Are we in an AI bubble relative to the technology being relevant and powerful? Absolutely not. 1,000%. I feel that in my bones 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. Among the many hats I wear as an investor, fund manager, mom, podcaster, I also co-chair the Fortune Brainstorm AI conference in San Francisco. I'm just back from the event and still buzzing from it. I learned so much about how leading companies are leveraging AI and where money is flowing next. I got to host several conversations on stage and we're bringing those to Pioneers of AI. This week, my stage session with ARM CEO Rene Haas. Many of you have probably heard of ARM, but even if you haven't, you've no doubt used devices powered by their chips. ARM-designed CPUs are in basically every smartphone and a ton of other devices. For his role as ARM CEO, Haas was included in Time magazine's list of the 100 most influential people in AI. Our conversation touches on the role CPUs are playing in both AI training and inference, the fragilities in chip supply chains, and yet again whether we are in an AI bubble.
Welcome to the Fortune stage.
So excited to have you. So before we dive in, I want to give the room a sense of scale because 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 maybe 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. It started in a barn in Cambridge with an ambition to be the global standard for CPUs and that mission they accomplished.
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.
No, it's a little counterintuitive. And also back to the history of ARM. We started in a barn in Cambridge. 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, as a chip to run off batteries, which fast forward served us incredibly well because back to the mobile phones, 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. 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 into the accelerator. There's just a huge code dependency 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 like 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.
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, maybe that's not something I spend a lot of attention on. If you asked the question of are we in an AI bubble relative to the technology being relevant and powerful? Absolutely not. 1,000%. I feel that in my bones. And you might ask, why are you so convicted about that? You simply 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, 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 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 just 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.
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 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 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 a moment to give you the answer. That's inference. So the giant use case as you think about it is inference. 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, 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, 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, how it works.
