Rene Haas, Arm CEO: AI demand won’t slow

0:00This BBC podcast is supported by ads outside the UK.

0:30originally deposited, and any profit you might have made. This is not a recommendation or offer to buy, sell, or attain any specific investment or service. Decisions made in Washington can affect your portfolio every day. But what policy changes should investors be watching? Washington Wise is an original podcast from Charles Schwab that unpacks the stories making news in Washington right now and how they may affect your finances and portfolio. Listen at schwab.com slash Washington Wise. That's schwab.com slash Washington Wise.

1:06Rene Haas:Hello, I'm Faisal Islam, the BBC's economics editor, and this is the interview from the BBC World Service. The best conversations coming out of the BBC. People shaping our world from all over the world. I want to get freedom. I like their freedom. A gender equal world would be a better world for men too. We need this is fire. We need healing. We need trust. These companies don't really, they don't care what governments do. This is a war. The first thing that we want is the war to end. For this interview, I met René Haas, the American chief executive of the British technology firm Armholdings in Cambridge.

1:48Rene Haas:Founded in the early 1990s, the firm began its life operating out of an old turkey barn in the English countryside near Cambridge. As technology shrank and became increasingly mobile, Arm, whose main focus is the design of efficient chips, positioned themselves at the heart of the boom. It's now mostly owned by a large Japanese tech investment group and their chips can be found in almost every part of modern life, from smartphones and cameras through to cars and supercomputers. With the AI revolution continuing apace, analysts have said that ARM will have a key role to play in the coming years.

2:23ARM 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 for every electronics device because that's what essentially runs the chip or the program or the end device. We'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. We are the brain in today's robotics. 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, demanding building bridges, doing repairs.

3:10Rene Haas:Welcome to the interview from the BBC World Service with Rene 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 you've 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? Gosh, it's even hard to say what it's going to look like in five years, but 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.

4:02It 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 will be ai yeah and you have experience not just if you like in a core supply chain of the chips that drive the ai but also in the applications you know talk to us about you know the key growth area you're in every corner of smartphones or automotive or whatever you know robotics for example is something that you're yeah you've been pushing So ARM is the heart of the semiconductor industry and the electronics industry to that end.

4:45We 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. We'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. 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, building bridges, doing repairs.

5:33We will see... So actual, I mean, people talk about robots and they're not quite, you're due to the sci-fi future of actual robots. Absolutely, 100%. As workers, physical workers, fixing stuff, cleaning stuff. Absolutely, yeah, 100%. If you almost work backwards, you'd ask, well, why can that not happen? Because artificial intelligence today, one of the things that enables robots is for the robots to learn. That's a very key point because robots prior to 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.

6:12With 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.

6:55but that's just an engineering cost issue that we're going to get our hands around. Well, massive consequences for that. I wanted to just take you back to the origin, your origins. For those that don't know, I mean, obviously you're ubiquitous in the tech industry, but you're not a brand that 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.

7:33But 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. So 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.

8:24So we are, in every major device you can think of, over 350 billion chips have been shipped since the company was invented. That's an astonishing number. That'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. Right. So 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.

9:05Oh gosh, yes, we're in a beautiful facility right now, so I can assure you that this is not where it all started. It started in a barn not far from here. It was a joint venture between a number of companies, 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.

9:45Back 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. 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.

10:19And 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 on to something. We've got something that's incredibly power efficient, which is kind of the heart and DNA of the company. 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.

10:57But 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. And 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, 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.

11:32Anything you can do to be more power efficient is incredibly critical.

11:38Rene Haas:You're listening to The Interview from the BBC World Service.

11:56capitalize on around-the-clock access to highly liquid global futures and options markets across all major asset classes visit your online broker and get started see what adding futures can do for you at cmegroup.com forward slash podcast derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made this is not a recommendation or offer to buy sell or attain any specific investment or service if you are currently overpaying on software to run your business remember this number 10 ,000. That's the number of new businesses that join Odoo per month.

12:30Join Odoo today at odoo.com. That's O-D-O-O dot com.

12:37Rene Haas:I'm at the Cambridge headquarters of Arm Holdings to interview its CEO, René Haas. His company is the hidden hand in the chips that control hundreds of billions of devices and AI data centres. The company is rooted in British know-how and a culture of the experiments with 1980s computers, such as the Acorn and BBC Micro. It now plans to be at the heart of a world of self-learning humanoid robots and AI data centres. Okay, let's return to my conversation with Rene Haas. You're now involved in the sticky business of supply chain manufacturing, trying to get hold of materials, testing equipment, everything, you know, and the moments when the world's most advanced industry just can't make enough of this stuff.

13:22Yeah, yeah. 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. They were like, who are you and what do you do? They know us all very, very well. But yes, to your point, we are in an absolutely supply-constrained environment, which I think we will be in for a bit because the demand for artificial intelligence touches everywhere and everything.

13:59And as a result, chips are at the center of it and supply is going to be tight. 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 of the chip market or is could there be something more fundamental going on here if it's a bump it's a really really big bump yeah because the 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 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.

14:48When 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 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, the capital expenditures for building out new factories is immense, it's tens of billions of dollars. It takes a long time to build these factories, two, three years.

15:25And then when you look at the amount of spending that's taking place with the hyperscalers, hundreds of billions of dollars 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. 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 it is 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.

16:04And at the dot-com bubble, you know, we went through, obviously, a huge build-out of infrastructure. At that time, there was, you know, 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, you know, will we go to the level of capital correction or valuation correction? I think that's not impossible. I think it quite potentially could happen. That doesn't mean that the demand for artificial intelligence goes away. Again, let's go back to that time frame with the Internet build-out. We did not have an issue that the Internet 20 years later is not a valuable utility.

16:42In 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. And 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.

17:14We 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. So 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? Like, you know, so much of the valuations have gone towards the creation of the models and towards the AI companies.

17:55Do you think that could be up for grabs as AI evolves in the direction that you set? Well, right now, I think the value has kind of gone to anybody who's in the electronics industry. If you look at the top 10 market cap companies, NVIDIA, Apple, Google, Alphabet, Amazon, Meta, go back 20 years, it's General Motors, it's Exxon, it's a completely different industry. So now all the top 10 value companies are really in our world. Then when you sub-segment inside the world, semiconductors, memory companies are hugely valuable. NVIDIA is very valuable. AMD is very valuable. Broadcom is very valuable.

18:38We are quite valuable. I think it's a recognition that semiconductors are essential. ARM, we're very unique because we have two ways that we deliver value to our customers. One is our IP business model, providing this blueprint to everybody in the industry. And then our new business, the AGI CPU business, is hugely valuable because, frankly, CPU growth has been outpacing GPU growth over the last year or so. But it may be that the frontier AI companies that are investing so much in getting the absolute best form of their AI models, it might be that the impetus is towards maybe more value for money models, cheaper models, generic models.

19:22We've seen this move between open and closed. How are you positioned if there's a move away from the more expensive frontier models towards something that's a little bit more commoditized? Yeah, it's a great point because I think the story is still to be told relative to the delta between these frontier models that run the best you can possibly buy versus the open source models, which are 90 % as good, but much less costly. You think they can be 90 % as good? Could be, yeah, could be. Thankfully for me, I don't care. Because the frontier models are going to run on ARM, and the open source models are going to run on ARM.

20:00We're the plumbing that all that's going to run through independent of it. So thankfully, I don't have to pick a winner in that case. I think it'll be interesting to see how it plays out, but in either way, it's fine for us. But surely in our world, do you not benefit if the AI happens more closer to what we wear and what we use as consumers rather than in some data center that's operated in the desert? I do well in both cases. You're hedged. Okay. I do well in both cases. We are over 50 % market share today in the data center. Amazon builds on us. Okay. Microsoft builds on us. Google builds on us.

20:36NVIDIA builds on us. and they build on us whether it's open source or closed. So it's fine for me. And then if it ends up on the edge, which it will, and you'll have some hybrid, whether it's to your point on your lapel or in your car or some new device that's not your phone, that's all going to be on arm. When I'm told by the frontier AI companies that they are just at the point of exponential growth of capacity of these frontier models, that they'll be able to replace all knowledge work by 2030, you're at the core of the supply chain here aren't you it's like do your chips have the capacity to supply given all the constraints we've talked about that level of compute in two or three or four years time there's a bunch of assumptions being made that that'll just carry on going up but like aren't we hitting the buffers now in terms of of how much compute is available to fulfill these sorts of promises?

21:32The talk now is about multiple gigawatts of data centers, right? There was a data center announcement that SoftBank made, for example, a 10 gigawatt build out of a data center in Ohio that we just did as part of SoftBank Group. There's been multiple gigawatts of data centers announced in France, up to 5 gigawatts. These are giant scales, right? And then people are talking about Elon and Jeff Bezos about terawatt data centers in space. The constraint for making all that happen is going to be back to the earlier chat about wafers and memory. Can you get enough chips? We can't. Right now, it's quite constrained.

22:06It's quite constrained. We need more fabs before we can put a data center in space. I'll tell you that much. And we'll probably only put data centers in space when the biggest impediment to data centers is the cost of the data center. I'm struck that some of the main deployments, most advanced deployments of AI is happening in the AI industry, in the tech industry. Tell me, how have you used AI within your company almost to make the AI? It becomes a little bit circular, but is it replacing jobs? Is it augmenting? What's the balance? I think it's definitely augmenting. I wouldn't be able to tell you today that proudly my headcount is reduced by 25 % because we have AI bots doing all the work.

22:46But we have a lot of engineers using the work just to make them allow their work to be easier and to go faster. One of the things that's a big part of when you design a product or a chip is not the design. It's verifying that design and going through all the bugs and testing the bugs, identifying the bugs. AI is wonderful at solving bugs, addressing bugs, making it easier to find out what was the cause of the bugs. and we have engineers literally, I've had many of them tell me, you know, they'll run a report on a weekend and come in on a Monday. In the past, they would have to do all the Pareto's of exactly what they saw.

23:23When they come back, two-thirds of this might have been done and fixed. That just allows them to go faster. So I think probably we have 90 % of engineering use it on a daily basis. I use it all the time myself personally. So, yeah, we use it very heavily in Sidearm. And what about in your home life? How would you advise people use it with their children? Some concerns as well as some opportunities there? Yeah, you know, for people to say, gosh, you know, AI is going to replace humans and it's going to replace jobs and knowledge work. AI is going to be there, period. We know that. And that's for all things we're talking about.

24:00So I would tell anyone who's talking to their kids or in my case, grandkids, learn it, embrace it and become comfortable with it. Because ultimately, AI is a utility that's going to allow us to be more productive and effective. So don't be afraid of it. And then, well, you say don't be afraid, but it's quite difficult not to hear from the AI leaders that keep on saying, you know, how doom mongery the future might be and how we all need to protect against it and governments need to be quite hard. You know, you're actually running the core technology that provides the capacity for this exponential power to replace human labour, to do some mildly scary things, we're told by the AI companies.

24:36Do you think about that responsibility? Is that your job or are you just going to provide as much compute powers as possible? You know, a little bit, but I would say at the same time, the estimates of jobs going away and being completely replaced by machines is a bit overstated. I was looking at it, I was reading an article this morning, I can't even tell you why I was reading it, but it was about the economy in 1776 when the United States became an independent nation. 96 % of the revenue and labor was farming. Now in the U.S. it's about 4%. but are people worried about, oh, the farming jobs are going away, and as a result, because the farmers have no jobs, there are no jobs?