okay. Hi. Tal Liani from Bank of America. Just one clarification before my question. The clarification that I think the most positive surprise here was how little money you need to raise for the next three years. So I just wanted to make sure that I understand the slide. It's 10 and 10, so $20 billion for 2027 and nothing for the other two years after that? Yep, that's what we're committing to today, is we have been able, through these flexible mechanisms we explained, vendor financing or sort of vendor financing similar, bring your own hardware, prepayments. We can really have that confidence to commit to that $20 billion today. And that's a real commitment. We don't expect other forms of structured financing or other things using our Oracle credit out in the market, aside from, of course, we'll continue with the same business model that we have today of leasing data centers. Got it. My question is about pricing of capacity and your strategy around pricing. There is a difference between pricing for 5- to 6-year deals and pricing of 1- to 3-year deals and spot pricing. What is your plan? What is your strategy behind selling this capacity? Yeah. It is a good point. The way that we think about it is that you have to pick a portfolio mix. It comes down to what is your risk appetite and how you want to balance off your different options. As an example, if you sell something on very short-term reservations, you can get a much higher price at the beginning of the life cycle of the hardware. But you are taking on more of the risk about, okay, what is going to happen in the future? There is a game to be played around how much cash do you front, how much of a return do you get at what period of time, which partners you are using, how the customer is allocating their capital towards you. All of that comes into play. What I would say is that ignoring the how do we think about how many 1-year deals versus 5-year deals that we are doing, I think the thing that everyone is massively underappreciating is that the data centers that we built that came online this year, 5 years from now, whether it is we made X or Y or how we got there, these are going to be incredibly cheap data centers compared to what people are able to build 5 years from now. The hardware that will be in them, I think, will be incredibly cheap. Regardless of when there is a renewal cycle, and that is part of the reason why Hillary showed the renewal options that we are seeing, there has been a lot of fear in the generic industry that, hey, people are assuming a much longer life cycle for this hardware. That is very much the opposite of what we are seeing. The way I would think about it is in situations where we have plentiful cash and that we think the risk appetite is there, we are going to invest and take more risk, but we think we can get a higher return. In situations where we think that that is not appropriate and we need a lower risk profile, maybe we need access to different capital or we want to lock something in, we then do longer-term contracts. But we are constantly balancing that off. The thing to remember is that you do not just get one bite at the apple. If you sell a 4-year contract tomorrow, 4 years will be here sooner than we all like, as we age very rapidly. When that happens, do you renew it another 2 or 3 years? Do you sell that into the spot market? It is like asking what is an airline or a hotel chain's philosophy to pricing. Well, it fundamentally has to be extremely dynamic. That is a huge part of what we do, is to actually have extremely dynamic pricing, and you put a portfolio together to optimize your risk along with your overall returns. I would also add, Clay, you mentioned the word partnering, which I think is very important. As we think about these relationships, we also keep in mind that we ultimately become a distribution channel for these customers as well. Fusion Claw is a terrific example. There is a lot happening behind the scenes in Fusion Claw. We are aggregating some of our model customers. There is another chance for us to monetize that output as well because we want those customers to also become partners of ours too. That is unique to Oracle because we are delivering the entire ecosystem, the entire stack. Everybody else is basically in the business of providing computers. We are in the business of providing the computers that also enable outcomes as well. So it certainly factors into what we do. Yes. Hi, Stefan Slowinski from BNP Paribas. Thanks for the presentations and for taking the questions. Hillary, so much has happened in the last year in the industry, yet your guidance has not really moved for 2030. Just wondering how that all nets out. Obviously, we have had lots of inflation over the last 12 months and lots of debate about the inflation escalators in your contracts. Interest rates have gone higher. Just wondering if that guidance a year ago, there is just a lot of cushion in it, or how you would characterize the guidance today, and how those moving parts have impacted the lack of change in the guidance. Well, to be fair to Hillary, because she was not here, the number went from a number to a number plus. Did notice that. I just, Yep. Just Indeed. I hope that we clarified the question around pricing in the large AI infrastructure contracts that we have today. We do have effectively some form of cost-plus pricing. For the rest of the CPU business, we are anyways contracting much closer to when we bring the compute online. We feel pretty confident in being able to cover the increase in costs, for example, that we might see during the build-out of projects that we have, whether for CPU or for GPU. In terms of the guidance, look, I think adding $158 billion is pretty impressive. Not 100% sure that the market has quite priced that in yet. I think the $225 billion-plus, we are one year into the journey. We feel even more confident. We feel very confident with that $225 and that $21 in EPS. We added the plus really exactly for this reason. Hey, guys. Brad Zelnick, Deutsche Bank over here waving in this big room. Thanks so much for a great presentation all around. Really very helpful. Hillary, you made reference to this cycle as you presented the next few years. How should we think about future cycles beyond the current one and your ongoing commitment to growing free cash flow beyond the $30 billion in FY 2029? How also should we think about your appetite to take down more deals and the size of the types of commitments that you will make from here to customers? Thanks. First, I think the $30 billion-plus leaves us with opportunity to continue to grow the business. Like I mentioned, I think we're showing truly differentiated growth in what we're showing to the 225-plus in future. That's in fiscal 2030. This business is a very strong free cash flow positive business driver on all three business models. I think we have plenty of space to grow that business. I think that we'll be in a very different place in fiscal 2029, fiscal 2030, if we're growing far in excess of $30 billion-plus in free cash flow. I'm quite confident that we'll have all of the opportunity that we need for that business beyond 2030 to keep growing at the rates that we should as the business and keep up with the market we have available to us. I don't know, Clay, if you want to add anything else. Yeah. I think that clearly we don't want to. The world is dynamic, and we want to have flexibility there, but it's very clear that I think Hillary's talked about this in the past. It's a lot about ratios, right? Going from one to 10 gigawatts, right? You're going up a curve that's very steep. As those things throw off cash, I'd love to tell you that I think that from 2030 to 2034, that we could go from 10 to 100 gigawatts. I don't think that's going to happen. Part of, I think, what you're seeing is that we have gone up a very fast ramp. While we will continue to push as hard as we can and grow, I don't think it's possible to add 90 more gigawatts in 2030 to 2034. The ratio of how things get funded is just inherently going to be different in that environment. Hey, guys. Right next to Brad. Alex Zukin from Wolfe Research. Echoing the congrats and the presentation and the clarity and the, obviously, the confidence that it conveys in what you guys are doing. Maybe just given, I think, some of the dynamism that's inherent right now in the industry, and particularly the pricing dynamics, I think, Clay, that you're alluding to, it feels like you guys have discovered something pretty meaningful in this creativity to drive down that level of financing that you need, whether it's from the price points, the compute that you're able to take online, the price points that you're able to deliver it from. Can you maybe get a little bit more specific on what changed in the last, call it, year, that's enabling this decline in the density of financing that you're going to need to complete this cycle? Well, I think if you actually look at what we've been saying over the year, it's been consistent, right? Hillary did an amazing job of showing you the different forms. It isn't like, surprise, we invented bring your own hardware and prepayments and vendor financing in the last 3 weeks. We've been talking about that for many quarters. I think that part of what Hillary has done is she's explained to you the size and the scope of it. I think if you now take a step back and go back and read everything we've told you over the past several quarters, you'll be like, "Oh, this is entirely consistent with what we have been saying." I think there's just other people didn't calculate it the same way that we were. Yeah, and I think I'd mentioned today that we've now solidified quite a bit of this planning. There's a big difference between when we first set out our vision and now what we've put into place, which is all the mechanics around it. So we're really in a place today where we have enough. These aren't just forecasts. Where we have enough done that we can really make the commitment that we're making today on those lightening of capital intensity mechanisms. They're not just some dream. Now we actually have confidence, and we have those mechanics in place. Thanks. Jackson Ader at KeyBanc Capital Markets. Thanks for taking our question. The question I had was on the mix, I guess, of vendor financing, bring your own hardware, prepayments. What kind of mix is assumed in these 7 gigawatts that's expected to come online in the next couple of years? Is there, I guess, number 1, any risk that if that mix changes, it's like, "Oh, shoot, we actually do have to go out and raise outside capital"? Are there certain type of customers that you might have to say no to in order to maintain that mix? Thank you. So the 7 gigawatts that I showed, this is for contracted capacity already. And that mix between vendor financing, bring your own hardware, and prepayments is primarily solidified. That is the kind of confidence that. Otherwise, we wouldn't have the confidence to say what we do today on the $20 billion. We don't take that lightly as a company. That's a big, big statement to make. So we really feel comfortable with what we have in place with those 7 gigawatts, with the customers that we have, with the suppliers that we have today. Now, will we have some additional growth? Primarily probably beyond fiscal 2028. I think between now and 2028 is about what we have coming online. After Oh, hi. Karl Keirstad at UBS. Yeah. Clay, I've got two chip-related questions for you. Okay. One I think Hillary just answered, which is that if you are relying a lot more on vendor financing, given that you are very heavily weighted to one particular chip supplier, just how solidified is this commitment from that chip supplier? Is it locked in agreement? Clay, congrats on the big news around hosting Google TPUs. For me, that was actually one of the biggest announcements from Oracle this week. Maybe you could just spend a second on how meaningful that is for Oracle, and does that have any effect on the revenues, and backlog, and margin outlook? That might be helpful. Thank you both. Yeah. I think on the first question, I think that is presumptuous. I do not think that we said that we have specific financing with any specific vendor or in what magnitude or quantities. I do not think you should walk away here with any of those assumptions. I think there is an intentional reason that we are very confident in the different ways in which we are financing the business. Now, onto the second question about TPUs. Look, I think TPUs are a really cool accelerator, and if you look at our overall strategy, it is that we want to have all the accelerators that our customers want. Do I think that every customer will suddenly want to buy TPUs? No. That is okay. In the same way that we have AMD and Intel CPUs, we also have Nvidia ARM CPUs, and we have Ampere ARM CPUs. We have a lot of different options. What I would say in general is that our overall strategy has been to make the expense to us and the expense for our partners to be extremely low to integrate. Meaning, if we can make it very simple and easy for us to do the integration between our products, then why would we not want to offer those options? I think that has served us well. Not just because it helps you have accessibility to more customers, but that strategy actually makes us more flexible across the ecosystem today, but it also gives us confidence that we are being flexible across the ecosystem in the future. So if you wonder why is it that we are confident that we are going to be able to plug in different types of hardware five, or six, or seven, or eight years from now, it is because we are able to plug in a lot of different types of hardware today. That is the only way we can be confident that the stuff that we are designing is actually truly flexible. So look, in summary, TPUs are great. There is many customers of those. We have some of them that are very excited to use them on our cloud. But obviously, a lot of people would like to buy a lot of Vera Rubin chips as well. Same thing, right? People are excited about Cerebras. Broadcom Jalapeño is also very, very exciting. So that's our overall strategy, and we're working with all of the different players and all the different customers to try to give them the accelerators that they want. What I would say in aggregate is that demand for all of it is far exceeding the ability to go out and supply it, at least for what I see for the next several years. Hey, Derrick Wood at TD Cowen. Over here. We all read the news, obviously, on some of the challenges with permitting and data centers. Clay, you've responded recently on some of that. Can you just talk about the fungibility that you have in case there are delays, and the ability to put equipment into other shelves, and how much slack you have in withstanding some of those delays out there? Yeah. Well, first thing I would say is I think there's a misunderstanding in that I think a lot of people who are not very familiar with certain industries, it becomes very popular for some reason, and then what are just kind of normal course of action suddenly becomes front-page news. Sometimes it feels like you wake up and there's like, "Actors showed up to the set today, and they had to wait until the lighting was right before they could record their lines again." It's like that's how movies are made. The process that we use, like building large construction projects of this scope are complicated. And I think anyone that goes into those projects without assuming that they're going to be complicated is sadly mistaken. Now, in terms of the actual technical piece, there's what we put inside of the data centers themselves, but there's also the data center components. I think that people misunderstand that if you look at the total capital that goes into a completed data center, Mahesh, I thought, had a pretty cool video up front. The building itself does cost money, but it is not as significant. You take out the racks and you take out the network gear, you take out all the fiber optics, and you take out all the cabling, because all of that you can put somewhere else. You take out all the chillers because you could move those. You could take out all the UPSs because you can relocate those. Suddenly what you find is that as a percentage of the total site expense, yes, you have to do a bunch of grading, and there is specific, if you're hooking into the grid, certain substation transformer work and certain things. In terms of the overall percentage of the project, it's very small. Sometimes it's a little bit like Bob Ross would say, you have happy little accidents. You may have something that's delayed over here, but you can use those components to accelerate a different project over here. That's part of what Mahesh was trying to explain with, hey, a single quarter delivery we have on the order of 40 or more things delivering. You have a complex apparatus that you're hyper-optimizing all the time. Look, do I think that it requires a huge amount of effort and focus every day for us to wake up and focus on it and to make them work? Yes. That is our job. That's why people trust us to deliver capacity for them. I think if you see the predictions that we've made in terms of revenue and capacity and the success that we're having about hitting the deliverables that we're talking about. People keep asking, "Well, are you confident that you can plug in this stuff?" It's like, yes, and you can see it. We show you the videos of the sites. We show you the revenue that's associated with the customers paying for it. We show you the profitability that comes directly and operating income and how that flows through to EPS each quarter. This is not the future. When we said we delivered 850 megawatts last quarter, it doesn't take many quarters to get to That's a lot. It's not some abstract thing in the future. This is happening right now every day. Hi. Siti Panigrahi from Mizuho. Thanks for all the clarity today on funding and free cash flow inflection. Great job today. I'll switch the topic to application. I think this is one of the underappreciated topic today on AI World. Chris talked about the AI units, how you're launching now as a new revenue stream, usage-based revenue stream that doesn't depend on your adding seats with higher margin. Hillary, in your guidance for double-digit SaaS growth, how much contribution are you assuming now from the AI units or consumption-based? When could AI consumption become material enough to accelerate that apps growth beyond where it is today? Well, double digit obviously encompasses a fairly large range of outcomes. We're just getting started, I would say, in terms of the outcome-based pricing. So at least in my financial models, we haven't incorporated too much yet, but the opportunity is certainly there. Mike, I don't know if you want to talk a little bit more. Yeah, I would say, look, this is new, but things move pretty quickly in the world of AI. The catch with outcomes-based business is you want to be sure that you can generate the outcome. Since we've been an operator and a custodian for so long, our confidence is quite high. Our customers have shown us what good looks like, and in many cases, what great looks like. If we can replicate those heuristics across all of our customers by industry, we think that becomes a terrific business for us. As Hillary said, double digits is a wide range. We'll stick with that range right now and certainly provide more clarity as we roll it out. I think the other thing that's equally important, I said it this morning. You still have, in the ERP base, about half the customers still running on-premises. Getting them to cloud is incredibly important. I think it's also incredibly important in the age of all these advanced models that are discovering, as Sam said on stage yesterday, incredible vulnerabilities. The on-prem stuff is going to become even more vulnerable. It's going to be very difficult to patch that stuff at scale. I think that business is actually really important for us, too. Automating implementations, Chris said 50%-70% faster. That is an unlock that customers have been waiting for a long time. So we're really thrilled with both of those announcements. Mark Moerdler, Bernstein Research. Over here. Thank you for all the great detail. Impressive, and congratulations. Ken, it's been great working with you, and enjoy the next stage of life. We'll miss you. Hillary, you mentioned that you've built in data center delays. Clay answered this from a technical perspective, but Hillary, can you give us more color on how you're modeling in and managing for the potentials of regulatory or other delays on the data center? Does the delay create potential for clients to walk away? Does it allow you to manage the price with the clients? Any color so we understand what happens if there's a data center delay from a financial perspective. Thank you. Sure. Coming out of infrastructure for some portion of my career, and I think Clay said it a few times on a few of our quarterly calls as well. One would be crazy probably to assume a particular exact set date with yourself and your partners. That extends into our customer contracts as well. I think the customers understand that we're building not so much something new, data centers have been built for a long time, but at a scale that's quite new. I would say I wouldn't raise any particular concerns from a customer standpoint. We really view it more like a partnership with the large-scale AI sites. Another thing I would say is that, and I mentioned this throughout my presentation, the costs generally ramp up as we build. If we're going to have a delay, the costs won't ramp up. We're able to fairly match, and I thought Clay did a good job describing that, not a lot of the cost that we have in front of us are actually the shell or sort of flattening the land or anything like that. Most of the costs ramp up as we go by data hall, by building. We're able to manage, I would say, well financially, if we did run into any delays, and we do have fungibility between the sites for the vast majority of what we're spending money on. I think we've all said that, Mahesh, myself, Clay today as well. In fact, from a financial modeling standpoint, there's a forgivability, I would say, in terms of the plus-minus of the cost that you actually have. The financial targets that we've put out, we've already actually included into those, and I mentioned during my presentation. In my mind, infrastructure is effectively on time if it's a quarter or a couple of quarters around what you anticipated, again, given that we have customers that are more like partners. We're not talking about LDs or anything like that. Our financial modeling takes into account any of those large sites being delayed up to a couple of quarters here and there, and I think I mentioned that in the presentation. Patrick Colville here in the middle from Scotiabank. I have a clarification question for Hillary, please, and then I guess, Clay, more of a strategic one for you. I guess, Hillary, in this slide about cutting funding by 50% with vendor financing, bring your own chip, and prepayments, optically from the slide, it looked like it was a third, a third, a third. Indeed. Is that mix directionally right? The slide is just illustrative. We did not give you the perfect breakdown between those. I think that is probably a little bit too much information to give out for our partners being customers, partners being suppliers. So we have just given an illustrative mix there. But each one of those three plays a decent part in providing that around 50% capital intensity reduction that we put in the graph. Okay. Very clear. Clay, I guess the more interesting question, if I may, your comments earlier were very clear about demand exceeding supply. I think we all know that right now, but looking at the industry pipeline, there is about 40 gigs that is due to come along next year and then 60 gigs worldwide in 2028. If I think about Oracle, I think probably uniquely well protected given longer term contracts. But I guess could you talk about that demand supply balance industry wide and whether you expect that to persist and what that means for Oracle? Sure. Well, I will start maybe from the beginning. First, I think it is really important to separate out the marketing from reality. One of the things that I think people get It is like people will read the press releases, and they seem to ignore this really important term called up to. Up to is a very important concept. It means irrelevant. Right, I could be up to 100 feet tall. I am not, but I could be up to. I think if you actually understand our industry, there are some built-in self-governors. There is only so much memory in the world. There is only so much wafer fab. I think everybody who spends time thinking about how much power is coming online is missing the point. I think you can actually look at how much memory is being produced and how much fabrication capacity is, and that is a much clearer sample of how much you could put in because we do not need power without chips, right? That is a much smaller set of things that you have to analyze. I do not understand why people get caught up in I have got people emailing me that they have got a farm in Iowa that I could put up to 300 gigawatts on. If you add that to your number, then it is 340 just next year. But it does not mean that I could actually get any computers to put into And what I can tell you is that any smart provider, and to be fair, I think, again, ignoring the marketing, a lot of the people who are making most of these decisions are actually quite intelligent. If they are not building data centers, they do not have network fiber optic cables, transceivers. You do not go out and put all this money into mechanical, electrical, plumbing if you do not have the stuff to go with it. Your job is to make match sets. I think there is that. Now, that is the supply side. I also think that people do not understand the immense nature of the demand. I think if you look at any of the stuff that we talked about over the past couple of days at Oracle AI World, it is changing very, very rapidly, and I do not think people can understand yet the rate at which compute is going to be needed to fund the AI demand. The cool part is that we continue to A year ago, using AI for software development was an idea, but it was not rocking. It is there now. I think every year you are going to see significant new industries that are turned on. If you look at what is happening with mathematics or science, and then you look Part of this is it is nice to be in my position because you get to talk to a lot of the people doing the research, and you see what is coming. I just do not think people understand the breadth of demand that is out there. I am not worried about a situation. I think people are like, "But what if somehow we are able to build just so many more GPUs than there is demand for?" I just do not see that. Not for the timeline that I feel like I can confidently have some perspective on over the next three to five years. I do not see it happening. Hey, guys. This is Billy Fitzsimmons from Piper Sandler. Thanks for taking the question. I appreciate the free cash flow targets. We talked about prepayments and the ability to pass on certain costs already, but given everyone is setting the CapEx dollars in their models right now, my question is when we think about CapEx dollars and the expected financing needs, if we look back over the last couple of years, the cost to bring on a megawatt or a gigawatt of capacity has increased for a variety of reasons, whether chips, components, turbines, or energy. Can you just frame what has been contemplated in the targets in terms of additional potential cost inflation in future years, and what capital expenditures have potentially been locked in already? Can I give you one second, Hillary? Look, I feel like you are all asking questions that are missing the point. The next 18 months are the area of the biggest growth for us. That is also the area where we have the most certainty around pricing and clarity. We then, as Hillary said, hit free cash flow positive. The question around, well, plus or minus this money, great. But the cool part is if we understand how much revenue is going to be had and how much cash that is throwing off, that gives us a lot of flexibility to absorb things out in 2029 or 2030. So when people say, "Hey, well, how are you confident that this is how much capital you need?" It is for exactly that reason. People say, "Well, why didn't you give this out a year ago?" Because we needed some more of the certainty that we have available now to get from that period over the next 18 months. Thankfully, 18 months is within the time horizon where you actually have issued POs and have locked prices. That is what I mean. I do not know what a chip will cost five years from now, but I am also very confident my free cash flow looks very different five years from now. Yeah, and that one gigawatt example we gave, that is up to date. So I think last year we talked about $10 billion probably per gigawatt. This year it shows $16 billion. A lot of that is actually the pricing increase that we might have seen in memory, for example. So we have given you updated economics. I mentioned that cost-plus mechanism we have in pricing, so therefore, that revenue amount might change, that CapEx amount might change. Whereas the gross margin and the return on investment, that is really what we think about in terms of creating value for our business. So this is a business where there is enough demand, we have enough technological differentiation that we have pricing power. So that is what we are doing with the business. So in three years, will those numbers look different in the one gigawatt example? I would imagine, yes, in one way or another, aside from the strength of the business model that we have. Great. It's Tyler Radke from Citi here in the front. Thanks for the great presentation. One of the stats that stood out to us was just the diversity of capacity coming from the smaller sites, the sites outside of the ones we all track. Can you just talk about how you see that opportunity going forward? Where is this demand coming from? What's your appetite to really expand this globally, and is that mix the right way to think about the large site versus small site contribution going forward? There was many questions there. Is your question how do we think about the mix of when to do large versus when to do small? Or, because it felt like it was a Did you understand the question? Well, I think one thing we definitely wanted to demonstrate today is exactly what you said, which is that all of the growth that we're seeing doesn't come from the few sites that you tend to ask us the most questions about. And I think, Clay, you've been thinking more and more about In fact, we like diversification of sites. We like diversification of size of sites. And maybe you could talk a little bit more about customers- Yeah and what you think on that. I think the way to think about it is that large sites are great, except for all the downsides of large sites. In other words, I actually think that the gigawatt scale campuses make a lot of sense for training workloads, because you need to be able to harness all of that capacity in one area. But as you see inferencing get larger and larger and larger, inferencing is not going to be 2 MW sites everywhere, but we're delivering a lot of 50, 100, 200 MW sites, which, by the way, used to be considered very large sites. And those are available all over the world. They need to be distributed, partly for latency reasons around certain inferencing use cases. But it's where is there power available and where does it make sense? I think you'll continue to see a mixture because the customer demand is a mixture. You have training, but you also have lots of inferencing demand. There's that. Then in terms of the customers, I think Mahesh did a good job with, we have 1,000 plus AI customers on the platform. We see it from all angles. It's the big model providers that you think about, but it's this trading analytics firm who wants to do their own analysis, or this startup who's going into space and needs this thing. I think there's a misunderstanding of where AI demand is coming from. As AI becomes more and more ubiquitous, this is just computing. I had a slide yesterday in my keynote where I showed that we're installing far more AI accelerated compute than traditional compute, even while traditional compute has gone way up. This is not a fad. When the mini computers came along, people didn't ask, "But what about the punch cards?" The reality is this is the new normal for computing, and the demand for it is immense because it has a fundamentally different capability than we've seen before. I think we will see massive demand from the big players, but also we see huge demand across the portfolio from new startups to established enterprises. We will continue to build a more diverse set of options just from a risk management perspective. But obviously, big sites fill a role. The thing about a big site is you can carve it up into a lot of small allocations. You can't take a bunch of small data centers and join them together as well as you can as a large campus. You will continue to see a mixture of both from us, and I think you will see that throughout the entire industry. It is not just Oracle. You will see a wide diversity of that. What I would say, though, I think that people focus on the big ones. There is a lot of little stuff. If you look at that 10 gigawatts, forget how we got here, Oracle will be definitely less than half of its capacities in these gigawatt sites. That is what 5 out of 10 is, 50%. That is what I mean. I think the difference is that it is so much harder to write a headline about 17 different data centers spread across 4 countries, but that is why we are able to actually go out and predictably deliver on things. It is how you manage risk around regulation. It is how you manage risk around supply chain. I think you will continue to see that from everybody. Okay. We will take our final question here on your far right. Thanks. Kirk Materne with Evercore. Clay, just following up on that train of thought, one of the things that has differentiated you for a long time is the composable nature of your data centers. As we think about the diversity of AI demand, including what I think is going to be a lot of sovereign AI and countries wanting to have their own version of AI, can you just talk about your positioning within that world, espec- Today, we have the option, with a lot of our different providers, to actually offer Alloy and they are partner operated, and they have complete control of those regions. All of those regions are the same. We deploy GPUs in all of those. What limits the scale and scope of them? It is the appetite of that customer and/or partner provider. If we have Alloy partners who show up and they are like, "Hey, I want to deploy 10,000 GPUs," we go, "Awesome. Let us do it." I think as you look through how sovereign AI evolves or just sovereign compute evolves, we are very well set up for that. You will continue to see, I think, more and more evolution from us in different geos around the world. We have invested very heavily in that kind of strategy in the Middle East. We have many partners there. We have a lot of partners in Asia. I think you will continue to see that evolve, and as different areas of the world become more concerned with sovereignty, we have the product that I think is by far the best poised. It allows them to start small. We grow with them incrementally. We actually have a proven track record having done this for 5, 6, 7 years. Yes, I think there will be an immense amount of demand for that. But it is a little bit slower. It is kind of like the smaller data centers. It does not rise to the level of headline. But it is funny, on Hillary's slides, she was sitting there and showed, I think by 2028, we will have 200 plus regions. It was just like, of course. Of course, you will just have. That is not normal I think if you look at many of our competitors, that is not how they do it, and we just take it for granted now. But that will continue to pay dividends for us over the next few years. Thanks, Clay. Before we let you guys go, we do have one more thing. We would like to bring Ken to the stage, along with Bill. I think most of you know, we mentioned it a little bit throughout the day, that Ken is retiring as the head of Investor Relations from Oracle. So we wanted to make sure that we give him a big, big thanks. I think he's played a big role here. And thanks very much. Actually, Ken's going to read the safe harbor statement one more time. Well, thank you. Thank you, Bill. Thanks, Ken. Wow. Okay. I am going to take a picture. In the spirit of disciplined capital allocation, we did not get you anything, but As a shareholder, thank you. Wow. Okay. Well, first of all, my plan to slip out the back door has not worked out so well. I was saying, I would rather just very understated. In all the years that I have been here, working with Safra, she taught me a ton of things. A ton. One of my favorites is, "Less is more." So I am going to be brief. I want to let everybody know how appreciative I am to Safra for having given me the opportunity all those years ago to run the IR program. It fundamentally changed my life, all for the better. For that, Safra, thank you. Truly, thank you. I also was very fortunate to have great mentors, and they are here somewhere in the room. I do not know if they are hiding somewhere. But Doug Kehring and Ken Glueck were phenomenal mentors to me. They guided me along the way. I was also lucky enough to have Luis Montano on the team, Luis quietly and efficiently did a lot of the hard work in the group. Most importantly, I do not want to get emotional about this one, but I am most grateful to my wife, Barbara, and my family, who have supported me through all these years. The reality is, Oracle, it is a passion project, and it takes a lot of time away from your home life. Barb was awesome. Love you, babe. Now for the good news. The good news is I could not be more pleased to have Bill coming on board. Bill Choi, I have known for almost 20 years. In all those 20 years, I have been a fan. The guy is absolutely great, and I know as I step out, I know that you all are in great hands with Bill. With that, thank you all. God bless. Bill, let me turn it to you for closing. Thank you, Ken. As you said, I have known you for a very long time. Yeah. I have personally very much appreciated our relationship over the years. Congratulations on your retirement, and thank you for your many years of service to Oracle. Thank you. I am out. Well, it is great to see all of you. I am extremely excited to be here at Oracle at this important time. As some of you know, I have been on your seat for much of my career as a sell side analyst covering technology, and then joining in an in-house role as head of investor relations and strategic finance. I see many familiar faces, so I certainly look forward to reconnecting with many of you. For those who I do not know yet, I look forward to meeting you. Before we close, I also want to make a quick announcement that all of our slides today will be uploaded to the Oracle Investor Relations website, and the video replay will also be available shortly. With that, I want to thank Mike, Clay, and Hillary, and all of our presenters today. I also want to thank every one of you here who showed up in person and for those joining us on the webcast. We thank you for your interest in Oracle, and we look forward to seeing you soon. Have a great afternoon.

Data Source: Quartr Last updated: Oct 28, 2026

Earnings call transcripts are provided by Quartr and are published within hours after each earnings call.

Update frequency:

Quarterly

Data Sources Report a data error