Redefining Chip Architecture with Arm CEO Rene Haas

0:00There's no computing problem that's ever been invented that doesn't utilize the cant 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 and two and four. After that go to the library that we have down the hall to be anarchy. The genie's out of the bottle and there's no stopping that. Hi listeners, welcome back to No Pires. Today I'll add a nyer 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

1:00in 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. Pleasure. Congratulations on the chip presentation 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 and through better understanding the chip supply chain recently. Before 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. 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, the Samsung who's got their own fab or the vast majority companies that take their chip designs and go to TSMC and get them

2:00get them taped out. So in that world, 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. Automotive, data center, smartphones. So we see the supply chain situation from all angles. We also introduced our first product last March. When you just mentioned that at hot chips, the Army, GICP, 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 up to our waste and everything on the supply chain. Well, I'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 the different chip components are put together the new license that other people actually manufacture and incorporate into their designs. Why did you decide to start making some of your own CPUs? Yeah, it was an evolution from the early days of where we just supply simply the IP components, the pieces, the CPU IP, the GPU IP, the system IP, et cetera, et cetera.

3:07A 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 the one that did the road show 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 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's 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 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 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.

4:11So meta was that first example. They wanted a general purpose, a gentics CPU. There wasn't anybody out who could give it to them. They came to us and said, hey, why don't we do this together and that's how we got into it. How has that landed with the rest of your customer base? 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, how do you feel about this as direction we're going? And surprisingly, we got a lot less pushback 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 a product last March, we had Genson,

5:16we had Ronnie Bocher, we had Amen, we had James Hamilton, all the folks from those customer that I mentioned, all saying congratulations. That's a great thing. So it's been okay. What is the, where are you in the learning cycle as a business now selling physical chips that feels like a lot of new capabilities? Yeah, so we obviously to deliver a product and we're a fabulous semi company, right? We don't have a fab and we have no intention to build a fab. But we fit in that ecosystem. But that means you need supply chain operations people, you need to work with, as I said, the TSM season, the Samsung of the world, you need to work with the Samsung's and the microns, ESK, high-necks to get memory allocation. And then on the engineering side, you need a lot more different capabilities. You need back-end 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. I remember discovering that ARM had a 98.5%

6:20gross margin. That's kind of beautiful. I don't think I've seen that otherwise. 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 not to like? So we had ads 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 from video. So we have the leadership that's done this before in other companies. So we've been able to build up that muscle pretty quick. Have you ever first day I had options? So we were speaking earlier that there's news from Open Editor today about jalapeno and you chip that they designed their claim as it was a very fast time to market and part of that was using AI tooling to design chips faster. How much adoption have you seen there? I know other companies have also talked about things like adopting formal verification and Amazon or other places for the Trinium 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

7:21our level. Well, 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 get started super quickly and for industries, whether it's healthcare, infrastructure, robotics, every industry is going to use artificial intelligence as utility and stop. So since I have such a believer in this, of course, we use it very heavily 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 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., AI is really good at that. I would say we

8:25probably have 80 to 90 percent 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 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 up and look all this information up, people are to be anarchy. So the genie's out of the bottle, right? 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 on what's available publicly and a lot of 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. Have you been fine tuning models to try and address that gap given the proprietary information that you've been working with model makers around that

9:27absolutely. 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 a wait a minute. There's no documentation. There's no explanation on how you ever going to be able to use this. It's un-testable. Yeah. Right. And if it's un-usable and untestable, it's actually untrainable. And if it's untrainable, it's not usable for AI. I think we have 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. So exciting. How much do you think, if you word extra played out of this a little bit of a uncertain question, but if you're extra playing 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

10:31shrinks 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 pre-dramatic ramifications in terms of the clock speed of the entire industry? 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 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 get press a button and have it happen right away. But I think in five to 10 years, our industry as well, we're going to see some amazing differences relative to how chips are designed.

11:34How 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 want to have their own chip designs now. And the Vera and the Gravichons 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. 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 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 it. It's 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

12:38with substrate vendors. So companies are going to have to be much more... Or access to a three-nanometer line, a six-nanometer line, advanced packaging line. 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 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 have to need a lot of other things just to be able to get access to capital, wafer, everything you just talked about. We've just had a cast skating series of things that have been the bottleneck to more compute for the AI industry. So 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

13:40sense 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? 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 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 build out could be a headwind, just relative to everything going on, which may be end quote, okay? Because if infrastructure build out 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 are going to throttle the growth of this, which

14:43just expounding for a second. I've been on a bunch of panels and I get a lot of questions about AI bubble and when it's 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, oversupply to demand, not even close. I think again, that's because the demand is sensational, just given that these models work and infrastructure build out, access to wafers, access to memory, all of that's combining. You mentioned that, and I think a lot of companies are learning today, that strategic use of the cap table, access to capital in, and are where you either need to consume a lot of compute or you need to put a lot of catbex into the ground or you're just doing big technical projects like coming up with CPU, IP, you run soft bank group international, you have this one dominant shareholder arm itself as a business. It's just like a beautiful cash flow

15:43machine from the outside, right? I'm sure you think a lot about the leverage of soft bank and how to use that. Well, what advice do you have for entrepreneurs navigating these catbex intensive industries from where you said? 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 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 soft bank as our largest shareholder that we look to be very, very innovative around. To your point in terms of young companies, I would say strategic partnerships incredibly early, whether it's with people inside the supply chain, people in private equity, the banks that they've banks themselves, it's a different game. Now, on one hand, semis are kind of back because you now have a wave of

16:46semiconductor 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 ecosystem is going to be super, super key. And we at soft bank, 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 soft bank, we just announced that we being soft bank, a soft bank neo, 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 evidence for that. Can you talk a little bit more about the portfolio things that Paul under your per view at soft bank? I know as mentioned, there's arm and then there's this sort of broader suite

17:51of things. Yeah, I'd love to hear more about what else you're responsible for and we have some specific questions for some of us. Yeah, so the way we think about it is soft bank group, which is head in Japan and that is Maasa. Have a lot of different operating companies underneath them. One of the largest ones is soft bank KK, which is essentially soft bank soft bank mobile. Inside the US, there's a lot of investment activity that's going on with soft bank group international. There's soft bank vision fund, but increasingly, a lot of the strategies that we're trying to do around soft bank is helping the strategies that Maasa talked about publicly at the, at the his a cheerholder meeting in Japan, which is around robotics, open AI, infrastructure, and arm. So I've probably got my eyeballs on a lot of stuff to be honest with you in terms of helping Maasa really realize the execution of that vision. So yes, I'm leading the direction of Ampere

18:52and Graphcore and another company called Stack AV that's doing things around autonomous. But maybe a better way to think about it, Eli, is that I'm kind of in the room on a lot of discussions that Maasa's having and helping him sort of formulate that strategy and more importantly, help execute it. How is being part of the soft bank group or working with all these different companies, even SP Energy and the broader ecosystem change your point of view on what you can do with arm? 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. So it doesn't need to be the home, but it certainly can be a home, which is also a big help when we think about the verticals that soft banks that fall with robotics, energy, data center infrastructure, and then

19:52you look at the products that arm has, the only one we've announced so far is the RMAGIC CPU. You can start to connect the dots and say, gosh, there could be some very interesting 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 can be just doing products simply back for soft bank. We're a couple years into serious efforts in more generalized robotics at this point, if you compare to about a decade for LLMs, there's increasingly interesting demo results from companies on generalization of task environment, more robustness, maybe even in context learning, but not like wide-scale deployment quite yet. First, would you agree with that character? What predictions do you have about the robotics market and any opportunity for arm there? Oh, broadly speaking, I think whether it's humanoid or dedicated machines to do certain level

20:57tasks that can be retrained is going to be enormous. 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, etc., etc. 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? Then when you layer on top of that, the cost coming down, you look at and say, oh my gosh, what would not be able to do? It's almost like something out of the Jetsons, where a lot of things will ultimately be done by robots, construction, infrastructure,

22:01service, security. Right now, you see a lot of stuff on Instagram or TikTok of Olympic races with robots, etc., etc. 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. A lot of the hypotheses people have about the form factor of robotics, tends to split in a 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. 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. You have a hypothesis on I guess both. There's a lot of jobs and work tasks that are optimized around a person being six feet tall and having arms of a certain length, etc., etc., but I think it'll be both. I think the fact that they're going to have to be smart and can learn and to answer your earlier question, arm is going to be everywhere. We have a tremendous amount of technology from

23:06a real-time sensing stamp running around. Micro processors, they'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 humanites, those are all running on arm today. So I think for us going forward, the robotic energy will be powered by these things. Early indications, 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 see the future before others in terms of where adoption is happening or where ships 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? 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, because the cost of robots are so high, people buying the robots themselves. That's a tough model to get people's heads around to do, those are actually replaced. So I think costs need to come down and the business model need to

24:09be ultimately vetted. Because when another robotic footprint tend to be things like automotive or certain surgical robots or data centers, or any distribution centers, there's a few various sort of bespoke applications that I think are most of the robotic sales today. So that's a little bit curious. This distribution centers for sure. That can ultimately go completely automated, it's my relative to and even to the ultimate to 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. There is increasing debate and very quickly, like policy or eos around supply, chain controls, and usage controls around both robotics and chips and data centers. Sorry, I'm going to throw export controls in there. So four types of controls. All of these controls are relevant for you.

25:11And now either from your end customer perspective or as a relatively new entrant to, we're going to own the end product and have a supply chain organization of your own. Like, what's your stance on how protectionists, I realize it's not an American company, but you do a lot of business here. How protectionists the US or the West should be about manufacturing of chips, creation of data centers, robotics, like what are your overall stances here? So putting my American citizen hat on for a moment, and arm as you said, is not an American company. Or HQs 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% age. So we're a real company, but 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 semi's,

26:17and Japan ink 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 seam attack back in the day, which is really around how to refortify the American semiconductor industry, which I thought at the time was the right move, and there was a lot of energy around that. Internet hit, SaaS companies were all the rage, people kind of forgot about semi's being a strategically important asset, but I think it is incredibly important for the United States to have as much of that technology inside on US soil. And I would say the same thing to the UK, less or just because of the scale of the UK. 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

27:18diversification of supply chain. So I'm a big believer in terms of that as a strategy, I think it's really, really critical. As far as the export controls go, and we're going to limit up, we're going to limit the chips because we don't want China to win the race, end quote. And 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 people say, well, you know, the cost 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 technology because it drives innovation, but it also drives ecosystems. If you think about the US auto industry in the 1950s, post-World where Detroit was the center of the universe, you had spots across Wisconsin, Ohio, Illinois,

28:20whether it was Firestone or Bridgestone or Bridgestone, Japanese, but other companies in that ecosystem that fit 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 in the United States and be making sure that we stay staying in the lead. On the data centers, how 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 why there has been this sudden, unexpected outcry on data centers from certain corners? I think there is a, we were mainly saying about this

29:23a 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, I'm not fear is well grounded or because I know it seems like it's only creating. No, I think it's, I don't think it's well grounded at all. I I think the electricians labor union specifically said, please don't ban the data centers. We need these jobs very recently. 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 due to the highly educated job or you don't need 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 data centers. So I think to your question, I think part of the backlash is just from fear. 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?

30:27There's a lot of America just getting on the America political scene who's tough to make the mortgage. The paychecks haven't gone up and now they got this AI thing that just look it's going to even harder. So I think the data centers become a bull's eye. Unfortunately for all the things that could be bad about AI, which I think is just, you know, also are holding up like fake tan and 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. For sure. Yeah. For sure. And fortunately, it's become the boogie man for a lot of a lot of things. 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, Renegh, 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,

31:29external wealth for the communities around them. But it's on the industry to go communicate that. 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 there's just this is maybe the most important point. There's just not downside from being the leader. Yeah. There are there are second or third or effects that you may not like. But to be the laggard, you, 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 economically and socially, they're left behind. And governments 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. What are you most excited about in the coming year or two for

32:33being 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're right in the middle of it all because AI, 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, you know, in this industry, my entire career had a lot of times thinking about gosh, what's the next product you're going to need? Do people really need another tablet? And there's need to be 8.9 inches or 9.2 inches. And now it's like, 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 all. The need for chips is driven by like massive change in workload, right? And

33:35we have continual massive change in workloads, 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 future devices and robotics as well. But there's, there's the CPU in the rack. This is the vera's and the grabichron set 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, etc. 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? Well, from the CPU standpoint, and I think when when the data center things was kind of exploding with, let me back up, when chat GPT 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,

34:38the answer to the 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, etc. 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 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 been defined or invented, the CPUs can be doing

35:42just fine and there's going to be a lot of demand for it, a ton of demand. In addition to the accelerators and generate the tokens, but the way to think about it is it's a system. What's again, going back to memory? Well, of course, memory is needed because in a computer-vonement 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, that 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 CPUs 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 instructions that are 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

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