Arm CEO Rene Haas on AI: Nvidia Lessons, Intel's Decline and the US-China Chip War

Thank you so much.

David. Hey, good to see you. Hi.

I will go with the nine. With Jensen, you have to go big.

Well, I will say Nvidia is a customer of ours. So, I'm not going to say Jensen is my competitor today, but you know, I worked for Nvidia for many, many years as you know. And he's fantastic, and I learned so much working there, working for him, working with him. And then Nvidia almost acquired ARM in 2020, so I almost had a chance to work with him again.

You know, one of the things about Jensen that is amazing, I think it's also true for people like Michael Dell, Masa. You have these entrepreneurs who started their companies 30 years ago, 40 years ago, and they're still running it. So, you have this amazing set of characteristics of vision, speed, fearlessness, taking risk, and an ability to pivot very, very fast. And I saw that a lot at Nvidia. You know, when I was there, we were only about $4 billion in sales. And at that time, we were looking at lots of different ways to grow business models and such. And I just remember being, you know, one story: we were at a strategic offsite and it was supposed to be a review of roadmaps where we were looking at each one of the general managers going through what they projected in their business. And what was intended to be a roadmap review turned into: we're changing the strategy. We're abolishing this product line. We're going to move 2,000 engineers off of project X onto project Y. And by the way, we were only about 6,000 people at the time.

So we were involved at that time in trying to do mobile chipsets connecting to an Intel processor, right? And back in the day, for those who remember PC architecture, doing these chipsets competing with Intel was really hard business and Intel was making it very, very hard to compete relative to the integration that they did. And in fact, that was the genesis of starting to pivot to ARM in a very big way inside Nvidia because at that time Jensen looked at what was going on with SoCs and ARM-based architecture and moved everybody onto the program.

Took it private. Yeah. For $32 billion.

Yes.

Could not find a bidder.

That's right.

So it's like a slow kind of process of just building the shareholder base, but you've done phenomenally well as a business.

Mm-hmm.

Oh boy. A lot there to describe. So the way to think about Nvidia, and to some extent I don't, even though I'm the CEO of ARM, I don't want to tie it necessarily back to ARM, but in our world what really drives demand is compute workloads. At the end of the day, it's compute workloads. And when a new workload is essentially either identified or invented, then it comes down to what is the best architecture processor-wise to address that workload. So let's look at AI. The lightning bolt moment of AlexNet and the work actually that the Demis team were working on. AI, particularly training, is a very, very complex parallel problem that is well suited for a GPU. And in fact, the very first work done by the engineers on AlexNet was not with Blackwell. It was not with an AI processor, but it was with a gaming GPU, a gaming card. So Nvidia was in a very, very good place to seize that moment relative to the DeepMind moment, AlexNet, slash the transformer, and fast forward: training these complex AI models as Demis was just talking about, this is a huge, huge amount of work. Now, what role does ARM play there? Every one of these workloads requires a CPU to not only run the computer but help the accelerator run. And that's where Nvidia is a customer today. Their most advanced chip called Grace Blackwell is 72 ARM CPUs with a Blackwell architecture. And that's where Nvidia plays today. So, back to where does Nvidia fit? There is competition. Demis talked about prior with Google, they do their own chip called TPUs. Obviously Nvidia is the leader with general purpose, but right now we're in this interesting world where people are looking at: is it a general purpose chip, is it a custom chip, etc. It's a fascinating time to be in this industry for sure.

Maybe a little bit of both. I mean today the role we play is we are now increasingly that microprocessor that connects to these accelerators, whether it's something that's done by Cerebras or it's something that's done by Nvidia, something done by Google. They're connected. Could we do something ourselves custom? It's possible. Could we also supply the intellectual property to somebody building a custom chip? We're doing that today. So to some extent we're in a very unique place that not only can we provide the solution whether it's standard or custom, but as AI moves from gigawatt data centers to running in these headsets or running in a wearable or running in something that needs to be energy efficient, you still need to run the compute workload but now you need to run the AI workload and that is a place that I think only ARM is uniquely positioned to address.

I'm not going to say that today, but could we do that? I hinted in the last conference call that we're looking at going a little bit further than we do today.

Yes. And I also think you have a third bucket where training distills down to simpler training chips that you don't need to run a trillion parameter model. You could have a giant model that now treats and teaches smaller models, mixture of experts, 20 billion parameters that can be a mix of inference and training doing reinforcement learning where the chip is now helping learn trained areas. It's almost like the professor teaching a student who can also be a student teacher, right? Who can do a little bit of both. And then there's inference that over time will be very dedicated, and particularly as you get to end points that you can't have a GPU that runs at a kilowatt of power. You just, it's impossible, right? So if you have robots in the field, we have 500 million robots.

Yeah, physical AI is going to be a gigantic market. I mean today quite candidly they're using

Yeah, I think so, and because I think they're going to, today they largely use repurposed automotive chips, things that have functional safety compliance around ADAS, but they're not specific for actuators or specific for smaller parts of the joint. So physical AI, particularly AI that can learn, is, I think, going to be a giant market because the robots themselves will have tens of chips, hundreds of chips. So yeah, from a unit standpoint, it could be huge. The numbers are going to be well beyond what we see today.

To some extent, although we don't build anything. Right, our business model is we do the design, someone else has the chip built, mostly at TSMC, some at Samsung, even Intel. But because we are early in the value chain relative to the software ecosystem, in other words, we probably see what people are doing earlier than anybody else because ultimately we're the link between the hardware and the software. So on export control, yes, to some extent we have a very big lens into it. Now today the China ecosystem actually follows the global ecosystem, which is good from the standpoint that every mobile phone in China doesn't run Google Android but it runs a version of Android and it leverages the app ecosystem that comes off of Android. Same thing with autonomous vehicles. They leverage the ADAS stack that was created by ARM and then Qualcomm and Nvidia. So right now the China ecosystem on software looks a lot like the West, which for us is obviously great, and we have a very market opinion in terms of where we want things to go. It's great if the global ecosystem remains open.

So, you know, semiconductors, which I've spent my entire career at. I started at TI in 1984, and I've just been semiconductors my whole career. There are long product cycles. It takes a long time to develop chips. It takes a long time to invest in fabs. It takes a long time to define architectures and ecosystems. If you miss a few, you will be punished for that. And I think Intel has unfortunately been punished on a few areas. They were punished on mobile. Obviously, they missed that completely. They were also punished in terms of manufacturing of going to EUV. EUV is an advanced methodology for building the smallest chips on the planet. They decided not to invest in that probably a decade ago at the rate that TSMC did and they fell behind. Once you fall behind in chips, it's very, very difficult to catch up because the cycle gets on top of you. TSMC now has the best fabs in the world. The leading edge companies, Apple, Nvidia, AMD, they all build at TSMC. TSMC gets better at what they're building.

And Intel, a Samsung, they don't get the opportunities. It just compounds and that flywheel once it compounds and it compounds, it compounds, it's very hard to catch up.

Oh, 100%. I mean if you look at one of the most critical components in building chips are these rare earth compounds and there's a belief that China has cornered the market because they have all the access to these rare earth minerals. The access for the minerals are global. There's no issue in getting access to materials. The issue is in the refinement and actually building the factories that can refine the materials. Again, that's a decades level of investment. And I'll tell you one thing: when I lived in China for a number of years, one of the things that I was very impressed with when I lived there and still am is the industrial policy that sits inside the central government that will last, respectfully, an election cycle, and it will essentially be something that they require a lot of the folks who are in the Ministry of Technology to be engineers to be thinking about a policy on building. So to your question, should the US do it? Absolutely.

I think there probably needs to be more of some of the US companies working together. And I'll say this: because ARM is not a US company, but we, I would do the same if I would. Working together, pooling capital for some of these initiatives to essentially get some type of grounding. You need universities, but you need corporations to get behind this as well as financing, private equity, all kinds of different capital because this is a huge capital investment that also requires investment from companies and private equity, but at the same time needs to last for years.

Yeah, I don't want to take anything away from David. He's doing an amazing job as the AISAR. You've hit a very key tenant though relative to world-class manufacturing inside the United States and what is required to make that happen. We had it decades ago. Believe it or not, there was a time where the leading contract manufacturers in the world were US-based companies and we knew how to do that. And if you go back 30 years ago, when Apple and Compaq used to build their own PCs and they had their own factories, believe it or not, then all of that went to companies like Flextronics and SCI etc. So we had that. Ultimately for cost reasons that began to move all the way to the Far East into Foxconn in China etc. A great book, Apple and China, documents a lot of this. To your point in terms of, you know, could we get that back? In some ways there's no reason why we couldn't, but it is a mindset. TSMC is a 24/7 operation where if a line goes down or a customer's got a problem, not only do the technicians need to be ready to go, the engineers need to be ready to go. And that is something that I think we've lost the muscle memory inside the United States, frankly, on how to go do that. I mean, we may have had it a generation or so ago. I don't know that we have it now. And we certainly haven't trained a generation of folks to look at manufacturing jobs as being something that is as lucrative and prestigious. They sort of think, oh, it's a blue-collar job, I don't want to go into that way. It's not viewed that way in Taiwan, right? And in Taiwan, if you say you're working for TSMC or studying to go off and do that, it's a highly prestigious kind of thing. So it's not just the AISAR's problem. I think it's deeper than that in terms of us getting.

I think we've seen a huge amount of work already done by universities. I was at Carnegie Mellon a couple weeks ago. They now have microelectronics classes for chip design. That was gone a number of years ago. There weren't people designing chips. So I think getting manufacturing operations excellence into the universities, making that a field of discipline that the universities get behind to build up that capacity in the US, I think that's required. Let me go back to export controls which Jamath mentioned. And I'm not sure people here know exactly how these things work, but basically if a product like an advanced semiconductor is put on the export control list, it means that the company that's selling it or the buyer, they have to apply for a license from the Commerce Department to get their purchase order fulfilled. And the Commerce Department will then process that license request and it goes through some inter-agency committee and five different departments will basically have to sign off on it. And best case scenario, it takes months, but there are license applications that literally have been in the hopper for 2 years, by which time the chip is obsolete. And believe it or not, there are a lot of people in groups in Washington right now who are calling for literally every sale of an advanced semiconductor worldwide to be a licensed sale, because they think that GPUs are like plutonium or something and they're inherently scary. I mean, this is seriously the discourse that's going on right now. In fact, there was a major rule that was put forward called the Biden diffusion rule in the last 5 days of the Biden administration that basically did require every sale of a GPU worldwide to be licensed subject to some carveouts. We rescinded that, but there is a neverending clamor and pressure in Washington to bring back these sorts of rules. And the irony is that the people who are advocating for these things called themselves China hawks. But it seems to me that the whole basis of the semiconductor industry, the reason why it's moved so fast, why you get new chips every year, is it's really been left alone by the government for the most part and it hasn't been a highly regulated industry. And I'm curious, what do you think will happen to the industry and the pace of innovation if the government now makes it heavily regulated in the way that I'm describing?

You brought up a great point and I think we may even have a couple of those in the queue that hasn't been approved for a couple of years. You're right. Semiconductors have not been regulated traditionally, and because of that, if you look at the real heart of what drives semiconductor growth, compute, whether it's Intel, whether it's ARM, whether it's Nvidia, that's the West. And why is that the West? Because that requires both innovation at the chip level and a global software ecosystem. And the world works really well when it's flat and there isn't constraints relative to who you sell to or how ecosystems get built. If you shut off supply of a computing architecture into other parts of the world, what will happen? Certain parts of the world that have the capabilities either in terms of people, technology, innovation, they will find a way, and they will find a way around the problem. And once that happens, you've now created two parallel universes and then the US and the West would be at risk of that other ecosystem being an ecosystem of choice. So if you can navigate for those licenses being expedited, the world works really well in semis when it's flat and a global ecosystem. May the best company win.

Company was started in the UK in Cambridge, in a barn, part of a joint venture for the Apple Newton, building a processor. Combination of a joint venture of Apple and VLSI Technology. They needed a low-cost chip that could run off a battery. They contracted a company to build the chip. The chip wasn't so good, but a bunch of guys said, 'You know what? The design's pretty good, and why don't we try to build a business from it?' And that's how ARM was born. I'm the fourth CEO. I'm the first one that is not from the UK. And I've been trying to do in the three and a half years that I took over is to keep the great scientist and technology innovation that we have in Cambridge, but inject a bit of a Silicon Valley aggressiveness and twist to moving faster and going quicker. Now, as you said, half the employees are in the UK, but we've got folks globally: 2,000 people in Bangalore, probably over a thousand in the United States, different parts of Europe. So it's a highly global company and we go where the talent is and we look for great engineers.

We need far more investment. Our business is not one yet where I can say I'm hiring less people because of AI. I'm certainly hiring less finance people and legal people. Sorry Jason and Spencer if you're in the audience. But for engineers, AI for development, AI for creation, AI for science, that's still a hard problem to solve. Which is why we need more engineers to develop chips, which is great. I think back to: is there more demand for compute? Is this AI wave that we're seeing going to continue in the world of generating AI for science and creation? I think there's a ways to go.

I'm going to be an optimist here, Jason, and say I think yes. I think that China views some of the things around AI in terms of whether there are things like guardrails or policies or things to keep things in such a way that we've got the right level of safety checks. I think their minds are in the right space. And I say this just based upon conversations I've had with folks over there. I wouldn't necessarily compare it to the nuclear arms race, but in some ways it's not dissimilar in the sense that you need the countries that have the capabilities to be willing to sit at the table to have the conversations. And China, in my experience, has shown that so far.

Thanks, man.