← The Master Investor Podcast with Wilfred Frost
The Master Investor Podcast with Wilfred Frost
The Hidden Force Behind the AI Revolution: Arm CEO Rene Haas
00:47:12
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- [00:00]
A
We're at a very fascinating time in terms of where the winners and losers are going to be, particularly with business models. I was listening to your interview with Dan Ives and he was talking about the Internet. Whether this is 1995 or 1996, I think it's more like 1992. And the reason I say that is AI is so much larger than the Internet. AI will touch every single piece of human interaction with almost any device or any piece of software or hardware.
- [00:27]
B
Will it prove to be that there's been overinvestment in certain areas? Almost certainly.
- [00:33]
A
I would say at some point, yes. Are we there now? I don't know. I mean, there's. If you can talk about. There's a, there's a valuation bubble, which I'm not going to comment on, and then there is an over investment, overinvestment model. I don't think we're overinvested at the moment when you look at large language models. But I do think large language models at some point in time top out in terms of their efficiency relative to the amount of capital required and more efficient or different models will be invented. One of the things that I'm very passionate about, as anybody has to be who's leading a tech company, is there are no sacred cows you have to look all the time at where is the market going, where can I provide value, and ultimately where do I need to take the company in terms of different directions? So we look at things like that constantly. So do I know the answer of what the model looks like in the future? No. Do I have a very good sense that ARM is going to be at the heart of it 100%?
- [01:29]
B
Welcome to the Master Investor Podcast with me, Wilfred Frost, where we celebrate and learn from the success of the greatest investors, business leaders and politicians in the world, giving you, our listeners, an edge. The Master Investor Podcast is sponsored by BMY Investments, LSEG and Interactive Brokers. Please do remember the views expressed in this podcast are for general information purposes only. Nothing in the podcast constitutes a financial promotion, investment advice, or a personal recommendation. More on that in the show notes. My guest today, Rene haas, is the CEO of perhaps the most important company in the UK, Arm Holdings. He's been CEO since February 2022 and at Arm since 2013. Before that, he spent seven years at Nvidia and started his career back at Texas Instruments in 1994 and has spent essentially his entire career in semiconductors. He's also on the boards of AstraZeneca and SoftBank. Rene welcome to the Master Investor podcast.
- [02:34]
A
Thank you. Happy to be here in 1994. You gave me 10 years of extra shelf life.
- [02:39]
B
Oh, did I?
- [02:39]
A
1984.
- [02:40]
B
1984. There we go.
- [02:41]
A
I got 10 years younger. Thank you.
- [02:43]
B
Do you prefer to be younger or have more experience in this framing of it?
- [02:46]
A
That's a good morning. I'll take the more experience.
- [02:48]
B
Okay. Okay, perfect. Sorry for that. I got that wrong. Anyway, the key part, or the most important part of that is being CEO of ARM since February 2022. And I said in the intro, the most important company in the uk. I don't think it's talked enough about in this country, but I guess the first point before getting to the importance is to reiterate, you are a British company. A lot of fanfare and coverage that you listed when you IPO'd in New York, but you are. You consider it a UK company, will always be a UK company.
- [03:20]
A
Yeah. I mean, the company was born in the uk, right? So we started a barn in Cambridge. Our headquarters are in Cambridge. The vast majority of our employees in the company are in the uk. So by all accounts, we are and proud to be a British company.
- [03:35]
B
And I guess I said that too as well. Most important British company. That's, I guess, a slightly open debate. But my point on this is your chips. The chips that you design appear in every device around us here in this room, but around the world, if we.
- [03:50]
A
Define importance by ubiquity and popularity and quantity, then ARM is extremely important. Inside this little room that we're in right now with a number of cameras and. And video players, et cetera, et cetera, probably 50, 100 ARM processors that are running all the machines. The ARM product is a cpu, which is the brain of every electronics device. So when you think about the importance of a brain to the human body, pretty important. And literally every electronic device uses ARM as its brain, whether it's a security camera, whether it's earbuds, data centers, as I said, inside this room, cameras. So by those definitions, yeah, we're quite important.
- [04:31]
B
So just for the uninitiated, another kind of setup question about what ARM does. What is a gpu? What is a CPU you just mentioned? And why does the Nvidia Grace Blackwell sort of sum up the evolution of the industry in the last five or 10 years and highlight to our listeners why ARM is so central to everything and so tied to Nvidia, which is the Goliath Fortune dollar company that a lot of people have heard of.
- [05:02]
A
Right. Quite a mouthful there. So maybe a way to break it down is if you think about the semiconductor supply chain, it's quite disaggregated. And what do I mean by that is there are companies that physically manufacture the chips, actually build the chips themselves. And for our world, there's really only three left that do it. Tsmc, intel, and Samsung. Then there are companies that design the entire system on chip. This is Nvidia, this is MediaTek, this is Qualcomm, this is Apple. Myriad companies who, end quote, build chips, but what they actually do is design the chip and then the chip is manufactured, as I said, by TSMC or Samsung or Intel. So where does ARM fit in? We do the actual design of a component, and our chief component is the cpu. And. And we license that product to a company who's trying to put together an entire soc. So again, we design the brain, we license the brain to someone building the chip. The chip is the body, the arms and the legs and the feet, and then someone puts it all together, the chip manufacturer. So where we sit in the value chain, Wilfred, is we do the design of the cpu, which again, as I mentioned, is arguably the most important component in every digital device. Every single electronic device that runs software has to have a cpu. That's just by definition. So you said, well, what is a gpu? A GPU stands for Graphics Processing Unit. Traditionally, what they've done is run graphics. They draw triangles, they run images. So in a PC, the central processor runs all the software, and the GPU is what actually puts the images on your display. Now, what's happened over time Is, particularly with AI, GPUs are very good at certain problems, AI being one of them. So Nvidia has had this huge explosion in growth around artificial intelligence, using their GPU as the engine that does the processing. But that GPU can't run alone. So they have a product called Blackwell. That GPU needs a cpu. That CPU Nvidia calls Grace is based on arm. So when you think about Grace Blackwell, what is Grace Blackwell, it's a CPU based on ARM designed by Nvidia, but using our intellectual property, using a GPU designed by Nvidia and then ultimately built by tsmc.
- [07:16]
B
And that was such a great explanation. And I think it's going to be super helpful to everyone, including me, even though I've researched this tons. You mentioned that intellectual property. I mean, just again, to dwell on this point, you are really a pure IP company. You make these blueprints, these designs, as opposed to a manufacturing company. And in that sense, innovation is at your very core.
- [07:40]
A
100%. The company started in the early 1990s as a design shop and there was a product way back in the day called the Apple Newton. And that was a PDA way, way ahead of its time. That PDA needed a microprocessor, needed a CPU to basically run the display in the machine and the actual unit. Back in the 1990s, what was very important, it had to be very low power, had to run on batteries and it actually had to be fairly low cost and very efficient on heat. Because you're putting this into a small device here and you don't want your hands to melt when you've got the PDA in your hand. There wasn't anything on the market that could fit that need. So there was a joint venture between Apple, VLSI Technology and they went to a number of engineers who came out a company called Acorn, early British computer company, and designed a custom microprocessor, a CPU for that pda. That was the Newton. Now the Newton failed. The joint venture didn't really go anywhere. But ARM was born from that innovation. And the founders of ARM had a very brilliant idea back in the early 1990s was we're not going to build this chip based on ARM. We have this design that's very, very good at certain things, low power chiefly, and very, very efficient. And we're going to license it to companies who want to build chips around it. Again, back in the 1990s, early 1990s, that was a crazy idea because there wasn't an ecosystem that allowed companies to really take the ip. There wasn't really a third party ecosystem to design it, but what it did is it lowered the barrier for companies to actually adopt a microprocessor that a software ecosystem could be built on. So that's how the company was born. Robin Saxby, one of the founders of, not the founder of ARM, but one of the first CEO, he wanted to build a global standard for CPUs, and that was his vision for ARM. And it came true.
- [09:37]
B
And just to dwell again on the location, over half, I think you said your head count is in the uk, particularly in Cambridge. Is that still the right place in the world to do this sort of innovation work?
- [09:50]
A
Yeah, it's a wonderful question. Microprocessor design is really hard, really, really hard. And getting very, very good at it in terms of building products and generations and iterating is very, very hard. So when you build a critical mass of people in a location who can understand how the architecture is designed, can teach engineers how it's built and can bring on new legions of engineers. It's huge. We have, so we've been in Cambridge ever since we started, you know, early 1990s. So 30, 30 plus years. There's a number of engineers, I think finally the last founders have retired, but there's a number of folks, Wilfred, who are there, have been there 25 years, 28 years. My chief architect, I think he's been there 28 years. Our head of engineering 25, 26 years. It's hard to replace that critical mass. I mean to pick that up and say, okay, we're going to move it to North Africa because the talent's cheaper. There is, it's, it's nonsensical just in terms of attracting people and critical mass in terms of just getting the projects done.
- [10:50]
B
And what about young talent hiring up in.
- [10:53]
A
We hire a lot of people. We have a graduate program, a few hundred a year, two to three hundred a year that are hired in here, Cambridge, Oxford, Imperial College. So it's a big effort for us.
- [11:04]
B
At the same time, I've heard you speak since coming in in 2022 about lacking some of the American hunger or mindset when it comes to scaling the company. Talk to me a bit about that and what you kind of observed and how you're trying to marry all of that. Obviously your own background, having spent a huge part of your time in Silicon Valley.
- [11:25]
A
Yeah, no, I think I've heard you on one of your podcasts talk about this since you lived in the States for a while.
- [11:30]
B
That's kind of why I launched the podcast.
- [11:32]
A
Yeah. So my career early, part of my career was with larger companies but I always had an itch to do startups, so moved to Silicon Valley in the middle 1990s and did startups when I got there and then joined Nvidia, which is a big startup, a founder led company, et cetera, et cetera. So my training and development and my DNA sort of morphed working with startup companies, I found that, that a fit my nature. Well, the risk taking, the entrepreneurship, trying something new. So when I joined ARM in 2013, wonderful company and obviously was a startup at one point in time, what I was struck by was a bit of we would go into meetings and we would talk about new projects and the first six or seven things we would talk about all the things that go wrong. Right. All the things that could, that why were the reasons that we shouldn't do this. And I don't know if that's a British thing. I don't know if that was an ARM thing. But I've definitely sensed it working on both sides of the pond, that for whatever the reasons are, and you were born here, you can probably say better than I can, there definitely is less appetite for risk, and maybe that comes from less of an appetite for failing. Whereas in Silicon Valley, if you fail to some degree, it's a badge of honor. Here, I didn't sense that as much. And even inside arm. So one of the things I've really tried hard to do since I took over in 2022 and even before that was inject some of that Silicon Valley appetite for risk, moving quickly, making mistakes, but still keeping the things inside the ARM culture that are amazing, super bright people, highly innovative, highly collaborative. So I've tried very hard to mix them both.
- [13:16]
B
I love that. And it's clearly worked. And the share price has performed very, very well. And this brings me to my next question, which is not a valuation multiple question, which maybe we'll come to more. I guess I'm getting to the business model point, which is that ARM has been incredibly successful valuation today, $120 billion or so. But you mentioned a couple of other companies already that are as central, as exposed to this AI revolution as you are. TSMC is worth 1.5 trillion. Nvidia worth 4.4.5 trillion. I guess my question on this is you're so central to the extraordinary success that they've had. Is it a bit unfair that you're only worth 120 billion? Is that a. Is that a business model question, or is it.
- [14:07]
A
Perhaps, but it's all relative.
- [14:10]
B
Your success has been huge as a company, so relative is the right way.
- [14:13]
A
I'll put this a bit of context of what we've been trying to do in terms of arm, in terms of growing our position, which has been pretty dramatic for the last number of years. So our business model is licensing, and that essentially is customer wants to use the technology, they pay a license fee, and then on every unit shipped, we collect a royalty. The royalties have been very modest in the early years, and for some senses, that was a very good thing because what it did was it allowed the architecture to proliferate. Many people used it low friction, et cetera, et cetera. But when I took over, I looked at, well, how are the things that. What are the things that we can do that can grow the top line and the bottom line, but also at its core, deliver more value? And one of the things we looked at was the IP as a CPU is one component. But there are many things that we Deliver with the CPU to build a full system on chip. And these are different aspects of ip. We started to put them together into what we call compute subsystems. And this is blocks of IP. Let's say Microsoft wants to build a 128 core chip. We actually provide the entire 128 cores together. We verify it, we validate it, we provide all that value to the customer that drives much better royalty rates for us. And as a result we put a very high focus on delivering more value. And the royalties have really, really grown. So the proof point of that is 30 plus year old company took us 20 years to get to a billion in revenue. It took us another 10 to get to 2. So now 30 years to 2 billion. It took us two years to get to 3, one year to get to 4. So we're growing and that's a combination of the strategy has been working and we've been able to get into new markets such as Grace Blackwell, into Data centers, into AI. So we'll get there. I was at Nvidia 20 years ago and our market cap was 20 billion when I started and it was 20 billion when I left. Went up and down during the time in our industry it does take time. I don't want to play the valuation game but in terms of ARM's long term growth prospects, I am incredibly bullish on it.
- [16:24]
B
It's a really interesting point that accelerating growth, I guess the question that then always follows is whether or not you should or you're considering manufacturing chips more yourself. And it's always sort of rumored. You do talk about it a little bit. Is that something that fits with that and follows on from that question or is it not really worth it?
- [16:44]
A
Yeah, it could. And to be just to put a fine line on it, manufacture chips. In other words, will I build a factory like TSMC? No. I mean it's $30 billion of capex. Even my boss Masa doesn't really have the appetite for that. But could we design what Nvidia does the full system on chip? Potentially, because some of these chips now Wilfred, if you look at all the intellectual property that's inside them, in some cases 95% of it is ARM. And if we can provide something to the market that allows our end customers to get to market faster and sooner with a better product. So we'll look at it. One of the things that I'm very passionate about as anybody has to be who's leading a tech company is there are no sacred cows. You have to look all the time at where Is the market going? Where can I provide value and ultimately where do I need to take the company in terms of different directions? So we look at things like that.
- [17:37]
B
Constantly and I guess the rumor or the everyone's very focused on is OpenAI as a potential partner and not least because of the kind of tie up as well with SoftBank and Masterson. Is that far down the line? Is it close? What's the kind of update there?
- [17:57]
A
I mean they're a wonderful partner. One of the benefits of having SoftBank being a chief owner is alignment with your biggest investor. Masa. He's all in on AI as we are at ARM. He's very big on OpenAI, as are we. OpenAI is a magnificent company and they're doing things all across the value chain. Obviously what they're doing with the models is obvious. But they bought Jony I've's company IO. They've got some interesting ideas there. There's a lot of ways we can partner with OpenAI.
- [18:27]
B
And do you think it would upset your other core current customers like Nvidia?
- [18:32]
A
One of our challenges. We were chatting a little bit earlier about upsetting different people in the ecosystem. Everybody is an ARM customer. I mean literally everybody is an ARM customer. If you were to ask me who doesn't use arm, I would have a hard time answering that question. So we think about that all the time. If we are going to go off and do X, is it going to upset Y? Give you an example. The two largest ecosystems, the three largest ecosystems for software are Android and chrome, Windows and iOS. They all run on ARM. So I have to think about what am I doing that upsets the Apple ecosystem versus the Microsoft ecosystem versus the Google ecosystem. So it's something we think about all the time and I'd like to think we're pretty good at navigating through that.
- [19:20]
B
This podcast is sponsored by Interactive Brokers. Building wealth starts with the right broker and Interactive Brokers helps you reach your goals with powerful tools, global market access, low costs and unmatched financial strength. That's why the best informed investors choose IBKR. Learn more at ibkr.com masterinvestor. You mentioned that these companies that you work with, all the greatest tech companies in the world. I just wanted to reflect on who you think are some of the great tech leaders at the moment. Kind of two different sort of styles in terms of operationally, but in terms of innovation, I mean first, who is the greatest innovator do you think, alive today?
- [20:09]
A
Oh boy. I'm going to make sure I don't insult anyone. I have a predisposition, of course, for the work that Jensen has done at Nvidia. I think anyone who is still running a public company 30 plus years after, with the level of energy and intensity and tenacity that he has, is remarkable. And Nvidia, as we mentioned, they were not an overnight success gaming company for many, many years. A lot of criticism for all the investments they were making in artificial intelligence and CUDA before it bore out. So Jensen would be on the list. But I think about the semiconductor ecosystem. I think Hock Tan is brilliant. I think Lisa Su is brilliant. When I think about those large ecosystem players, each in their own way, I think what Satya Nadella has done at Microsoft is utterly remarkable. A company that didn't look like they could get out of their own way. And he was an inside guy and I know Satya well and I have the deepest respect for him. What he's done has just been amazing. I feel like I'm giving accolades to everyone, but I think what Sundar has done at Google is also amazing. A couple years ago, people maybe 18 months ago sort of left Google for dead in the AI race and felt like it's game over. It's all going to be around ChatGPT and the work that they've done with Gemini, with DeepMind, Demis. So. So one thing that's wonderful about technology is it's never dull. There's always someone to look at, look out for.
- [21:36]
B
You were very skillful there on not upsetting any of your customers. The next part on who's a great operational leader. I mean, I was wondering whether you'd say Tim Cook and Apple had just another great quarter of earnings again last night. But what I think so interesting when it comes to Apple is how core they were in ARM's history. And again, I heard you talking about this on the AQ2 podcast and talk us through how all of a sudden things changed when the iPhone came along and how central you guys were to that.
- [22:09]
A
Yeah, so Apple has an amazing history of market makers, if you will, relative to the end products. And if you go back in time, they were historically, if you go back to Steve Jobs time, when they were building early Mac products, they were all based on Motorola and then something called PowerPC, which is now an extinct entity. But that was an IBM and Motorola joint venture and Apple was involved in that as well in terms of using the product. And literally overnight in our world they flipped over to intel and everything went away from PowerPC to Intel and intel at that time was the dominant supplier in the Windows ecosystem. And Apple did a lift and shift of their operating system away from PowerPC to Intel. Huge, huge event. So the penny drop moment for us was at the moment where Apple was looking at we want to build a mobile device. All of our software, our complex software is written on intel for macOS. Do we take macOS and try to put it into a mobile device, aka an iPhone? And back then they were building the ipod. The ipod was based on ARM because we were really good at that, right? Runs off a battery, had a number of complex things to go off and do, but it wasn't a computer. But the big leap they made was we're going to build basically a handheld computer that looks like the capability of a MacBook but fits in your hand, aka the iPhone. And the big debate inside Apple was do we stay with intel or do we try to go from the ipod, which is this ARM based thing? And they chose arm, which was a wonderful outcome for us obviously. And that is where ARM became the global standard in smartphones. Android with Andy Rubin quickly followed. And next thing you know, in 2008, 2009, every smartphone on the planet, I'll be careful with the word every, is now based on the ARM architecture.
- [24:14]
B
It's so fascinating. And what I love about hearing that and got me thinking is you're ahead of the game on knowing what the next transformational product is that's about to coming out. I mean, as you said, smartphones transformed everyone's lives. I'm sure the work that went into it wasn't overnight, so you must have a two year lead time on what's about to come. And I'm sure you can't tell me what's about to come, but I guess it comes back to why you're so bullish on AI generally because you must know in a year or two years or three years, the next big thing that's coming.
- [24:49]
A
Yeah, I don't know if we know, but we have good instincts. And by that I mean AI at the end of the day is a workload, it's a computer workload. And today that computer workload almost exclusively runs in data centers. And you're training these large models, which takes a lot of compute. And you're also then running inference, in other words, basically now running the output of those training models. And today that's all done inside the data center. And traditionally, and that's a loose word here, because we're so new inside this, that's been a really 100% GPU bound problem. Yes, you need the CPUs. The CPUs work in conjunction with the GPUs. But people have looked at it and says a that's a GPU problem and B that's a data center. Only that's not where it's going to go on a couple different reasons. So let's just talk about the data center. As you start moving to more and more agentic AI and these inference are running agents and these agents are working with other agents, those are much more CPU bound problems and those are CPUs talking to CPUs, launching the agents together. So I think what you're going to start to see in the data center, and we've seen it already on the Vera Rubin platform that was announced by Nvidia, where The number of CPUs from platform to platform have gone up almost 6x. So back to the CPU relevance. So when we look at the trajectory of the CPU in the data center, we have a very, very good sense that's just going to continue to increase. Now then you think, well, is AI only going to run in the data center? Well, of course not. And history has shown us time and time again is that ultimately workloads move to local devices. Local devices, meaning the mobile phone, the PC, wearables, glasses, watches, something that's not invented yet. And what we have a very good sense is that they're going to move to these low power devices, not away from the data center in totality, but you're going to run AI locally. ARM is there locally already. We have a huge opportunity to maximize the potential of running AI in these glasses or in these wearables or in these cameras using arm. So when these models get smaller or they get parsed out in a certain way, we have a very good sense of that. So do I know the answer of what the model looks like in the future? No. Do I have a very good sense that ARM is going to be at the heart of it? 100%.
- [27:13]
B
And will you know the answer before the rest of us? Yes, even if you don't know the answer exactly at this moment. It brings me to my next question though, which is obviously you're super bullish on the theme overall and very confident of ARM's exposure to it. But is it fair to say that there will be big losers in terms of the scale of investment that has gone in? Will it all have big returns? And I guess following that answer, one has to think particularly to data centers, if going forward more of the compute will be done locally as opposed to centrally.
- [27:47]
A
We've gone from 10 megawatt data centers to 100 megawatt data centers to 1 gigawatt data centers. Are we going to 100 gigawatts? Are we going to a terawatt? That I cannot see. I think for a number of reasons. The cost of capital, the amount of land required, the sustainability issues. There's just a myriad of reasons why that scaling can't continue, which I think puts a lot of opportunity at different parts of the edge. On the flip side, we are still very, very early in terms of the capability of the models and I think there could be some breakthroughs there. What do I mean by breakthrough of the models? Today everything is based upon a large language model, and that's exactly. A large language model is exactly what the words say they are. Uses words to solve a certain level of problems. Not every kind of problem uses language to get solved. What do I mean by that? Physics problems, medical problems, biological problems, they may be more attuned and Yann Lecun talks a lot about this in terms of different kind of models potentially emerging that could be very specific for certain areas of domains. They may not use as much energy as the current large language models do. So I think it's very early days for people to project and say, oh my gosh, yes, the scaling is just going to continue until the end of time. History has also told us that that doesn't happen. And there's also a huge amount of innovation going around it. So I think the next number of years are going to be fascinating in terms of where things are going to go again. It's not just because I'm the CEO of arm. Everything needs to run through a central processing unit anyway. So we have a bird's eye view and a huge opportunity to direct where that goes.
- [29:35]
B
But I guess just to follow up on that point, will it prove to be that there's been overinvestment in certain areas? Almost certainly.
- [29:46]
A
I would say at some point, yes. Are we there now? I don't know. I mean, if you can talk about there's a valuation bubble which I'm not going to comment on, and then there is an over investment bubble. I don't think we're over invested at the moment when you look at large language models, but I do think large language models at some point in time top out in terms of their efficiency relative to the amount of capital required and more efficient or different models will be invented.
- [30:13]
B
I know you don't want to comment on the bubble territory and the valuation multiple specifically for your stock, including ahead of earnings. But I guess one takeaway from it is you can't blame you for listing in the US where you can get a very steep PE in your sector, which you wouldn't have got hit. I mean, that's a fair observation.
- [30:34]
A
I think so. I mean, at the end of the day, one of the things we looked at very hard was access to a lot of capital and ARM we felt was going to be in the league of a company that wouldn't want and need access to capital, which is why listing in the US markets at the time we did it with that made the most sense.
- [30:49]
B
Just one final question on this sort of industry overall, if we all use more AI compute power at the edge locally going forward as opposed to in the cloud, does it then suddenly, if we think of Apple, so much has been, oh, it's more of a services company over the last decade, and you shouldn't think of it as a hardware company where you only lay down $1,000 once every three years, even if that's actually how you transact, does this make it more about the hardware again? I mean, if we're doing it locally, will it matter the product that individual consumers are buying much more again? There's more risk, I guess, associated with Apple again.
- [31:31]
A
It certainly could. It certainly could. We're at a very fascinating time in terms of where the winners and losers are going to be, particularly with business models. I was listening to your interview with Dan Ives and he was talking about the Internet. Whether this is 1995 or 1996, I think it's more like 1992. And the reason I say that is AI is so much larger than the Internet. AI will touch every single piece of human interaction with almost any device or any piece of software or hardware. If you think about the Internet today in terms of how it's being used relative to booking flights, ordering groceries, making doctor appointments, if you were to look back in the mid-1990s and try to pick winners around those applications, impossible. What's changed now is that the big guys are really big, right? Amazon, Microsoft, Meta, Google, so, and Apple, of course. So in one sense, it's a little bit hard to think of a world where they're displaced just because the gap is so large relative to their access to capital, the size of their markets, their access to consumers. Now, OpenAI has got a lot of ambition to try to change that, and we'll see. But I think one thing that may be a little different this time is that the past winners may still be the next set of winners just because of the size of their scale.
- [32:51]
B
That's really, really interesting. The scale is certainly enormous.
- [32:53]
A
The scale is so enormous. It's so different this time than it was last time.
- [33:02]
B
This episode of the Master Investor Podcast is brought to you by lseg, the leading global financial markets infrastructure and analytics provider. To learn more about how ELSEG connects businesses, investors and markets worldwide, visit elseg.com. Hi guys, it's Wilf. I hope you're enjoying this episode. Just a quick reminder to please hit, follow or subscribe on your podcast or video app so that you never miss an episode. And if you've got time, please do give us a five star rating and leave us a comment. It really helps other people find the podcast too. Now back to the episode that brings me on to the next topic of China. And obviously all of those huge scale players you've just been touching on are US Companies. Is it fair to say that China has a couple of companies that are as good or close to being as good or not?
- [34:04]
A
Oh, Huawei is an amazing company. Just an amazing company. They are highly vertically integrated. They build networking equipment, they build cell phones, they do a lot of work in software. So they have amazing capability, as does Alibaba, as does ByteDance Tencent. The challenge that Chinese companies have is that compared to 10 years ago, their access to the rest of the world markets is much less than it was. But from a technological standpoint, technology standpoint, they have amazing capability.
- [34:38]
B
And when we talk about the advantages that China has versus the us a lot of people talk about energy costs. Is that the main one or are we overlooking talent or other factors as well?
- [34:51]
A
Certainly a big advantage just in terms of pure access to energy. But the US actually has a lot of energy. But one thing China is much better at at the US is building things really fast and moving through a lot of red tape. You know, what do I mean by that? In the U.S. to move power from 100 mile facility, you have a substation, let's say 100 to 200 miles, to an area where you have a data center. And let's say you don't have transmission lines, you have to get individual permits and approvals from counties and villages and towns. That's a huge bureaucratic process. Huge bureaucratic process. Now the US government is trying to help where there's federal land, they're trying to make that easier. I lived in China for a number of years. I can assure you that my observations in China is that when they want to build Something it just sort of gets done and there isn't a butterfly study that's done in terms of migration or anything relative to what other impacts might be. They just decide to build it and then it's done. So to your question, they have a big advantage in terms of energy, but they have a big advantage in terms of how fast they can build things, how quickly they can put up infrastructure. It's remarkably, remarkably fast.
- [36:01]
B
And talent as well.
- [36:03]
A
And let's go now saying on talent, China's a billion plus people and they have a huge access to talent in terms of people coming out of the university system, entrepreneurs who may have been in the west and went back to China. So, yeah, they absolutely have the access to talent.
- [36:18]
B
And I guess it comes to my next question of, so where do you stand on the cut them off, try and contain or embrace them and try and have them build on our tech stacks or the Western tech stacks?
- [36:29]
A
Yeah, I've said publicly many times that I believe global ecosystems raise all boats. And back to when ARM had started, Robin's mantra was for ARM to be the global standard. When we have open markets, when the world is flat, that's the best actually for the West. When you start putting up artificial boundaries or walled gardens or areas where you have two different ecosystems, actually innovation tends to slow down. So I'm a big believer in open markets.
- [37:03]
B
And I guess in light of that, I mean, it's interesting timing. Of course, the UK prime ministers in China as we speak, those moves you think are sensible, but it sounds like. But with open eyes at the risks.
- [37:17]
A
I think, yeah, you have to go in with your eyes wide open. Fair trade is super critical. Openness to markets is also super critical. So I think that's key to any time you've got an open ecosystem. You also have to ensure that the playing field is level and fair. And if exports are allowed from one country to another, it should be a quid quo pro.
- [37:40]
B
And I guess just to continue on the sort of nationwide comparison we've touched on why you love the UK for ARM, what are we lacking versus that list of China and US's assets to not build the next ARM, but to build the next sort of hyperscaler.
- [37:56]
A
Yeah, scale matters. Unfortunately, in this world, the UK is not at the scale that the US or China is. But I've been very encouraged by some folks inside in the government, Peter Kyle, Liz Kendall, they've been very aggressive on this point, looking to do some things around data centers in the North. But I think also back to the risk Appetite. I think if we could get more venture capital inside the UK and then access even to secondary capital where people who want to start companies can do so in the UK and have their companies thrive in the UK and obviously go public in the uk, I mean, that would be a home run on all levels. I met, and this is a bit of a sadder commentary, but I was at Imperial College about a year, year and a half ago and met with some young students. Brilliant folks came up to me afterwards and said they've got an idea for business plan, blah, blah, blah, and they wanted to move to Silicon Valley or move to Texas and such. And I was thinking, gosh, a shame, that's the kind of work they should actually be doing here because there's so many bright people here.
- [39:01]
B
Yeah. The scaling aspect has been a massive problem in recent years. You're one of the few companies that has stayed to cross a sort of huge market cap. We've got only sort of five or so five, ten minutes left. And I just wanted to talk about innovation generally and how as a leader, you keep making sure that the innovation comes every year and you don't get stale from it. I saw one quote that you kind of were applying to some of those, I guess, tech companies that do fail at some point, and that was a mindset of good enough. And how do you then make sure that's not the mindset that people adopt after great success has already come their way?
- [39:42]
A
Yeah, there was a famous book from Andy Grove, only the paranoid Survive. And I certainly felt that doing in startups and working for Jensen, on a personal level, I definitely have a FOMO problem when it comes to new technology. That's why I love living in Silicon Valley. My chief of staff can attest I overextend myself in terms of if I see a new startup or get connected to somebody working on something innovative, whether it's around a language model or AI for chip design or photonics or something of that nature. I'll take the meeting because I always want to learn and understand and have a sense of what's going on inside the ecosystem such that we, we don't miss out. And that's one of the benefits also of being connected into SoftBank. SoftBank's a huge investor in terms of the tech ecosystem and we're looking all the time, whether it's around robotics, whether it's around different areas of innovation. I think you have to keep your eyes wide open for the next great company because in this world, innovation happens so quickly and it's not always organic. So I spend a lot of personal time on that.
- [40:48]
B
Another sort of area that I've heard you talk about was in the 90s, early 2000s, all the capital that was going into tech kind of got diverted away from semis and into software. And that was one of the many factors that provided an opportunity for you guys to still be spending great deals on innovating in an area that suddenly then came back into vogue. Do you look at the ecosystem now and think, oh, that's happening again over here. People aren't focusing enough on this little area over here and therefore we're going to step into it. And how do you again see ahead? See around the curve almost.
- [41:25]
A
Yeah. I think one of the areas that we are looking at without giving away all of our secrets here, is that in. There was a time when semiconductor companies were not seen as a very interesting area to invest in. Why is that? A lot of capital required. It takes a lot of money to build a chip. It's a lot of investment until you actually ever see the light of day. Hundreds of millions of dollars when you look at mask fees, et cetera, et cetera. Now, that being said, there's a lot of things around the chip ecosystem, whether it's around materials, whether it's around different ways to innovate chips, whether it's around to design chips that I think are super interesting. And some of those areas are not getting as much attention as they should, but they could be the catalyst that says it's suddenly very, very efficient to build chips a certain way, in a certain methodology, in a very different way than they've been built before.
- [42:16]
B
And again then in terms of the leadership kind of aspects you need to be across, how much do you also think about being a salesman for a very complicated product? It's not typical. I mean, Jensen's probably a great example where you can have a great innovative mind that can drive the innovation, but then also commercialize it.
- [42:39]
A
Yeah, I think the sales part is interesting, right? Because what is one of the key attributes of sales is communication and being clear to articulate either a value proposition or why the product's better than the competition, you know, et cetera, et cetera. That applies a lot in terms of when you're communicating, communicating to your own teams and your own people. A lot of I lead a 10,000 person company, right? So I can't be involved in everything at all, but making sure that I clearly articulate what the vision is. What is the strategy we're using to execute that vision. That People can clearly understand it and then articulate that down to their teams is incredibly important and critical. Now, is that a sales skill? To some extent it is when you think about the areas that are kind of important in terms of leading teams. So I think both are critical. I think if you can do both, as far as a technology leader, communicate clearly what the vision is, what the strategy is, what you're trying to get done, and then also understand the speeds and feeds and the bits and bytes and how to articulate that when you're sitting down with engineers. If you can do both, that's magic.
- [43:42]
B
And then my final question, Rene, which is again, just rounding off this sort of advice section for our listeners, is to young people, because actually, particularly, I know this is across all Western countries, but particularly in the uk, and we've been covering this a lot on Sky News, how kind of disorientating it is for young people who maybe did all that they were told to do this course, this exam, had their degree, but the world has changed. And what is your advice to young people today with the things that are changing in the world and in the economy of what they need to study and be prepared for as the world changes around us?
- [44:21]
A
Yeah, the world is always changing. That's history. Probably not at a pace that we've ever seen before. But if I was to give one piece of advice is not to be afraid of AI, but to embrace it and deeply try to understand where it can be leveraged in the areas that you find curiosity and interest around. I've always told people, ask me a lot of times, oh, my gosh, when you Were in your 20s, 30s, did you anticipate being a CEO? Was that your life goal? I said it never was. But one of my life goals was doing interesting work and I was intellectually curious and wanted to try new things by doing those things and being a hard worker and spending a lot of time at it, opportunities fell into my place. So I would advocate that for young folks. Find what you're curious about, learn as much as you can, try new things, because that exposure and then with an overhang of AI, because AI is going to be everywhere. That's the advice I would give.
- [45:18]
B
And just one follow up on that. If you're 16 years old and you're choosing your A levels or you're graduating from high school, picking your specialization at university, what are the courses that ARM looks for when you hire graduates?
- [45:32]
A
We still look very, very hard at computer design, hardware design, computer science. I think computer science is an area where AI will accelerate and probably be able to do more work than have done in the past. But things around physical design, making things, et cetera, et cetera, back to the physical AI, that's something that the map model still aren't that great at, and I think it's a huge area for opportunity.
- [45:57]
B
Well, I'm GLAD I graduated 20 years ago because all of that stuff.
- [45:59]
A
Me too.
- [46:01]
B
Straight over my head. Rene, it's been such a pleasure. I know you're incredibly busy, so to have this 45, 50 minutes with you has been a real thrill. Thank you so much for joining us here on the Master Investor Podcast.
- [46:11]
A
This was great. Thank you so much.
- [46:13]
B
Make sure to subscribe and hit follow if you haven't done already. Next up on the Master Investor Podcast, we'll be speaking to Sonali Basak, formerly of Bloomberg, now chief market strategist at iCapital. Make sure to stay tuned for that one, but for now, our thanks again to Rene Haas.
- [46:29]
A
Thank you.
- [46:31]
B
The Master Investor Podcast is sponsored by BNY Investments, Elseg and Interactive Brokers. Please do remember the views expressed in this podcast are for general information purposes only. Nothing in the podcast constitutes a financial promotion, investment advice, or a personal recommendation. More on that in the show Notes this podcast is produced by Paradine Productions and Master Investor limited In association with Birdline Media. If you've enjoyed the show, please do subscribe on YouTube or click follow on your podcast platform and you'll be automatically notified each time a new episode drops.