# Redefining Chip Architecture with Arm CEO Rene Haas No Priors: AI, Machine Learning, Tech, & Startups · 2026-09-03 Arm CEO Rene Haas argues that the CPU, not the accelerator, remains the heart of every system: AI workloads still depend on microprocessors to orchestrate tokens, so Arm moved from licensing IP to building full compute subsystems, including the Arm AGI CPU for Meta. He says the shift was driven by customers like Meta who wanted complete solutions rather than components, and that Arm's design wins in smartphones, data centers and automobiles prove the model. Haas contends supply constraints in memory, substrates and advanced packaging will throttle industry growth for three to five years, making US fab capacity a national-security priority. He predicts AI will become a utility embedded in every device, from edge sensors to robots, and that fears of job loss are overstated because electricians, engineers and skilled tradespeople will be needed in greater numbers to build out data centers, fabs and the physical infrastructure AI requires. ## Questions this episode answers ### Why did Arm move from licensing IP to building its own physical CPUs? Rene Haas explains it was an evolution: Arm moved from individual IP components to compute subsystems, using a Lego analogy for stitching pieces together, and demand was insane because it saved time to market and cost. Meta then asked Arm to jointly build a general-purpose agentic CPU nobody else could provide, becoming the first product example, announced at Hot Chips. [2:52](https://nopriors.podhood.com/2ea5dced-01a5-4bd5-9790-55a3ccfdbe9d?t=172000) ### How is Arm using AI in its own chip design process? Rene Haas says chip design takes 24 to 36 months, and the largest portion is verification, validation, debug, and documentation rather than the actual architecture design, which AI handles well. He estimates 80 to 90% of Arm engineers use AI tools daily, though RTL generation and physical design remain immature because models lack proprietary training data. [7:20](https://nopriors.podhood.com/2ea5dced-01a5-4bd5-9790-55a3ccfdbe9d?t=440000) ### Why do CPUs remain crucial for AI workloads despite the focus on accelerators? Rene Haas argues there is no computing problem that can't utilize the microprocessor. In a system, something must do the orchestration and arbitration around where tokens go — the token factory generates tokens, but CPUs are the trucks delivering them to users. He says demand will be enormous across data centers, automobiles, robots, phones, and edge devices. [34:24](https://nopriors.podhood.com/2ea5dced-01a5-4bd5-9790-55a3ccfdbe9d?t=2064000) ### What is Rene Haas's stance on US semiconductor manufacturing protectionism? Wearing his American citizen hat, Rene Haas says it is critically important for the US to have more fabs on US soil for national security and supply chain diversification, citing the 1980s Japan Inc. competition and SEMATECH. He calls the chip race an infinite game with no winner, but warns the US must stay at the technology forefront. [25:44](https://nopriors.podhood.com/2ea5dced-01a5-4bd5-9790-55a3ccfdbe9d?t=1544000) ## Key moments - **[0:00] Cold Open & Intro** - **[0:49] Arm and Chip Supply Chain** - [1:37] Arm sees the entire chip supply chain from smartphones to data centers because its CPU IP is licensed everywhere - **[2:37] Shift from IP to Manufacturing** - [2:54] Meta couldn't find a general-purpose CPU anywhere, so Rene Haas partnered with Meta to build Arm's first physical chip - **[4:23] CPU IP and Customers** - [6:17] "No inventory, no RMA, no scrap — what's not to like?" Rene Haas on Arm's 98.5% gross margin IP business - **[7:04] AI Adoption at Arm** - [7:59] 80 to 90% of Arm's engineers use AI daily because chip verification and debug — not design — eat most of the 24-36 month cycle - **[10:15] Changes in Chip Time to Market** - [10:16] Rene Haas predicts idea-to-GDS2 chip design could compress to months within five to ten years for straightforward designs - **[12:27] Data Center Buildout Bottleneck** - [12:42] Rene Haas predicts the chip industry stays supply-constrained for at least three to five years - [13:44] Data center buildout — not wafers or memory — is the next AI bottleneck, and demand is nowhere near oversupply - **[14:53] SoftBank Leverage and Capital Strategy** - [15:13] Q: What advice for entrepreneurs in capex-heavy chip industries? A: Form strategic partnerships with the supply chain and banks early - **[17:23] Robotics Opportunities for Arm** - **[20:09] US Manufacturing Protectionism** - [20:39] Rene Haas predicts robotics 2.0 will be enormous, replacing human labor in construction, service, and security - [22:45] Humanoid vs task-specific robots: Rene Haas says it will be both, since the world is built for six-foot humans - [25:44] Rene Haas argues more US fabs are critical for national security, citing the 1980s Japan Inc. memory scare - [28:28] Rene Haas calls the data center backlash ungrounded fear, pointing to electricians' unions begging for the jobs - **[28:39] Arm Outlook** - [31:35] "There is no downside from being the leader" — Rene Haas on why laggards get the script dictated to them - **[33:31] Conclusion** - [34:24] "Where are the trucks that take the tokens away?" Rene Haas on why CPUs orchestrate every AI workload ## Speakers - **Elad Gil** (host) - **Sarah Guo** (host) - **Rene Haas** (guest) ## Topics Semiconductors & Chip Design ## Mentioned Ampere (company), Arm (company), Graphcore (company), Meta (company), Micron (company), Nvidia (company), OpenAI (company), Qualcomm (company), Samsung (company), SoftBank (company), TSMC (company), Arm AGI CPU (product), CPU (product), ChatGPT (product) ## Transcript ### Cold Open & Intro **Rene Haas** [0:00] There's no computing problem that's ever been invented that doesn't utilize, and can't utilize, the microprocessor. It is the heart of everything. All roads lead through it, around it, past it. Something has to do the orchestration, arbitration, decision around where those tokens go. That's what CPUs do. Chip design can take anywhere from 24 to 36 months, depending on the complexity of the chip, etc., etc. The actual design is not the largest amount of time. The largest amount of time is in the verification, the validation, the debug. AI is really good at that. And if we were to shut it off, it's like being in the 1990s: you've got internet, and you're now saying, you know, "Only internet between the hours of 2:00 and 4:00." **Elad Gil** [0:37] Yeah. **Rene Haas** [0:37] After that, go to the library that we have down the hall. It'd be anarchy. **Elad Gil** [0:40] Yeah. **Rene Haas** [0:40] The genie's out of the bottle, and there's no stopping that. ### Arm and Chip Supply Chain **Sarah Guo** [0:49] Hi listeners, welcome back to No Priors. Today Elad and I are here with Rene Haas, the CEO of Arm and SoftBank Group International. We talk about the position of Arm within the chip industry, the resurgence of interest in chip innovation, the challenges of the supply chain, the future of robotics, energy, his place in the SoftBank Group, and how he sees workloads changing in the future and for Arm. Rene, thanks so much for doing this with us. **Rene Haas** [1:14] Pleasure. **Sarah Guo** [1:14] Congratulations on the chip presentation at Hot Chips and, you know, all of the progress that Arm has made. I think there's an enormous amount of interest from the technology industry and the software industry in just better understanding the chip supply chain recently. For anybody who's not super familiar, can you explain Arm's position in it? And then we'll get into sort of more recent topics. **Rene Haas** [1:37] So we have, we have two positions in the chip supply chain. Our primary business is licensing IP, the CPU core that finds its way into smartphones, data centers, automobiles, you name it. Our customers are the ones who either build the chips themselves, a Samsung who's got their own fab, or the vast majority of companies that take their chip designs and go to TSMC and get them, get them taped out. So in that world, and this is the cool thing about Arm, because we're so broad in terms of the markets that we serve, we kind of see everything. We have a very good sense of what's going on in automotive, data center, smartphones. So we see the supply chain situation from all angles. We also introduced our first product last March, the one you just mentioned at Hot Chips, the Arm AGI CPU. So now we're in that soup ourselves from the standpoint of we're also having to figure out how to buy substrates and buy wafers and buy memory, etc., etc. So we're, we're, we're up to our waist in everything on the supply side. ### Shift from IP to Manufacturing **Sarah Guo** [2:38] Why did you make the move now? So for, you know, Arm, I believe existed for a few decades now. The focus was always on IP, which is effectively like designing the way that different chip components are put together, and then you license that out to other people to actually manufacture and incorporate it into their designs. Why did you decide to start making some of your own CPUs? **Rene Haas** [2:54] Yeah, it was, it was an evolution from the early days of where we just supplied simply the IP components, the pieces, the CPU IP, the GPU IP, the system IP, etc., etc. A few years ago, what we were starting to see was that product cycle times aren't slowing down, chip manufacturing times are extending, the ability to get solutions out faster was becoming more and more important. So we moved from these individual components into what we called compute subsystems. I used when we went did the roadshow a few years ago, I used the Lego analogy where essentially we're providing the blueprint on here's how you stitch it all together. Demand for that was, was insane. And what we were finding was we were, and we initially, people thought, well, people aren't going to want these subsystems because that's what a chip designer does. Why are you providing that piece? But it saved time to market, and it saved a whole lot of things in terms of cost, speed, etc., etc. The physical product was sort of the next, the next leap, if you will. And there are certain sets of customers that will license IP to, and they've got all the capability in the world to, to build chips based on Arm. There's a lot of companies who want to have product based on Arm. Not all of our customers build products that serve those markets. So Meta was that first example. They wanted a general purpose agentic CPU. There wasn't anybody out who could give, give it to them. They came to us and said, "Hey, why don't we do this together?" And, and that's how we got into it. ### CPU IP and Customers **Sarah Guo** [4:24] How has that landed with the rest of your customer base? **Rene Haas** [4:27] So one of the things that we were very careful about was getting, making sure the ecosystem was on board with this. Because we do CPU IP, which is really only as good as the ecosystem. The ecosystem of chip people and the ecosystem of software folks and people who build around that. So we talked to just about everybody who were customers and said, you know, how do you feel about this as direction we're going? And surprisingly, we got a lot less pushback than I, than I thought. And the reason for that was the more software that's available in the wild, whether it's proprietary and/or open source, benefits the broader ecosystem and the customers themselves. So whether it was Nvidia, Amazon, Microsoft, Google, all people who build Arm-based server chips, they were all on board. And I think the ultimate proof point was when we announced the product last March, we had Jensen, we had Ronnie Bowker, we had Amin, we had James Hamilton, you know, all the folks from those customers I mentioned, all saying, "Congratulations, it's a great thing." So it's been okay. **Sarah Guo** [5:27] What is the, you know, where are you in the learning cycle as a business now selling physical chips that feels like a lot of new capabilities? **Rene Haas** [5:36] Yeah, so we, we obviously to, to deliver a product and, and we're a fabulous semi company,right? We, we don't have a fab and we have no intention to build a fab, but we, we fit in that ecosystem. But that means you need supply chain operations people. You need to work with, as I said, the TSMCs and the Samsungs of the world. You need to work with the Samsungs and the Microns, the SK Hynix to get memory allocation. And then on the engineering side, you need a lot more different capabilities. You need backend people, layout people, implementation people, bring up labs, physical stuff,right? We didn't have a lot of physical stuff, which was kind of the beauty of the business, the original business. **Sarah Guo** [6:17] I remember discovering that Arm had a 98.5% gross margin. **Rene Haas** [6:22] Yeah, kind of beautiful. **Sarah Guo** [6:24] I don't think I've seen that otherwise. **Rene Haas** [6:25] I came from Nvidia before I came over here. Most of my career was in the chip world. And I remember coming to Arm in 2013 and thinking, "No inventory, no RMA, no scrap. What's, what's not to like?" So we had to add a lot of those capabilities. We have a lot of people on the leadership team who've come from that world. I've got execs from Broadcom, Qualcomm, Nvidia. I work for Nvidia. So we have the leadership that's done this before in other companies. So we've been able to build up that, that muscle pretty quick. **Sarah Guo** [6:56] How did your first AI adoption? So, you know, we were speaking earlier that there's news from Open Editor today about Jalapeño and a chip that they designed. Their claim is it was a very fast time to market. And part of that was using AI tooling to sort of design chips faster. ### AI Adoption at Arm **Sarah Guo** [7:10] How much adoption have you seen there? I know other companies have also talked about things like adopting formal verification at Amazon or other places for their training chips. So I think the chip world is starting to evolve in terms of AI usage. And I'm just curious about how you've done that at Arm. **Rene Haas** [7:23] Personally, I'm a huge believer in AI as a utility that's going to help productivity for every single industry. It is going to be the great leveler in terms of companies that can get started super quickly. And for industries, whether it's healthcare, infrastructure, robotics, every industry is going to use artificial intelligence as a utility and stop. So since I'm such a believer in this, of course we use it very heavily, you know, inside of Arm. On the non-engineering side, we're using it all over the place, but on the engineering side, we've seen huge, huge benefit. You mentioned verification. Chip design can take anywhere from 24 to 36 months, depending on the complexity of the chip, etc., etc. The actual design of the architecture, the RTL generation, if you will, the mapping of the architecture is not the largest amount of time. The largest amount of time is in the verification, the validation, the debug, the documentation, etc., etc. AI is really good at that. And I would say we probably have 80 to 90% of engineers today inside Arm who use it on a daily basis. And if we were to shut it off, my analogy I give to people, it's like being in the 1990s. You've got internet and you're now saying, you know, only internet between the hours of 2:00 and 4:00. After that, go to the library that we have down the hall that's got all the books that you can go up and look all this information up. People, it'd be anarchy. So the genie's out of the bottle,right? There's, and there's no, there's no stopping that. Now, there's certain things that the tools are still not that mature of. One of them is really around RTL generation and then physical design and implementation in best of class. And that's simply because the models, you know, they're trained on what's available publicly. And a lot of that information is quite proprietary. That being said, there's massive opportunity between the ecosystems and everyone in the industry to make that better. It's only going to get better. **Sarah Guo** [9:22] Have you been fine-tuning models to try and address that gap, given the proprietary information that you all have? **Rene Haas** [9:26] We've been working with model makers around that. Absolutely. And I think that's a big, big opportunity. And one of the things that I'm proud of at Arm is given our business, our core IP business, we probably have the richest IP portfolio, both in terms of not only the IP, and this is the killer, the documentation, the test benches, you know, how you build the IP. You know, I've worked for chip companies in the past that have said, "Hey, why don't we license this IP that we've got? Because it's really, really valuable." And then you get into, "Wait a minute, there's, there's no documentation. There's no explanation on how you." **Sarah Guo** [9:59] No one's ever going to be able to use this. **Rene Haas** [10:00] It's unusable and it's untestable. **Sarah Guo** [10:01] Yeah. **Rene Haas** [10:01] Right? And if it's unusable and untestable, it's actually untrainable. And if it's untrainable, it's not usable for AI. I think we have some, some built-in advantages based on our business model that'll allow us to really be able to take advantage of the tools as they get better. ### Changes in Chip Time to Market **Sarah Guo** [10:16] So exciting. How much do you think, if you were to extrapolate out, this is a little bit of an uncertain question, but if you extrapolate out two, three years and all the tooling that's likely to come in AI and the ability to fine-tune models against, you know, some aspects of the design that you mentioned, do you think that 24 to 36 month cycle shrinks to a year to six months? Do you think it stays roughly where it's at? I'm a little bit curious about how does that really impact these cycles and time to market? Because that has pretty dramatic ramifications. **Rene Haas** [10:41] Yeah. **Sarah Guo** [10:41] In terms of the clock speed of the entire industry. **Rene Haas** [10:43] I don't know. I don't know if it's in two to three years away, but five plus years, can you go from idea to a GDS2 file, GDS2 file being the file that you actually send to the fab to go get built for certain designs? Quite possible. So it takes that whole design piece out of the way. It takes that whole piece out of the way relative to the verification. So I think for the more straightforward designs, quite possible. Now, if you go into the tool and say, "Design me something that's 10% faster than Vera Rubin, 20% cheaper, and 30% more efficient on this model," you're not going to be able to press a button and have it happenright away. But I think in five to ten years, you know, our industry as well, we're going to see some amazing differences relative to how chips are designed. **Sarah Guo** [11:35] How does it change, I'm sure you had some prediction of this, but how does it change the way you look at the business given there's just a big diversity of large players and new players that all, you know, want to have their own chip designs now? And, you know, the Veras and the Gravitrons of the world, they all use Arm. It's a big step up for them, but it's a big diversification of the customer base,right? That can be only good. **Rene Haas** [12:00] Oh, absolutely. I think what's going to matter, back to the earlier discussion we had on supply chain, is understanding the supply chain impacts, how all of that gets built and put into ultimate end products. I think that's going to become a much more important muscle as we go forward. Because it's one thing to, say it another way, there's a lot of really great young companies today doing AI chips. Well-known companies getting tons of funding, innovative designs, etc., etc., selling into an industry where the capital requirements are just massive. ### Data Center Buildout Bottleneck **Rene Haas** [12:34] And the relationships with memory vendors is incredibly critical, or the relationship with substrate vendors. So companies are going to have to be much more. **Sarah Guo** [12:42] More access to a three-nanometer line, a six-nanometer line, advanced packaging line. **Rene Haas** [12:46] All of it. Yeah, all of that. And I think that is, that's not going to stop in 12 months. It's not going to stop in 24 months. I think we're going to be in this constrained environment for three to five years at least. So long as the transformer is the unit of energy relative to how you generate AI training and AI inference by design, it is a, it is, it's very compute intensive, it's very memory intensive. So if you think about that, that's going to drive a lot of demand on having supply chain acumen, which then goes back to people who've got great ideas on chip design. They're going to have to need a lot of other things just to be able to get access to capital, wafers, everything you just talked about. **Sarah Guo** [13:27] We've just had a cascading series of things that have been the bottleneck to more compute for the AI industry. So, you know, two years ago or so, I think it was like packaging and packaging-related items. And then eventually now people talk about how it's memory and things like that that are in some sense limiting to certain systems being built at sufficient scale. Do you have a view of what is the next sort of bottleneck that's coming? **Rene Haas** [13:47] I think building out the data centers is going to be a bottleneck. And when I say building out, if you look at all the projects that are being done today, not a lot of them are ahead of schedule and needing less labor than they thought. And then when you, when you layer on top of that, a lot of buzz that's coming from different parts of the country and the United States here relative to slowing down data center development or putting restrictions around it, I think that infrastructure buildout could be a headwind just relative to everything going on, which may be, you know, "okay." Because if it wasn't, if infrastructure buildout was not a headwind, I think capacity for wafers, capacity for memory, that probably would be a headwind. So I think you're going to see a number of different governors, if you will, not governors of states, but different things that are going to throttle the growth of this, which just expounding for a second, I've been on a bunch of panels and I get a lot of questions about AI bubble and when's it going to stop? And there's setting aside the valuation bubbles, which is a stock market index component, the bubble in terms of are we over, over supply to demand, not even close. And I think, again, that's because the demand is insatiable, just given the way these models work and infrastructure buildout, access to wafers, access to memory, all of that's combining. ### SoftBank Leverage and Capital Strategy **Sarah Guo** [15:13] You mentioned that, and I think a lot of companies are learning today that strategic use of the cap table, access to capital in an era where you either need to consume a lot of compute or you need to put a lot of capex into the ground, or you're just doing big technical projects like coming up with CPU IP. You run SoftBank Group International. You have this one dominant shareholder. Arm itself as a business is just like a beautiful cash flow machine from the outside,right? I'm sure you think a lot about like the leverage of SoftBank and how to use that well. Like what advice do you have for entrepreneurs navigating these capex intensive industries from where you sit? **Rene Haas** [15:56] Yeah. So one of the benefits we have at Arm, publicly traded, yes, but a very, very large single shareholder. So I have lots of informal investor meetings with my chief shareholder, you know, all the time about this. We have a big advantage in that there's a lot of things symbiotically we can do together that can help Arm advance its initiatives by having SoftBank as our largest shareholder that we look to be very, very innovative around. To your point, in terms of, you know, young companies, I would say strategic partnerships incredibly early, whether it's with people inside the supply chain, people in private equity, you know, the banks, the banks themselves. It's a different game, you know, now. On one hand, semis are kind of back because you now have a wave of semiconductor startups. There was a long time where that was just not happening, investment in the industry. Now we've got a lot, but access to capital is going to be the gate for them in terms of how they get through that. So I think getting much more creative in terms of how they work with the ecosystem is going to be super, super key. And we at SoftBank, that's one of the things we look at very strategically, you know, companies that we can bring into the portfolio that we can help, that we can provide a combination of either the backstop, you know, and/or if you think about SoftBank, we just announced that we being SoftBank, SoftBank Neo, which is our intent to become a NeoCloud. ### Robotics Opportunities for Arm **Rene Haas** [17:29] And in that world, we could become a home for these young companies who have chip technology that in other worlds, they'd have to go up and figure out how to get a design win at Microsoft or Google. We can provide a lot of interesting avenues for that. **Sarah Guo** [17:44] Can you talk a little bit more about the portfolio things that fall under your purview at SoftBank? I know as mentioned, there's Arm and then there's this sort of broader suite of things. **Rene Haas** [17:52] Yeah. **Sarah Guo** [17:53] So I'd love to hear, we'd love to hear more about what else you're responsible for. And we had some specific questions for some of those as well. **Rene Haas** [17:58] Yeah. So the way I think about it is SoftBank Group, which is headed in Japan, and that is MASA, have a lot of different operating companies underneath them. One of the largest ones is SoftBank KK, which is essentially SoftBank, SoftBank Mobile. Inside the US, there's a lot of investment activity that's going on with SoftBank Group International. There's SoftBank Vision Fund, but increasingly a lot of the strategies that we're trying to do around SoftBank is helping the strategies that MASA talked about publicly at his shareholder meeting in Japan, which is around robotics, OpenAI, infrastructure, and Arm. So I've probably got my eyeballs on a lot of stuff, to be honest with you, in terms of helping MASA really realize the execution of that vision. So yes, I'm leading the direction of Ampere and Graphcore and another company called Stack AV that's doing things around autonomous. But maybe a better way to think about it, Elad, is that I'm kind of in the room on a lot of discussions that MASA's happening and helping them sort of formulate that strategy and more importantly, help execute it. **Sarah Guo** [19:07] How does, how has being part of the SoftBank Group or working with all these different companies, or even SV Energy and the broader ecosystem changed your point of view on what you can do with Arm? **Rene Haas** [19:20] Well, one thing it does, it gives us a huge bird's eye view relative to where the broader industry is going, whether it's around infrastructure, whether it's around capital, or whether it's around energy. But also you can imagine it could provide a home for our products,right? So it doesn't need to be the home, but it certainly can be a home, which is also a big, you know, a big, big help when we think about the verticals that SoftBank's involved with: robotics, energy, data center infrastructure. And then you look at the products that Arm has. The only one we've announced so far is the Arm AGI CPU. You can start to connect the dots and say, gosh, there could be some very interesting opportunities that could be an opportunity for Arm, which necessarily doesn't mean that we're getting into the broad merchant chip business. ### US Manufacturing Protectionism **Rene Haas** [20:09] We could be just doing products simply back for SoftBank. **Sarah Guo** [20:14] We're a couple of years into, you know, serious efforts in more generalized robotics at this point,right? If you compare it to like about a decade for LLMs, there's increasingly interesting demo results from companies on generalization of task and environment, more robustness, maybe even in-context learning, but not like wide-scale deployment quite yet. **Rene Haas** [20:38] Yeah. **Sarah Guo** [20:39] First, would you agree with that characterization? **Rene Haas** [20:41] 100%, yeah. **Sarah Guo** [20:42] What predictions do you have about the robotics market and any opportunity for Arm there? **Rene Haas** [20:49] Oh, well, broadly speaking, I think the, whether it's humanoids or dedicated machines to do certain level of tasks that can be retrained, is going to be enormous,right? The robotics 1.0, which is a purpose-built industry, you had a piece of mechanics designed to do a certain task and the software that was optimized for that task. If you had to, a brand new automobile line came up or some different piece of equipment, if the robots weren't well suited for that, rip up the line, et cetera, et cetera. So as you can imagine, then the barrier was pretty high. Getting to a world where the robots can learn just based upon either being trained or what they see. And then when you then combine that with, can you design something mechanically general purpose enough that can take advantage of being reprogrammed? And then when you layer on top of that, the costs coming down, you look at it and say, oh my gosh, what will it not be able to do? So it's almost like something out of the Jetsons,right? Where a lot of things will ultimately be done by robots: construction, infrastructure, service, security. You know,right now you see a lot of stuff on Instagram or TikTok of Olympic races with robots, et cetera, et cetera. I don't think anyone's going to have any interest in watching a sports league of robots. There may be an enthusiast class who might be interested in that, but the broader utility is going to be around a lot of human labor tasks that can ultimately easily be replaced by robots. That's no question. **Sarah Guo** [22:25] A lot of hypotheses people have about the form factor of robotics tends to split into two or three camps. One of the camps is that they're going to be humanoid or roughly sort of the human footprint, because so much of the physical world is already designed that way and the tooling is designed that way. And so you can just slot robotsright in. Others view it as there's going to be much more sort of specialized task-specific form factors. Do you have a hypothesis on? **Rene Haas** [22:45] I think it's both. Yeah, I think it's both. There's a lot, there are a lot of jobs and work tasks that are optimized around a person being six feet tall and having arms of a certain length, et cetera, et cetera. But I think it'll be both. And I think the fact that they're going to be smart and can learn. And to answer your earlier question, Arm is going to be everywhere. We are, we have a tremendous amount of technology from a real-time sensing standpoint around microprocessors that will be out at the fingers. They can do perception and sensing. That's all going to be Arm-based. Today, whether it's NVIDIA or some of the work that Qualcomm does, most of the brains, the brains that you see in the humanoids, those are all running on Arm today. So I think for us going forward, the robotic industry will be powered by Arm. **Sarah Guo** [23:31] Are you seeing any early indications? I mean, you have this great seat to your point where given the ubiquity of Arm and a lot of these different types of devices, you can kind of see the future before others in terms of where adoption is happening or where shifts are happening from a technology perspective. Are there specific pockets that you think will be most likely the early adopters of robotics that you're starting to see some signal from? **Rene Haas** [23:51] I think it's still a little bit early because the business models have not been actually figured out. The cost of robots are so high,right? Because the cost of robots are so high, people buying the robots themselves, that's a tough, it's a tough model to sort of get people's heads around those that actually replace. So I think costs need to come down and the business model need to be, need to be ultimately vetted. **Sarah Guo** [24:12] Becauseright now the robotic footprints tend to be things like automotive or certain surgical robots or data center or, excuse me, distribution centers,right? There's a few very sort of bespoke applications that I think are most of the robotic sales today. And so that's why I was a little bit curious. **Rene Haas** [24:25] Distribution centers for sure. I mean, that can ultimately go completely automated,right? Relative to, and even to the, ultimately to the delivery,right? And you can question, to me, loosely speaking, a truck that has an autonomous is a robot of sorts. So around factory automation and delivery and distribution, that will be one of the very first to be automated, no doubt. **Sarah Guo** [24:50] There is increasing, you know, debate and very quickly like policy or EOs around supply chain controls and usage controls around both robotics and chips and data centers,right? Sorry, I'm going to throw export controls in there. So four types of controls. All of these controls are relevant for you. And now either from your end customer perspective or as a relatively new entrant to, you know, we're going to own the end product and have a supply chain organization of your own. Like what's your stance on, you know, how protectionist—I realize it's not an American company, but you do a lot of business here—how protectionist the US or the West should be about manufacturing of chips, creation of data centers, robotics? Like what are your overall stances here? **Rene Haas** [25:44] So putting my American citizen hat on for a moment, and Arm, as you said, is not an American company. **Sarah Guo** [25:51] Because of the UK. **Rene Haas** [25:52] Our HQ's in the UK, but we have a lot of employees. I wouldn't say half our employees, maybe 30% are in the US. I think 40% are in the UK and maybe 30% Asia. So we're a global company, but with a huge, you know, with a huge US footprint. But as an American citizen and someone who grew up in semiconductors, and I remember in the 1980s when the US was the leader in semis and Japan Inc. started to really get very, very aggressive in terms of memory pricing and essentially taking a lot of market share. The US started something called CEMATEC, you know, back in the day, which is really around how to refortify the American semiconductor industry, which I thought at the time was theright move. And there was a lot of energy around that. Internet hit, SaaS companies were all the rage, people kind of forgot about semis being a strategically important asset. But I think it is critically important for the United States to have as much of that technology inside on US soil. And I would say the same thing to UK, lesser just because of the scale of the UK. **Sarah Guo** [27:02] Right. **Rene Haas** [27:02] But when you think about the size of the US market, the criticality of semiconductors to what the US does, whether it's Intel, whether it's Micron, I think we need more US fabs. It's critical for national security. It's also critical for diversification of supply chain. So I'm a big believer in terms of that as a strategy. I think it's really, really critical. You know, as far as the export controls go, and we're going to limit the chips because we don't want China to win the race, you know, end quote. You know, my personal view is that it's an infinite game, I believe, first in terms of the race, that there's not going to be a winner. The race is going to be over. But you could get to a situation where a lot of the critical technologies are not US-based. And that's not going to be a good thing,right? Because when people say, well, you know, the costs will go down and goods are cheaper. But ultimately, and I'm a big believer of this, number one, for both national security reasons and economic, you want to be at the forefront of the technology because it drives the innovation, but it also drives ecosystems. If you think about the US auto industry in the 1950s, post-World War II, where Detroit was the center of the universe, you had spots across Wisconsin, Ohio, Illinois, whether it was Firestone or Bridgestone or Bridgestone is Japanese, but other companies in that ecosystem that fed into it. Data centers are kind of the same way. People look at data centers and say, oh, it's a big Costco box and there's two parking, two cars in the parking lot, and all of that is being driven automatically. So there's no jobs. I call BS on that because if you think about whether it's around energy, liquid cooling, all of the things that make the data center better, that's all, those are all jobs that can be created and done here. So I think as a national policy, it's incredibly important for us to be investing A, in the United States, and B, making sure that we stay in the lead. ### Arm Outlook **Sarah Guo** [29:00] On the data center side in particular, it seems like a lot of the actions that are being taken to try and prevent future data centers feel more coordinated than not. I know it phrases grassroots efforts, but it seems like there's some coordinated function there. Do you have a hypothesis in terms of like why there's been this sudden unexpected outcry on data centers from certain corners? **Rene Haas** [29:21] I think there is a, we maybe chatted about this a bit earlier, that there's a fear that AI means job loss and job loss means for all these things kind of implications. So I think unfortunately. **Sarah Guo** [29:34] And you think that fear is well-grounded or do you? Because sometimes it seems like it's only creating jobs. **Rene Haas** [29:37] No, I think it's, I don't think it's well-grounded at all. **Sarah Guo** [29:39] I think the electricians' labor union specifically said, please don't ban the data centers. We need these jobs very recently. **Rene Haas** [29:46] Completely. I mean, and these are jobs that make people, it's a great, it's a great example,right? Because here's one where there may have been a stigma to being an electrician,right? Electrician is either, it's not, maybe viewed as a highly educated job or you don't need a PhD. It's a highly skilled job that requires a lot of training and certification. And you need tons of them, you know, to do this kind of work. And that's very, very critical to the data centers. So I think to your question, I think part of the backlash is just from fear. I think there's just a fear that my jobs are going to go away. The AI boom, for good or for bad, has benefited a lot of people. And there's a lot of people who have no benefit from it,right? And there's a lot of America, just again, on the American political scene, who it's tough to make the mortgage, you know, their paychecks haven't gone up. And now they've got this AI thing that just, look, it's going to even harder. So I think the data centers have become a bullseye, unfortunately, for all the things that could be bad about AI, which I think is just. **Sarah Guo** [30:47] People also are holding up like fake tainted water and claiming that, you know, it's ruining the water supply. So I feel like there's other kind of things that are just being made up about data centers as a way to try and create fear. **Rene Haas** [30:57] For sure. Yeah, for sure. And unfortunately, it's become the boogeyman for a lot of things. **Sarah Guo** [31:01] I think it's also pretty clear that there's like, you know, organized media influence around these issues as well. But it doesn't, I think, you know, you can have all three separate points here, including yours, Rene, which is there are benefits from the construction of essentially like a rapidly growing new industry that can create new technology and new jobs and create, you know, external wealth for the communities around them. But it's on the industry to go communicate that. **Rene Haas** [31:35] Yeah. I mean, on first principles, whether it was smartphones, the internet, personal computers, fill in your favorite technology, there is no downside from being the leader. There's just not. **Sarah Guo** [31:46] This is maybe the most important point. **Rene Haas** [31:48] There's just not downside from being the leader. There are second and third order effects that you may not like, but to be the laggard, you are having the entire script dictated to you and everything that kind of comes with it. I mean, look at other parts of the world that are just not the leaders in this space. Economically and socially, they're left behind. And governments, you know, carry the large tax burden of it. So if you're on the wave of some technology innovation, and I would argue to some extent, AI is a little bit of the final frontier of what can be done with essentially intelligence, of course you want to be in the lead. Of course you want to be driving that because the benefits for society are going to be enormous. **Sarah Guo** [32:30] What are you most excited about in the coming year or two for Arm? **Rene Haas** [32:34] Being in the center of all that. Yeah. Honestly, I think we are, I feel fortunate every day that we are in the heart of all of this. And the fact that we can be a participant in that ecosystem, we can help drive the innovation, we can be involved with leadership companies, develop leadership products. We'reright in the middle of it all because A, all the AI needs some level of compute. That's what Arm does. And that compute needs to be power efficient. That's what we're really, really good at. So those roads all lead through us. So what I, and I've, you know, been in this industry my entire career, had a lot of times thinking about, gosh, what's the next product we're going to need? Do people really need another tablet? And does it need to be 8.9 inches or 9.2 inches? And now it's, there's no, the abundance of opportunity innovation is so great with AI. So yeah, I'm just, I'm super excited and feel blessed to be leading a company that's in the center of it all. ### Conclusion **Sarah Guo** [33:32] The need for chips is driven by like massive change in workload,right? And we have continual massive change in workload. So no better place, time to, you know, go work on chip designs and sell to all of the people working on that innovation. My understanding of like the CPU opportunity in this era is like two core pieces and then, you know, future devices and robotics as well. But there's the CPU in the rack. This is the Veris and the Gravitrons of the world. And then there's the use from an agent perspective, like, you know, sandboxes and agents being able to use all of the software we already have and API calls, tools, et cetera. Do you have any guess as to, you know, both these things are growing, but the scale of opportunity, or am I missing things that you guys are really excited about from the CPU perspective? **Rene Haas** [34:24] Well, from the CPU standpoint, and I think when the data center thing was kind of exploding, let me back up. When ChatGPT had the explosion thing and everything was all about the accelerators, I think there was so much focus on no matter what the question is, the answer is the accelerator. There's no computing problem that's ever been invented that doesn't utilize and can't utilize the microprocessor. It is the heart of everything. All roads lead through it, around it, past it, et cetera, et cetera. You look at fundamental system design and you have to have CPUs. They just don't kind of go away. They were a little bit forgotten as this accelerator thing kind of took off. But what then became very obvious was as more and more of the data was moving away from training. Training is obviously very important to recursive learning, reinforcement learning, to inference, the use of the tokens, the use of the information. Well, of course, in a system problem, something has to do the orchestration, arbitration decision around where those tokens go,right? The token factory just generates all these tokens. It's like literally where are the trucks that are going to take the tokens away and give them to the users? That's what CPUs do. So until something's invented that says the CPU has gone away and we're now doing it through some other mechanism, which has just been defined or invented, the CPU is going to be doing just fine. And there's going to be a lot of demand for it, a ton of demand. In addition to the accelerators that generate the tokens. But the way to think about it is it's a system, which again, going back to, you know, memory. Well, of course, memory is needed because in a computer von Neumann architecture or computing architecture, you have a CPU, you have some accelerator, whether it's a floating point, a GPU accelerator, and memory. System design hasn't changed. I think some of the focus kind of moved around, but for Arm, and by the way, that applies whether I'm talking about a data center, it applies when I'm talking about an automobile, a robot, a phone, wearables. And in fact, as you get to the smaller footprints where more and more AI is going to take place, that's going to be a sweet spot for Arm because the CPU's table stakes anyway, you have to have it to do all the things that are required in the edge device. But now we have an opportunity with our instruction set architecture to do a lot of things where you just can't put a 50-watt GPU on your head,right? You're going to have to do that AI processing somewhere locally. So it's a great place. **Sarah Guo** [36:49] Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.