# ‘Like Disneyland when it just opened’: Arm CEO Rene Haas on the AI revolution - **Show:** Pioneers of AI - **Published:** 17 Dec 2025 - **Source:** https://prfx.byspotify.com/e/dts.podtrac.com/redirect.mp3/tracking.swap.fm/track/7G0M4p2bOLP0mP50O3CD/pscrb.fm/rss/p/rss.art19.com/episodes/a016add9-04fb-4d06-9be4-1498ec46f903.mp3?rss_browser=BAhJIhlwb2RjYXN0Lm9wZW5uYXNoLmNvbQY6BkVU--4dcea1dd70768d62b2466884c2f8513b5f848fbf - **Transcript:** https://podcasts.opennash.com/shows/pioneers-of-ai/like-disneyland-when-it-just-opened-arm-ceo-rene-haas-on-the-ai-revolution-cc8e42/ - **Provider:** OpenNash Podcasts (machine-generated) ## In brief Arm CEO Rene Haas argues that AI is not a bubble because the technology is genuinely powerful and still early. He says Arm's power-efficient CPUs (the general-purpose chips that manage a computer's work) are essential at every scale, from giant data centers to tiny wearables. As AI shifts from training models to running them, he expects Arm to benefit. The central tension is between a concentrated, fragile supply chain and a fast-growing, competitive market. Haas admits that chip manufacturing depends on single points of failure, such as TSMC, ASML and Zeiss. He also concedes that Nvidia leads while rivals multiply. Arm's response is to stay at the center of the platform and explore building chips, because customers want products faster. ## Key insights 1. **Arm began as a battery-saving chip for Apple's Newton** Haas says Arm began as a VLSI Technology and Apple joint venture to make a low-power CPU for the Newton PDA. That battery-first design later made Arm ideal for phones and power-limited data centers. 2. **AI data centers still need CPUs beside GPUs** Haas says a CPU manages and prepares the data that feeds the GPU accelerator. This is why Nvidia chose Arm for Grace Hopper and Grace Blackwell, and why Arm claims about half of top hyperscaler compute. 3. **Inference will spread AI across every device** Training builds a model over months, while inference is using it to answer questions. Haas expects inference to grow everywhere, from cloud to cars to earbuds, and says it will mostly run on Arm CPUs. 4. **Chip manufacturing depends on a few single suppliers** Haas points to TSMC for AI chips, ASML for EUV machines and Zeiss for the mirrors, each effectively the only source. He says reshoring manufacturing to the US is a step in the right direction but will take decades. 5. **Customers are pushing Arm to build chips itself** Haas says AI models move faster than hardware, so customers ask Arm to build products rather than wait for its designs. He says Arm continues to explore this, mainly because customers want to ship faster. ## Questions this episode answers ### What does Arm actually make, and why do I use its products? Arm designs CPUs, the 'brain' of electronic devices, which other companies build into products. Haas says they are in every smartphone, plus cars, TVs, washing machines and data centers. ### Why does AI need CPUs if GPUs do the heavy lifting? Haas says the CPU manages and prepares the data that goes into the GPU accelerator, so the two are codependent. In tiny devices like glasses with about two watts of power, there is no room for a GPU, so the AI must run on the CPU. ### What is the difference between AI training and inference? Training is teaching a model, which can take months. Inference is using the finished model to answer a question. Haas says companies still spend more compute on training today, but he expects inference to grow much larger. ### Is Rene Haas worried AI is a bubble? He says stock valuations are not something he focuses on. He says the technology itself is powerful, so he is certain it is not a bubble in that sense, and he predicts people will stop talking about a bubble within a year. ### How risky is the global chip supply chain? Haas says it has many single points of failure: TSMC makes nearly all AI chips, ASML is the only maker of EUV machines, and Zeiss makes their mirrors. He cites COVID-era shortages as an example and says moving manufacturing back to the US will take decades. ## Facts - **Ad read** [0:02:05]: "I also co-chair the Fortune Brainstorm AI Conference in San Francisco. The annual two-day event brings together luminaries from across the industry" - **Rana el Kaliouby** [0:03:00]: "For his role as Arm CEO, Haas was included in Time Magazine's list of the one hundred most influential people in AI." - **Rana el Kaliouby** [0:03:00]: "An electrical engineer by training, he also worked at Nvidia and Mythic before joining Arm." - **Rene Haas** [0:04:14]: "we are in literally every single mobile phone uh built. We're in all the iPhones, we're in all the Android phones" - **Rene Haas** [0:05:05]: "started in a a barn in Cambridge with the ambition to be the global standard for CPUs, and uh, that mission, they accomplished it." - **Rene Haas** [0:05:30]: "it was a joint venture between chip company called VLSI Technology and and Apple. And they were looking for the first chip for a first PDA known as the Newton." - **Rene Haas** [0:05:58]: "what it needed was a low-power CPU, something that could run off a battery" - **Rene Haas** [0:06:25]: "ARM had gotten into the cloud data centers a number of years ago, general purpose cloud with Amazon and Microsoft and uh, and Google." - **Rene Haas** [0:06:25]: "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 and they chose ARM" - **Rana el Kaliouby** [0:05:13]: "you recently announced that in 2025, 50% of all compute shipped to top hyperscalers will be ARM-based." - **Rene Haas** [0:07:17]: "the CPU does all of the the management of the data, it prepares the data that goes in the accelerator." - **Rene Haas** [0:07:43]: "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." - **Rene Haas** [0:08:59]: "If you look at one of the bottlenecks in drug discovery today, it's actually around trials, human human trials." - **Rene Haas** [0:10:54]: "Months and months and months are spent training a model, training chat GPT, and OpenAI takes three months, six months, eight months to train this giant model." - **Rene Haas** [0:11:27]: "First things today, still researching companies spend more time training than they do on inference." - **Rene Haas** [0:13:04]: "And when you lost your key fob, it took fifty two weeks to get a new one." - **Rene Haas** [0:13:28]: "Uh, TSMC is by far and away the provider of all these AI compute chips. Everything NVIDIA builds is from there." - **Rene Haas** [0:13:28]: "for EUV, it's a company called ASML Dutch company. They're the only people in the world who build EUV machines." - **Rene Haas** [0:14:01]: "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." - **Rene Haas** [0:15:01]: "You'll have hyperscalers doing their own chips, whether that's Google TPUs, and that's gone on for a number of generations, Microsoft, Meta, AWS," - **Rene Haas** [0:16:26]: "It's it's not the computer that's thinking, it's the computer is waiting for data to come into the computer to figure out how to make the answer. That's memory bandwidth." - **Rene Haas** [0:17:49]: "I was looking at Waymo, uh, and the number of accidents that Waymo had conventional accidents compared to human drivers." - **Rene Haas** [0:18:48]: "I think in the next five to ten years, and again, ARM will be involved. You're gonna see large, large sections of factory work replaced with robots." - **Rene Haas** [0:21:00]: "It's not unnatural for customers to come to us and say, if the computer and models are all running on your platform anyway, instead of waiting for your IP, why don't you just build something for us?" - **Rene Haas** [0:21:33]: "And I said, no one will be talking about an AI bubble a year from now." ## Transcript **[0:00:01] Ad read:** Stripe is built for whatever comes next because AI isn't just changing business, it's evolving fraud. Stripe Radar detects fraud with the fast scale of Stripe's 1.9 trillion dollar annual transaction volume. In fact, when a card is used, there's 92% chance the Stripe network has seen it before. That means emerging fraud patterns are detected sooner, blocking bad actors, welcoming good customers. Learn more about fighting fraud with Stripe at stripe dot com slash radar. **[0:00:35] Bob Safian:** The business world is moving faster than ever, and when change hits, we need to learn in real time. On rapid response, you'll hear candid conversations with CEOs and leaders making tough calls about AI, human talent, responsibility, and the bottom line. How they navigate uncertainty, pressure, and high-stakes moments. I'm Bob Saffian, former editor-in-chief of Fast Company, and I'll be your host as each episode breaks down what you need to know right now. You can find rapid response wherever you get your podcasts. **[0:01:11] Ad read:** On Masters of Scale, iconic leaders reveal how they've beaten the odds. **[0:01:24] Speaker 8:** Asking really strong questions is a superpower. **[0:01:27] Ad read:** You want to show up with something radically different. And how they've grown companies to incredible heights. **[0:01:33] Speaker 5:** The greatest rewards always come from the greatest risks. **[0:01:36] Speaker 9:** Let's hit the gas. **[0:01:37] Ad read:** Airbnb, Zillow, Microsoft, Liquid Death, and more. Hear from the founders who've changed the game. It's anything but business as usual. Find Masters of Scale on Apple Podcasts, Spotify, YouTube, or wherever else you get podcasts. **[0:02:05] Ad read:** 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. The annual two-day event brings together luminaries from across the industry to dive into the exact topics we cover on this show. 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. **[0:02:40] Ad read:** 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. I realize ARM is likely **[0:03:00] Rana el Kaliouby:** in my laptop, my car, even my refrigerator. And Arm's chips are helping power the revolution in AI by providing CPUs to data centers. For his role as Arm CEO, Haas was included in Time Magazine's list of the one hundred most influential people in AI. An electrical engineer by training, he also worked at Nvidia and Mythic before joining Arm. 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. **[0:03:38] Rana el Kaliouby:** I'm Rana El Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Rene, welcome to the Fortune Stage. **[0:03:56] Bob Safian:** Thank you. **[0:03:57] Rana el Kaliouby:** So excited to have you. So before we dive in, I want to give the room 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. **[0:04:14] Rene Haas:** Yeah, so what what we do uh is uh a product called a CPU, which is the brain of every modern electronic device. So I would say that each person probably maybe uses fifty to a hundred ARM CPUs on their person or in their home. Uh, I can assure you, everyone has one today because we are in literally every single mobile phone uh built. We're in all the iPhones, we're in all the Android phones, but we're in Ford F one fifty s, we're in Teslas, we're in data centers, we're in washing machines, the brain that powers everything. **[0:04:48] Rene Haas:** 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. Uh, company's been around uh thirty-five plus years. **[0:05:03] Rana el Kaliouby:** yeah, we're just talking. It's uh, Cambridge University, **[0:05:05] Rene Haas:** started in a a barn in Cambridge with the ambition to be the global standard for CPUs, and uh, that mission, they accomplished it. **[0:05:13] Rana el Kaliouby:** 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 yeah, so that's that's kind of not expected. **[0:05:30] Rene Haas:** it's not. It's a little, a little counterintuitive, and also back back to the history of ARM. Um, we started in in a in a barn in Cambridge, uh, and it was a joint venture between chip company called VLSI Technology and and Apple. And they were looking for the first chip for a first PDA known as the Newton. If people remember that, that device, which was a PDA way ahead of its time relative to running off batteries, had a display. **[0:05:58] Rene Haas:** 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 to the mobile phones, because the battery power is 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. **[0:06:25] Rene Haas:** Fast forward to these modern data centers that are running hundred megawatts, 500 megawatts, gigawatts. Energy efficiency I is everything. So ARM had gotten into the cloud data centers a number of years ago, general purpose cloud with Amazon and Microsoft and uh, 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 and they chose ARM, and because Grace Blackwell is literally every single AI data center uh compute **[0:06:56] Rana el Kaliouby:** You were there too. **[0:06:58] Rene Haas:** being done, we're there too. So hence, hence our big number, fifty **[0:07:01] Rana el Kaliouby:** Yeah, it's incredible. percent. Um, 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? **[0:07:17] Rene Haas:** So let's think about the data center application we just talked about, right? These giant AI data centers that are using GPU acceleration. Um, they all need a a CPU, uh, as well, because the CPU does all of the the management of the data, it prepares the data that goes in the accelerator. There's just a a huge codependency there. You can't have a a GPU without the CPU. That's fine when you've got fi fifty megawatts to deal with, a hundred megawatts. **[0:07:43] Rene Haas:** 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. Simple, simply put, you have to find a way to run that AI accelerator on on the CPU platform. So that's where we play. So we not only have a a solution up in the cloud, but we have a solution in the smallest devices. **[0:08:06] Rana el Kaliouby:** Yeah, are we in an AI bubble? **[0:08:08] Rene Haas:** I think it depends on how you define bubble. If if if the definition of AI bubble is that the stocks are overvalued and the P Es are running way ahead of themselves historically, you know, you know, maybe, uh, 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. 1,000%. I feel that in my bones. **[0:08:34] Rene Haas:** And and you might ask, Well, why are **[0:08:35] Rana el Kaliouby:** why? **[0:08:35] Rene Haas:** you? why are you so convicted a about that? You simply just have to look at the the areas that AI tends to help us with already. 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 and build a company that could solve this level of problem. Take life sciences. Uh, I've always thought that the killer app for AI is around health. **[0:08:59] Rene Haas:** If you look at one of the bottlenecks in drug discovery today, it's actually around trials, human human trials. If you could replace all of that with AI and have AI instantly tell you from the time you're starting R D that you know exactly what the trials are going to look like, and you've accelerated drug discovery by ten, fifteen years. That, by the way, all of what I described is going to happen. None of that is a if statement, it's a when statement. **[0:09:24] Rene Haas:** So when I think about AI and 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, I am a absolutely not believe we're in any kind of bubble. **[0:09:40] Rana el Kaliouby:** We'll be right back with more from Arm CEO Rene Haas after a short break. The companies winning right now aren't just building great AI, they're building the infrastructure to actually run a business on top of it, which is where Stripe comes in. Stripe is built for whatever comes next, with planetary scale financial infrastructure to help you power any business model. As a matter of fact, 86% of the Forbes AI fifty run on Stripe, along with industry leaders like Salesforce and Pepsi. **[0:10:16] Rana el Kaliouby:** Last year, Stripe users processed $1.9 1 trillion combined. See how Stripe can power what's next for your business at Stripe dot com. **[0:10:30] Rene Haas:** 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 gonna need more and more AI inference. And this is, you know, the shift is gonna benefit ARM. So tell us more about how you think about kind of the training inference. Yeah, and again, and and another example of why I don't think we're in an AI bubble. So just backing up for a minute a moment. **[0:10:54] Rene Haas:** What is training? What is inference? Training is teaching a model to do something, uh, whether that is teaching you to recognize images, answer questions about history, solving an enterprise problem, et cetera, et cetera. Months and months and months are spent training a model, training chat GPT, 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. **[0:11:27] Rene Haas:** Sometimes it thinks it takes amount of time to give you the answer. That's inference, okay? So the giant use case, as you think about it, is, of course, is inference, right? The the more sophisticated the training model is, the more inference we're going to going to use. First things today, still researching companies spend more time training than they do on inference. So all this compute, these hundred megawatt data centers, they actually spend more time training than doing inference. **[0:11:55] Rene Haas:** 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, you know, what is good for ARM? To your question, uh, those will all run in areas that generally will have an ARM processor. So we think as inference, uh, explodes, and it will, uh, as more and more of these models get sophisticated, you're gonna run these AI workloads everywhere. **[0:12:27] Rene Haas:** 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. **[0:12:35] Speaker 4:** yeah. Do we have any questions? The audience? Yes, there's a question at the back. If you s share your name and organization. Sure, Julie Michel Morris. I'm always here to ask about cybersecurity and the security of this. Um, so the single source, the supply chain, of course, makes me a bit nervous. Um, does it keep you up at night to know that the weight of the world is on your shoulders? We have Yeah, **[0:13:04] Rene Haas:** a very unique situation in the semiconductor industry, and where chips were not so interesting for for a number of years, and then they got really interesting. And and one of the events that made it so interesting was COVID. And when you lost your key fob, it took fifty two 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. **[0:13:28] Rene Haas:** Um, not just ARM, uh, there's TSMC, which is in a very, uh, uh, obviously interesting part of the world, geopolitically, where where it sits. Uh, TSMC is by far and away the provider of all these AI compute chips. Everything NVIDIA builds is from there. And, uh, 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. Uh, for EUV, it's a company called ASML Dutch company. **[0:14:01] Rene Haas:** 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, um, I don't know if it keeps me up at night, but it is something the entire semiconductor supply chain is been learning to live with. But I will say, uh, and you've seen this partially with the the Trump administration, this, uh, acceleration to move manufacturing back to the United States, I think, is a step in the right direction. **[0:14:31] Rene Haas:** I I. 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 to get to where it was because it took decades for it to kind of get to the point that it is. But we have to start, and I'm glad to see that starting because it's necessary. **[0:14:46] Speaker 4:** well, when we were, yeah, there's a question here. When we were prepping for our call, um, I mentioned that I'm an investor in an early stage chip company that's very focused on AI inference. So perhaps you can give us a sense of, like, what does the competitive landscape look like? **[0:15:01] Rene Haas:** Well, right now, uh, the leader is NVIDIA, uh, by far, by far and away, uh, and and they do almost everything relative to training and inference. Um, now, when you have such a large competitor in a such a large market, that, of course, begets lots of competition, and there will be lots of competition from a combination of sources. You'll have hyperscalers doing their own chips, whether that's Google TPUs, and that's gone on for a number of generations, Microsoft, Meta, AWS, and then you'll have specialized companies trying to do, uh, chips around inference. **[0:15:36] Rene Haas:** Not only for the cloud, but also for these smaller edge devices. So, uh, I would never s uh try to second guess or or underestimate Jensen and NVIDIA. I worked there for many years. He's an amazing leader and they're an amazing company, but the opportunity is so large and so, uh, the scale is so broad. You will see other players. Uh, no, no question about it. **[0:15:56] Speaker 4:** Animanant Kumar So I do **[0:15:59] Speaker 5:** I do agree on the Nvidia side. Uh, so building off of that, how do you see the hardware landscape evolve, right? So, you know, Grace Hoppers, for instance, the bandwidth between CPU and GPU is increasing. So as these models get bigger, you know, agents, long context, it's all memory and bandwidth centric. So how do you see the ecosystem evolve and what would the, you know, CPU of the future look like? **[0:16:26] Rene Haas:** Yeah, no, it's a it's a wonderful question, um, because ultimately, when you're handling these large AI models and the models are, if you, if you enter ChatGPT or enter a question into it, uh, or Grok, and it says thinking, and you're wondering, well, why is it thinking? It's it's not the computer that's thinking, it's the computer is waiting for data to come into the computer to figure out how to make the answer. That's memory bandwidth. Uh, so I think what's going to happen over time is you're gonna see some innovations in memory technology, you're gonna see innovations in packaging, you're gonna see innovations in in interconnect. **[0:16:59] Rene Haas:** In other words, what's the material used to connect between the devices? Is it light? is it a different kind of photonics? is it, um, a different area in terms of other interfaces? So I think back to your question on on innovation. If you're if you're investing in this space, it's a bit of the wild, wild west, uh, because people are looking for all kinds of different levels of of innovation there, uh, because it all has to get solved. **[0:17:22] Rene Haas:** Um, selfishly, I think ARM's gonna be in a in a wonderful place there because most of that traffic needs to run through or around our devices, and we're very involved in a number of, uh, activities in that space. **[0:17:33] Speaker 4:** can we talk about, um, physical AI, um, and that could be robotics, autonomous vehicles, drones, but also these small embedded, you know, AI first, um, human machine interfaces or wearables. And again, what does that mean for ARM? **[0:17:49] Rene Haas:** Well, again, because just about everything that you described is going to require something from a, uh, CPU standpoint to to manage the, uh, the activity we will be involved. Um, so then you get into physical AI, what's what's taking place there? Uh, and you see this a little bit with autonomous vehicles. Uh, in fact, I was reading an article, it was in the New York Times this Sunday. It's rather it was rather amazing. I was looking at Waymo, uh, and the number of accidents that Waymo had conventional accidents compared to human drivers. **[0:18:18] Rene Haas:** And it was about one tenth to one twentieth, just looking at, and they were looking at different kind of accidents, right? In terms of lane incursion or intersections. So there you have a data point that says, okay, autonomous physical AI is is starting to see benefits in in the cars. And Waymo, wonderful company, they're all over San Francisco, is kind of autonomous 1.0. It's got all kinds of equipment on there, it's got lidar, it's got radar, it's looking all over the place relative to how to drive autonomously. **[0:18:48] Rene Haas:** The next generation using LLMs are they need less cameras, they need less devices. And why is that? Because they're actually using AI as opposed to looking at all the physical areas to make decisions on how to drive and how to, uh, how to move forward. What does that mean? It's going to really accelerate robotics, humanoids, things that can do factories. I think in the next five to ten years, and again, ARM will be involved. You're gonna see large, large sections of factory work replaced with robots. **[0:19:21] Rene Haas:** And and part of the reason for that is these physical AI robots can be reprogrammed to do different tasks. One of the issues you had with factory robots in the past is if it was a pick and place machine for a factory, **[0:19:34] Speaker 4:** they're just optimized for one task, **[0:19:36] Rene Haas:** for one task. The the software was for one task, the hardware was for one task. Now, if you can design a general purpose humanoid that the software is all AI and it learns by doing, it's going to completely replace a large set of factory workers. So I think in five to ten years, and back to the geopolitical comment, I think it will level the playing field for countries because you will start to see physical AI be a great enabler for other economies. **[0:20:03] Rene Haas:** Quick question **[0:20:04] Speaker 5:** ARM is dominant. X86 is struggling. RISC five, we'll see. Um, you talked about so many customers, right? Why then is ARM going moving up the stack, going into building chips? Aren't you competing with customers? Um, you know, what's the calculation there? **[0:20:26] Rene Haas:** Yeah, I don't think I said anything about that. **[0:20:29] Speaker 5:** You know, it's a Silicon Valley rumor. **[0:20:31] Rene Haas:** Yeah, one of the things that we see is is huge demand for our technology. And one of the other things we see is that the the speed required to develop products is just the demand on us just getting faster and faster and faster. Part of that, and AI exacerbates it in the sense that the mod the models are moving far, far faster than the hardware can keep up. So when you sit where we do in the platform, in other words, the compute sort of starts with ARM. **[0:21:00] Rene Haas:** It's not unnatural for customers to come to us and say, if the computer and models are all running on your platform anyway, instead of waiting for your IP, why don't you just build something for us? It could be quicker. That is something we do listen to. So, as I've said, you know, in in our earnings calls, you know, very carefully, that is something we continue to explore. Largely because customers are pushing us for it, and why are they pushing us for it? **[0:21:26] Rene Haas:** They just want to get their end products out faster. **[0:21:27] Rana el Kaliouby:** Rene, last question What are you most excited about for 2026? **[0:21:33] Rene Haas:** You know, I, uh, I I think that this continuance of of AI and the acceleration of what we're doing it to me, it's like it's like being in Disneyland. It just opened. Uh, w we have hours and hours and hours to spend. Um, I was at a panel where someone said, give you a bold, give me a bold prediction for this time next year. And I said, no one will be talking about an AI bubble a year from now. **[0:21:55] Rene Haas:** And then someone else is because it popped. I said, Well, I'm not gonna say because of that, but I I just don't think we will. I think people have embraced a year from now that we're well on our way. **[0:22:05] Rana el Kaliouby:** I love that Disneyland just opened. Thank you for the conversation, Rene. **[0:22:08] Rene Haas:** Thank you. **[0:22:09] Rana el Kaliouby:** Thank you. I really enjoyed my conversation with Rene. The chip industry didn't used to be sexy, but now chips are central to the conversation as they become even more integral to AI. I have two main takeaways. One, as demand for AI increases, inference, not training, will dominate compute demand, and much of it will happen on CPUs at the edge as AI moves into more devices, vehicles, wearables, and robots. Two, in hyperscale data centers, more GPUs doesn't reduce the need for CPUs. **[0:22:47] Rana el Kaliouby:** In fact, it increases it, because while the GPUs do all the heavy processing, the CPUs handle everything else, like storage, networking, data, and security. But beyond the technical takeaways, my conversation with Rene reminded me that we're still in the early days of AI, full of possibility. But like Rene said, Disneyland when it just opened. Pioneers of AI is a Wait What original production. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa. Our senior talent executive is Stephanie Stern. **[0:23:27] Rana el Kaliouby:** Mixing and mastering by Brian Pute. Video editing by Eric Purcell. Original music by Ryan Holliday. Our head of podcasts is Lital Malad. You can join the conversation across social media platforms. Just look for us at Pioneers of AI. Thanks so much for listening.