Andrew Homan & Chris Miller - Redefining Semiconductor Progress - [Invest Like the Best, EP.396]

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1:29 My guests today are Andrew Homan and Chris Miller. Andrew has spent two decades at Maverick Capital and is a managing partner at Maverick Silicon, where he leads the firm's technology investments. Chris is a professor at Tufts and the author of the New York Times best selling book, Chip War. Which details the geopolitical to control the semiconductor industry. Today we get into a comprehensive discussion on the semiconductor ecosystem and the silicon backbone of our digital age.

1:55 Andrew and Chris share insights on how venture capital is navigating this complex industry and what it means for the future of computing. We discussed the AI driven revolution in chip demand, the geopolitics of semi-manufacturing, and the next wave of innovation behind NVIDIA. Please enjoy my conversation with Andrew Homan and Chris Miller. So guys, I thought a fun place to begin would be to talk about one specific thing in this ecosystem just as a jump off point, and Intel seems like an interesting business to do that with because Until it's been around for a long time. There's been periods when Intel was an unbelievably dominant and pioneering business.

2:33 And we're in the midst of a paradigm shift. So maybe you can describe why Intel the business is an interesting one to study. to learn about what's happening in the field of semiconductors today. So I guess maybe to kick it off. I was at an event last week with the global semiconductor alliance and this topic came up and someone asked, What film genre would you describe in terms of what is happening in Intel right now? Is it a horror show?

3:00 Will it end as a romantic comedy? And I'm not exactly sure what term I'll use to describe that right now. But someone from the company got on stage and

3:10 His comment was I would describe it as a Hong Kong action film, which is probably a pretty good example of explaining the state of play right now there. And I think There's a lot of moving pieces, you've got geopolitical tensions. Play on top of all them. You've got competitive issues on the design business.

3:28 They're trying to break into the foundry business, kind of a next new leg of the stool for them. But there are A lot of moving pieces, I guess, is how I would Kick it off, but Chris. You've been following the company for a long time, so

3:40 I think they illustrate how hard it is for a very successful company to pivot to a new business. 'Cause Intel's problems, in my view, are actually a function of their success. They were too successful in producing chips for PCs, too successful in data center for a long time, and it made them risk averse. And hesitant to invest in new products. They missed the mobile revolution. Famously Steve Jobs asked them to produce chips for the iPhone. They didn't do it. And now they're struggling with AI because it was always a huge risk to try something new. Why does this happen? I know that's the reason I've read the Innovator's Dilemma like everybody else. I understand juicy existing profit streams.

4:14 But also, this is so well documented across business history, especially in technology. These are all very smart. people running Intel. With lots of experience. Why don't they just say We're not gonna fall.

4:26 Prey to this specific kind of disruption. And we're willing to do what it takes, including betting lots of our free cash flow or whatever. to not lose this way again because we know this is the way companies like us lose. I think it's painful for institutions and for individuals to actually embrace the innovator's dilemma because it means you have to sacrifice the products that you made your career on. You have to

4:48 promote people from different business streams who are working on new products that threaten the old ones. And so there's just a lot of tension that's involved. It's much easier to keep the status quo, especially if the status quo looks like it's profitable. And it was profitable for a long time. And by the time you realize it's no longer profitable, it's often too late. Interestingly Andy Grove, one of the initial leaders in exactly like wrote a book about this. And when you read the book, you're like, why is this not mandatory reading? The entire industry, but certainly the intel management team. Cause as you talked about you missed this transition to mobile, it seems like they've largely missed the transition to AI and so

5:22 You've got one of the founders of the company that had highlighted these risks and these paradigm shifts. Now you need to be ahead of them and for whatever reason it's been Challenge for them. Can you describe that paradigm shift that we're going through right now? From your perspective. So I think when you look at

5:37 Technology every decade there seems to be what I would describe as an architectural change. And so you went from mainframe In the eighties to the PC. Then you went from PC to mobile. Cloud also was happening simultaneously and now this next paradigm shift that we're just in the very early beginnings of would be AI. And the implications from that I think are probably broader perhaps than any of those other paradigm shifts that we've

6:02 Touched on so far. Did you see it any differently you've studied the history of this? As much or more than anybody. Is it that simple or is there more nuance to appreciate? I think that's right in terms of the end markets. I think simultaneous to that there's been shifts in the business models along the way. So the history of the industry has started with companies that were entirely integrated. They designed and produced their own ships, then you shift to the fabulous foundry model with TSP C producing ships for everyone. And now you're in a position where you've got a small number of AI companies or cloud companies that are not just big buyers of chips, they're also reverting to the prior practice of designing their own chips and getting really deep into the weeds of the technicals of how their chips are produced because they want to have that entire integrated process where they can control from the software all the way down to the silicon.

6:47 Could you pick one company example that's making that transition? Away from being just a hyperscaler and going Vertical. And describe what's happening. In a lot of detail.

6:58 what this actually means in terms of outlay of investment, what they're actually building, why they're doing it. One of these transitions would be interesting to highlight what you mean. There's two directions it's happening. It's happening at the chip companies are going up, like NVIDIA. We can talk about that, but at the hyperscale level, they're all going down. They've all bought a accelerator. startups. They've all tried to integrate them. They're all trying understand how to bring the networking together.

7:20 And they're having to hire Hundreds in cases, thousands of new employees who have areas of expertise they've never had to hire in before. So it really Same story I think at Meta, at Microsoft, at Amazon, they're all essentially following the same playbook. Maybe describe the logic as you see it. I think you look at the enormous capital outlay that all these companies are making right now, primarily for NVIDIA GPUs, and they look at

7:43 where that spend is gonna go in the future, which I think that number is only going higher. And are there alternatives that they can do, at least for certain workloads, where they can custom build a chip that is very Purpose built. For one specific workload, whether that be search, for example, or some sort of social media app, that will allow them to potentially Lower their cost to serve those customers. Significantly.

8:04 It's fascinating to watch investors try to figure this out. And You can find a brilliant investor for just about any argument for where the value is gonna accrue in all this. I'm assuming here that AI is going to be a massive, huge thing on par with or exceeding mobile or cloud or whatever. Obviously, there's some version of the world probably where that's not true. I don't know.

8:23 Let's focus on the world in which it is true. I was with a a very well known investor in the private markets last week that said, look, we just think it's a hundred percent gonna be in the application layer. And so that's where making all of our bets. Your focus much more on the infrastructure. silicon layer and everything around the chip. Why are you making that bet?

8:42 What is it about? your reasoning through where the profits will accrue in the future because of this boom that has you focusing so specifically on silicon and the surrounding technologies. So when I think about the broader AI ecosystem, we think about it as a three layer cake, right? So the bottom layer, chips and cloud, that's where were focused. The middle layer would be the foundational models, and then the top layer would be the applications built on top of those. So the foundational models would be Open AI, Anthropic, Gemini, et cetera.

9:12 That chip cloud layer would be NVIDIA, AMD T S M C, et cetera, and then that top layer would be Chat GPT. office co pilot, test of full self driving and I think the challenge right now is that on those top two layers it's very foggy in terms of who the ultimate winners are gonna be and how much value is gonna get accrued to those layers probably. Eventually, yeah, there will be big dollars that go there, but in terms of who actually captures though, I think it's very

9:39 When I think about that. Chip player though, to me Almost in Any scenario, I see that being the area that's gonna capture a significant amount of economic rent. through this paradigm shift that we're going through right now with AI.

9:53 What would be the reason that it doesn't? Let's say that We get to simulate the future in a whole bunch of different ways and then some percentage of those futures Does not capture a lot of the profits. It's big.

10:04 But there's pricing pressure and it gets competed down at the cost of capital and There's just not a great competitive advantage period to earn money in. Draw that picture, or can you draw that picture? How could that happen? I think there'd be two dynamics. So one would be did these scaling laws change? Right now everything we see would say, no, they're not gonna change. And the more compute you throw at these problems, the better the model, the quicker you'll be able to train the model, et cetera. No, I think the question is do you get into a situation where there's much more competition, particularly in that AI accelerator.

10:33 specific area of the market where prices come down significantly. I think ultimately we do want prices to come down. That's the history of compute. But I think you've seen in prior transition whether it be mobile or whether it was with P C That silicon layer still captures a ton of value with PC and server. Intel clearly created an incredible business during those periods of time. And then specifically with mobile, you look at companies like ARM and Qualcomm and others that monetized significantly on that trend despite there was more competition. Prices did come down, but the TAM was so large. these still great companies were built and created over those time periods.

11:08 And I think the PC and Mobile era both show that describing it just a silicon actually misstates the source of those modes because it was silicon plus software and the interface between the two. Why are there Intel chips and half the world's PCs? It's because of the software's built on top of Intel's architecture. Same thing true for whether it's Apple and Mobile or Ballcom. Or arm at the base of it all. And so you you've actually had a long history of the silicon software interface playing a big role, and the software is often a big portion of the moat. And one other area that's worth mentioning on the scaling laws. Is it with the release of OpenAI Strawberry Model. an area of AI that was thought to be compute light, that being inference is now compute intense as well.

11:46 is for that model to be better at reasoning, it requires much more and a much longer period of inference. It's really interesting to think about the history just from an investing perspective up until now. And I'd love you to reflect on it where If you ask about semi investors in private markets, they'll often tell you Just don't do it. It's capital intensive and brutally competitive and

12:06 You're just not gonna make money as a semiconductor investor if you're like a venture investor. And in public markets, there are certainly people that have done tons of semis investing historically. But there aren't a lot of semis funds, semis dedicated funds. There's plenty of software dedicated funds, but not a lot of semis dedicated funds. So why? is the technology that has undergirded all of technology progress of the last forty, fifty years. Why has it been like somewhat of a backwater for public and private investors over, say, of the last twenty years.

12:34 I think there's a dynamic where there were a lot of semi focused funds twenty, thirty years ago and many of them did very well. And then you had this rapid rise of software that kind of captured everyone's imagination and many of the kind of particularly the early stage community pivoted fabulously well, right? Investing behind that trend. And then you had a situation where a lot of that expertise just retired

12:58 And left the industry. And it's probably it it is semis are challenging. They're technically complex. They are capital intensive. Though I think like comparing them to software today looking prospectively over the next ten years versus say the last ten years. My guess is that we're gonna see more and more capital start to flow into this hardware layer because so much value, I think, will get created there and there's just so much need for innovation. Where do you think the highest prospective returns are within the infrastructure layer. Everyone obviously is focused on NVIDIA'cause that's where all the market cap is.

13:29 They make GPUs, everyone's talking about GPUs. There's so much more going on around them. Data centers are getting built out famously, a record. pace with crazy amounts of power. There's all this cool stuff happening around the GPU itself. So where else

13:42 Are you most interested to study, look, meet with companies And so on. This ties into a point. That Chris made earlier. Less focused on

13:51 That core. GPU itself, and there's many companies trying to go directly after that AI accelerator market. I think it is challenging to go after the predator that it's at the very top of the food chain right now, NVIDIA. being that player. And there's all these interesting areas around that GPU, whether it's interconnect or memory or power related, where you're just gonna need innovation because the way of doing business that existed five years ago.

14:19 is just breaking. And the data center that existed five years ago where an Intel processor is king of that data center and there's some simple networking and then there's some storage attached to it. The example that we give when we're talking to folks is that Essentially you had a tsunami, an AI tsunami that you basically wipe that data center out and Jensen's riding his surfboard on top of that tsunami and

14:41 As a result You're seeing everything needing to get rebuilt because the power needs of these GPUs is are off the charts. You're trying to connect as many of them together as possible to create super GPUs. And so right now we're connecting a hundred thousand to get together. The next step is gonna be connecting a million of them together. And the complexity of doing that is creating, I think, a lot of opportunity. Chris I'm curious where You've studied the history of this in detail. We won't go through it all. People should read your book if they're interested. Which is excellent.

15:08 But I'm curious if you take all that history out through to today. And then you spend a lot of time with companies, with governments, with investors, with sort of everyone in this ecosystem. What people are not talking about enough. That is not yet enough in the mainstream narrative, but you're hearing a lot about in these conversations. I think everyone is beginning to register how much money will be spent on compute over the coming years, decade.

15:32 Driven by data center build out, driven by AI. The numbers are staggering. They're exciting if you're a data center builder or a power provider or if you're NVIDIA, but that's actually bad thing. We'd like cheap uh compute to be as cheap as possible. And so I think The key focus right now should be finding ways to minimize the amount of money that needs to be spent in order to build out the computing we need for AI. And We're right now at the stage of admiring the scale of the problem, admiring the billions of dollars that will be spent. And actually the real goal should be finding ways to economize to

16:03 bring new technology to bear to Find ways to bring down that cost because that's gonna be one of the key gating factors of AI development is can we bring the cost down? Can we talk about the political considerations around both the infrastructure and the first two stacks. We'll leave the apps for later, maybe, but if we're talking about the just the infrastructure, the fabs, everything that needs to go in to run these things. And then the model layers.

16:25 US companies seem to really dominate this, maybe except for T SMC. And a couple in Europe. Talk about the most important political considerations given that If this technology is the most important one ever.

16:38 Seems like every country's gonna want some answer to their foundation model or their data center or their infrastructure. What are the key variables in the political landscape right now as you see them? I think if you start at the level of where chips produced, you've got a whole lot of concentration, Taiwan As you mentioned, and the economies to scale in the industry are such that you're just not gonna have multiple locations at the size of Taiwan where ship are produced. Maybe a couple of other countries, Korea, but it's not gonna be every country producing its own chips. Totally impossible. The data center level, if you're talking about data centers for foundation models, those are huge, brutally expensive projects. And so there too, although there's a lot of governments that would like to have their own, the scale of investment is such that unless you're sitting on a lot of oil. Or unless you're a very large economy, you really can't justify the spend. And so that's why if you look at where big data center projects are happening in the US.

17:25 Happening In China. There's a desire to have them in the Middle East, and there's Political issues. that come up right there because the US wants most of the world GPUs to be used in the United States and is wary of letting too many

17:39 get deployed in other countries, both because there's concerns about China and competition with China, but also because the US thin will be really benefit to training most of the world's foundation models. If we had like a country leaderboard of who is the most advanced in a couple different areas, let's say foundation models and infrastructure. Who do you think will be the largest movers up and down? over the next say like five years. I think the the surprise player right now is the UAE. The Emirates have put a ton of money

18:05 into AI, they're Leadership is very And if you look at The Falcon models that they've developed, they're pretty sophisticated.

18:15 And I think most people wouldn't have expected five years ago that the UAE would be front and center. in AI. I've actually this surprising uh downside player is China. If you look at the huge capital expenditures that US big tech firms are outling right now, that's not really matched in China, with maybe the exception of Byte Dance. And that's partly for US China reasons and controls on technology, but also for China's own domestic economic situation is not making firms excited about putting out billions or tens of billions of dollars in data center spend and investment in AI.

18:46 Andrew, can you talk about the motes? in this business that people might have a hard time appreciating. T SMC is an interesting one to talk about because Everyone knows what a political danger hotbed this could be. Most of the world's cutting edge chips are built very far from us and very close to arrival. And it seems sort of insane that we just

19:04 Don't replicate this in the US. I know we're trying. But the fact that we haven't begs the question. Why is it so hard? What is it about?

19:12 the infrastructure businesses that makes them so hard to replicate or copy. It just seems crazy. It's changed over time, right?'Cause there was a period of time where Intel actually was at the leading edge and was The most technologically advanced manufacturer of chips in the world. And then T SM C

19:28 Lap frog than that. Probably happen. Around ten nanometer time when Intel was struggling with that node. So call that Gosh, that was probably almost Seven or eight years ago, something in the zip code. And so like why has TSM C been so successful at what they do? I think in part of it, it's because they're just maniacally focused on just one

19:44 business model, right? All they are doing is the foundry model. Whereas the other players, Intel has a very large design business that's designing the chips that will get made in the Intel Foundry. Samsung plays in multiple areas, memory Foundry, they also design some of their own chips, but if you're just singularly focused, right, on the task at hand in making the highest performant chips, right? And then learning From your customers'cause they have all the most sophisticated design customers at this point making their chips on the leading edge at TSM C. It just gives them the ability to continue to improve.

20:17 and sharpen that manufacturing process to a level that I think at this point's gonna be very challenging for anyone to catch up. I think the thing that strikes me is the ecosystem in Taiwan is just extraordinarily well developed. And we talk about the software ecosystem of Silicon Valley, but there's a manufacturing ecosystem in Taiwan. That's the chemical suppliers, the materials producers, the people who know how to fix the tool when the tool breaks. And they're all right there in a very small country within an hour and a half of each other on the high speed rail on the Western shore of Taiwan. And so if something goes wrong in your factory, or when you need to prototype a new machine or new process, it's very easy to get it right away. In a way that if you're in Arizona or if you're in Japan, uh you just don't have that efficiency. And so TSMC will say I think they're right, that the bullet train that goes up and down Taiwan bringing people back and forth in a single day between their fabs is one of the key sources of their comparative advantage.

21:08 Why is the yield so low in these things? If you're producing cars in a Ford factory or something, there's very few lemons. Whereas you tell me what the numbers are, but I think for some chips it's as low as like seventy percent of the ones produced actually then go get used in production. Which means you're just like literally throwing away a lot of the product. Why is that still the case despite the all that sophistication?

21:28 The manufacturing is the most complex that humans have ever done. So in comparison to a car, cars can have fault tolerances measured in millimeters for certain parts. In ships you're talking about nanometers, so a thousand times more precise. And if you look at just a chip in your phone, for example, and pop it open, there are billions of tiny transistors, each one of which is the size of a coronavirus, and almost all of which have to work for your chip to function, to let you like things on Instagram or send a text message. And it changes every year. Every year TSM C rolls out a new manufacturing process that is dramatically better than the old one, which means that the transistors are smaller or packed more densely together. And so the tolerances get even tighter year after year. And so the question of yield, how do you produce chips that work means gotta be yielding a new process on a regular basis. So you started out last year's process and you start all over again with the next process with even smaller tolerances for error.

22:21 And I'm curious what you think like the five ish most important companies are right now outside of NVIDIA and TSM C. I would put Brycom on that list. I would put S K Heinex on that list on the memory front. I would put AMD on that list right now.

22:38 I think when you looked further down, you would put some of these other smaller component players that are in the AI infrastructure orbit as well. There's actually probably a longer list of those more than two or three. But you definitely have a whole group of companies that are benefiting from what I think is gonna be One of the most significant Infrastructure build.

22:58 That we've seen. If you think about a company like ASML or something that seems pretty singular, which companies if they just Broke completely would most Mess us up. Well the A C M L's definitely on that list maybe that's a

23:10 time to chat about EUV and all these different acronyms that people throw around and what that means. I think it ties into the yield question and why you only get a seventy percent yield or eighty percent yield, or maybe even lower than that in some cases. But we were talking about at lunch how incredibly complex the EUV process is and what exactly you're doing. And we're gonna use this laser to heat this tin to a hundred times the temperature of the sun, and then we're gonna bounce it off these mirrors that are the most technologically advanced and smooth mirrors in the entire world and then we're gonna essentially the layer of complexity, I think the analogy I'm gonna

23:46 hit a golf ball from Earth and get a hole in one on the moon. That's what you're doing. And so when you're dealing with that scope, I think that Kinda gives you a sense for why. Sometimes these chips don't come off the line and

23:56 Perfect. Uh What do you are the top concerns of the US government? We can talk about Chip, we can talk about where we will accept external capital to finance companies or projects just by revealed preference. What does the US government care the most about? And In your interactions, how on the ball are they?

24:14 Like we're talking the day after Gavin Newsome vetoed this bill in California, which I think everyone in technology is very happy about. How well calibrated are our politicians around this stuff? Do you think they've been effective? What are they doing? I think there are three broad goals Gloman has. One is to mitigate Chipmaking concentration around Taiwan.

24:34 Number two is to keep US firms out in front technologically in every segment that's possible. And then three is to prevent adversaries China above all from accessing cutting edge AI chips. And I think we've seen a lot of moves the last couple of years on each of these fronts. The CHIPS Act, which subsidizes domestic manufacturing controls on technology transfer to China. Government is relatively clued in I think the people who are working these issues understand them well.

24:58 But I think government works at government speed. The chip industry works at Moore's Law speed. And so what really matters in the long run. is actually that Silicon Valley, the US ship industry can keep that exponential growth going. If it does, then the political issues will sort themselves out. And if it doesn't, we have more problems than just political issues because our entire economy is levered to Moore's Law.

25:20 Why can't China catch up? If so much money, so many incredibly smart People, so much talent there. Obviously the will. Seems like more In more of a centrally planned, more centrally focused effort. They can just say we're gonna devote

25:34 X amount of effort. We don't direct the private sector the way they might. Help me understand why they can't get to parity and Earlier you said they might be on the downgrade list. No one can do it on their own right now. The US can't produce cutting ships on its own, Japan can't, Taiwan certainly can't. They rely on imports of chemicals and software and technology. And so the fact that China's struggling to do it on its own is not a surprise. They're normal.

25:55 for struggling to do it on their own. In fact, they're trying something that's harder than anyone else has already tried. which is to actually be self-sufficient. The trend over the last thirty years has been to become less self-sufficient. We've collectively decided that we'd rather have specialization. So Taiwan specializes in manufacturing, Japan specializes in the chemicals, US specializes in design because there's huge efficiencies. that come from that. And did we accept a bit too much specialisation in the case of Taiwan? But I think China's effort to do it all. by themselves is much, much harder because they don't get the benefit of access to world markets and access to the whole world's expertise.

26:33 Because this is such a burgeoning space, I'm curious, Andrew, where If I got a room full of The semi investors that you respect. Whoever those people are. Where you think your personal views

26:45 would most diverge from that group. Couplefold. I think there's been a view, you know, you maybe take the biggest of them all, right? NVIDIA and what is the view on that company and how has that view changed? in the broader public markets over time.'Cause it's interesting. A lot of active managers that I've spoken with actually NVIDIA has not been a huge position for them, which is interesting because I think people always felt this is too expensive.

27:08 Or This story's already been discovered. And so that's probably been One of the most I don't know, puzzling or just interesting observations that I've had over time is surprisingly like few people actually own NVIDIA despite it feeling like it's a

27:20 Very well known. Story. Right. And I think people talk about it all the time, right? It's in the newspaper all the time, et cetera. But I think people have this fear that oh my gosh, the stock is too expensive or it's run, it's already up so much. But if you actually look at what's happened there, at least on a fundamental basis over the last two years since ChatGPT really was released, the fundamentals have tracked dead in line with the stock price, right? And so if anything, I think the burning's multiple is probably flat to slightly down versus where it was.

27:47 when ChatGPT was released. And so they've actually delivered on a fundamental basis, like over that time period. I think many smart some investors pr probably do own NVIDIA, so I don't want to say that's necessarily the case, but I think broader in terms of the broader market view. Of that. Company is always struck me as a little bit. Interesting, given that dynamic. Can you talk about the changing cost structures and

28:09 Business models. of the digital slash software world in this new era where It used to be Very people engineering intensive. Now it's becoming much more compute intensive. I know we talked about we want that cost to come down, but just the game on the field right now, the difference in the business models that you're seeing.

28:26 This like struck me probably like early twenty three, I was tag along a meeting with the Maverick Ventures team, meeting with one of the Frontier model players. And this was early in their life cycle and we were getting their financial model for what the business could look like over the next three years, five years, what the cost structure look like.

28:44 And This was one of these eye opening moments where You looked at the cost base and literally ninety percent of it was compute, right? So these are dollars that we're spending with Azure or AWS or G C P or Oracle Cloud.

28:58 And literally just for training these models. And that was Another one of these aha moments. People don't appreciate the magnitude of spend that's gonna be happening here. over the next decade the stats that people throw around sh train a competitive model And Twenty twenty two was ten million dollars in twenty twenty three.

29:17 It's a hundred million dollars. In twenty twenty four it's a billion. In twenty twenty five it's ten million and then I think the latest the Larry Allison was highlighting was hundred billion dollars in twenty twenty six, just like staggering amounts. What types of companies can actually keep up With that level of spend. to be able to be on that frontier.

29:34 Model. Edge than lead. That's one area just you're seeing just the cost of doing business and software in particular just drastically change where you had these old models that were very people intensive and you had all kinds of salespeople running around selling the product, very R and D heavy and programmers, et cetera. And is that now changing where just the cost of business is being much more compute and ship driven versus people driven? I think you're seeing that shift in the hyperscaler CapEx numbers, et cetera.

30:01 Can you outline what you view as the key sectors within Semis whether that's Design whether that's Manufacturing, whether that's

30:10 Whatever. What do you think are the key three, four, five Areas that matter most. So The design piece is critical, right? And that's Where the companies like NVIDIA dominate, right? They're designing the best chips and then

30:24 T SMC is making those chips. And then I think what is interesting Is that you're seeing as Moore's Law slows, and this is something that people are well aware of, there's been press talking about this for now for probably the last 10 years, where are the opportunities to create value as it gets harder and harder to get those Moore's Law benefits on the front end. So the initial manufacturing of that chip itself. And that's why you're seeing so much interesting innovation. on the back end side, so the advanced packaging side of the equation, for example. So all right, we've made this chip now, TSM C, it's on three nanometer How can we package that chip with other chips?

30:58 And memory, for example, and get that packaging to be as efficient as possible because historically people just hadn't cared about it because you were getting these massive cost downs with Moore's Law and every two years doubling the number of transistors in that chip itself. And so you're finding these areas that historically have been ignored that are now being in some cases bottlenecks. Like co-s is probably a term that many people have heard thrown around that was actually limiting the number of NVIDIA GPUs are able to get manufactured. And so finding those areas Some of the most interesting.

31:30 If you think about the emerging players, everyone's heard some of these big names mentioned a lot, ASML, NVIDIA, Cadence, and so on. What are the most interesting emerging players in your mind that will matter a lot more. Like my rankings question. Names that we'll hear a lot more in five years than we're hearing about today. I think the first thing you'd have to say is that You'd be surprised by how short the list is.

31:53 relative to any other sector in technology. Look at AI. and list the five most important companies in AI, half of them. were found in the last decade. Do the same as semiconductors and it's a very different ratio.

32:05 I think that speaks to the fact that there are some big incumbents who play a very big role and it's led to less company formation than I think you might expect, given that the history of the industry is actually in Startups merging in Silicon Valley. Andrew do you think about Returns. I know when you and I have talked about this in the past, it's really just this is like a massive

32:24 Cycle that could be bigger than the ones that have come before it and obviously technology in investing is about riding those waves and identifying them. How do you think about what that means for prospective returns relative to everyone's opportunity costs? The S P five hundred or something like that. Either player.

32:41 It's interesting when we look at opportunities on the private side this scenario where at least to date you've seen valuations be actually be quite palatable. Much different than what you see in software, particularly over the last few years. And so I think the opportunities for returns are actually gonna be quite attractive. Interesting looking at this performance of a semiconductor index broadly, it's actually done Quite well. I think something like

33:03 Twenty five percent. Kager over the last decade. And so I think when we think about the opportunity set in front of us right now, you look at the private world of semis and then you look at the public world, in theory that private world should outperform

33:17 The public companies given that There should be an illiquidity premium, et cetera. And what we see in the market now is actually a pretty attractive set of companies. They should be either large, independent, players in this space looking out three to five years or will become

33:34 Important parts of Many of the large Existing incumbents, right?'Cause as you see it very challenging for these public companies to merge with each other, they're gonna have the need to do these Tuck and MA. strategies to really beef up their internal flagship efforts.

33:49 Talk about antitrust. This seems like a really important I don't know, feature of the system when it comes to returns if NVIDIA wanted to buy all the companies that you backed. And they're not able to because of antitrust, that can impact your returns, maybe. What's the state of play with antitrust and what's gonna be allowed?

34:05 We've heard a lot about antitrust last week with discussion of a potential Intel Qualcomm deal and that would face antitrust. regulatory issues. Hard for me to know. There's actually two types that I trust that matter in the chip industry. One is real antitrust from Western regulators and two is

34:21 fake antitrust review from Chinese regulators. And a lot of the deals that we've seen Be sunk in recent years were from the Chinese refusing to give approval, not because there was actually real concern, but this was a part of the political game that they're playing with the United States. And so I think for every major transaction, even some minor transactions in the chip industry, the question of Chinese approval hangs over And it's something that companies themselves can't do much about. It depends on the state of US China relations and when's the next presidential summit. That's what matters more than the actual substance. Of the antitrusts. Can you give an example of that? Just a case study of that playing out?

34:56 The most recent, I think, was with Intel's effort to buy Tower Semiconductor. So Tower Semiconductor is a very small foundry. Company producing different types of chips than Intel produced. They're both in the foundry business, but Towers a small company and Western regulators all waved it through and then China refused to review it. And why can't we just say okay

35:17 Why isn't there some way around that? I think this is a really interesting question, and at some point we're gonna see a merger that's approved by everyone but China and the company's just gonna say screw it. Well the problem is China's a pretty big economy. And everyone's got assets in China. And everyone needs the Chinese market. And so unless you're a in a niche where you don't care about Chinese customers and you don't have any assets in China, you don't know exposure to the Chinese market. You've gotta listen when they say no. There was this meme going around earlier this week that

35:42 In the late nineties we really didn't have an internet bubble, we really just had a telecom bubble. Why Might this not be of a similar character. For nature to that. Alright, I've got a lot of thoughts on this one. The CapEx debate rages on and I think that Gavin Baker did a nice job setting the state of play on this about a month ago. So let's go back to that bubble, right? We spent a lot of time thinking about it. I started at Maverick in two thousand four, right, is it was

36:07 in the aftermath, right, of that explosion and We pulled the data and looked at the biggest offenders and biggest spenders of CapEx during that time period. So the Global Crossings, the level threes, the quests, et cetera. And you look at the amount of capital that they were spending was just totally unsustainable versus where we are today and I'll get into that. But the

36:26 CapEx. So the dollars that they're spending, whether that be on fiber or Cisco routers. Or other kind of optical gear. Wait. on average about two hundred percent of their operating cash flow. So for every dollar that the business generated on an operating basis, two dollars was spent on CapEx. So they weren't self funding, they had to borrow In the high yield markets or issue equity.

36:46 Or what have you, but it was not a self funding sustainable situation. In some years we pulled the data and it was even more hot popping. It would be five hundred percent of operating cash flow in a given year. And then you look at What was the utilization rate? Of that.

37:00 fiber in the ground. And I think as of even of like as late as nineteen ninety nine and early two thousand, only like two percent of it was actually lit up and being used. So it was all on this build it enable come mentality because the internet was in the early days back then. And so the thought was This is all gonna get consumed in very short order. And it just turned out that it wasn't. And I think it's very different. From today where you look at the spending that is primarily done by the hyperscalers, so

37:26 Meta Alphabet. Amazon and Microsoft. What are they spending relative to history on CapEx? And if you look at their operating cash flow in this case, so the cash the business is generating.

37:37 Right now their capex is only about fifty percent of that, which is dead in line with where it's been over the last five years. So we are very much in a sustainable level where You could. double catbacks and these businesses would still be self-funding. And you wouldn't have to have run into an issue where they're having to borrow or issue equity or something like that to fund these bills.

37:58 Furthermore when you look at the utilization rates of these GPUs In their clouds. It is nearly a hundred percent. There's nothing sitting around idle Underutilized.

38:10 Very different looking at that telecom. period of time where you had this massive amount of infrastructure that was built. It wasn't really built in a sustainable manner from like a financing perspective. It wasn't even being utilized. Whereas today we can debate What ultimately will get generated by these large language models and the value that will get created. But I can tell you the GPUs are definitely being highly utilized right now, so it feels like a very different period of time.

38:34 So if I were to take your analysis and apply it to today, the place that looks like telco is the foundation model companies. They are spending Five X there or or way more. They're operating cash flows. They've negative operating cash flows. That does exist. So Does that mean they're all toast? If we get to a hundred billion dollar

38:53 Model There's just not many places in the world you can get a hundred billion dollars. And the very few places you can are the companies that you've mentioned that have these amazing operating cash flows. So does that just mean that This technology is like an incumbent dream. And that these upstar, even the opening eyes of the world are in trouble.

39:11 That's an interesting way of cutting it. I think you look at the types of companies that could sustain that level of spend, that list is pretty short. And we talked about them just a few minutes ago. So that's a very interesting way of framing it and I guess the implications In terms of who ultimately can afford these. The list is not very long. Well it ties back into the Middle East, which we discussed before, because if you're looking for very large pools of capital, that's one of them. Will that be feasible? Will the US

39:34 We have in various cases pretty close ties to the countries with the most money. Talk about the range of feasibility. I think the US is pretty comfortable with Middle Eastern governments investing in US based companies to build US based data centers. And we've seen, for example, Microsoft Mabala. announce a joint investment vehicle. I think the US is a lot more worried about investment In the Middle East. Because then

39:59 The US has less leverage Middle East. Certainly there will be data centers built. all over the world, including the Middle East, but the US government really doesn't want the most advanced frontier data centers being built in the Gulf rather than in the US. And so there's a lot of regulation coming down the pipe right now. About that particular issue.

40:16 What are you most worried about? I worry about the time horizon on which we see the return that Andrew's talked about. I think. Making sure that lines up. is gonna be critical to sustaining if the scaling laws are true.

40:26 the CapEx scales up alongside the size and we need real returns right away to fund that CapEx. I think the next couple of years we have a runway in which the hyperscalers will spend regardless of return. And then at some point we're gonna need to see real business models emerging and so the time horizon for that. I think really matters. I think the other thing going back to the cost to compute is The more we can do to find ways to economize and compute, the more sustainable all this becomes. There's a huge incentive right now to economize on compute because NVIDIA's showing how large the market is to take a bite out of. And the best way to take a bite out of it is to produce something that is just as performant or even more performant, but at lower price.

41:04 Andrew, how about from an investing perspective? What worries you the most? You're obviously gonna Lay out a lot of capital to support companies trying to solve all these problems. What keeps you up? It goes back to some of the points that Chris just made around What is the

41:18 sustainability of all this and is this a situation where is there a digestion period? I've been doing semis for long enough to know that this is still a cyclical business, despite what people will say. I think it's a absolutely a growth cyclical business. And so I actually probably welcome Any sort of down cycle'cause it's a great Time to put capital to work. But I think just making sure that

41:38 there will be a tier of new companies that are formed that are able to Capture this broader paradigm shift they were going through and The challenge that the world I think faces today somewhat is you have one company, an incredible company that's captured the line share of the economics to date.

41:56 And When I think about NVIDIA and what they've done. this was company that was initially a chip company, right, that then became chip and memory company that then expanded onto networking and now they're selling this full system. And what happens if there's one company that's just capturing all the economic value?

42:14 of a transition like this, and you're gonna want to have I think a very vibrant startup community that's helping solve some of those problems, particularly like in and around the GPU. I think going directly after the GPU itself. We can talk about that more too. I think is it challenging? task, but there's no shortage of need for innovation, everything Around.

42:35 That GPU and so Are we gonna be able to create an ecosystem here? Opportunity that exists. We mentioned that our breaking earlier around power, around interconnect, around memory. Et cetera. And I think when you look at the big customers that we just talked about, they are highly motivated to have a diverse and healthy silicon supplier base. And

42:55 We were chatting about let's look at the last big paradigm shift and what happened. With mobile. And You had a situation where When Apple announced the iPhone, ATT is the launch partner.

43:07 AT had a market cap. of two hundred and fifty billion dollars when the iPhone came out, Apple had a seventy billion dollar market cap. You fast forward fourteen years. And Apple's market cap now is Three and a half trillion.

43:20 And A T and T is a hundred fifty billion. Apple just extracted all the economics out of that. transition capturing obviously the hardware with the iPhone, but then also the software with the app store. And I think when you think about the biggest buyers, Of a lot of this infrastructure right now. They want to make sure that they don't become whatever you want to describe a dumb pipe, right? And they wanna be able to be capture

43:42 Value from this AI. transition that we're going through right now. And not that that all accrued to one of their suppliers. I think it creates a tension in the ecosystem when you have a supplier that has a bigger market cap than you, that has more power than you, et cetera. I think there's a real incentive to have a

43:56 Healthy. EK Assistant. Generally speaking, what are the corporate strategy options available to those players that want to accomplish that? Just playing forward the Apple A T and T thing. How do you avoid that happening to you this time? Well, they're trying to develop their own chips and so that custom Acer work is probably very high on the list of what they're trying to do.

44:15 And I think they're very forward leaning. We were having lunch with one of the hyperscalers last week where they have a team they're whole role of this team is just to be talking to startups. in Silicon Valley, understanding what technologies are coming down the pike so that they can be put into their data center. And to see if they can help solve some of those pain points that I illustrated. So I think they're trying to have a very

44:36 wide aperture in terms of what they're looking at in terms of what other opportunities. Are out there, even for the incumbents, right? There's I think a strong desire to see AMD succeed in this market as well. I think we will see more and more success. come from them as they catch up And we're releasing new chips that are higher performant. They figure out some of the software challenges that they've had historically. So I think that's another piece of the equation. So it's gonna be a combination of

44:59 custom ships that these hyperscalers are developing themselves. It'll be looking at other incumbents like the established semi companies, and I think the third bucket's gonna be the startup ecosystem, providing value to help them diversify. I know you said Maybe less interesting to go directly after the GPU and better to attack the area surrounding. But let's say the GPU is the death star at NVIDIA and there's that one little arrow for Luke Skywalker to shoot the laser beam through to destroy it. What's your best guess as to where that vulnerability might be for NVIDIA?

45:27 I think the challenge Nvidia's chip is very flexible and nimble and that's why people like it. If the models change the GPU can still solve the models of tomorrow. If there is a scenario this transformer technology, for example, ends up being the structure that everyone is gonna be building models on.

45:44 Going forward. that will potentially be that opportunity for companies for startups that are able to just isolate that one very specific use case and do it much more cheaper. that will be an area I think that could end up proving to be a challenge for them. Like how does NVIDIA respond to that? They can obviously make custom chips that kinda go after these very specific markets too. To date they've chosen not to do so. Okay, I wanna talk about

46:06 the ways to stay informed in this very fast changing world. And I'd love for each of you to call to mine. three specific people if you only got to ever talk. To three people. With the goal of always knowing what's going on.

46:20 Just imagine you could access Whatever you want. What three people Where'd you pick? think it would definitely be someone at building out the infrastructure at one of the large hyperscalers.

46:31 They're on the leading edge of all this, or you could say it even one of the frontier model folks. Think that's definitely at the top of the list. I think it's gonna be someone at T S M C that's on the leading edge of the manufacturing front. I mean it's definitely someone in NVIDIA. That's on the front I mean, be someone NVIDIA, right? You can say who that would be.

46:48 Those are obviously three good answers. I think the other thing. aspect of this that I look at it from is understanding the business model shifts. And that to me is something that you understand not by talking at someone who's building the data center the hyperscaler, but actually who's running one of the hyperscalers because they're looking at this saying NVIDIA is now bigger than we are. What's our approach?

47:05 To the fact that our suppliers now beating us in scale and w what are the shifts we need to take to deal with that. I think that's one. I think the second Yeah. the capital allocation side of it. Where is money flowing from Into And I think you learn a lot talking to some of the smart investors.

47:21 In this space. And I think the third would be Actually politics matters more here than it has in Half a century. And so you really can't understand what's coming in terms of who has which access to which market, in terms of which technology can you sell abroad without

47:34 Considering the political side as well. What questions would you ask? Like let's say we had those six people in this room with us. What would be the couple most pressing questions that you don't already know the answers to? That you wish you Could have an answer too.

47:48 I think On hyperscaler build out. what are the key problems that they want to be solving next year, the year after, that will provide you a roadmap into A, what are they gonna be buying, but also B, how are they positioning themselves in the industry? And to me that's a hugely important question. That's where all the money is allocated in each hyperscaler's decisions. That's half the market right there when you're talking about AI. I think the government piece that Chris brought up's really important here too, right? And just understanding What is the focus particularly of the US government and where they are gonna be putting potentially restrictions in place or probably more importantly, like what are they trying to enable, right? Whether that's domestic manufacturing or getting the startup ecosystem in the US.

48:25 operated at a much more higher level I think that's another key question and topic to explore. What about beyond the hyperscalers? Half the market. What about the other half? What's going on there?

48:35 I think the other market that hasn't really taken off yet, but will is on the edge of networks. We're gonna see a whole lot of innovation. coming as we in order to deploy some models in the data centers, some on the edge, and we're not sure what ratio yet. I think we're in the very, very early innings of innovation in semis on the edge to be super power efficient, to provide just the right amount of processing power. for edge devices. That'll be different for cars, I think, than it will be for phones or for wearables or others. Unlike data center, where we're inning three or four right now for data center, we're still in inning one on the edge. What does that mean? So Does that just mean

49:10 Tesla and Apple are gonna develop better chips, does it mean The And user devices. are pretty concentrated and it's the biggest companies in the world. They already designed chips. Because it's just them doing a better job.

49:22 of what they're already doing, or is it some new paradigm shift there too? So in certain cases like Apple, obviously they will most likely be making their own chips for their iPhones. I think when you look out Little bit more broadly, there's definitely gonna be the opportunity for new companies to take advantage of this edge AI theme, I guess is how I describe it. The analogy and this is actually interesting because it ties into NVIDIA as well. Where NVIDIA has clearly shown that it dominates the data center. I think hands down. I think most people would not debate that. Whereas I think on the edge,

49:52 The story is very much unwritten at this point, and I'm not sure if the game has really even begun. And if I look at NVIDIA and Where their focus is I think Jensen is pulling all or the vast majority of the resources to focus on data center piece of the business because that's where all the value is getting created right now. Whereas I feel like they perhaps even decreased focus on some of their edge products. And you can see this even in their financial statements where

50:16 their data set business has grown at a rate that I don't think I've ever seen any company grow at that scale. over the last eight quarters, whereas on their auto business, for example That business has largely been stable over the past eight quarters. Whereas you've seen this explosion in the data center. Part of that is just it's not in their DNA as much. to be offering low power chips, which is a really critical for the edge when many of these things are gonna be battery operated.

50:41 And performance for wise the key metric. The joke in the industry is that Videa makes a killer gaming laptop. But if I'm getting on my flight from J F K back to San Francisco and I plug that laptop in, the plane, the whole plane drops two hundred feet'cause the power usage is so high. Is there an opportunity on the edge to create I think some transformative companies that can take advantage of I think what will be intelligence just

51:05 moving from that data center into your phone, into your car, into your PC. into the broader industrial footprint. That largely hasn't seen a lot of innovation in probably four decades. And so that's I think an area that's really exciting and worth exploring. I think the other point just in terms of who's buying these things, this whole rise of the start up GPU cloud, right? Companies like Corewe, for example.

51:30 that came really out of nowhere. just dominant franchises right now. There's one in the AMD ecosystem called TensorA that's trying to do a similar strategy and I think there's a real need for these startup clouds because they're just they can move more quickly. And it's interesting that we view Amazon and Microsoft these incredible businesses, which they are But they're also at a scale that they just don't move that quickly. So there is

51:54 The benefit of partnering. with a core even NVIDIA did like an ingenious job really bringing them up to scale. Where they can get chips out six months before. I think if you look at Corweed, they had their H one hundreds out in March of twenty twenty three. And

52:10 You couldn't spin up an instance of H one hundred in Azure or AWS until probably almost like six months later. And that's a lifetime, right? Given how quickly things are moving right now in the broader AI ecosystem. And so if you're able to be a nimble start up that can move quickly and align with A chip company in this case NVIDIA or Tensor Wave's case. A

52:32 Like there's an opportunity there to create a lot of value. Both for the chip company and for that startup cloud itself,'cause you just get this product out into the market. And these GPU clusters very finicky, right? And so if you just specialize in making those work, there's a big market. On the edge, one more point there that I think Since we're in the early stages

52:51 We underestimate the amount of change in products that will emerge. It's easy to assume it's gonna be AirPods and iPhones that are defining the edge in ten years, but if you Looked in. two thousand, what will be the primary use mobuting, you might not have guessed the smartphone. And so I think we should be pretty open minded about

53:07 Where will glasses be or whatever else? It's interesting to watch. Facebook. For two years ago the metaverse seemed like a idea that wasn't gonna materialize very soon. And now we won't call the metaverse, but actually there's some interesting glasses applications coming out on the consumer side. And I think the other thing is the industrial side, which is less exciting because it's not something we're gonna buy for our own use, but

53:27 As Andrew said, there's been so much less innovation on the industrial side and there's so many potential use cases for AI on the industrial edge. We were talking over lunch about tractors having GPUs in them. I think that's probably just the first of many use cases for industry, which Could be a very large market. Meaning a John Deere that has a couple of NVIDIA GPUs on it. Anything that could benefit from the deployment of a cutting edge AI model will actually have these cutting edge chips, not cheap chips like are in most things. On devices.

53:56 And any sort of robotics in the future is gonna need a whole lot of computing power. And maybe some of that will be calling back to the cloud. If latency matters, like in a self-driving car, but also in a drone, you probably don't want to call back to the cloud to ask it a question on a regular basis. You want enough compute on the device. What about power? Any commentary on power? There's all these Crazy headlines about building more nukes and All the things that we're gonna do to power the data centers and any layer deeper

54:21 Insights on what's going on in the world of power. So maybe this would be a little bit of a controversial view. My thought on the so power's critical and there's probably not enough of it broadly, but I think there is a dynamic here where capitalism usually finds a way to work and I remember If you go back to solar, for example, in the two thousand six, two thousand seven time frame, there was a fear that we were gonna run out of poly silicon to make The solar wafers and sure enough.

54:43 You know, capitalism finds a way capacity comes online and then there was actually oversupply in that case. And so with power in particular, when I talk to folks that I would view to be experts in this space, I wouldn't say I'm an expert in power by any means, but I think there is a dynamic when you see These forecasts for data centers that are getting built. Is there a dynamic? Where a data center says that it's gonna need pick a number.

55:04 Hundred megawatts, fifty megawatts. And will the real usage actually be A less than what they're saying kinda nameplate capacity will be just because there's almost like a double ordering dynamic where they want when they're telling the planner in that city or that state how much power they want, they're gonna give the absolute kind of highest number. that they can so that in the case that they might need that much, they can have access to that much.

55:26 Electricity, I think The other dynamic, and this is an area where I'm talking about intelligence at the edge, the broader energy infrastructure in the US, I think people don't actually have like a crystal clear sense of how much capacity actually is out there and what the utilization rate actually is. And there's this concept of you're building to handle that peak load. But are there creative ways Where In Atlanta, for example, in the heat of summer when everyone has their AC on in August, can you somehow incentivise folks to

55:56 turn their air conditioning to a warmer temperature and bring that peak level down and then all of a sudden maybe there is actually more excess capacity. Then people's a little bit of a provocative statement, because I think the mindset, probably, in the investment community is that power, and I think power is very critical, but I think there is A dynamic where maybe some of these Kind of shortfalls find.

56:16 Solutions. The other day we said In data center chips power has not really been a key focus the past decade. It's mattered, but it hasn't been the case that the primary driver has been bringing down power consumption.

56:28 And as a result, we haven't brought time power consumption. Whereas in mobile most people m twenty years ago would be shocked by the compute per watt that is in your iPhone because Apple and others have fixated on bringing that down, uh bringing rather compute per watt up because battery life is limited. And I think now NVIDIA's getting a lot of calls from his customers saying, Hey We'd like more performance, but also you gotta take power really seriously. I'm curious how things get done in this world. This is like a side door into

56:56 asking about the way that you're structuring Maverick silicon specifically. the players you have around the table. the investments of your time and energy that you're making that will ultimately be called back to benefit the companies that you invest in. It strikes me as a world that you could count on two hands and pretty quickly get to the players that really have the power. And whose influence and support you might need to be successful as a breakout company. Talk through that dynamic of how things are getting done, who matters, how you're cultivating relationships with them. Seems like a much more hands on

57:29 style of investing than maybe even Maverick has done in the past, where it's like mostly a public markets investment firm. You buy some NVIDIA, you don't have to do anything, you can benefit from it going up. Seems like you're going to be able to do it. Way the other direction with a really hands on approach. So How and why are you doing it that way? Yeah.

57:45 It's funny, as we spent more time in this and pulled on this thread, I think we've gotten more and more excited about the opportunity here and When you talk to Many of the founders that we've worked with There's three big pain points for a semiconductor startup. Pain point one is getting

58:02 the design tools. So that would be from a synopsis or a cadence. Pain point two is getting the right IP building blocks, right? Cause a lot of these chips, you don't need to reinvent the wheel on everything. There's off the shelf IP that you can buy for various of that chip. And then the third challenging bucket is obviously the foundry piece and that initial tape out and then broader manufacturing. Is there a way to work with the key players in each of those buckets. So EDA, IP, and Foundry. To

58:27 Just ease the burden for these startups. And the concept that we use is that many of these startups are on the airplane, they're in the middle seat. They're in the last row of the plane. How can we get them upgraded to first class? All the large semi players benefit when they're interacting with those foundry players, the IP players, the EDA players, et cetera. And so it's been exciting to have those conversations. Because the many of those incumbents on those three buckets will benefit from having these startups in their ecosystem rather than one of their competitors' ecosystems.

58:58 Not all these companies will be successful, but some of them will become big customers and which company will be the next and a partner or which company will be the next to Star Labs. There will be others like that in the future. And I think those different incomments are very excited about. working closely with that, but it's been fun. Maybe Jim Keller is a Well accepted genius of the field.

59:17 Who's also entrepreneurial. Is that generally the description of the founders in this space versus the classic Silicon Valley disruptor. Talk us through like the nature of the people building these things.

59:33 I would say the the vast majority tend to be much more experienced. So these are individuals that have run divisions at the large public semi companies, right, that are that have had the experience of building out chips at scale. Which is I think diff much different than the software where you have a much, I think, younger average age of your startup. founder. It may be even twenty year difference or something along those lines. There's some exceptions

59:56 to that I know with etched with Gavin does uh fascinating work there and I remember laughing with him the first time I met him. I was like, Did they teach you any of this at Harvard? And he's Nah, not really. But it is, I think for the most part I think that's probably more the exception. than the rule, this is gonna be a much more experience based management team that you're gonna be investing behind. Who do you compete with for deals? One of the things I've learned talking to you is the interesting distribution of where capital is available.

1:00:21 for investing in especially in private companies. And obviously this can change really quickly and supply rises to meet demand and so on. But it does strike me that there aren't a lot of people doing in a dedicated way what you're trying to do in private markets for the semi's ecosystem right now. So who in a given deal that you're trying to invest in that you you think is the best deal

1:00:41 of the year. Who are you up against? Most often. When I think about the lay of the land of the semiconductor, private semiconductor and vasting landscape, you have some very good early stage folks like Lipu at Walden, for example, Stefan at Sutter Hill. These

1:00:56 are just experts in their craft, right? At that early stage company formation period. And then I think when you look at the very latest stages and even the pre IPO rounds, you have some what I would deem to be Traditional public market players. Fidelity has got A very strong team. Adam Benjamin leading that. That will come into those pre IPO rounds, but in that middle section.

1:01:16 Let's call it series B and beyond where the capital needs to really start to pick up. I think there's a real dearth and shortage of capital in that market. And I think that's the opportunity that we're so excited about because as these capital needs pick up for these companies, and that's right, is that that tape out's happening and the IP costs are starting to ramp because you're actually starting to manufacture chips. the EDA and emulation costs start to pick up because you're testing that tip to make sure that it's working. And that can total Fifty million dollars. And there's just not that many players that are willing to commit that level of capital to these companies. I think once you're able to satisfy that need.

1:01:54 the reward can be enormous. We've seen that with some of these recent semi IPOs. I think just comparing it to software, for example, where I think there's a very long list of capital providers that are going after that market. I think it's just frankly just much shorter. When it comes to semiconductors. If you could

1:02:09 Fly anywhere tomorrow. And have a meeting of your choice. Based on what's going on right now, where would you go? is harder to understand right now is in China, actually. Just went back earlier this year, first time since Covid, but because of the politics, because the regulation

1:02:27 Industries that used to be very closely knit are being split apart. And so I think understanding, for example, Huawei, which is the AI leader in China, producing China's leading GPUs at a moderate scale. That is gonna be the company that defines where China is on the What's the scale comparison to like N Dia or something? It's small right now, relative to NVIDIA. And they're trying to ramp up and the US government's trying to slow their ramp. And one of the key questions I think over the next couple of years is will they succeed or will they face real supply constraints for the next couple of years? Can you tell the Huawei story, how it got to here and what's happened?

1:03:00 It really is a fascinating company and I think had it not been for the politics, we probably would see it like a Sony or a Samsung, a real electronics powerhouse. And in some ways it it still is it started out in telecoms and networking equipment. founded in Shenzhen, right across the border from Hong Kong and grew by selling first to the Chinese market, but very early on pivoting to international sales. And so they were to learn from their customers abroad, improve their technology, some IP theft along the way, but was it more or less than the average? Hard to say. And it was only really in the last

1:03:31 ten or fifteen years that the relationship between the Huawei and the Chinese state got closer. And now given the politics, Huawei's business model is to sell as a almost monopolistic producer into a Chinese market that is constrained both by the US and the Chinese governments. And so the company itself has had to shift a lot. And now it's a Chinese AI monopoly player. And maybe describe what the US government did vis vis vis Well wait just to complete the story. So the US government doesn't want China to produce cutting edge ships and so Huawei works with SMIC, the leading Chinese chipmaker, to actually manufacture its ships. Smick is like the T S M C. Of China.

1:04:06 But SMIC isn't allowed to buy the most advanced tools including the newer lithography systems from ASML. And so right now Smic is trying to produce pretty close to cutting edge GPUs using second tier machinery and it's learning Just how hard it is to

1:04:24 produce nanometer sized. Transistor is using tools that weren't really designed for that purpose. And so I think they're a scrappy company. They are innovating on the fly, but they're also hitting real scale limitations, at least for now. When you think about the components of returns, you mentioned the entry prices can be reasonable.

1:04:42 Sometimes in this space relative to some other stuff you've seen. What about just like the size they can get to? the capital intensity along the way that will dilute You as an investor, or if you're an early investor. And the paths to exit. Those seem like the remaining three components. I'd love you to just like pine on each of those three.

1:04:59 I think maybe we'll start with the last one in terms of the path to exit. If there's ten companies, my guess is one or two will end up going public, the the traditional kind of IPO route, I think. The majority will end up getting acquired by the large established semi companies that will Be using these startups to help.

1:05:17 Basically bolt onto their flagship. efforts where they can buy a engineering team or they can buy a product that can really solve a product gap that they have. And these are companies that are generating collectively something like two hundred billion dollars of free cash flow a year. So there's ample dry powder to pay for assets like this, even to the tune. One to two to three billion dollars and we've seen.

1:05:36 Several examples of that happened. Won't work. In terms of the capital needs and the dilution that's required, I think it's interesting because that there's this mindset with semi is that they are very capital intensive, which I think is broadly true. I mean

1:05:53 each company's not making its own fab, right? They're making their chip at T S M C but there are capital needs in terms of Some of those startup costs that we talked about, having a deep engineering team, et cetera. that are not inexpensive. But I think comparing to software Yeah, software's not very capital intensive when you start. You can only have three or four people and you can do it all in the cloud and it doesn't cost that much to spin up the first product. But I think to scale Software's actually pretty darn capital intensive. It's just if that capital intensity hits you later. In semis, it hits you early. But then I think actually the margin profile of a young semi company can be actually be far more attractive than

1:06:27 software. And so you can look at a semi company that is doing a couple hundred million dollars of sales and is operating at thirty five or forty percent operating margins relative to A software company that could be still burning significant cash. Which may be the right decision for that software company if the L T V of these customers that they're winning will s justify that level of spend. But I don't think that you should

1:06:49 Kid yourself that Software's not capital intensive. I think it's just when in that life cycle of the company. That it hits you. So that's how I'd frame that. What about the word Bubble. When does the word bubble enter your minds?

1:07:00 If at all. I think when people talk about bubble, it means you probably aren't in a bubble. One rule of thumb. It's usually only like afterwards. Yeah. I think that's probably one just broad observation whenever people talk about a bubble I think people were talking about this being a bubble like twelve months ago, right? And I think it's proven not be the case to date. And so I think that's just one overarching view.

1:07:23 As I mentioned earlier, I've been investing in semiconductor for long enough to know that This is a sickle clay industry and at some point there will be a digestion period, but I will say that this is a growth cyclical. And this is actually a very attractive end market to be putting dollars into. And I think if you look at where the industry structure is today and where the operating margins are relative to some of these other Sectors in tech.

1:07:47 It's Very attractive. And so if you have the stomach to deal with if there is a bubble or there's a digestion period or what have you, I think there's actually can create opportunity because I think we know the end point is gonna be up and to the right. Is that gonna be a straight line up there? Probably not.

1:08:01 But I think taking advantage of any down cycle, hopefully you can do that from a position of strength. Chris, if you had to study or encourage people to study any of the historical episodes in semis. to better understand the future. What would you have them study? The P C revolution.

1:08:18 And Intel's role in it gives you an example of what it's like to be early in the stage of a growth cycle. The chip industry was always cyclical around D RAM, the memory chips that were in all sorts of products, computers, consumer devices. But Intel was early to the PC revolution and was able to ride that wave. Two and a half decades, three decades, of which there were cycles in the midst of it, but because more people were buying PCs, you didn't really notice those cycles anywhere near the way you notice the D RAM cycle because they were just less protected. And so I think that's the optimistic view of where we are right now is that it's a cycle

1:08:50 as Andrew said, heading up to the right, you can suffer some ups and downs. And I think that's probably the right analogy because we're seeing an entirely new type of or new category of chips emerge to prominence that we hadn't used before. And that's what happened in the eighties with microprocessors that were used for the first PCs. A really a niche market before then, and PCs made them a mass market. How do we get to this state of the world where so much of the manufacturing is done? Overseas. I think there were two drivers of the shift overseas. I think one was that US companies naturally in some cases rightly focused on what they were good at, which was design.

1:09:26 And two is that Manufacturing was cheaper overseas. Today it's cheaper in Taiwan, not primarily because of labor costs. There's actually very few people that work in a fab, highly automated. It's cheaper because of the ecosystem dynamics and those are very hard to reverse. And I think for that reason is is also why companies are

1:09:44 less excited about investing a dollar in manufacturing versus design because if you're gonna invest a dollar in manufacturing and you've got higher costs and less developed ecosystem, well, you'd probably rather develop it. put it in design. Government's trying to change that with the CIPS Act, putting forty billion dollars at work in the US. And that's a lot of money. I think it's also important to keep that amount of money. In contact, T SMC spent almost that much in CapEx last year. And so even really substantial government money is enough to move the needle, but it moves the needle on the margin and it's gonna take, I think, a long time to really shift where manufacturing happens.

1:10:17 What could most disrupt the trajectory of this whole story. What could be an example, like a new architecture, not a transformer, but something different that re kicks off a different kind of rate of improvement or You already talked about maybe the scaling laws level out.

1:10:32 That could change things. What other things could happen that would drastically change the trajectory of the semis industry? Memory? One of the key limiting factors in performance improvements is the need to move data back and forth from processor to memory. And so there's

1:10:47 a variety of new memory architectures that are being experimented with. I'd emphasize experimente rather than prototyped, but a lot of researchers refer to this as the memory wall. The key pain point is that your memory is too far from your processor and this slows everything down. And so I'm not gonna bet on it next year or the year after, but down the road we might need new memory architectures. Maybe just explain. in a little bit more detail what is literally going on there. What is happening. And then if it were to get revolutionized, what it would unlock.

1:11:15 You literally have a memory chip and a processor chip. So in the case of an AI accelerator you have your GPU and your amount of memory and you need to move data between them, which means move electrons between them. And the distance coupled with the speed at which electrons move, which is slower than the speed of light, for example, you could move it photonically and increasingly do, creates friction in your training. And so if you were to design ships differently to have processor memory more intertwined or closer together, or if you switch to optical interconnects, those are all ways you can increase the speed at which that happens. And the knock on effect of that would be just a more efficient system.

1:11:50 More efficient system, but also potentially changing. Lower cost of compute. Lower cost of compute, yeah. And potentially changing Who's good at producing them? I think that's fair. You have this dynamic in the industry where the GPU performance has been increasing at a much faster rate than the networking and memory performance. And so you're not able to get as much utilization out of that GPU. as you would like because of some of these other bottlenecks.

1:12:14 what happens if the networking piece and that memory piece can't keep up? Does that end up just slowing down? Just the level of innovation that we're seeing in the market and ability to Train a bigger and bigger model. I think my expectation would be there's a lot of interesting activity happening that networking and memory level to try to solve some of those

1:12:33 catch up with the performance improvement that you're seeing with the GPU, but to me I think that's a key. issue to understand because if we are in a situation where You've got this incredibly powerful processor, but you can't get data in and out of it quickly enough, or you can't get enough data in and out of it. Does that end up being a bottleneck that slows down?

1:12:52 Innovation. Just imagine that your whole portfolio is already built. the general types of companies that you're gonna invest in. On average, where do you think the source of sustainable power and differentiation will come from

1:13:03 business power, not not literal power will come from for those companies. Is it mostly scale? Is it intellectual property? Is it cornering the best researchers as you think about not just who's gonna grow, but who's gonna grow in a defensible way. Where do you suspect most of that defensibility will come from?

1:13:21 I think it's gotta come from the intellectual property piece, right? And the value that these companies are bringing to the table and the problems that they're solving, right? Because the scale piece comes from the foundry players. These startups are designing the chips for the most part, and then they're gonna outsource that piece. So the scale gets Solved by that third party. foundry. Whereas I think the value really is gonna be created from the pain point that company is solving and are they able to do it in a way that no one else can. Is that world like AI model research where frustrating how few people seem to really matter? And it's like dozens of people that seem to have any real impact on frontier models.

1:13:54 I think it's broader than that, to be totally candid. I think you've got In Silicon Valley in particular, right, you've got thousands of engineers, tens of thousands, probably frankly even hundreds of thousands, right, that are very technical. Or are they all gonna be startup founders? Definitely not. I think it is actually probably at a wider town pool than maybe what you're seeing with some of these

1:14:12 AI kind of frontier model places where it seems like there's these hundred X engineers, right? If you're able to have one of them, that solves all your problems. I think there are folks like that in the semi industry for sure. You highlight Jim Keller, his reputation. I mean clearly precedes him. And there's others, but I think in the broadly speaking, I don't think you have quite that same Pain point. What do you two most commonly debate or disagree on? I don't know if we just I mean, we talk a lot about government stuff, right? In terms of when headlines come out and there's a lot of news flow coming out of D C and just trying to understand what it means and what the intentions are of the various restrictions that are coming out or whether it's Chipsack related, what are the end goals that they're trying to achieve with both the Fab side and the R and D side.

1:14:51 Chris has got incredibly tight relationships there. I remember I told you I was in D C last minute and then you had meeting with Biden's Chip liaison. I was like in the West Wing of the White House like four hours later. So you definitely have Those

1:15:04 People really trust and respect what you have to say. But I think it's more of a collaboration, just trying to like what does this really mean? 'Cause I think you are probably more effective than I am at reading the tea leaves a lot of this. Yeah, I think the other facet this is a fair statement. There are more different ecosystems that need to be brought together to understand the full picture. You need to understand the manufacturing IP.

1:15:24 The end users. In the data center. The supply chain is longer than A typical software company, for example, and you've got a understand where all the players are to see where technology's going.

1:15:34 That's a very interesting point, right? Cause I think you look at software that tends to be much more siloed and In the semi connector ecosystems and we touched on these different players. earlier in the conversation where like so many different folks just interlap and it's very interwoven where everything from semicap that's making the tools to the foundry player to the design folks and even with design, there's these different buckets like mobile and compute and there's analog.

1:16:00 And then you have the EDA folks that are selling the tools that are designing the chips. And then you have I P players like ARM, et cetera, but like everyone Needs to work together to push the industry forward and so being able to see that bigger picture and have the connectivity with those different players, I think is a really critical piece of

1:16:19 successfully pursuing this market. And I just think it's different than what you see in in software, for example. I was struck when I first started learning about the smite hunter industry, I figured there'd be someone I could call To get a view of the whole thing.

1:16:32 And I quickly realized that There are brilliant people who know their slice and know a bit about the slice below and above, but there are so many steps in the supply chain that actually it's really hard, almost impossible to have a view of the whole picture. What are the most sensitive parts of the supply chain? The manufacturing step is sensitive, but so to are the tools that make manufacturing possible, the ASML level, if you will. But then you can't do the manufacturing of the chemicals. And the chemicals are made in many cases by a couple of Japanese firms which have the unique knowledge to produce chemical X with ninety nine point nine nine nine nine nine percent precision. And that's only the manufacturing side. Then you look at design where the IP and the E D eight tools are, it's hard to say

1:17:13 One of them is more critical than the other because you need all of them. Starts to feel like a Rackish or something in Dune. It's just like precious. compute resource that just fuels everything else and There's a revelation going on.

1:17:27 in it that kind of we've never seen before. How big do you think this gets? How big is this industry in five, ten years? And how big is it today? Today the semi connector industry is probably seven hundred billion dollars, give or take. Software's probably a little bit higher than that, like eight hundred billion dollars.

1:17:43 When I think about where we go, maybe this is a bold statement, I think the semi intermarket will exceed the software market by the end of the decade. Which just given this flow of Shift into compute from people into computers The primary. resource that's gonna drive AI.

1:17:59 I see the other thing that Makes me optimistic about the future rate of progress is the application of compute to solving chip design and chip manufacturing problems. There's this sort of flywheel of we make better chips, we produce more compute, and we use it first and foremost to make better chips and produce more compute. So whether it's that the chip design where Leading chip design companies have essentially been doing AI for chip design for decades now. You can't lay out a chip with ten billion transistors and a whole lot of computing to do it for you. Or whether it's the lithography process, how do you know what shape to print?

1:18:31 There's now uh extraordinary amounts of computation that go into the actual figuring out how to get the shape you want on your transistor or the shape of the transistors in your piece of silicon. There's already a huge use of computing in the design and production process, and I think there's immense excitement about ways to find even more applications for AI to make the chip design and production process more efficient. Anything we've missed that you think is critical to what's going on in this world that you spend time on that we haven't talked about.

1:19:00 The thing that stands out to me is that one might think the more advanced ships we need, the fewer less advanced ships we need, but it's actually the exact opposite. The more advanced ships we need, the more less advanced ships we need to be in the systems around them. So take a new car, for example. You've got a couple of pretty high end processors for the self driving. But if you need self-driving, you need a ton of sensors around that. You need more chips managing the data transfer between different sensors. And so a a new car will have more lower tech chips than an old car will. In addition to having a couple of higher end ships. I think that's an interesting dynamic for the entire industry because it means Even if you're not NVIDIA.

1:19:36 if you're an analog devices which produces a lot of the sensors, for example, AI is a really interesting transformation for you too because the sensors that you produce are gonna be converting the data that will be necessary for AI training or inference. And you see that in basically every corner of the industry. Is that an investment opportunity? That strikes me as something that is a far less sexy story and therefore perhaps more interesting price.

1:19:58 But nonetheless. Similar long term prospect in terms of market size. Yeah, absolutely. I think that kinda ties into that broader edge AI bucket. And we talk to a lot of these companies, they're more analog based, right? And so not the leading edge digital chips. Where

1:20:12 You try to make it very clear with them. What just happened in the data center, that tsunami is coming for you next. And if you don't embrace change here and widen your aperture to be able to put intelligence into your historically kind of dumb chips, you're gonna be on the wrong side of change and someone's gonna come disrupt you just like you saw NVIDIA.

1:20:31 Disrupt. The incumbent. in the data center market. And so I think anyone who's on the edge should realize that that wave is coming for them next. If you both to leapfrog

1:20:41 one or two degrees away from just core semis focus. What? is going on in the world right now that most interests you. The place I'm spending lot of time is in biotech genetics. Space.

1:20:54 not this decade, but next decade, it'll be a very interesting intersection of improved semi technologies. And BioToc. And I think if you spend time at places like leading universities and ask what are the PhD researchers working on. There's a whole lot of interesting work being done there, which I suspect will lead to company formation and new technology.

1:21:13 Down the road. So I've been living and breathing semis here in particular over the last Nine months so this is it's a refreshing question. I think just try to zoom out and this ties into the semi piece of the equation, but like how do you help create a more vibrant

1:21:27 Healthy. Startup ecosystem for this set of companies in for this industry. And how do you collaborate with folks in DC, which I think to their credit, the Chip's office is doing a great job being proactive, talking to investors, talking to companies, not trying to be in that DC echo chamber. What can be done. To

1:21:46 D bottleneck. all these pain points that makes it so challenging for a chip company to start up. How do you motivate that really smart engineer who's at NVIDIA right now or AMD or Broadcom to say, hey, I'm gonna start this. New company,'cause I think the rewards of that are gonna be both financially and personally. Massive.

1:22:05 And so Up creating that culture is something that I try to think about Because I think it's really important. For the US broadly to being competitive. On

1:22:14 The design side where it dominates today but the fear. Is it does that slip away because you don't have a very active start a pipeline that's pushing the incumbents to innovate and keep moving this ball down the playing field. And so that's something that I think about a lot and try to figure out how can you collaborate with a different players in this ecosystem, whether it be the incumbents, whether it be the US government or other governments. To create

1:22:38 A really vibrant Healthy. Ecosystem. Having read the book and Talk to you a lot about what you're building at Maverick Silicon. I'm really excited to see the companies that start to come through here. Like you guys have put so much time and thought and attention into

1:22:51 Not just who to back, but how to help back them. So I'm sure the signal coming off of what you invest in is gonna be a fascinating way. You'll be my answer to my earlier question about who to call to figure out what's going on. My traditional closing question for everyone, what is the kindest thing that anyone's ever done for each of you? The caveat here will be this is excluding family, but So this ties back to when we had lunch this summer and I was leaving

1:23:14 for that trip my first trip to Taiwan and so If I go back to high school and college, the mentorship that I got from my teachers and my coaches over that period of time had such a huge impact on who I am today. That probably is the kindest thing that anyone's ever done for me. And the reason why I bring up that Taiwan trip. So that ended up turning into two Taiwan trips over the course of eight days. And I was at an airport hotel working out at the gym, trying to maintain some semblance of fitness, and got an email from my rowing coach from high school that probably isn't coached me on the water. for over twenty years just, hey, Andrew, checking in. How are you doing? How's your family doing? And it just totally brightened my day and just individual still cares about me long after I left his program. I left that university. I left that high school. Just like really

1:24:03 brought a smile to my face. And I think just trying to do that at Maverick, where can we mentor that next generation of leaders of the firm and spend time with them and invest in them. I try to do that like people did with me. I don't think necessarily do the best job, but I think that's just like a really valuable lesson. I just encourage everyone take the time to do that because I think it's important. I'd mention a mentor as well, my advisor when I was doing my PhD in history, John Gaddis. Who Often when you have a PhD advisor, your advisor wants you to study what they study. That's The standard trend.

1:24:32 And John said, Study whatever you want to study and gave me sort of an intellectual blank check and trust that I could find something interesting and accurate to say in whatever field I wanted to. And at the time that was a niche area of Russian history when I was studying them, but it I think helped me realize that if you have the right set of tools, you could have confidence to go into a new area. Understand what was happening there, make sense of it. a value at the end of it. And that initial

1:24:57 Willingness to take a bet and write that blank intellectual check is something that I value today and hopefully I've produced value from. The world's infrastructure is changing. Thank you for teaching us about what's going on. Thanks for your time. Thank you. Thanks.

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