Arm CEO: AI Will Cure Cancer

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1:18Welcome to Big Boss Interview. I'm Liana Byrne and this week we have an interview that our economics editor Faisal Islam has done with a British company that designs the brains behind most of the world's electronic devices, making them one of the most influential tech companies there is. Who have you been speaking to, Faisal?

1:36Rene Haas:I've been speaking to René Haas, the CEO of Arm Holdings. Not the most famous company in the world, but perhaps one of the most ubiquitous in terms of the design of the central brain of microchips that control not just billions of devices or even tens of billions but hundreds of billions of devices most famously the iphone but also cars and now increasingly at the heart of the build-out of ai data centers and that is such a boom that they've changed a long-held strategy only to design and get other people to do the manufacturing they're now actually with the help of tsmc um the taiwanese they're manufacturing chips for direct sale to help fuel the astonishing build-out we're seeing of artificial intelligence data centres.

2:32Rene Haas:The consequences of that boom, René Haas tells me, could be a cure for cancer that would not be possible with humans alone. A world within five years, ten years, of ubiquitous self-learning humanoid robots, and the self-learning is made possible by AI. What is the consequence of that for our economies, for our jobs, how sustainable is even what we're seeing right now financially, and who's going to win from the simply staggering amounts of money being raised and spent by the world's biggest tech companies, including Arm Holdings. Let's hear it then. Here is Arm Holdings CEO, René Haas.

3:20Rene Haas:I want to start pretty big right I mean you sit somewhere almost nobody else gets to sit in this period of extraordinary change in technology and in our economy you've worked inside NVIDIA worked in the semiconductor industry for three decades or more and you now run ARM and your technology is in nearly every phone every device on earth so what does the world look like in 10 years time because of AI? And what's the thing the public hasn't understood that you can tell us about what's yet to come? Yeah, thank you for having me. What does the world look like in 10 years? Gosh, it's even hard to say what it's going to look like in five years.

4:00But I think in 10 years, artificial intelligence will be ubiquitous into every market that we know, every vertical we can think of, whether it's health, whether it's education, It will be pervasive everywhere. It will have some pretty dramatic effects, I think, on how we live, play, and work. Some jobs will go away. Some jobs will be created that we don't know about. But at the heart of it will be AI.

4:26Rene Haas:And you have experience, not just, if you like, in the core supply chain of the chips that drive the AI, but also in the applications. Talk to us about the key growth area. You're in every corner of smartphones or automotive or whatever. Robotics, for example, is something that you've been pushing hard. Yeah, maybe I'll link back to your first question with robotics. ARM is the heart of the semiconductor industry and the electronics industry to that end. We are the CPU or the brain that sits inside every electronics device. And the brain is so important, as you can imagine, for every electronics device because that's what essentially runs the chip or the program or the end device.

5:13We're in data centers, we're in automobiles, we're in PCs, we're in smartphones, we're in earbuds. But we are the brain that makes those things go. Robotics, to your first question, I think in 10 years will be a huge change function. We are the brain in today's robotics. One of the leaders today in robotics is NVIDIA. Their Jetson series, all of the things that they do there, the heart of that uses an NVIDIA GPU and an ARM CPU. The work that Qualcomm does in robotics, they use ARM CPU. So we are the brain of all these modern robots. And I do think in 10 years, we will see physical AI robotics in a very large way, whether it's around manufacturing, cleaning services, security, infrastructure, manning building bridges, doing repairs.

6:03Rene Haas:uh we will see actual actual actual i mean people talk about robots and they're not you're due to the sci-fi future of actual absolutely 100 as as workers physical workers fixing stuff cleaning stuff absolutely yeah 100 the if you almost work backwards you'd ask well well why can that not happen yeah because artificial intelligence today one of the things that enables with robots is for the robots to learn that's a very key point because robots prior AI were built for a certain purpose. You see them in factories today, they install a tire in a factory or inside a distribution area, they're the forklifts that move up and down.

6:43With artificial intelligence, these robots can see, learn, and essentially be reprogrammed for new tasks. So in the service industry, the robot that was programmed to make a bed can now make a bed, but can also learn how to arrange the towels in a room or clean the dustbins or whatever you want to go off and do. This is only enabled by AI. AI allows these robots to learn, these humanoids. So what will happen is you'll have these general purpose systems that because of AI will be able to learn and deliver new types of workflows. That is going to happen 100%. We need to get the costs down. We need to be able to get them to be lighter, faster.

7:28But that's just an engineering cost issue that we're going to get our hands around.

7:31Rene Haas:And then just tell us, in terms of the application of AI scientifically, where could we go? What problems that are currently unsolvable could be solved? Yeah, I've always thought that the killer app for AI is health. Now, drugs can take 20 years to be developed. 95 % of those R &D efforts fail. AI is going to not only shorten the amount of time that those drugs can be invented, it's going to shorten the amount of time that you test them. You'll start to replace some human trials with AI. But more importantly, you'll find a cure for cancer that today, you and I, other humans, not in our lifetimes.

8:06I believe in our lifetime, AI will help cure cancer.

8:09Rene Haas:So this is your company that built on science, but it will transform science as well. I mean, obviously, you're ubiquitous in the tech industry, but you're not a brand that a consumer would buy at this point. Just describe what it is, the core of what you have done and where you've come from, and just how many of these devices you're in. Yeah, so ARM is business to business, meaning that we're not a consumer brand. We don't sell to consumers. Our customers are people in the semiconductor value chain or people in the electronics value chain. But the way to think about it is if you're building a chip, and that chip is essentially inside of your vehicle, your data center, your automobile, your consumer appliance, that chip that makes all those things go, we are the company that supplies to those chip makers.

9:00So what does that mean exactly? We build a CPU, as I mentioned earlier, which is the brain, it's the essential element. But what we do is we design that brain and we license it to a company building chips. So it's the blueprint. If you're building homes, we're providing the blueprint to how you build that house. And then it's a very wide business model that essentially Apple, NVIDIA, Qualcomm, Samsung, Tesla, Amazon, Google, Microsoft. It would be very hard to name a company that doesn't use us as opposed to who does use us. So we are, in every major device you can think of, over 350 billion chips have been shipped since the company was invented.

9:42That's an astonishing number. It's an astonishing number. That's dozens for every human. Yeah, and it's probably low. I think we have figured out that not only have we shipped enough chips for every person on Earth, we've shipped more chips than every person who's ever lived on Earth. Add them all up, and we're still larger than that. So it's the most ubiquitous compute platform ever invented.

10:02Rene Haas:And yet, where we are now is the potential start of an exponential curve up in the usage of these sorts of chips. We are at the start of a growth phase unlike anything we've ever experienced, certainly I've ever experienced. And it's really around what drives artificial intelligence, which is, when you think about the logic of it, it makes sense. Because artificial intelligence, number one, requires a lot of chips. It requires a lot of compute. That's what we do. And artificial intelligence can touch everything, every human, every industry, every workflow. So just to go back to the origin for a second, this all begins where we are right now in Cambridge in the 1980s, a team behind the BBC Micro, the Acorn computer, some engineers with not a great deal of money, I understand, in a barn, designing chips that were so frugal that they even worked when the circuit board wasn't plugged in.

10:58Rene Haas:Just describe that and how central is Cambridge in the UK to your story? Oh, gosh. Yes, we're in a beautiful facility right now, I can assure you this is not where it all started. It was a joint venture between a number of companies, but the real driver was VLSI Technology, Apple. It was a spin out of Acorn. And what the company was looking to do was to design a new microprocessor, a new CPU, for use. In fact, the first use case was the first PDA, something called the Apple Newton. And there were two big requirements for that first PDA. this is 1990s now, a long, long time ago, it had to be low power, because you're running off a battery, and it also had to be rather inexpensive, because it had to go inside a plastic package.

11:46Back then they were in very heavy ceramic packages, so we needed it so it didn't generate a lot of heat. So that was design requirement. Make it inexpensive, and don't draw a lot of power. And to your comment, yes, the engineers overachieved, because what they found out was the very first microprocessor, the way these test boards are built is you have a test board, has a bunch of other components, and you plug it into a wall and the processor runs. But what they found out overnight on the very first run was when they unplugged it, so no power is on the board anymore, so technically the CPU should not work, the CPU was still working.

12:21And it was still working because there's tiny little bits of leakage currents that exist on the board. That sipping amount of power was still enough to make the CPU go. So they thought, oh my gosh, we're really onto something. We've got something that's incredibly power efficient, which is kind of the heart and DNA of the company.

12:38Rene Haas:And the critical point that you still retain to this day is the combination of compute power and power efficiency in terms of energy use. That's right. The company then grew quite a bit and the first designs were the GSM phones and then smartphones. And now, as I said, we're really everywhere. and power efficiency is the key to it. But I think one of the most important things that happens with any company when it's conceived are the habits you learn relative to designing products. And we were fortunate, and I'm super fortunate now as the CEO of the company, that that DNA was forged around low power and being very power efficient.

13:18And that really found itself into every single product that we build, which is why now even in data centers, when people think about, oh my gosh, Arm, ARM, aren't you known for smartphone chips? How are you possibly being used in these data centers? Well, these data centers use hundreds of megawatts and gigawatts. Anything you can do to be more power efficient is incredibly critical, and ARM is twice as power efficient as the next leading CPU.

13:44Rene Haas:Okay, right. I'm going to jump forward to this year now. And so famously, you designed the chips. You did the blueprints for them, and others manufactured them. A big moment this year when you start essentially manufacturing chips for a specific purpose. Yeah, so just to put a finer note on that, we're having someone else build it for us. Manufacturing chips is a big lift. But what we were faced with a couple of years ago was many customers were starting to come to us and say, hey, look, you're licensing this blueprint to me, and increasingly you're expanding the blueprint. What does that mean?

14:21We were moving away from single blocks of IP to the entire subsystem. What does that mean? Data centers, for example, used not only one CPU, but hundreds of CPUs. So we had customers that we were starting to deliver hundreds of CPUs in this blueprint, connected together, all just working. What was happening was customers saying, we need more and would like more. Could you complete the job for us and actually have the entire blueprint of the chip and then have it manufactured? And in some cases, these chips, most of what was inside the chip was ARM intellectual property. Maybe in some cases, 90%, 95%.

14:58So we worked very closely with Meta a couple years ago, and Meta came to us and said, we would like to have you actually have the chip manufactured, and you be the supplier to us of that product. That product is the ARM-AGI CPU. We announced it in March. Meta was the lead partner, but since then we've announced many other customers, Cloudflare, SK Telecom, Oracle, multiple customers, and the takeoff has just been amazing.

15:29Rene Haas:Yeah, and you said the demand was double what you could supply already. Yeah, what we said back then was we had a billion dollars of demand. What we said, and that was in March, in May we said that number was$2 billion. And two months later we said that number is north of$2 billion. So the demand has been off the charts for the product. Okay, and that maybe answers my next question, which is this is why you break the habit of a lifetime, a company so known for sort of not manufacturing the complexities of the manufacturing supply chains. But doesn't it mean you're competing with your own customers?

16:07I don't look at it that way. In this case, we were asked to build the product in a market that was largely underserved. And I think the proof point would be, are we seeing demand for the product? We're seeing huge demand for the product. At the same time, there are other ARM-based CPUs that companies can go to. NVIDIA being one, the NVIDIA VeriChip. They announced their intent to sell that product. And last I heard from Jensen, he's got great demand for Vera as well. So right now what we're looking at is a market that needs efficient CPUs, particularly in this world of AI, which we can get into as why are CPUs so important again.

16:44But no, I think it's a market that's well underserved.

16:48Rene Haas:But you're now involved in the sticky business of supply chain manufacturing, trying to get hold of materials, testing equipment, wafers, everything.

17:00Rene Haas:moments when the world's most advanced industry just can't make enough of this stuff. Yeah. My entire career, as we chatted about earlier, was around semiconductors. So all those muscles that I had learned, and we have a lot of great people inside the company who spent decades also as well, we're now having to exercise. But as you can imagine, given where we sit inside the ecosystem, relationships with people who make memory, the Microns, the Samsungs, the Hynex of the world, the TSMCs of the world. These are all people that we know and have strong relationships with. It's not like we showed up and said, hey, we're going to now start to become a customer of yours.

17:38They were like, who are you and what do you do? Well, they know us all very, very well. But yes, to your point, we are in a absolutely supply constrained environment, of which I think we will be in for a bit because the demand for artificial intelligence, as we chatted earlier, it touches everywhere and everything. And as a result, chips are at the center of it and supply is going to be tight.

18:02Rene Haas:And memory chip prices have obviously nearly doubled because of the AI data centers hoovering up all the supply. It's making smartphones more expensive. It's affected demand for smartphones as well. Is this just a bump in the road, what we're seeing in terms of the chip market? Or could there be something more fundamental going on here? If it's a bump, it's a really, really big bump. Yeah. Because the demand for chips and artificial intelligence and memory is at a scale we've not worked on before. Right. And you say, well, why is it different this time? If you look at artificial intelligence and the way it works today, these models with lots and lots of parameters that need to train and then essentially provide all the information from an inference standpoint, there's a lot of things it needs to remember.

18:53When people say trillions of parameters, that's really the amount of information being used to weigh out how answers are derived. So when people say, oh, the model has taken all the information that we have on the internet and then some, all the information on the internet and then some, that's a lot of memory, right? when you think about what's required there. So it's different this time just because of the sheer scale. And when you think about the scale, and then whether you're TSMC or Micron or Samsung or SK Hynix, the capital expenditures for building out new factories is immense. It's tens of billions of dollars.

19:31It takes a long time to build these factories, two, three years. And then when you look at the amount of spending that's taking place with the hyperscalers, hundreds of billions of dollars of CapEx to build these new data centers that essentially will get the compute and the power and the coolers, et cetera, et cetera. These are big, big numbers that are somewhat circular in terms of driving overall demand.

19:54Rene Haas:Right. And sustainable? I mean, is there trillions of demand kind of going in a particular direction? I mean, people are saying that it'll run out of steam. Are you seeing any sense of that at all? You know, the way I look at is we were chatting earlier about when ARM went public in 1999 and I was at a different company at the time and so I lived through that dot-com bubble. And at the dot-com bubble, we went through obviously a huge build out of infrastructure. At that time there was, end quote, a lot of dark fiber that was not being used. Today we don't have that issue, all the GPUs are being used, but one could argue, well gosh, will we go to some level of capital correction your valuation correction.

20:38I think that's not impossible. I think it quite potentially could happen. That doesn't mean that the demand for artificial intelligence goes away. And again, let's go back to that timeframe with the internet build out. We did not have an issue that the internet 20 years later is not a valuable utility. In fact, it's almost too valuable. If you don't have access to a smartphone, try to get into a sporting event or a concert. It's not going to happen. That's the internet at its backbone. So artificial intelligence will be the same. It's going to be table stakes for everything that we do. We may see a short-term bump in the road or some pause or whatever you want to call it.

21:18And then people will freak out and say, you know, end quote, AI is dead, which is complete nonsense because it's only going to get better from here. And it's going to be a greater utility. So I don't worry about it too much. We may see something like that, but the long-term demand prognosis, I don't know how people can argue against it. It's not logical to me.

21:37Rene Haas:Trillions of market value have been created in recent years. I'm intrigued by Arm's position in that ecosystem. Clearly the value has gone to the GPU, graphics processing units, creators, because they help create the models, train the models. and where you sit has traditionally helped more with the use of the models, the inference of the models. Do you think that that's up for grabs? So much of the valuations have gone towards the creation of the models and towards the AI companies, the LLMs, the Anthropic valuation, the OpenAI valuation. Do you think that could be up for grabs as AI evolves in the direction that you set?