I don't think everyone here is familiar with how pervasive ARM is. You're in our phones, laptops, fridges, stoves, you name it. So, give us a sense of how pervasive ARM is.
Yeah. So, what we do is a product called a CPU, which is the brain of every modern electronic device. So I would say that each person probably uses 50 to 100 ARM CPUs on their person or in their home. I can assure you everyone has one today because we are in literally every single mobile phone built. We're in all the iPhones. We're in all the Android phones, but we're in Ford F-150s. We're in Teslas. We're in data centers. We're in washing machines. The brain that powers everything. So when you turn on your smart TV and all those apps come up and you're basically trying to pick whether it's YouTube or Amazon Prime, that whole operating system that's running on the TV, that's all running on an ARM CPU. The company's been around 35 plus years. Yeah, we're just talking it's a Cambridge University started in a barn in Cambridge with an ambition to be the global standard for CPUs and that mission they accomplished it.
Yeah. I also want to double click on it's not just devices. You talked about data centers and you recently announced that in 2025, 50% of all compute shipped to top hyperscalers will be ARM based.
Tell us more about that because that's kind of not expected.
Not it's a little counterintuitive. And also back to the history of ARM. We started in a barn in Cambridge. And it was a joint venture between a chip company called VLSI Technology and Apple. And they were looking for the first chip for a first PDA known as the Newton, if people remember that device, which was a PDA way ahead of its time relative to running off batteries, had a display. There were a million things wrong with it. But what it needed was a low power CPU, something that could run off a battery. And that's how ARM was conceived was a chip to run off batteries which fast forward served us incredibly well because back to the mobile phones because the battery power is so key there and you're running off batteries. Energy consumption is key. Power efficiency. So that is really the application space where we're quite strong. Fast forward to these modern data centers that are running 100 megawatts, 500 megawatts, gigawatts. Energy efficiency is everything. So ARM had gotten into the cloud data centers a number of years ago, general purpose cloud with Amazon and Microsoft and Google, but when Nvidia made the choice with their Grace Hopper chip and then Grace Blackwell, they needed a CPU to pair up with the accelerator and they chose ARM. And because Grace Blackwell is literally every single AI data center compute being done, we're there too. So hence our big number 50%.
Incredible. You've made a bold statement that ARM is the only compute platform delivering AI everywhere from milliwatts at the edge to gigawatts in the world's largest data center. What makes ARM uniquely positioned to play in this space?
So let's think about the data center application we just talked about, right? These giant AI data centers that are using GPU acceleration, they all need a CPU as well because the CPU does all of the management of the data, prepares the data that goes in the accelerator. There's just a huge codependency there. You can't have a GPU without the CPU. That's fine when you've got 50 megawatts to deal with, 100 megawatts. But when you go to a very small application where wearable glasses where you have maybe two watts of power, you can't put a GPU there. Simply put, you have to find a way to run that AI accelerator on the CPU platform. So that's where we play. So we not only have a solution up in the cloud, but we have a solution in the smallest devices.
Yep. So we've been talking about this all day, but I still would love a raise of hands if you believe we are in an AI bubble because perhaps you've changed your mind since the morning when Ally asked the question. Okay. Who thinks we're not in an AI bubble? Okay. I think and whose answer is like a depends, complicated. Yeah, I would say like third third. What do you think? Are we in an AI bubble?
I think it depends on how you define bubble. If the definition of AI bubble is that the stocks are overvalued and the PEs are running way ahead of themselves historically, you know, maybe that's not something frankly I spend a lot of attention on. If you ask the question of are we in an AI bubble relative to the technology being relevant and powerful? Absolutely not. 1000%. I feel that in my bones. And you might ask, well, why are you so convicted about that? You simply just have to look at the areas that AI tends to help us with already. The previous speaker was talking about vibe coding where you go in and you ask a question relative to write me a piece of software. Well, imagine a world where you go to AI and say write me a business plan, right? Or write me a way that I could come up with a novel concept to build a company that could solve this level of problem. Take life sciences. I've always thought that the killer app for AI is around health. If you look at one of the bottlenecks in drug discovery today, it's actually around trials, human trials. If you could replace all of that with AI and have AI instantly tell you from the time you start R&D that you know exactly what the trials are going to look like and you've accelerated drug discovery by 10, 15 years. That by the way, all of what I described is going to happen. None of that is an if statement. It's a when statement. So when I think about AI and the changes it can make for humanity and the planet and where we are relative to its capability, we're so early and the benefits will be so profound. So I absolutely do not believe we're in any kind of bubble.
Yeah. I'm going to take audience questions in a moment. So please tee up your questions. You know, a lot of attention goes into the training of these AI models. But as demand for AI increases, the prediction is we're going to need more and more AI inference and this is the shift is going to benefit ARM. So tell us more about how you think about training versus inference.
Yeah, again another example of why I don't think we're in an AI bubble. So just backing up for a moment, what is training, what is inference? Training is teaching a model to do something, whether that is teaching it to recognize images, answer questions about history, solving an enterprise problem, etc. Months and months of time are spent training a model. Training ChatGPT and OpenAI takes three months, six months, eight months to train this giant model. That's training. Once the model is done, it's released to the world and on our phones we can ask a question and the question comes back with an answer. Sometimes it takes an amount of time to give you the answer. That's inference. So the giant use case as you think about it is of course inference, right? The more sophisticated the training model is, the more inference we're going to use. First things today, still researching companies spend more time training than they do on inference. So all this compute, these 100 megawatt data centers, they actually spend more time training than doing inference. And you say, well, why is that? Because there's just so much still that the models can get smart at. Inference is the use case that happens everywhere. It happens in the cloud. It happens in your car. It happens in your earbuds. And again, back to what is good for ARM to your question. Those will all run in areas that generally will have an ARM processor. So we think as inference explodes and it will, as more and more of these models get sophisticated, you're going to run these AI workloads everywhere. It will be ubiquitous. We will not think about AI running in a cloud or AI running in an edge device. It's just software, that's how it works.
Yeah. Do we have any questions from the audience? I'm not. Yes, there's a question at the back. Please share your name and organization.
Sure. Julie Michelle Morris. Always here to ask about cyber security and the security of this. So the single source, the supply chain of course makes me a bit nervous. Does it keep you up at night to know that the weight of the world is on your shoulders?
How do you think about? We have a very unique situation in the semiconductor industry and we were chatting about this in the green room prior where chips were not so interesting for a number of years and then they got really interesting. And one of the events that made it so interesting was COVID and when you lost your key fob it took 52 weeks to get a new one. It was a bit of what is going on here? Well, that was just a function of the semiconductor supply chain has many single points of failure. Not just ARM. There's TSMC which is in a very obviously interesting part of the world geopolitically where it sits. TSMC is by far and away the provider of all these AI compute chips. Everything Nvidia builds is from there, AMD, Broadcom. That's a single point of failure. There is a very sophisticated device that has to go into these fabs that comes from one company on the planet for EUV. It's a company called ASML, a Dutch company. They're the only people in the world who build EUV machines. There are a set of mirrors that go into that EUV machine made from a company in Germany called Zeiss. They're the only company in the world that makes those mirrors. So yes, I don't know if it keeps me up at night, but it is something the entire semiconductor supply chain has been learning to live with. But I will say, and you've seen this partially with the Trump administration, this acceleration to move manufacturing back to the United States, I think is a step in the right direction. There's a lot of criticism in terms of how long it will take and there's no question that it will take decades to get to where it was because it took decades for it to get to the point that it is, but we have to start and I'm glad to see that it's starting because it's necessary.
