Redefining Chip Architecture with Arm CEO Rene Haas

There's no computing problem that's ever been invented that doesn't utilize and can't utilize the microprocessor. It is the heart of everything. All roads lead through it, around it, past it. Something has to do the orchestration, arbitration, decision around where those tokens go. That's what CPUs do. Chip design can take anywhere from 24 to 36 months depending on the complexity of the chip, etc., etc. The actual design is not the largest amount of time. The largest amount of time is in the verification, the validation, the debug. AI is really good at that. And if we were to shut it off, it's like being in the 1990s. You've got internet and you're now saying, you know, only internet between the hours of two and four. After that, go to the library that we have down the hall. It'd be anarchy. The genie's out of the bottle and there's no stopping that.

Pleasure.

So we have two positions in the chip supply chain. Our primary business is licensing IP. The CPU core that finds its way into smartphones, data centers, automobiles, you name it. Our customers are the ones who either build the chips themselves, a Samsung who's got their own fab, or the vast majority of companies that take their chip designs and go to TSMC and get them taped out. So in that world, and this is the cool thing about ARM, because we're so broad in terms of the markets that we serve, we kind of see everything. We have a very good sense of what's going on in automotive, data center, smartphones. So we see the supply chain situation from all angles. We also introduced our first product last March, the one you just mentioned at Hot Chips, the ARM Neoverse CSS CPU. So now we're in that soup ourselves from the standpoint of we're also having to figure out how to buy substrates and buy wafers and buy memory, etc., etc. So we're up to our waist in everything on the supply side.

Yeah, it was an evolution from the early days of where we just supplied simply the IP components, the pieces, the CPU IP, the GPU IP, the system IP, etc., etc. A few years ago, what we were starting to see was that product cycle times aren't slowing down. Chip manufacturing times are extending. The ability to get solutions out faster was becoming more and more important. So we moved from these individual components into what we called compute subsystems. I used when we went did the 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 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 leap, if you will. And there are certain sets of customers that we'll 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. So Meta was that first example. They wanted a general purpose agentic 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.

So one of the things that we were very careful about was making sure the ecosystem was on board with this because we do CPU IP which is really only as good as the ecosystem. The ecosystem of chip people and the ecosystem of software folks and people who build around that. So we talked to just about everybody who were customers and said, you know, how do you feel about this as a 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 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.

Yeah. So we obviously to deliver a product, and we're a fabless 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 TSMCs and 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 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.

Yeah. Kind of beautiful.

Yeah. 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 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 worked for Nvidia. 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.

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. So since I'm 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 on the complexity of the chip, etc., etc. The actual design of the architecture, the RTL generation if you will, the mapping of the architecture is not the largest amount of time. The largest amount of time is in the verification, the validation, the debug, the documentation, etc., etc. AI is really good at that. And I would say we probably have 80 to 90% of engineers today inside ARM who use it on a daily basis. And if we were to shut it off, my analogy I give to people, it's like being in the 1990s, you've got internet and you're now saying, you know, only internet between the hours of 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. 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 what's available publicly and a lot of that information is quite proprietary. That being said, there's massive opportunity between the ecosystems and everyone in the industry to make that better. It's only going to get better.

We've been working with model makers around that. Absolutely. 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 only the IP, and this is the killer, the documentation, the test benches, you know, how you build the IP. You know, I've worked for chip companies in the past that have said, hey, why don't we license this IP that we've got because it's really, really valuable, and then you get into, wait a minute, there's no documentation, there's no explanation. No one's ever going to be able to use this. It's unusable and it's untestable, right? And if it's unusable and untestable, it's actually untrainable. And if it's untrainable, it's not usable for AI. I think we have some built-in advantages based on our business model that'll allow us to really be able to take advantage of the tools as they get better.

I don't know if it's in two to three years away, but five plus years, can you go from idea to a GDS2 file? GDS2 file being the file that you actually send to the fab to go get built. For certain designs, quite possible. So, it takes that whole design piece out of the way. It takes that whole piece out of the way relative to the verification. So, I think for the more straightforward designs, quite possible. Now if you go into the tool and say design me something that's 10% faster than Ver 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. But I think in 5 to 10 years, you know, our industry as well, we're going to see some amazing differences relative to how chips are designed.

Oh, absolutely. I think what's going to matter, back to the earlier discussion we had on supply chain, it's understanding the supply chain impacts, how all of that gets built and put into ultimate end products. I think that's going to become a much more important muscle as we go forward because it's one thing to, 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, 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, have access to a 3-nanometer line, a 16-nanometer line, advanced packaging line, all of it. All of that. And I think 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. So long as the transformer is the unit of energy relative to how you generate AI training and AI inference, by design, 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 have great ideas on chip design, they're going to need a lot of other things just to be able to get access to capital, wafers, everything you just talked about.

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, right? And then 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 infrastructure buildout was not a headwind, I think capacity for wafers, capacity for memory, that probably would be a headwind. So I think you're going to see a number of different governors, if you will, not governors of states, but different things that are going to throttle the growth of this. Which just expounding for a second, I've been on a bunch of panels and I get a lot of questions about AI bubble and when's it going to stop. And setting aside the valuation bubbles, which is a stock market index component, the bubble in terms of are we oversupply to demand, not even close. And I think again that's because the demand is insatiable just given the way these models work.

Yeah. So, one of the benefits we have at ARM, publicly traded, yes, but a very, very large single shareholder. So I have lots of informal investor meetings with my chief shareholder, you know, all the time about this. We have a big advantage in that there's a lot of things symbiotically we can do together that can help ARM advance its initiatives by having SoftBank as our largest shareholder that we look to be very, very innovative around. To your point in terms of 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, it's a different game. You know, now on one hand semis are kind of back because you now have a wave of semiconductor startups. There was a long time where that was just not happening, investment in the industry. Now we've got a lot, but access to capital is going to be the gate for them in terms of how they get through that. So I think getting much more creative in terms of how they work with the ecosystem is going to be super, super key. And we at SoftBank, that's one of the things we look at very strategically, you know, companies that we can bring into the portfolio that we can help, that we can provide a combination of either the backstop, you know, and/or if you think about SoftBank, we just announced, we being SoftBank, a SoftBank Neo, which is our intent to become a neocloud. 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.