Redefining Chip Architecture with Arm CEO Rene Haas

From data center orchestrators to AGI and robotics, CPUs remain the heart of modern computing. Arm CEO Rene Haas joins Elad Gil and Sarah Guo to explore how Arm is positioned at the epicenter of AI-driven demands for compute. Rene explains Arm’s position in the chip supply chain, and how Arm transitioned from an IP licensing model to producing physical chips like the Arm AGI CPU for Meta. He also discusses bottlenecks in hardware supply chains, SoftBank’s ecosystem and capital strategy, why US semiconductor manufacturing independence is critical, the future of robotics, and why CPUs remain crucial for executing AI workloads. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @renehaas237 | @Arm Chapters: 00:00 – Cold Open Trailer 00:49 – Rene Haas Introduction 01:14 – Arm and Chip Supply Chain 02:37 – Shift from IP to Manufacturing CPUs 04:23 – CPU IP and Customers 06:55 – AI Adoption at Arm 10:15 – Changes in Chip Time to Market 13:27 – Data Center Buildout Bottleneck 15:13 – Softbank Leverage and Capital Strategy 17:43 – Softbank Portfolio Overview 20:13 – Robotics Opportunities for Arm 24:49 – US Manufacturing Protectionism 28:59 – Data Center Backlash 32:30 – Arm Outlook 33:31 – CPU Opportunity 37:06 – Conclusion

• 0:00Speaker 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, et cetera, et cetera. 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 two and four.

• 0:36Speaker 01

  Yeah.

• 0:37Speaker 00

  After that, go to the library that we have down the hall, it'd be anarchy.

• 0:39Speaker 01

  Yeah.

• 0:40Speaker 00

  The genie's out of the bottle and there's no stopping that.

• 0:49Speaker 02

  Hi listeners, welcome back to No Priors. Today, Alad 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 those with us.

• 1:13Speaker 00

  Pleasure.

• 1:14Speaker 02

  Congratulations on the chip presentation at Hot Chips and, you know, all of the progress that ARM has made. I think there's enormous amount of interest from the technology industry and the software industry into 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.

• 1:36Speaker 00

  So we have, we have two positions in the, 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 a 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, from all angles.

• 2:18Speaker 00

  We also introduced our first product last March, the one you just mentioned at Hot Chips, the ARM AGICPU. 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, et cetera, et cetera. So we're, we're, we're up to our waist in everything on the supply.

• 2:37Speaker 01

  Why'd 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. Then you license that out to other people to actually manufacture and incorporate into their designs. Why did you decide to start making some of your own CPUs?

• 2:54Speaker 00

  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, et cetera, et cetera. 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 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.

• 3:36Speaker 00

  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, et cetera, et cetera. 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.

• 4:18Speaker 00

  They came to us and said, hey, why don't we do this together? And, and that's how we got into it.

• 4:23Speaker 02

  How has that landed with the rest of your customer base?

• 4:27Speaker 00

  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 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 the 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.

• 5:10Speaker 00

  And I think the ultimate proof point was when we announced the product last March, we had Jensen, we had Ronnie Boker, 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.

• 5:26Speaker 02

  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.

• 5:35Speaker 00

  Yeah, so 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.

• 6:17Speaker 02

  I remember discovering that ARM had a 98.5% gross margin.

• 6:21Speaker 00

  Yeah, kind of beautiful. I, I, I.

• 6:23Speaker 02

  I don't think I've seen that otherwise.

• 6:25Speaker 00

  I came from Nvidia before I came over here. And 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.

• 6:55Speaker 01

  Have you approached AI adoption? So, you know, we were speaking earlier that there's news from Open Editor today about Jalapeno, a new 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. 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 the 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.

• 7:22Speaker 00

  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, et cetera, et cetera.

• 8:07Speaker 00

  The actual design of the, the architecture, the RTL generation, if you will, the, 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, et cetera, et cetera. 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'd 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 two and four. After that, go to the library that we have down the hall that's got all the books that you can go, go up and look all this information up, people, it'd be anarchy. So the genie's out of the bottle, right?

• 8:51Speaker 00

  There's, and, 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, they're trained on, on what's available publicly. And a lot of the 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.

• 9:21Speaker 01

  Have you been fine tuning models to try and address that gap, given the proprietary information that you have?

• 9:25Speaker 00

  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, 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, oh, wait a minute, there's, there's no documentation, there's no explanation on how.

• 9:58Speaker 02

  No one's ever going to be able to use this.

• 9:59Speaker 00

  It's unusable and it's untestable.

• 10:00Speaker 02

  Yeah.

• 10:01Speaker 00

  Right? And, 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 advantage the tools as they get better.

• 10:15Speaker 01

  So exciting. How much do you think, if you were to extrapolate out, and this is a little bit of an uncertain question, but if you extrapolate out two, three years and all the tooling is likely to come in AI and the ability to find two 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.

• 10:40Speaker 00

  Yeah.

• 10:41Speaker 01

  In terms of the clock speed of the entire industry.

• 10:43Speaker 00

  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? A GDS2 file being the, 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 happen right away.

• 11:27Speaker 00

  But I think in five to 10 years, you know, our industry as well, we're going to see some amazing differences relative to how chips are designed.

• 11:34Speaker 02

  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 Vera's 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.

• 11:59Speaker 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 it 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 today, said another way. There's a lot of really great young companies today doing AI chips. Well-known companies getting tons of funding, innovative designs, et cetera, et cetera, selling into an industry where the capital requirements are just massive. 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-

• 12:41Speaker 02

  Or access to a 3 nanometer line, a 6 nanometer line, advanced packaging line.

• 12:45Speaker 00

  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 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 have, need a lot of other things just to be able to get access to capital, wafers, everything you just talked about.

• 13:27Speaker 01

  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 want to 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?

• 13:47Speaker 00

  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 in 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, end quote, "okay," because if it wasn't, if infrastructure buildout was not a headwind,

• 14:28Speaker 00

  I think capacity for wafers, capacity for memory, that probably would be a, 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, 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, oversupplied 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.

• 15:12Speaker 02

  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?

• 15:55Speaker 00

  Yeah, so one of the, 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, the banks, the banks themselves.

• 16:40Speaker 00

  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, 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 a super, super key. And we, and we at SoftBank, that's one of the things we look at very strategically, you know, companies that we can bring in, 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, we being SoftBank, SoftBank Neo,

• 17:24Speaker 00

  which is our intent to become a Neo cloud. 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.

• 17:44Speaker 01

  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. So I'd love to, 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.

• 17:57Speaker 00

  Yeah, so it's really the way we 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 the, his shareholder meeting in Japan, which is around robotics, OpenAI, infrastructure and ARM.

• 18:39Speaker 00

  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 him sort of formulate that strategy and more importantly, help execute it.

• 19:07Speaker 02

  How does, how has being part of the SoftBank Group or working with all these different companies, or even SP Energy and the broader ecosystem, changed your point of view on what you can do with ARM?

• 19:19Speaker 00

  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, 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, a big, a 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

• 20:00Speaker 00

  opportunities that that could be an opportunity for ARM, which necessarily doesn't mean that we're getting into the broad merchant ship business. We could be just doing products simply backed for for SoftBank.

• 20:13Speaker 02

  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 is 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.

• 20:37Speaker 00

  Yep.

• 20:39Speaker 02

  First, would you agree with that characterization?

• 20:40Speaker 00

  Hundred percent, yep.

• 20:41Speaker 02

  What predictions do you have about the robotics market and any opportunity for ARM there?

• 20:48Speaker 00

  Oh, 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? And 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 the barrier was was pretty high.

• 21:28Speaker 00

  Getting to a world where the 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 cost's coming down, you look at it and say, oh, my gosh, what 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 of robots.

• 22:12Speaker 00

  There may be an enthusiast class who might be interested in that, but the the broader utility is going to be around a lot of human labor tasks that can ultimately easily be replaced by robots. There's no question.

• 22:24Speaker 01

  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 robots right 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?

• 22:44Speaker 00

  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, you know? 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'll 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 all running on ARM today.

• 23:25Speaker 00

  So I think for us, going going forward, robot, the robotic industry will be powered by ARM.

• 23:30Speaker 01

  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?

• 23:50Speaker 00

  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, that's a tough, it's a tough model to sort of get people's heads around, does it actually replace? So I think costs need to come down and the business model need to be, need to be ultimately vetted.

• 24:11Speaker 01

  Because another robotic footprint tend to be things like automotive or certain surgical robots or data, or 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.

• 24:24Speaker 00

  Distribution centers for sure. I mean, that that can ultimately go completely automated, right? Relative to, and even to the ultimately to the delivery, right? And 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.

• 24:49Speaker 02

  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? So I'm going to throw export controls in there. So four types of controls. All, all of these controls are relevant for you and now either in it from your end customer perspective or as a relevant 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, what's your stance on, you know, how, how protectionist, I realize it's not an American company, but you do a lot of business here, how protectionist

• 25:34Speaker 02

  the US or the West should be about manufacturing of chips, creation of data centers, robotics? Like, what are your overall stances here?

• 25:43Speaker 00

  So putting my American citizen hat on for a moment, and ARM, as you said, is not a, it's not an American company.

• 25:50Speaker 02

  You guys are UK based.

• 25:51Speaker 00

  Our HQ's in the UK, but we have a lot of employee, 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, 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 Semitec, you know, back in the day, which is really around how to re-fortify the American semiconductor industry, which I thought at the time was

• 26:35Speaker 00

  was the right move and there was a lot of energy around that. Internet hit, SaaS companies were all the, 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 our, on US soil. And I would say the same thing to the UK, lesser just because of the scale of the UK.

• 27:01Speaker 02

  Right.

• 27:02Speaker 00

  But when you think about the 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 of supply chain. So I'm a, 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, we're going to limit the chips because we don't want China to to win the race, you know, end quote. You know, my, 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

• 27:47Speaker 00

  US based.

• 27:48Speaker 02

  Mm hmm.

• 27:49Speaker 00

  And that's not going to be a good thing, right? Because, well, people say, well, you know, the cost will go down and 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 technology because it drives 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's Japanese, but other companies in that ecosystem that fed into it. Data centers are kind of the same way. People look at data center and say, oh, it's a big Costco box and there's two parking,

• 28:32Speaker 00

  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, stay in the lead.

• 29:00Speaker 01

  On the data centers hat 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's 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?

• 29:21Speaker 00

  I think there is a, we were maybe chatting 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.

• 29:33Speaker 01

  And you think that fear is well grounded? Because sometimes it seems like it's only creating.

• 29:36Speaker 00

  No, I don't think it's well grounded at all.

• 29:39Speaker 02

  I think the electrician's labor union specifically said, please don't ban the data centers. We need these jobs very recently.

• 29:45Speaker 00

  Completely. I mean, and these are jobs that make people, that's a great, that'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 have a PhD. It's a highly skilled job. It 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, to the data centers. So I think to your question, I think part of the backlash is just from fear. There's just, 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,

• 30:28Speaker 00

  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, the data centers have become a bullseye, unfortunately, for all the things that could be bad about AI, which I think is just.

• 30:46Speaker 01

  Who else 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.

• 30:56Speaker 00

  For sure. Yeah, for sure. And unfortunately, it's become the boogeyman for a lot of, a lot of things.

• 31:01Speaker 02

  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, Renee, 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.

• 31:34Speaker 00

  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.

• 31:46Speaker 02

  This is maybe the most important point.

• 31:47Speaker 00

  There's just not downside from being the leader.

• 31:49Speaker 02

  Yeah.

• 31:50Speaker 00

  There are, there are second and third order effects that you may not like, but to be the laggard, you, you, 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, 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.

• 32:30Speaker 01

  What are you most excited about in the coming year or two for ARM?

• 32:33Speaker 00

  Being in the center of all that. Yeah. Honestly, I think we are, we are, I feel fortunate every day that we are in the heart of all of this. And, 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're right 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, 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

• 33:18Speaker 00

  inches or 9.2 inches? And now it's, I, there's no, the abundance of opportunity innovation is so great with AI. So yeah, I'm just, I'm, I'm super excited and, and feel blessed to be leading a company that's in the center of it all.

• 33:32Speaker 02

  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 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, future devices and robotics as well. But there's, there's the CPU in the rack. This is the Veras 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, the software we already have and API calls, tools, et cetera. Do you have any guess as to, you

• 34:15Speaker 02

  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?

• 34:23Speaker 00

  Well, from the CPU standpoint, and I think when, when, when the data center thing was kind of exploding with, let me back up, when ChatGPT had the explosion thing and then everything was all about the accelerators, I think there was so much focus on, no matter what the question is, the answer is accelerator. There's no computing problem that's ever been invented that doesn't utilize and can't utilize the microprocessor. It is, it is the heart of everything. All roads lead through it, around it, past it, et cetera. So you do, 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

• 35:06Speaker 00

  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, 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 yet 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

• 35:47Speaker 00

  the tokens. But the way to think about it is, 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 is table stakes anyway. You have to have it to do all the things that are

• 36:30Speaker 00

  required in the edge device. But now we have an opportunity with our instructions, our 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.

• 36:48Speaker 02

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