Redefining Chip Architecture with Arm CEO Rene Haas Transcript, AI Summary & Key Points

00:00 There's no computing problem that's ever been invented that doesn't utilize and can't utilize [music] the microprocessor. It is the heart of everything. All roads lead through it, around it, past it. Something has to do the [music] orchestration, arbitration, decision around where those tokens go. That's what CPUs do. Chip design can take anywhere from 24 to 36 [music] months depending on the complexity of the chip etc etc.

00:19 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 [music] 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. >> After that, go to the library that we have down the hall.

00:38 It'd be anarchy. >> The genie's out of the bottle and there's no stopping that. >> Hi listeners, welcome back to No Priors. Today Allad and I are here with Renee Hos, the CEO of ARM and SoftBank Group International. [music] We talk about the position of ARM within the chip industry. the resurgence of interest in chip innovation, [music] the challenges of the supply chain, the future of robotics, energy, his place in the softbank group, [music] and how he sees workloads changing in the future and for ARM.

01:11 Renee, thanks so much for doing this with us. >> Pleasure. >> Congratulations on the chip presentation at hot chips and you know uh all of the um progress that ARM has made. I think there's an enormous amount of interest from the technology industry and the software industry in sort of better understanding the chip supply chain recently. Um for anybody who's not super familiar, can you explain ARM's position in it?

01:33 And then we'll get into sort of more recent topics. >> So we have uh 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 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 companies that take their chip designs and go to TSMC and get them get them taped out.

01:59 So in that world, and this is a 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 from all angles. We also introduced our first product last March, the one you just mentioned at Hot Ships, the Army GIC CPU.

02:22 Uh 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 waste and everything on the supply side. >> Why' 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.

02:46 Then you licensed 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? >> Yeah, it was it was an evolution from the early days of where we just supplied simply the IP components, the pieces, the the CPU IP, the GPU IP, the system IP, etc., etc. Few years ago, what we were starting to see was that product cycle times aren't slowing down.

03:10 Uh chip manufacturing times are extending. uh 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 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.

03:32 Demand for that was was insane. Uh 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 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.

03:52 And there are certain sets of customers that will license IP to and they've got all the capability in the world to uh 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.

04:15 There wasn't anybody out who could give give it to them. They came to us and said uh hey why don't we do this together and uh and that's how we got into it. >> Uh 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 CPUIP which is really only as good as the ecosystem.

04:36 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 push back than I than I thought and the reason for that was the more software that's available in the wild whether it's proprietary andor open- source benefits the broader ecosystem and the customers themselves.

05:03 So whether it was Nvidia, Amazon, Microsoft, Google, all people who build ARMbased 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 Boker, we had Amen, we had James Hamilton, you know, all the folks from those customers I mentioned all saying congratulations.

05:23 It's a great thing. So uh it's been okay. 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. >> Yeah. So, we we obviously to to deliver a product and 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.

05:47 You need to work with, as I said, the TSMC's and Samsungs of the world. you need to work with the Samsung's and the Microns, the SKH Highix to get memory allocation. And then on the engineering side, uh 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.

06:11 Um which was kind of the beauty of the business, the original business. >> I remember discovering that ARM had a 98.5% gross margin. >> Yeah. kind of beautiful. I I I I >> I don't think I've seen that otherwise. Yeah. >> I came from Nvidia before I 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?

06:35 Uh so we had to add a lot of those capabilities. Uh we have a lot of people on the leadership team uh who've come from that world. Uh I've got execs from Broadcom, uh Qualcomm, Nvidia. I work for Nvidia. So we have the leadership that's done this before and other companies. Um so we've been able to build up that uh that muscle pretty quick. >> How have you approached AI adoption?

06:55 So you know we we were speaking earlier that there's news from open editor today about Jalapeno and 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. Um how much adoption have you seen there? And I know other companies have also talked about things like adopting formal verification at Amazon or other places for their training chips.

07:15 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 our >> per personally I'm a huge believer in AI as a utility that's going to help productivity for every single industry. Um 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 utility and stop.

07:44 So since I'm such a believer in this, of course, we use it very heavily, you know, in inside of ARM. Um, 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.

08:05 the actual design of of the the architecture, the 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. Uh, and I would say we probably have 80 to 90% of engineers today in Sidearm who use it on a daily basis.

08:30 And if we were to shut it off, uh, 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, there'd be anarchy.

08:48 So, the genie's out of the bottle, right? 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. uh 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 uh and a lot of that information is quite proprietary.

09:12 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 offer? >> We've been working with model makers uh around that. Absolutely. Uh and I think that's a big big opportunity and one of the things 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

09:40 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 wait a minute there's there's no documentation [laughter] there's no explanation No one's ever going to be able to use this.

09:59 >> It's unusable and it's untestable, 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 uh able to take advantage of the tools as they get better. >> Really exciting. How much do you think um if you were to extrapolate out this is a little bit of a uncertain question but if you extrapolate out 2 3 years and all the tooling is likely to come in

10:23 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 6 months do you think it stays roughly where it's at a little bit curious about how does that really impact these cycles and time to market because that has pretty dramatic ramifications >> in terms of the clock speed of the entire industry >> I don't know I don't know if it's uh in two to three years away, but five plus years, >> can you go from idea to a

10:50 GDS2 file? GDS2 file being the the file that you actually send to the fab to 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 Ver Rubin 20% cheaper and 30% more efficient on this model you're not going to be able to get press a button and have it

11:26 happen right away. Uh but I think in 5 to 10 years you know our industry as well uh we're going to see some amazing differences relative to how chips are designed. How does it change um I'm sure you had some prediction of this but how does it change the way you look at the business given uh there's just a 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 the Vera and the gravitrons of the world they all use ARM um it's a it's a big step up for

11:55 them but it's a it's a big diversification of the customer base right that can be only good >> oh absolutely uh I think what's going to matter back to the earlier discussion we had on supply chain. It's understanding the supply chain impacts uh how all of that gets built and and put into ultimate end products. Uh I think that's going to become a much more important muscle uh as we as we go forward because it's one thing to en said another way there's a lot of really great young companies today doing AI chips well-known

12:26 companies getting tons of funding innovative designs etc etc 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 access to a 3nometer line, a 16 line, advanced packaging line, all of it.

12:46 Yeah. All of that. And I And I think that is um 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 3 to 5 years at least. Uh 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 comput intensive.

13:09 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 have got great ideas on ship 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. We've just had a cascading series of things that have been the bottleneck to more compute for the AI industry.

13:30 though you know 2 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. Uh do you have a view of what is the next sort of bottleneck that's coming? I think building out the data centers is is going to be a bottleneck.

13:49 And and when I say building out, um if you look at all the projects that are being done today, um not a lot of them are ahead of schedule uh and needing less labor than they thought, right? And then when you when you layer on top of that a lot of buzz that's coming from different parts of the of the of the country in the United States here relative to slowing down data center development or putting restrictions around it.

14:14 I think that uh infrastructure buildout uh could be a headwind just relative to to everything going on which may be you know end quote okay because if it was if if infrastructure buildout was not a headwind uh 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 uh which just expounding for a second I've been on a

14:44 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 a stock market index component the the bubble in terms of are we over um over supply to demand not even close and I think again that's because the demand is insatial just given the way these models work >> and infrastructure buildout access to wafers access to memory all of that's combining >> uh you mentioned that Um and I think a lot of companies are learning today

15:16 that um 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 um uh 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?

15:43 I I'm sure you think a lot about like the 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? >> Yeah. So, one of one of the benefits we have uh at ARM uh publicly traded, yes, uh but a very very large uh single shareholder. So I have lots of informal investor meetings with my chief shareholder you know all the time about this.

16:10 Uh we have a big advantage in that uh 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 um innovative around. uh to your point in terms of you know young companies I would say strategic partnerships incredibly early uh whether it's with people inside the supply chain uh people in private equity you the banks the banks themselves it's a different game you know now on one hand semis are kind of

16:42 back because you you now have a a wave of semiconductor startups u there was a long time where that was just not happening uh investment in the industry now we've got a lot but access to capital is going to be the the 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 super super key.

17:06 And we at 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 uh that we can provide combination of either uh the backs stop you know andor if you think about SoftBank we just announced we being SoftBank a SoftBank Neo uh which is our intent to become a uh a neocloud and in that world we could become a home for these young companies who have uh chip technology that in other worlds they'd have to go up and figure out how to

17:37 get a design win at Microsoft or Google. Uh we can provide a lot of interesting avenues for that. >> Can you talk a little bit more about the portfolio things that Paul under your purview at SoftBank? I I know as mentioned there's ARM and then there's uh this sort of broader suite of things. 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.

17:57 >> Yeah. So the way the way to think about it is uh SoftBank Group which is uh headed in Japan and and and that is MASA uh have a lot of different operating companies underneath them. One of the largest ones is SoftBank KKK which is essentially SoftBank SoftBank mobile. Uh inside the US uh there's a lot of investment activity that's going on uh with SoftBank Group International.

18:20 There's SoftBank Vision Fund. uh 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 uh at the his uh shareholder meeting in Japan which is around robotics, open AAI, infrastructure and ARM. So I probably got my eyeballs on on a lot of stuff uh to be honest with you in terms of helping MASA really realize the the execution of that vision.

18:46 So yes, I I I'm leading the direction of Ampier and Graphcore and uh 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 Moss is having uh and helping him sort of formulate that strategy and and more importantly help execute it.

19:05 Mhm. >> How does um how has uh being part of the soft bank group or working with all these different companies or even SP energy and the and um the broader ecosystem changed your point of view on what you can do with ARM? Well, one thing it does, it gives us a a huge uh bird's eyee view relative to uh 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 for our products, right?

19:35 So, it it doesn't need to be the home, but it certainly can be a home. Uh which is also a big, you know, a big help. Uh 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 uh that could be an opportunity for ARM, which necessarily doesn't mean that we're

20:06 getting into the broad merchant chip business. We could be just doing products simply back for for SoftBank. We're a couple years into um you know serious efforts in more uh generalized robotics at this point, right? If you compare it to like about a decade for um 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 widescale deployment quite yet.

20:38 >> Um first, would you agree with that characterization? Yeah. >> What predictions do you have about the uh robotics market and um any opportunity for ARM there? >> Oh, well, broadly speaking, uh I think the whether it's humanoids or or dedicated uh machines to do certain level of tasks that can be retrained is going to be enormous, right? And the the robotics 1.0, zero, which is a purpose-built industry.

21:04 Uh you had 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 as you can imagine, then the the barrier was was pretty high. 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

21:39 general purpose enough that can take advantage of being reprogrammed and then when you layer on top of that the cost coming down you look at 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 uh construction, infrastructure, service, security.

22:01 You know, right now you see a lot of stuff on Instagram or Tik Tok of Olympic races with robots, etc., etc. I don't think anyone's going to have any interest in watching a sports league of of robots. There may be an enthusiast class >> who who might be interested in that, but the the broader utility is going to be around uh a lot of human labor tasks um that will can ultimately easily be replaced by robots.

22:23 There's no question. A lot of the hypothesis 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 uh 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.

22:38 Others view it as there's going to be much more sort of specialized task specific form factors. Do you have a hypothesis on >> I think it's both. Yeah, I think it's both. There's a lot there there are a lot of jobs and work tasks that are optimized around >> a person being six feet tall and and having arms of a certain length, etc., etc. Uh but I think it'll be both you and and I think the the fact that they're going to have be smart and can learn and to answer your earlier question.

23:01 ARM is going to be everywhere. Uh 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. uh 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 uh today so I think for us going going forward robot the robotic industry will be powered by ARM >> are you seeing any

23:30 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?

23:50 >> I think it's still a little bit early because the business models have not been actually figured out. The co 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 uh ultimately vetted because another robotic footprints tend to be things like automotive or um certain

24:13 surgical robots or data or uh excuse me distribution centers right there's a few very sort of bespoke applications that I think are mostly robotic sales today and so that's why I was a little bit curious >> distribution centers for sure I mean that that can ultimately go completely automated right relative to and even to the ultimate to the delivery right uh and and you can question >> to me loosely speaking a a truck that has an autonomous is a robot of sorts.

24:41 So around factory automation and delivery and distribution that will be one of the very first to be automated. No doubt >> there is increasing you know debate and uh very quickly like policy or EOS around supply chain controls and usage controls around both robotics um and chips and data centers right um sorry I'm going to throw export controls in there so four types of controls um all all uh all of these controls are relevant for you and now um either in from your end customer perspective or as a rel relatively new

25:18 entrant to, you know, we're going to own the end product and have a supply chain organization of your own. Like, um, what's your what's your stance on, you know, how uh how protectionist I I realize it's not an American company, but you do a lot of business here. Uh, how [clears throat] protectionist the US or the West should be about manufacturing of chips, creation of data centers, robotics, like what what are your overall stances here?

25:41 So putting my American citizen hat on for a moment and ARM as you said is not a it's not an American company by our HQs in the UK but >> we have a lot of employee we have 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 we're a global company but with a huge you know with a huge US uh footprint but as a as an American citizen >> and someone who grew up >> in semiconductors and uh I I remember in the 1980s uh when the US was the leader in semis and Japan

26:16 Inc. uh 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 Semitech, you know, back in the day, which is really around how to refortify uh the American semic industry, which I thought at the time was was was the right move and there was a lot of energy around that.

26:36 [snorts] Uh internet hit, SAS companies were all the were all the rage. People kind of forgot about semis being a strategically important uh asset. But I think it is critically important for the United States to have as much of that technology inside uh on our on US soil. And I would say the same thing to to UK lesser just because of the scale of the UK.

27:00 But when you think about the the size of the US market uh the criticality of semiconductors to what the US does whether it's Intel, whether it's Micron, uh I think we need more US fabs. uh it's critical for national security. It's also critical for diversification of supply chain. Uh so I'm a I'm a big believer in terms of that as a strategy. I think it's really really critical.

27:25 You know, as far as the export controls go and we're going to limit we're going to limit the chips because uh 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 US-based.

27:49 >> 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 and goods are cheaper. But ultimately, and I'm a big believer of this, um, 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.

28:08 Uh 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 Japanese, but other companies in that ecosystem that fed into it. Data centers are kind of the same way. People look at data centers 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."

28:37 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 uh stay in the lead.

28:59 On the data center side in particular, it seems like um a lot of the the actions that are being taken to try and prevent future data centers feel more coordinated than not. I know it's phrases grassroot efforts and uh 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?

29:20 I think there is a to to we were maybe chat about this a bit earlier that there's a fear that AI means job loss and and job loss means for all these things kind of implications. So I think unfortunately >> you think that fear is well grounded or because somewhere it seems like it's only creating jobs. >> No, I think it's I don't think it's well grounded at all.

29:38 I I >> I think the electricians labor union specifically said please don't ban the data centers. We need these jobs very recently. Oh >> completely. Yeah, I mean it and these are jobs that may 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 need a PhD.

30:00 It's a highly skilled job that requires a lot of training and certification and you need tons of them, you know, to to do this kind of work. Uh and that's very very critical to the to the data centers. Um so I think to your question, I think part of the backlash is just from fear. just there's just a fear that my jobs are going to go away. Uh the AI boom for good or for bad has benefited a lot of people and there's a lot of people have no benefit from it, right?

30:26 And 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 looks going to even harder. So I think the the data centers become a bullseye unfortunately for all the things that could be bad about AI which I think is just people are holding up like fake tainted water and claiming that you know it's ruining the water supply.

30:51 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 unfortunately it's become the boogeyman for a lot of a lot of things. Mhm. >> I think it's also pretty clear that there's like um you know uh organized media influence around these issues as well.

31:08 Um uh but it doesn't I I think it you know you can have all three separate points here including yours Renee which is um there are benefits that uh from the construction of essentially like a rapidly growing new industry that can create new technology and new jobs and create you know um external wealth for the communities around them. But it it's on the industry to go communicate that.

31:34 >> Yeah. I mean on 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 not >> this is maybe the most important point. >> There's just not downside from being the leader. Yeah. >> There are there are second and third order effects that you may not like, >> but to be the lagard, >> you you the you are having the entire script dictated to you >> and everything that kind of comes with it.

32:00 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 of what can be done with essentially intelligence.

32:24 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 ARM? >> 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 um the fact that we can be a participant in that ecosystem.

32:47 We can help drive the innovation. We can be involved with leadership companies, develop leadership products. Uh 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?

33:15 Do people really need another tablet and does it need to be 8.9 in or 9.2 in? And now it's I 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 and and feel blessed to be leading a company that's in the center of it all. >> The need for chips is driven by like massive change in workload, right?

33:33 And we have continual massive change in workload. So no better place time to uh uh you know go work on chip designs and and sell to all the people working on that innovation. Um my understanding of like the CPU opportunity uh in this era is like two core pieces and then you know future future devices and robotics as well. Um but there's there's the CPU in the rack.

33:59 This is the veros and the gravitrons of the world and then there's the um use from a agent perspective like you know sandboxes and um uh agents being able to use all of the the software we already have and um 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?

34:23 >> Well, from the CPU standpoint, um, and I think when when when the data center things was kind of exploding with, let me back up. When Chad GBD had the explosion thing and and everything was all about the accelerators, uh, 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.

34:44 It is it is the heart of everything. >> All roads lead through it, around it, past it, etc., etc. You 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.

35:14 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.

35:42 Uh 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 is is it's a system which again going back to you know memory. Well, of course, memory is needed because in a computer vonomian 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.

36:06 I think some of the focus kind of moved around, but for ARM, and what by the way, that applies whether I'm talking about a data center, it applies 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.

36:27 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 uh 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 Find us on Twitter at no prior [music] pod.

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