Heat. Heat. N.
There's a specific kind of silence right before the world changes. It's that split second of realization. The moment when you know that life as you'd lived it is about to become something entirely new.
For more than 35 years we've learned to recognize that moment. From Cambridge to the world, we partnered with our ecosystem, solving problems together. We've been here from the start of AI, preparing the world for what's next. Empowering intelligence everywhere in how we live, work, play, and move together.
This is our moment. Because when you know you have the power to change everything, you step forward.
Please welcome Arm's Chief Executive Officer Rene Haas.
Such a nice warm welcome. Thank you. So, welcome to our live stream audience watching the ARM Everywhere event. I don't think we've ever done a live stream event like this. And to the folks here in the audience, thank you so much for coming to the historic Fort Mason. And you may not know that Fort Mason here in California was actually an official defense site for the Civil War. And this is where a very famous battle between Alabama, Georgia, and California took place. Now you're thinking to yourself, I don't remember that battle. That's why this area looks so pristine. There actually was not a battle, but it actually was a fort. So I thought that was actually kind of neat. I didn't actually know that. So, thank you again for attending a big day for us. We have a lot to share with you. So, I'm going to jump right into it. When we thought about, you know, how to name this event and how to talk about our company, we thought ARM Everywhere was really appropriate because one of the things that we're very proud of that we don't always think about in our daily lives at ARM, but is really quite impactful is just the scale of the company and the scale, the magnitude we have. So when we start looking at numbers, 117 billion, what is that number? That's the total humans ever to live on Earth. So that's if you count up by all of our calculations, how many people have lived on the planet since inception, about 117 billion.
350 billion plus are the number of ARM chips to have ever shipped. That is three times the total number of humans who have ever existed on the planet. So it's not just one for every human, it's three for every human that have ever lived. Seven times the total number of non-ARM-based CPUs shipped combined. Just think about that number. And 160 ARM chips for every global household. Mine is probably larger than 160, but 160 is about the average. So, that just gives you a sense of the scale of what we've done, and it's really important because it feeds into everything that makes us what we are today. And of course, could not be done without our ecosystem partners.
Now the company's DNA was really born to run off batteries. Company started in the early 1990s. It was a spinout of a British computer company named Acorn. And that company had a mandate to build a chip and that chip had a couple of requirements. One was it had to run in a plastic package which back then was really important. And number two, it had to be really low power. The first part was important because of heat. The second part was important because battery life meant everything because this was going into the world's first PDA. So the company we nailed that objective so solidly that and this is a true story that when the first ARM development board that had the first ARM1 processor was powered up and these were plugged in now into a back of a wall. So you had a development board, lots of logic chips plugged into an AC outlet. When the AC outlet plug was removed, the chip kept running and the chip kept running based upon the leakage current that was coming off all the other chips on the board. So the folks came in the next night and they saw the oscilloscope was still driving a signal. And that is really what for us launched the revolution of smartphones.
We were designed into the very first GSM phone for those who remember that Nokia brick on the far edge. But then the Blackberry which many of us who had loved still love, wish it came back, all the way to the modern smartphones of Android and iPhones. That is where we started in terms of the battery life. It launched a generation of smartphones.
Now, one of the breaks we got about 10 years ago was when SoftBank bought ARM. Yeah, it was about 10 years ago. It was 2016. And when SoftBank bought ARM, MASA gave us an opportunity now that we're a private company to invest into areas that we were not able to invest in before. And that gave us the opportunity to expand the platform to a number of other verticals. We took everything that we knew about smartphones and then expanded that out into the cloud. We launched Neoverse. We got our first design wins in the data center. And then we were also able to invest into autonomous automotive physical AI. We could not have done that without that 2016 moment. And this is my thank you to MASA for allowing us to do that. We could not have made that all happen. It's paid significant benefits for the company.
However, as good as our products are, as competitive as the platform is for physical AI, for autonomous, for the cloud, it is really what I like to call the ecosystem of ecosystems that really differentiates us. And this is where the partnership really comes to life because that mobile platform that we built cannot happen without the software. And the software layer in the case of the mobile area is iOS, it's Windows, it's Android, it's Mac OS. And then the litany of applications that not only run on the ARM compute platform, but they're highly optimized, highly tuned, and allows the partners in the ecosystem to build great products. That formula applies to every vertical that we participate in.
It applies to what takes place in the cloud whether it's Linux or OpenAI or Anthropic and then the platform that runs with it and this is why we like to call this the ecosystem of ecosystems because it's not just one vertical and you can see when we look at the physical AI platform with automotive, same formula. 22 million plus software developers that are very unique to a vertical but they leverage a lot across the ecosystem that allows people to get started in other areas. So this is the magic and this is what is uniquely ARM. This is what's very unique about our compute platform. There's no one on the planet who can serve the edge to the cloud in the way our ecosystem does.
Now over the past few years we've been evolving our strategies largely because we see the demands in the marketplace are around the chips are more complex. The cycle times to build these chips are getting longer. 5 nanometer to 3 nanometer to 2 nanometer means longer fab times longer packing times. There's a need to do more and to do it faster. We've traditionally provided IP, IP in a standalone form, the CPU, the GPU, system IP, and that has served us well for the first 30 plus years of the company. But as I said, we were starting to see huge demand for the need to go faster, make products better, and get time to market sooner. And we introduced something called compute subsystems. We did this about three four years ago. We invested very heavily in terms of the engineering requirements to do this. And what this does is it takes all the blocks of IP and puts them together in a finished way verified, performant, tested that the end customer can then take to market. And in some cases, it shaves a year, in some cases 18 months off the time of starting design to getting to production.
It was a very significant investment for us. We put a lot of effort and engineering into it, but we've already seen massive benefits in terms of the customer base. We introduced this three or four years ago. Our business model is a license plus royalty. Royalty is the laggard. So royalties start to show up two three years after we license a product. Already CSS represents almost 20% of our royalties and growing.
Now that's our evolution. But of course, we're now in an era where everything is different than we knew it before. And when I think about artificial intelligence and I get a lot of questions when I talk to analysts or media about did AI just come up on us by surprise and I think back to a time I was in Bletchley Park about a year and a half ago and the Bletchley Park is where the original crypto work was done by Alan Turing to help the West against the Germans in World War II. There's an area there where you can go in the museum and you see papers from Alan Turing about can machines think. I think those papers were written in the 1940s. So the idea of AI is obviously not new. And if you're a sci-fi fiction fan, I certainly was growing up. Arthur C. Clarke was one of my favorite authors. 2001, a Space Odyssey. Now we have people who weren't even born in 2001 who are here.
I always looked at this and said, of course, this is going to happen. I just didn't think in my lifetime I would see it at the pace that we've seen it. And for anyone who says, you know, this is a bubble and it's going to pass. It may be a financial bubble in the case of investment may slow down and it may be an investment bubble in the sense of the valuations may not be what they are today tomorrow. But if anyone thinks that this is something that is going to go away, it's a little bit of an ostrich syndrome. This is here with us. And it's really changed how people think about computing.
However, somewhere along the way, people kind of thought CPUs were dead. And there was a thought that the only way you handle AI is through accelerated computing that the CPU's role in the AI world is no longer relevant. Now if we think about the role of the CPU and what happens in the cloud, now this is the cloud before AI. I'm going to say it's before that last slide that I showed. Huge growth in compute cloud. We saw growth from AWS, Microsoft, GCP. And the conventional use of the cloud was you type in an answer, you do a search, any seats left for the Warriors game. I think there are a lot of seats left for tomorrow's game, by the way. I have seen or tonight's game. You got the prompt back. This is the cloud. Very simple. You do search but CPU is very heavy. So when we look at the growth of SaaS 10 plus years ago, 10 15 years ago and all the growth around cloud, the CPUs were doing literally all the work.
Now, when you add the AI cloud, if you will, and now you are a human and you're putting in a prompt into your device, whether it's your phone or your PC. Well, of course, there are still CPUs involved. The cloud is servicing that request and that request gets sent for a token which the accelerator generates and the CPU in that data center orchestrates and sends a token back. The token being a word or an answer that provides the request to the query. So this is all the work that's being done by the AI data center. So CPUs are involved both in the cloud and obviously they're involved in the AI data center and we estimate that in this data center there's probably 30 million CPU cores per gigawatt. So there's a lot and data center here is a combination of what sits right in the AI cluster whether it's your head node to your accelerator or what sits next to a dedicated rack. But the math is basically about 30 million CPU cores per gigawatt. Okay. And that is the world that we've seen coming up to about the last year or so or maybe even less.
And what has changed in the last number of months has been this explosion of agents. Agents are essentially tools that act on a request and come back with a full flow of answers. So, it's not just a query for an answer, but it's actually work. It's run a payroll task, do a scheduler, go off and write a number of analyses relative to a tool flow and provide me an answer. And we heard so much about Open Claw here in the last few weeks as an example. And it's not the only example. Now, why is this important? Why am I talking about this? Because as we move to agentic AI, the number of tokens per human go up 15x if not greater. And if you think about the why of that, it's pretty straightforward. Agents can generate requests a far faster than humans and b they don't sleep. They're at it 24/7. So the agents are now pushing these requests into the cloud into the data center. And what's happening?
The data center is choking. These accelerators which are very expensive that generate the tokens now need to send those tokens back through the cloud. Now if we think about what an agent is, an agent is a workflow. As I said, it's a payroll task. It's a scheduler task. It's asynchronous. It is a lot of work relative to scheduling. That's what CPUs do. That is what CPUs do. That is not a work that can be done by an accelerator. The way to think about this is the accelerator generates the tokens, but it's almost like pushing a dump truck up and someone's got to move all that dirt. The CPUs are the pieces of equipment that move that dirt and agentic AI only increases that. So what you see is a huge bottleneck now in terms of flow. So what does that mean?
You need more and more CPUs, lots of them. CPUs near the headnode, CPUs next to the accelerator rack, more CPU racks inside the data center, you just need more. And by our calculations, and we think this may be a little bit light, goes up about 4x 120 million CPU cores for that same gigawatt. Okay, so in that same profile, we now need 120 million CPU cores.
Now we're trying to put four times the amount of CPU cores in that same power envelope. Power is precious. Obviously, the capital required for it is precious. So trying to put all those extra CPUs into a data center that is already stuffed to the brim with accelerators and CPUs doing the core work. That is a problem.
Now, every tough problem needs a good solution. And we're announcing our first silicon chip that we are selling to customers for revenue, the ARM AGI CPU.
Now, this is a big big deal and I would love to tell you every feed and speed about the product right now, but Muhammad will kill me if I do that. So we'll go into a lot of detail about the product and how we conceived it and the why. But let me be clear, we are now in a new business for ARM and we are supplying CPUs as chips. The biggest reason we're doing this is that our partners have asked for it. But we're also really doing this to solve the problem I just described. As agentic AI becomes mainstream, all of the work required to make that happen is CPU-bound and you need a CPU that has the DNA of being born to run off a battery. So, as I said, reason zero is our partners have asked us for it. And one of the partners we worked the closest with on this is Meta. And I'm super pleased to have Santosh Janardhan with me today who's gonna do a better job than I can to tell you why Meta made that choice. Santosh, please welcome to the stage Meta's head of infrastructure, Santosh.
Thank you, sir. Get the clicker.
Hey folks, welcome. It's funny as every year I try to run the San Francisco half marathon and they distribute the bibs the day before you run right here. I can tell you it looks very very different compared to what I guess you're seeing now. So hi my name is Santosh Janardhan. I lead infrastructure at Meta. So what does that mean? Well, it means that we traditionally go and custom build and design our data centers. We run it. We custom build our hardware, our GPUs, our CPUs, and we'll get into that quite a bit. The network that connects them, and obviously the software that sort of binds it all together. It's a fancy way to say that if your Instagram is not working, if your WhatsApp is not working, your message is not arriving, I am the person to blame. Okay.
Now, if you think through our family of apps, that about three three and a half billion users that use our products daily. Every single day about half of humanity logs into one of our sort of apps and hammers away at it. And as you can imagine that creates a decent amount of scale. We run a decent amount of the internet. And we're probably the only hyperscaler that's not a cloud, right? So if you think about gigawatt of capacity, tens of millions of servers, and increasingly more and more, you're seeing bigger and bigger CPU and GPU AI clusters. Rene sort of went through that quite a bit. I think it's interesting to go and look at how this has grown over the last years. AI clusters are a fairly new thing really started sort of post-COVID 2022 23 just after sort of ChatGPT came along and our initial clusters were pretty small in fact when I look back for this in 23 our initial clusters are about 128 GPUs that's it. But as you can see even in 23 we started scaling quite a bit and as you sort of fast forward it really started growing the demand for this has far surpassed sort of what any one of us could imagine. We are in the tens of thousands of GPUs stitched together in a single cluster now. And if I project it forward and this is the thing I really want to assert context there is absolutely no sign of this slowing down. In fact it's almost exponential. I only see it accelerating. Right? So the demand is exponential and as Rene was saying power is constraint.
I want to talk a little bit about some of our clusters. That is Prometheus. Prometheus is one of our bigger clusters. It'll surpass well over a gigawatt by the end of this year. That's a lot of GPUs I can tell you. And we've stitched together a bunch of data centers, a bunch of tents. That thing you see, the blue colored thing is actually a tent. It's a fancy tent, but still a tent. Right it's weatherproof it can survive about a category 2 hurricane but we putting together all of this stitching it together with a network and so to our developers to our researchers what they end up getting is about a gigawatt worth of an AI cluster in a single combined entity which is pretty powerful as you can imagine but like I was saying the demand is exponential to put it mildly that is Hyperion is going to go up to five gigawatts in a few years. Most people can't fathom what a gigawatt is. A gigawatt is about 10 Palo Altos. Town of Palo Alto 10 times what it consumes is one gigawatt. This will be five. That's 50 Palo Altos right that's what we are building out. So it's going to go and go really really big.
So why do we do this? At Meta, we have this vision of delivering personal super intelligence for every single one of our users. This means creating models that can go and figure out the most relevant experience, the most engaging experience for every one of you on our platforms. It means creating a personal assistant for every one of you. Right now, if you have to go and deliver sort of personal super intelligence to billions of people, what kind of systems would that take? We're talking about billions of people each using sort of exacting amount of compute over and over and like I said over three billion users a day right. This if this advances there you go so what does it take? Well it takes power it takes land it takes a decent amount of hardware software obviously and most of all it takes silicon a lot of silicon right and this is why I think ARM is such a natural partner for us what we want is a partner who can match our ambition who can match our cadence of velocity of innovation and what we realized when we were sitting down with ARM is that they co-developed it they were as hungry as we were and most importantly for us were as power conscious and as efficient as we wanted them to be. This is why while ARM is now the primary co-collaborator and the primary partner the CPU that we are ending up developing is pretty foundational. It's not just a Meta CPU. It's not just an ARM CPU. This is something that I think will end up being a foundational CPU for the whole ecosystem. I think we are at the threshold of something pretty sweet here because you're going to hear more and more about sort of the constraints that data centers are facing. You're going to hear more and more about while the demand for compute is growing, the power is not growing at the exact same curve. So this marriage is I think about it personally as a win-win situation, right? So it's extremely sort of heartening to see ARM moving on from not just being an IP license provider but actually getting into the game of building something that is production scale and production ready. Exciting times.
Yeah, you should. Yeah. 2 years, 2 years, 3 years in the making, but I think about this as the sweetest things takes some time, but we're getting there. Now, like I said, we are obsessed with efficiency. And if you think about one of the biggest appeal that ARM has had over the years, it's power profile. ARM can go Rene had this fascinating experience that he was talking about taking 30 million cores instead of 30 now making it 120 million and fitting it the same power envelope but that's one thing you don't want to compromise on performance right this is the thing that I really want to make sure we drive here the biggest reason why we sat down with ARM and had this conversation was we want to put in a lot more cores per watt but we do not want to compromise on the performance piece. That marriage is why I really think it's a win-win situation here. In fact, about two two and a half years ago, we sat down with ARM. We actually first surveyed the market to see was there a CPU that could meet the specs that we wanted. If we met the performance, we couldn't get the power. If we got the power, we couldn't get the performance. And this is why ARM ended up being such a partner. The ability to scale that ARM gives us when you push in a lot more cores and if you think about personal super intelligence if you think about the orchestration that Rene showed you don't want to starve your CPUs nor do you want to starve your GPUs that marriage that you end up doing is I think that most people are going to realize pretty soon.
Now the design point that we chose for this was something to minimize risk for this iteration. We wanted to make sure we get our first CPU right get it working out of the box. But this is a multi-generational partnership. I just want to emphasize this when we look at subsequent iterations of things that already in the hopper of what we're going to build out. I truly believe that this chip is going to expand sort of the performance on multiple axis. In fact, in this ecosystem is actually going to be awesome. When you challenge the incumbents, you see innovation across the board. That I think is what all of us will end up achieving.
Now, I want to talk about why I want to take us back I guess to why we do this work. Like I've saying three three and a half billion people use our products every single day. This means that's your friends messaging each other on WhatsApp. It could be a small or medium business messaging the users on a platform. It could be somebody going and doing an AI interaction with Meta AI. None of this is possible without infrastructure. Infrastructure has now become has gone from being on the back side of sort of technology innovation to being the enabler of technology innovation. Right? AI is built on the backbone of infrastructure. So every interaction, every post, every feed, every call is done on the basis of what we build out on the back end. And at least for us, we custom building data centers, we're custom building hardware and custom building silicon. That's why ARM I think is such a big partner for us because for us, we want to squeeze every bit of performance out of what we build out. We think about optimizing things like performance per watt, performance per gigawatt. And ARM allows us to do that. It allows us to go and increase the efficacy of everything we build out. Why? So that we can go and serve more users so that we can hopefully improve every one of your lives in some way, shape or form. And that's why I think ARM has been an awesome partner. So thank you Rene and Tim. It has been absolutely a pleasure to work with you and hopefully we'll do this for years together. Thank you.
Wow. Amazing. Santosh, thank you. That was terrific. I have someone else I'd like to ask to join us to also talk about how they plan to use our ARM AGI CPU and that's Kevin Weil from OpenAI. Kevin, thank you, sir. Kevin, thanks for joining us.
Welcome to Fort Mason. Have you been here before?
I have for a few conferences in the past.
Yeah. Well, welcome. So, first off, just tell us and tell me why does this launch today matter to OpenAI?
Well, I thought you did a good job painting this. AI performance these days is system performance. GPUs kind of get top billing wherever they go, but really the CPU is playing an incredibly important role as an orchestrator, but also I think as AI becomes more agentic. When you look at a rollout that an agent is doing, it's using tools inside containers that's CPUs. It's running Python scripts as it performs skills. Those are CPUs. So the CPU plays an incredibly important role and it's really the whole system together that makes this all possible.
Now your role at OpenAI is a pretty cool one, right? You're doing math and science and the stuff that's super compute. And when you think about compute constraints and I know when I talk to you or Sam or Mark or anyone at your company, it's I need more compute. Tell us about that.
That is one of the most common things I hear inside OpenAI. I need more compute. It's kind of the coin of the realm. I mean the root of it is we have more demand from customers. We have more ideas internally that we want to experiment with. We have more things that we want to do than frankly the industry can keep up with. And when you get to the bottom of all this, it's certainly it's about silicon, but it's also about power. And so if you have a CPU that can draw less power, it could be just as performant but use less power, it means you have more left over for everything else that you want to do. That means more inference and more compute. That means more intelligence. And if there's one thing that I've learned in my couple years now at OpenAI, it's that more intelligence leads us to be able to build better products for all of you. The thing that I keep coming back to that I try and remind myself of all times is as amazing as the models are today and every year I'm blown away by the amount of progress we make. As amazing as the models are, the model that you use today is the worst AI model that you will ever use for the rest of your life. It's the worst AI model you're going to use for the rest of your life. And a year from now, you're going to be you couldn't imagine coming back to the AI models of today because they're getting better at such a rapid pace, which just means there's basically infinite demand for intelligence. So, we're not stopping from here.
And in your world, in your new role where you're looking at verticals that are somewhat untapped today, yeah, math and science and things of that nature, when you think about the ARM AGI CPU, or more broadly, what does more compute do for you in that space?
Well, I mean the more compute you have, the more inference you're able to do, the longer the rollouts you're able to do. AI as we go, you know, as we're sort of progressing from this world of AI as chat to AI solving harder and harder problems. And just like you or me, when you solve harder and harder problems, you're going to need to think a little bit longer. So the more important problems we solve as we start to think about things like enterprise AGI science, you're going to need more compute. Which means if you can draw the power that you have, which will always be finite, you can draw that more efficiently. You can do more. We can solve more problems.
And for you personally, what are you most excited about broadly in terms of everything we see going on with AI?
Well, I mean, I kind of I think I have the coolest job in the world. I get to work on accelerating science with AI. And you've seen sort of a revolution in the past even just three months with GPT 5.2, 5.4 codecs. I mean, it used to be that people said, oh, well, these are just stochastic parrots. You know, they're sampling from a distribution of data that they were trained on, but they can't do novel things. Now, we're seeing everyday AI solve open problems in science, in mathematics, in physics, and biology. We're seeing AI help us understand the nature of the universe. We're seeing AI work for weeks on end using a robotic lab to run 36,000 different experiments to optimize the synthesis of a new protein faster and better than any human could. So, it's an exciting world. I think science is going to move faster than ever and it's all built on the kind of infrastructure that you're providing here.
We are grateful for your support, Kevin. Thanks. Hey, thank you so much.
I love the idea that the model that we're using today is about as bad as it's going to get. And that's crazy. I want to repeat in case I wasn't crystal clear on the first go around. We are now delivering IP, CSS, and chips. IP, CSS, and chips. Contact your local sales representative. Will is here. He can be reached afterwards. Now, seriously, I talked earlier about the ecosystem of ecosystems and none of this could be done without the ecosystem that we have particularly around Neoverse. We have many partners that we work with on the supply side, whether it's around memory or connectivity, but we've also got great customers who use our IP today and they are so supportive of what we're doing. You know, Santosh talked about the demand. The market is so large, the demand is so significant that no one company can serve it. So, what I'd like to do is rather than me going on and on and talking about it is have you hear from some of our partners and friends who I think you'll probably recognize a few.
Rene, congratulations on launching ARM's first data center chip. Congratulations to ARM on the launch of the AGI CPU. Congratulations on the launch of the ARM AGI CPU. Today's announcement of the ARM AGI CPU is a significant milestone in AI optimized compute and for the ecosystem. Congratulations to the ARM team on the launch of an incredible milestone for the ecosystem built on innovation, scale, and openness. The continued growth of the ARM ecosystem with its AGI CPU is a significant milestone in continuing to bring customers the flexibility to optimize for their specific workloads and ensuring accessibility of a new generation of purpose-built compute. AI systems are evolving rapidly. They are becoming more autonomous and more data intensive. And that means performance is no longer defined by compute alone, but by how efficiently compute and memory work together. ARM's latest platform opens new opportunities for system level innovation across compute, memory, and storage. We look forward to continuing our partnership with ARM to advance next generation AI platforms and ecosystem. We are proud to partner with ARM in building this open, scalable, power efficient AI future. Accelerated computing didn't make CPUs irrelevant. It made them essential partners. ARM architecture has become foundational across all of our platforms from Jetson, our robotics system to Drive, our autonomous vehicle system, our data processing units called Bluefield to Grace, our CPU. Without the ability to mold and shape and modify the ARM ecosystem and the ARM platform, it's impossible for us to build these systems that we build. And so ARM's adaptability, modifiability, if you will, really has made it possible for us to integrate ARM across all of our platforms. This collaboration with ARM has been great for both companies, and Graviton continues to provide better price performance for AWS customers. We see the AWS ARM partnership continuing to deliver big for customers. We are excited about the opportunity this creates to expand the ARM AI and data center ecosystem. Our Azure Cobalt 200 CPU built on Neoverse compute subsystem is an important part of how we optimize and accelerate every layer of our stack. Deeply integrating cloud-native capabilities and delivering the best price performance and efficiency across our fleet. Partnership with ARM is a key part of that vision. We've been a longtime and early adopter of ARM-based systems and a strategic partner in advancing the ecosystem. Having a diverse portfolio of ARM silicon and software gives OCI greater flexibility and differentiation through deeper comprehensive collaboration in memory foundry design and advanced packaging. I believe ARM and Samsung Semiconductor can deliver exceptional ARM-based AI CPUs worldwide. Google is proud to partner with ARM to support organization's most demanding cloud-native and AI workloads. This builds on a long and productive relationship between our two companies working together across standards, technology, and products to help deliver the infrastructure for the world's leading data centers. We're proud to partner with ARM and we look forward to building the next generation silicon for AI infrastructure. We look forward to continuing our partnership together and to helping customers and users everywhere build AI systems that are smarter, faster, and more scalable. That's why our longstanding partnership with ARM is so important. One seamless platform from cloud to edge to AI factories. We look forward to building this future with you. Congratulations on bringing ARM's first data center chip to market.
Charlie and Matt and Sanjay and even my old boss did better than I could in terms of talking about this. But this has not happened without fantastic partnership and support from the ecosystem. Now, I know you are dying to hear about this product, as am I. And I'm now going to turn over to Muhammad Ahmed, who's going to tell you all about the ARM AGI CPU and why it is absolutely amazing. Muhammad, please welcome to the stage ARM's executive vice president, Cloud AI, Muhammad Ahmed.
Thank you. Wow. Thank you, Rene. Thank you, Santosh. Thank you, Kevin. Thanks to all of you. Thanks to the entire ARM team that made today possible. We have been looking forward to this and it is so exciting to be here. It's so exciting to talk to you guys. Thank you. Thank you. Thank you.
Rene talked about how the world is transitioning from sort of legacy data centers to AGI data centers to agentic data centers heading down this path and how the CPU is at the heart of it. We've designed our AGI CPU around three simple principles. We believe that's the heart of what we're doing. It's the heart of what we focused on. It's the heart of how we
Think about it. First, performance. Performance. With this many threads going on, with this much work to do, with this much orchestration to happen, you can't slow down. 24 hours a day, as Rene said, these agents are going to be running. And if they're not performing fast enough, then the rest of that infrastructure that's relying on it grinds to a halt. So we focused on performance.
Second, we focused on scale. The scale of what we're talking about here is just incredible. You heard Santosh talk about gigawatts. Scale at the CPU level, scale at the board level, scale at the rack level, scale at the warehouse level all the way up. We focused on that.
And finally, we focused on efficiency. Maybe most importantly, because at the end of the day, with this much at stake, with this much compute we're trying to deploy, we're not going to get there unless we provide that performance, we provide that scale, and we do it in an efficient package. Those are the principles that have guided us and we refuse to compromise. We've designed on all three. Play the video now.
