Gavin Baker on Orbital Compute, TSMC, and Frontier Models | Edited Transcript

A professionally copyedited transcript of Gavin Baker and Patrick O'Shaughnessy on AI infrastructure, watts and wafers, orbital compute, frontier models, chips, and geopolitics.

Chapter Timestamps

• 00:00 Intro

• 07:55 Anthropic and OpenAI Valuations

• 12:58 Watts, Wafers, and Infrastructure

• 14:39 Orbital Compute and Data Centers in Space

• 22:49 Avoiding the AI Bubble

• 28:26 Terafab and the Future of US Manufacturing

• 32:16 Returns to the Frontier

• 37:23 Continual Learning

• 42:03 New Chip Companies

• 48:52 Extending GPU Lifespans and Private Credit

• 51:22 The Application Layer

• 57:32 The Token Path and Open-Source Dynamics

• 1:01:37 Cybersecurity

• 1:05:46 Diversity Breakdown

• 1:11:59 Assessing the Big Tech Players in AI

• 1:19:02 Geopolitics, Personal Safety, and the AI Horizon

Made with: The Transcript Desk Chrome Extension

Full video:

In this episode of Invest Like The Best, Patrick O’Shaughnessy is joined by Gavin Baker to dissect the unprecedented AI technology boom. Gavin explains why recent growth from companies like Anthropic represents the most extraordinary moment in the history of capitalism. They explore the critical constraints of the AI build-out—”watts and wafers”—and how capitalism, from TSMC’s chip manufacturing dominance to SpaceX’s potential orbital compute, is racing to solve them. Gavin also unpacks the competitive landscape of chip design, the struggles at the AI application layer, and the shifting dynamics between giants like Google, Meta, Amazon, and Microsoft. Finally, they discuss the geopolitical risks of AGI alongside the incredible potential for AI to revolutionize biotech and extend human life. An essential listen for anyone navigating modern tech markets.

Transcript

00:02-00:40

Gavin Baker: What was happening in AI, I think, was the most extraordinary moment in the history of capitalism, the history of American business. Anthropic added $11 billion of ARR. The three highest-profile SaaS companies founded in the last 10 or 12 years are Palantir, Snowflake, and Databricks. These three companies spent 10 years building their businesses. Anthropic added their combined businesses in one month. Nothing like that has ever happened in the history of capitalism. Forget my career—just the flat-out history of capitalism, the history of business.

00:55-01:24

Patrick O’Shaughnessy: All right, so this is our sixth time doing this, if you can believe it, which puts you back into first place—or at least tied for first place—with Gurley. And I think even since last time when we did this, which was so exciting and spectacular, I think we’re in an even more interesting time now. Maybe just start by riffing on how it felt for you living through March and April of this year, which felt to me just like a completely unique economic, technology, and market environment. You’re the biggest student of history and of these times. So what does it feel like?

01:25-02:12

Gavin Baker: I would say, broadly speaking, there are two kinds of drawdowns. There are drawdowns where you’re wrong—a company misestimates, your hypothesis was invalidated, and you have to take your medicine and crystallize that loss. And then there are drawdowns or periods of underperformance where you’re underperforming because of companies you know really, really well and where you profoundly disagree with the price action. You can lean in and, instead of crystallizing negative performance, you can kind of build pent-up alpha—pent-up future performance. For me, that is what March felt like. It felt like the NASDAQ was selling off, and at the same time, what was happening in AI was, I think, the most extraordinary moment in the history of capitalism, the history of American business.

02:13-03:12

Gavin Baker: What I mean by that is that Anthropic added $11 billion of ARR. What is astonishing to me about this is that the SaaS and cloud revolution created, let’s call it, between $5 and $10 trillion of value. Arguably, the three highest-profile SaaS companies to have been founded in the last 10 or 12 years are Palantir, Snowflake, and Databricks. These three companies employ thousands—tens of thousands—of people collectively. They’ve all spent 10 years building their businesses. And Anthropic added their combined businesses in one month. Nothing like that has ever happened in the history of capitalism. Forget my career—just the flat-out history of capitalism, the history of business. It’s wild. And then Krishna comes on this show and shares some stats—500% in DR.

03:15-04:07

Patrick O’Shaughnessy: Yeah. You do the math on that for three years. Insanity.

Gavin Baker: So there’s just no precedent for this. Tech investors—you hear a lot of discussions about S-curves and investing in exponentials. I’ve just never seen an exponential like this. It felt even more extreme than DeepSeek, which was a very similar setup. If we go back to ‘25, there was a huge sell-off on DeepSeek, which was very strange because the paper gets published seven days before DeepSeek Monday. I believe it got published on a Monday that was a holiday in America. I read it and thought, “Hm, this feels like it might not read that positively for the AI trade.” I took action. We had DeepSeek Monday, where AI really imploded a week later, and that was really strange because by DeepSeek Monday, it was super clear that this was going to be the most positive thing that had ever happened to compute demand.

04:08-05:13

Gavin Baker: Prices in the AWS availability zones in Asia had already doubled. You were seeing GPU availability go down. This was just the first time we saw how much more compute-hungry reasoning models are during inference than non-reasoning models. That was a similar setup, but you had to do some work to see that. I mean, it’s not that hard to say, “Oh wow, stocks are selling off, the price of DRAM is going vertical, the price of GPUs in Asia is going vertical, GPU availability is going down.” Then, two or three days later, GPU prices in America started going up—GPU rental prices. All you had to do in March was simply observe what was happening to Anthropic. There are all these people who seem to regret not buying during ‘22, not buying during COVID, not buying during DeepSeek. You had the same valuation setup at the beginning of April and an even clearer AI inflection. There have been all these chances to buy into AI.

05:14-06:07

Gavin Baker: Of course, what complicated it was the Strait of Hormuz. I became a believer—and am a believer—that maybe one thing the market was mispricing, and I’m no macro expert, but I do a lot of pro-national security investing. So I do have access to people who are experts and are excited to share their thoughts and opinions with me. The Strait of Hormuz being closed is actually relatively awesome for America.

Patrick O’Shaughnessy: Why?

Gavin Baker: Because, particularly for the goals of the current administration. Electricity is a very important industrial or manufacturing input. The key input into American electricity prices, which feeds into AI, is NG1 natural gas on Bloomberg. That was down 20%. Natural gas in Asia, Europe, everywhere else doubled or tripled. So our relative manufacturing competitiveness improved overnight, and for better or worse, that is what the Trump administration seems to care about. They are very focused on America’s relative position. I think a lot of people had memories of the 1970s.

06:08-07:44

Gavin Baker: What made the ‘70s so traumatic was it wasn’t just that prices went up—there were actual gas shortages. Then you go through, “Okay, well, the US economy is dramatically less energy-intensive than it was. The United States is now the world’s largest producer of oil and gas, and we’ve become the world’s largest exporter of oil and gas.” On top of that, there’s this relative manufacturing advantage. That made it, I think, easier to stay focused on AI fundamentals, to stay focused on what were historically attractive valuations. I think, on a relative basis, tech essentially got as cheap as it’s been versus the rest of the market at any point over the last 10 years. Just think about that in the context of market efficiency. We have the most extraordinary moment in the history of capitalism that’s wildly bullish for AI, and you get a chance to buy AI at really attractive valuations.

07:45-08:01

Patrick O’Shaughnessy: What do you make of the multiples that specifically Anthropic and OpenAI—

08:03-09:02

Patrick O’Shaughnessy: Specifically Anthropic and OpenAI, which in my mind are like the reference assets—the most pure play takes on this trend. Is it really not that crazy? If you just look at the sales multiple and compare it to maybe what Databricks and Snowflake and these companies traded at their peak, how do you make sense of it?

Gavin Baker: I do think OpenAI and Anthropic are pretty different animals from a capital efficiency perspective. Anthropic clearly has a dramatically lower cost per token than OpenAI—they just do. You can see that in the amount of money they’ve burned to get to a roughly similar revenue scale. I think they’ve burned maybe 80% less than OpenAI.

Patrick O’Shaughnessy: So as businesses, they clearly have very different structural ROICs.

Gavin Baker: I think OpenAI is doing a lot. I think Sarah Friar is one of the most exceptional CFOs. They’re doing a lot of things to try to improve this.

Patrick O’Shaughnessy: And they’ve secured a lot of compute—more than anyone else.

Gavin Baker: That’s another big difference. It turns out being aggressive really paid off. But yeah, Anthropic at $900 billion for $50 billion in ARR and growing at a thousand percent...

Patrick O’Shaughnessy: Yeah, growing at ridiculous rates. Maybe a true statement is that if Anthropic had all the compute, they’d probably be doing well north of $100 billion today—maybe $150 billion.

Gavin Baker: And I do think they’ve clearly deprecated the intelligence of Claude. There’s an analysis that Claude, even on Opus, is generating 70% fewer tokens for the exact same question. As we talked about last time, token quantity equals quality of answer and quality of thinking at some level. There is an intelligence density per token that also matters. I think I’ve felt that as a user. So I think they would be doing materially more—$100, $150, maybe $200 billion. So you might be buying it at more like five times...

09:02-10:09

Gavin Baker: ...unconstrained—I’m going to make up a new number—URR: unconstrained run rate revenue.

Patrick O’Shaughnessy: Why do you think they don’t raise $100 billion at a $3 trillion valuation or something like this? If you were the Anthropic CFO—Krishna is awesome, we just had him on—or if you’re Sarah at OpenAI... Certainly, if the inbound I received following the Krishna episode is any indication, everyone I’ve ever met is trying to invest in both these companies.

Gavin Baker: So I think it’s wise. The future is uncertain. You are clearly in a very capital-intensive game, even if you are Anthropic. I’m sure they’re at very positive gross margins on inference today. I think they probably start generating cash this year, if they’re not already generating cash—which I think is probably the case. But still, you probably want to be able to raise more capital, access more compute. The world is uncertain. Ukraine is starting to really, really win—how is Russia going to respond? There’s still a lot of uncertainty in Iran. All this uncertainty, I think, probably amplifies geopolitical uncertainty over Taiwan. So, it’s an uncertain world.

10:09-11:54

Gavin Baker: If I think about Elon, Elon has always made investors money. He treats it like a sacred covenant. And as a result, because he’s made people money for now 20 years, he has a superpower: he can essentially raise as much capital as he wants, whenever he wants. And I think it’s wise that these companies are taking—I don’t know if that’s how they think about it—but I do think being focused on making investors money is wise and creates benefits that don’t just last for a year or two. They can last for the next 20 to 30 years.

Patrick O’Shaughnessy: And the way Elon did this was sort of systematically underpricing SpaceX or whatever else. What is the actual method?

Gavin Baker: Just never being greedy on valuation. Never pushing valuation.

Patrick O’Shaughnessy: Just that simple?

Gavin Baker: Just that simple. My friend Antonio pointed out SpaceX compounded in the low 30% per year for whatever that was—a decade. And that was just because Elon was, I think, focused on preserving the superpower and trying to strike a fair balance between investors and employees. But I think it’s wise. Could Anthropic raise money at probably at least a 100% premium to this rumored latest mark? Of course.

11:54-12:39

Gavin Baker: Most software companies try to maximize your time on their app to juice engagement. Ramp does the exact opposite. Ramp understands that no one wants to spend hours chasing receipts, reviewing expense reports, and checking for policy violations. So, they built their tools to give that time back, using AI to automate 85% of expense reviews with 99% accuracy. And since Ramp saves companies 5%, it’s no wonder that Shopify runs on Ramp, Stripe runs on Ramp, and my business does, too. To see what happens when you eliminate the busy work, check out ramp.com/invest.

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12:39-14:17

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Let’s get to the “watts and wafers” part of the discussion—always my favorite thing to talk about with you. The importance of this infrastructure buildout. I feel like every time I think it’s getting overheated, the next time I talk to you, it seems like we should have done way more than we did. You’ve studied S-curves and the steepness of those S-curves a lot, and you know a lot about history. Talk us through how you’re thinking about watts and wafers today as the key inputs into this whole thing.

Gavin Baker: Yeah, I would say I think capitalism is going to solve the watts shortage...

14:17-14:58

Gavin Baker: ...absent big regulatory or political blowback, which I think is a real possibility. The head of data center infrastructure investing at one of the big PE firms—I think Blackstone, Apollo...

15:00-16:08

Gavin Baker: You know, I think Blackstone, Apollo, KKR—they used to say energy and chips were our biggest gating factors. Now, it’s zoning and approval that’s much more important. And I think a lot of companies are waiting until after the midterms to take action in terms of maybe workforce reductions. Nobody wants to be a piñata during the midterms, but you’ve seen a lot of companies that make turbines announce plans to significantly increase capacity. There are only two of these machines that can cast these big blades. We haven’t made one in 80 years in the West. We don’t know how to make them anymore, etc., etc. All of that is true. And by no means am I minimizing the industrial engineering magic and artistry that goes into those, but capitalism is very good at solving problems like these over time. There are other sources of energy besides these turbines, with a longer time frame. So I think the watts shortage will probably begin to alleviate in ‘27 or ‘28, and then I think orbital compute will really solve that.

16:08-17:05

Gavin Baker: And I do want to reframe orbital compute, because I think when people hear “data centers in space,” which we discussed on our last episode, they picture a Pentagon-sized building in space. They’re like, “Well, we can’t do that.” That’s not what it is. A Blackwell rack weighs 3,000 pounds. It’s eight feet high, four feet deep, three feet wide. It’s racks in space. And SpaceX has shown you an illustration—it’s a rack, that’s the satellite. It’s probably about the size of a Blackwell rack. It has these solar wings that are probably 500 feet long on each side. You keep it in a sun-synchronous orbit, so those solar panels are always in the sun. And then, because it’s in an exactly sun-synchronous orbit, the radiator extends behind it for hundreds of feet.

Patrick O’Shaughnessy: This is a common criticism—how are you going to go...

Gavin Baker: I’ve spent a lot of time at Starbase over the years, and I’ve talked to a lot of SpaceX engineers. I do think it is the most talented group of engineers on planet Earth, and they’re very confident they have solved this. And they’re not always confident. Like, I think probably there’s some engineering that needs to happen to turn the Starship into a Mars colonial transporter. Will they do that? Absolutely. What are they more focused on? I would say probably the repair and maintenance.

Patrick O’Shaughnessy: These are the two big responses: the radiator, and how do you repair whatever issue goes wrong in the rack?

17:05-18:04

Gavin Baker: And the answer is, until you have probably floating Optimuses, you don’t. Now, I do think Starship is going to change the space economy in ways we cannot imagine. Particularly if regulation becomes a constraint to data centers, none of it’s going to matter. You’re going to sell as much orbital compute as you can make. And then, obviously, you link these racks using lasers traveling through vacuum, which are already on every Starlink. It’s just mind-blowing to me that SpaceX operates the world’s largest satellite fleet—which is like 98 or 99% of all satellites in orbit. Every Starlink—they’re cooling it today. And, you know, I think Starlink V3 is going to operate at 20 kilowatts. A Blackwell rack is only 100 kilowatts. And people talk a lot about density. Well, if you’re connecting the racks with lasers through vacuum, you can make the rack bigger physically. You’re focused on weight, not size.

18:04-19:19

Gavin Baker: In a data center on Earth, where you’re trying to connect racks—ideally using copper, minimize lengths, etc.—cabling is a big cost. You do want that rack to be small, because, you know, copper when you can, optics when you must. But in space, there are all sorts of things that SpaceX can do that I think maybe some of these naysayers are not contemplating. But it’s just—they operate more satellites than anyone. They have a 20-kilowatt satellite today, so maybe you just scale that up to 60 kilowatts to start. They seem very confident they’re going to go right to 100 to 120. And the same company now also operates the largest data center on Earth. They have the world’s best hardware engineers, and all sorts of people—almost all of whom are not smart enough or practical enough to work at SpaceX—are these armchair skeptics.

You know, I don’t want to quote Larry Ellison, but somebody was being skeptical, and Larry was just like, “Listen, he’s out there landing rockets. I don’t see anybody else landing rockets.” And the reality is that 10 years later, no other company is consistently capable of landing and fully reusing an orbital rocket. And none of this works or makes sense without reusability. That means you have to land it.

19:19-20:28

Gavin Baker: I would like to redefine orbital compute as racks in space, not giant floating Pentagon-sized data centers in space, which is just silly. But you can—what makes a data center is you’re connecting these racks with lasers. So it’ll be racks in space that are connected with lasers into a virtual data center. And if you think about that state of the world—let’s say that all happens and we’re really good at getting these things up economically, running matrix multiplication all over space—what does that mean for terrestrial data centers? Someone once said, “America was going to suck as hard as it can on every energy source it can get.” And I just think the same is true of compute.

Patrick O’Shaughnessy: It’s why I’m probably less worried about an edge AI bear case than I was.

Gavin Baker: We’re going to consume as much compute as we can. And inference, I think, is very sensible for orbital compute. Training will be done on Earth for a long time. So I don’t think that this is super bearish for terrestrial data centers. I think those are going to be valuable for my lifetime.

20:28-22:14

Gavin Baker: But I do think if you are in this ecosystem of power production and cooling, and you are massively ramping capacity—and a lot of these capacity ramps are going to be hitting just as all of the silly skeptics start to understand that orbital compute is very real—I think it’s worth thinking long and hard about that if you’re one of those companies. And then all sorts of cool stuff is happening in the interim. We’re getting really good at repurposing jet engines. There’s that Boom Aerospace that is doing this.

Patrick O’Shaughnessy: So, there’s a lot—capitalism is hard at work on watts. On wafers, though, it’s just this group of flinty older humans in Taiwan who are the most important humans in Taiwan. They are the overwhelming fraction of the country’s GDP, water usage, electricity usage. They talk about the Silicon Shield. They all view themselves as inheritors of Morris Chang’s sacred legacy. I vividly remember visiting Science Park more than 20 years ago and, you know, talking to them. Do you think you...

22:35-22:43

Gavin Baker: Talking to them, I asked, “Do you think you could catch Intel?” And they said, “This is such a beautiful dream, but it’s a dream for our grandchildren.”

22:43-22:51

Gavin Baker: And they did it—partly because of Intel’s self-inflicted wounds, but also because they think very differently. One reason Jensen flies over there so much is he wants them to expand capacity.

22:51-23:09

Gavin Baker: I do think it’s wild that Jensen has never had a contract with Taiwan Semi. They do business on what seems fair—on handshakes. Just fascinating. No contract. It’s going to be fair over time. We’re partners; we’re going to be fair to each other.

23:09-23:29

Gavin Baker: And the truth is, based on every prior market precedent for a foundational new technology like AI, you’ve always had a bubble. Carlotta Perez wrote this great book about this. Basically, markets are efficient—they correctly understand that this is a foundational technology.

23:29-23:39

Gavin Baker: There’s what Michael Mauboussin calls a breakdown in diversity. Everyone becomes bullish on this new technology. And I am beginning to worry a little bit about a diversity breakdown. And then you get a bubble.

23:39-24:06

Gavin Baker: That bubble funds the buildout of this new technology, but supply gets ahead of demand. And you get a crash—and it’s a particularly severe crash if it’s a debt-fueled buildout, like in the year 2000. One thing I’m really happy about, really good about the current buildout, is it’s still overwhelmingly funded out of operating cash flows, which is a really important fundamental difference versus the year 2000.

24:06-24:19

Gavin Baker: Another difference is that every GPU is running at 100% utilization, whereas 99% of fiber was unutilized back then. So there are all these fundamental differences, but history doesn’t repeat—it rhymes. As investors, we have to be very cognizant of that.

24:19-24:35

Gavin Baker: We have to recognize that, based on the last two or three hundred years—forget the internet bubble—we had a railroad bubble, a canal bubble. We should expect a bubble. And that’s terrifying. Nobody wants a bubble. A bubble is terrible.

24:35-24:59

Gavin Baker: The reason it’s terrible is if you’re valuation-sensitive, you massively underperform. You get fired by probably all your clients. George Vanderheiden, who is no longer with us—a great Fidelity portfolio manager—he fought the bubble in ‘99 and he retired in early 2000 because I think he just couldn’t take it.

25:00-25:18

Gavin Baker: He knew it was wrong, and his clients were deeply skeptical. “George, you’re out of step.” He had white hair. He was a truly great man. I only overlapped with him briefly, but he was a very important mentor and friend to my good friend and mentor, Jennifer Urick. So I have a lot of Vanderheiden DNA through her.

25:18-25:47

Gavin Baker: He was the same person who said, “Being early is the same thing as being wrong.” George retires because he can’t take the underperformance and he can’t take clients saying, “What’s wrong with you? You don’t get it.” He had like 40% of his fund in tobacco, 40% in homebuilders, and literally, he probably outperformed the NASDAQ by like 20 or 30x over the next three years.

25:47-26:22

Gavin Baker: I’ve been optimistic that this fundamental shortage of wafers—which really today is controlled by Taiwan Semi—will prevent a bubble. If Taiwan Semi did what Jensen wanted, I think Nvidia could sell two trillion dollars of GPUs in ‘26 or ‘27, maybe two and a half trillion, maybe three trillion. But there is a limit where consumers would consume so much that you probably would be in an overbuild. So if we don’t get a bubble, we need to throw a party for Taiwan Semi, because they will have single-handedly prevented a bubble.

26:22-26:29

Gavin Baker: Okay, you are starting to see companies go to Intel and Samsung.

26:29-26:35

Patrick O’Shaughnessy: Let’s just assume TSMC stays super supply-constrained versus the latent demand. What happens?

26:35-26:59

Gavin Baker: Well, one of the things about the history of markets is—I don’t know who, but one of Intel or Samsung—they’re not going to stay disciplined. They will break, and then at some level, that will force everyone else to break. So I think a lot of this may come down to the degree to which Taiwan Semi can maintain a lead over Intel and Samsung. You have to remember, whatever it is—it’s nine, twelve, fifteen months.

27:00-27:11

Patrick O’Shaughnessy: Sort of like the leading node edge, you mean?

27:11-27:15

Gavin Baker: Exactly—the pace at which they expand capacity. If I were to watch one thing to understand whether there’s a bubble, it’s Taiwan Semi’s capacity decisions.

27:15-27:38

Gavin Baker: I think there’s a Goldilocks zone where they expand enough that they make it hard for Intel or Samsung to really, truly emerge as a scaled second source with well north of 30% market share. And yet, they also keep this fundamental constraint on wafers that helps us avoid a bubble.

27:38-27:48

Gavin Baker: And then, obviously, I think the Terafab is going to play into this too.

27:48-27:51

Patrick O’Shaughnessy: Say more about that for people that don’t know.

27:51-28:17

Gavin Baker: The Terafab—it’s a SpaceX, I believe Tesla’s involved as well—joint venture to build the world’s largest fab here in America. I think they’re going to be successful. They have a partnership with Intel, which is very important because they’re getting access to 50 years of institutional knowledge. That’s just nine months, a few quarters, twelve months, three to five quarters behind the front. That’s an advantage.

28:17-28:40

Gavin Baker: It’s also an advantage that I believe Terafab is going to get attention from the A-teams at all the semi-cap equipment companies. One big reason Taiwan Semi caught up is ASML, KLA-Tencor, Lam Research, and Applied Materials—they wanted them to catch up. They don’t like having a monopsony, so the A-teams were in Taiwan working. Intel made some mistakes, and presto.

28:40-28:54

Gavin Baker: So the A-teams will be here because of Elon’s reputation in hardware engineering. And then, to a degree that I think is maybe hard for people to imagine in America—where politics has replaced religion—because Elon had his foray into politics, that makes it hard for some people in America to see him clearly, which is sad.

28:54-29:14

Gavin Baker: Because I do think he’s probably doing more for America than any other American. He’s single-handedly bringing manufacturing back to America. He’s revived defense tech. SpaceX is, in some ways, the most important defense contractor in America. What he’s doing with Starlink is amazing for the world. He’s creating all these blue-collar manufacturing jobs, which is a goal, I think, of a lot of liberals and good for America.

29:14-29:49

Gavin Baker: He’s done more than any living human to decarbonize the world. And if you are upset about data centers on Earth for environmental reasons, well, here you go. So it’s sad, but he is a living deity in China, Taiwan, South Korea, and Japan.

29:49-29:59

Gavin Baker: And having watched him for a long time, what he’s going to do is they’re going...

30:00-31:00

Gavin Baker: What he’s going to do is recruit the best people, because the best engineers want to work for Elon, especially in hardware engineering. He’s going to recruit incredible engineers. And then, next to Terrafab, there will be a Taiwan Town—these are your favorite restaurants, I’m going to move them and their whole staff from Taiwan to Texas, and we’re going to make everything the way they like it. And then we’ll have Japan Town, same thing. Then we’re going to have Korea Town. We’re going to have all these things, exactly, but dialed to recruit the best engineers. And that’s just not the way that the people who run Intel at Seung think. So he’s going to have the best talent. He’s going to have the A teams at the wafer fab equipment companies. He has Intel, which is important. It’s so good for any administration’s political goals. And I think it’s different enough that it will not alienate TSMC.

Patrick O’Shaughnessy: And these have long lead times, right? So Terrafab is going to be pumping out Nvidia or whatever GPUs, whatever chips, quite a long time from now.

Gavin Baker: Elon tends to do things differently. Everybody else has taken three years to build a data center. He built one in 122 days. You know, Samsung had to give him an office in their fab in Texas because he was so unhappy about the pace at which they were expanding a building. We’ll see.

31:00-32:00

Patrick O’Shaughnessy: Are you surprised by—you mentioned DeepSeek earlier. The simple reaction to that was, “Okay, these models are just going to get 95% as effective for some tiny fraction of the cost—still Chinese open source models. We’ll be able to use these for most of what we want to do.” Fast forward a little bit of time, you know, two years from now, there’s no reason I have to spend a million dollars a year in my small little firm on tokens or something. But then the actual reality seems quite different than this. And I’m curious why there’s that dissonance in your mind.

Gavin Baker: I do think it’s fascinating—the returns to the frontier. All the economic returns to AI at the model layer—not all of them, but an overwhelming amount—have been at the frontier, which is surprising to me. And I think it’s been surprising to a lot of people. I think this is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor. Are frontier tokens going to continue capturing the overwhelming majority of economic value created at the model layer? And it is surprising. I just remember when Gemini 3.1 Pro came out, and it was mind-blowing to me. It was so good. And today, it’s intolerable.

Patrick O’Shaughnessy: Intolerable.

32:00-33:00

Gavin Baker: And you know, there’s probably a little bit of a dynamic where companies prototype with frontier models, then when they put something into production, you’re hearing a lot of people do use Vertex or open source. But still, it is a fact today that the overwhelming majority of these economic returns come from frontier tokens. And that’s surprising, and whether or not it continues I think is a very interesting question. I’m much more open-minded to that, having had the experience I’ve had with Gemini 3.1 and then Opus. And then I do use Grok 4.3—it is on the Pareto frontier. The companies that are on the Pareto frontier are—and this is, by the way, a big change and a consequence of what we talked about last time—Google losing their per-cost token leadership as a result of making very conservative design decisions with TPU V8 to try and take it away partially from Broadcom, and Nvidia continuing to make aggressive choices. But Google dominated the Pareto frontier—the Pareto frontier being intelligence versus cost. And I think this is the most important thing to look at to analyze AI labs. Google dominated that nine months ago. At every point on the Pareto frontier, OpenAI, xAI, and Anthropic were inside of them. Now, the Pareto frontier is dominated by Anthropic, OpenAI, and then Grok 4.3 is on the Pareto frontier. It’s clearly the best lowest-cost 500 billion parameter model. And then Gemini 3.1 is hanging on to the Pareto frontier. And if I were to bet, I’d bet that they’re subsidizing that out of pride. I would just say, one, a violation of Richard Sutton’s bitter lesson is for sure the biggest risk to this trade—to all of AI.

33:00-34:00

Gavin Baker: Now, the closer someone is to AI, the more skeptical they are this will occur. One thing I think contributed to weakness in March was a much more stupid version of DeepSeek, which was this thing called TurboQuant. TurboQuant is some Google memory optimization that was written up in a paper a year ago. And then, during the middle of an agreement while Google was negotiating with Micron, Samsung, and Hynix to sign some LTA that would lock in really high prices for a long time, they released this. You know, what people do is always more important than what they say. And they just kind of publicized it on X, and it goes viral like, “Oh my god, DRAM is cooked. Here’s this DRAM optimization.” I was unable to find a single AI engineer on planet Earth who believed that TurboQuant would have any impact on DRAM demand, but nonetheless, a violation of Richard Sutton’s bitter lesson—you know, more compute will always outperform human algorithmic ingenuity. More compute and data, or Chinchilla optimal, I guess. What people increasingly do today—that’s a real risk, man. And I think the people who are building these models are skeptical of that risk. The reason I am a little less skeptical is I think we are very close to ASI, and who knows if the bitter lesson holds for 400 IQ models.

34:00-35:00

Gavin Baker: Or maybe we get a temporary period where these—if you get to ASI, the first thing it wants is probably to be smarter and have more resources. How does it do that? It makes itself more efficient. I think that is an actual risk—that the bitter lesson literally, I believe, includes humans in it. So we’re about to find out whether the bitter lesson applies to 300 IQ AIs, then 400, then 500, and 600, and at some point we may have a temporary violation of the bitter lesson based upon AI and ASI.

Patrick O’Shaughnessy: So I’m curious how you think about some other parts of the innovation around the model—continual learning and memory being two that people seem to be most focused on as things that might create yet another new paradigm that we would enter. What do you think about the role of those two things?

35:00-37:54

Gavin Baker: Yeah. Well, I think we’ve done a lot with memory through these harnesses. And it turns out that harness engineering is not as important as the model, but it really matters. And these harnesses in these models are increasingly being co-developed. One of the big things a harness does—which you can just think of as a runtime that the model operates in—knows where the pool tools are, creates context, memory, state, has very specific prompts or instructions, and just makes a huge difference. Even simple versions make an incredible difference. I think the last time I was on here, or one of the other times, I just said, “Hey, as an investor, it’s very important that you pay for the $250 a month version to get your own intuitive sense.” That’s no longer possible. To understand what frontier AI is capable of today, even for a non-coding use case, you need to have cloud code or Codex, and you need to be on an enterprise plan. And the reason for this is—and this is another dynamic that’s enabled by Google losing their cost leadership—

37:54-41:02

Gavin Baker: Google losing their cost leadership is these AI models just shifted to usage-based pricing. If you’re on that $250, $280, or $300 a month plan, or whatever it is, you are getting severely rate-limited. You’re getting a lobotomized version of the AI because, like we talked about, Claude now produces 70% fewer tokens. If you want the tokens that Claude and its harness really think it needs to produce to get you a good answer, you need to be on a usage-based plan.

And by the way, this is so bullish for AI. I was a telecom analyst in ‘05 to ‘07, and cellular had been a great growth industry for the last 10 years. The reason was you had a combination of fixed pricing—you had 900 minutes for whatever it was—and then usage-based pricing over that. When did cellular stop being a great growth industry? When everybody just went to all-you-can-eat. And by the way, long distance was the same thing. AI is just shifting from all-you-can-eat to pay-by-the-drink. And it turns out people really like to talk to their friends long distance. They really like to talk to their friends on the phone. And people really like to use AI, particularly now that one person can have 100 agents working. So I think the shift to usage-based pricing is probably why you will see OpenAI and Anthropic exceed well over $200 billion in ARR this year. Because not only is more compute going to come online, but they’re going to be able to push frontier token pricing with these usage enterprise models.

But it’s sad. It’s sad for the world, because it just means if you can’t afford that, you’re not at the frontier. But yeah, continual learning—man, I mean, if we solve that...

Patrick O’Shaughnessy: How do you conceptualize that? There are so many mysteries about the human mind. We’re such sample-efficient learners relative to AI. I forget what it is, but an AI needs orders of magnitude...

Gavin Baker: Yeah, many orders of magnitude. Now, we have a crude variant of continual learning today when something is verifiable, and that’s just reinforcement learning during mid-training. But yeah, continual learning is a model that dynamically adjusts its weights or adjusts in some way in real time, like as a human.

Patrick O’Shaughnessy: That’s what you do.

Gavin Baker: Yeah. Like, if the first time I touch—or, you know, put my hand in a fire, I’ve learned. I never put it in there before. That model today needs to put its hand in the fire a million times and then have the designers effectively put a fire in the next training run or an RL gym for it to learn. I think it has to be dynamically updating the weights, but I think people are working on really smart techniques beyond this. But if we get that, then we have a really fast takeoff, and people seem confident that continual learning is kind of just around the corner.

And I do think this is the third big question: Bitter lesson violation as a result of ASI, or less likely, human ingenuity. Will frontier tokens still command the premium they do? And will we get continual learning? And if so, when?

41:02-42:06

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42:07-45:17

Patrick O’Shaughnessy: What is the role of new chip companies in all of this? We’ve talked a lot about Nvidia and their relationship with TSMC and Intel and all these sorts of things. There’s a thousand flowers blooming—literally, probably a thousand flowers blooming—trying to create a new chip to address some part of this bottleneck. I’m curious how you process this space, this opportunity, what role it will play, what role they’ll play.

Gavin Baker: So, I think this is good and healthy for the world. It’s good for Jensen too, because a different administration might take a different view. Competition, I think, is good for everyone. In tank design, they talk about the iron triangle. The iron triangle of tank design is that all designers of a tank have to make trade-offs between attack, defense, and mobility. For obvious reasons, the more defense you have—which is just armor—the heavier the tank is, the less mobile it is. So you have to live in this triangle and make trade-offs. Like the Merkava in Israel, it’s optimized for defense. Russian tanks and the Leopard are generally more optimized for mobility.

Chip design is the same. There are these fundamental constraints imposed by the laws of physics, as embedded in the Taiwan Semi design rules, that you need to live within. You have TPU, Tranium, and AMD, which are all essentially trying to be a better GPU. Today, I think probably Tranium is doing the best. Nobody’s a better GPU, but Tranium is—I think they’re tugging on Superman’s cape.

Patrick O’Shaughnessy: And this hasn’t started yet. The Tranium 3 needs to ramp into production because it has a switch scale-up network, which you really need to economically inference models. A lot of companies have a torus architecture. That’s where Google was, and AMD. We’ll see. The MI450—we don’t know yet. We’ll see. We probably know more about Tranium 3 than the MI450, but that’s a hard game to play.

Gavin Baker: So you have to do something different, and you have to do something different that is also hard to do. I think the best path for these startups—my rule of thumb is 1% market share is going to be worth $100 billion. $100 billion is a pretty good venture outcome. I think what Jensen would say is, “Okay, if somebody does something different and it gets to one or two or three percent share, we’ll make that chip,” and that’s coming for everyone. But if you’re trying to make a better GPU, good luck. If you’re doing something different, it also needs to be hard to do.

And you can make different trade-offs. The disaggregation of prefill and inference really have opened the aperture for making these different trade-offs, because you can make very aggressive trade-offs for decode, aggressive trade-offs for prefill.

Patrick O’Shaughnessy: Prefill being taking in the context, decode being, you know, write the output.

Gavin Baker: Yeah. I have a great colleague named Andrew Fox who said, “Picture a British naval ship from the 18th century. Prefill is loading the cannon, decode is firing it.” And what prefill literally is, is just the model understanding the question, the prompt...

45:19-46:00

Gavin Baker: Understanding the question, the prompt, and then keeping track of its own decisions—that is fundamentally a memory capacity-bound problem. Decode is a process of generating new tokens, and that is memory bandwidth-constrained. So, if you’re a chip designer, this gives you a richer canvas to paint on. But even so, it needs to be hard, because if you make different trade-offs in that iron triangle to optimize for memory capacity, and they’re not hard trade-offs to make, well then Nvidia is going to make those same trade-offs. They get better prices from Taiwan Semi than you’re ever going to get. Good luck. And they have the advantage of working with every model company and optimizing their designs.

46:00-47:00

Gavin Baker: And by the way, another very funny thing is if you’re a VC and you’re investing in a semiconductor company that is telling you they are going to have an advantage because of a Taiwan Semi process that they have special access to—I promise you that Jensen saw that process when it was a twinkle in Taiwan Semi’s eyes, and they know more about it than this little company with 200 people can imagine. Taiwan Semi, everybody in the supply chain is showing Jensen everything, the same way they’re showing Amazon everything, AMD everything, TPU everything. And that’s another reason: don’t go try to make a better GPU. You can do something different. You can paint in the pre-fill canvas, you can paint in the decode canvas, but you also have to do something hard, because if it gets to scale, those four companies have very fast followers.

47:00-48:00

Gavin Baker: My firm was a venture investor in Cerebras. What Cerebras has done is something hard and fundamentally different—a new way for scale computing. It comes with a set of trade-offs, but that architectural decision they made was hard and lets them do something that no one else can do. And we’ll find out how big that is. They’re working on really cool things. One of the problems Cerebras has: once you start needing to glue a lot of chips together and scale up or scale out networks, you need a lot of IO, and IO is bound by what’s called the shoreline—the sides of the chip. Cerebras has an overwhelming ratio of on-chip compute and memory relative to shoreline IO. They’re really smart people. They did something really hard. They’re trying to see if they can put an optical wafer right on top of that, and then that solves that problem. I’m sure they’re looking at hybrid bonding of DRAM to get around these alleged limitations that are not true. A Cerebras machine can theoretically run any size model. There are sizes of models where they’re much better than others.

48:00-49:00

Gavin Baker: So, Cerebras—what I think is interesting is they did something different that’s hard to do: really hard to do wafer-scale computing. I do think there’s a role for these, and I would just encourage them all—make a different trade-off and try and do something hard, because everybody’s going to get funded after the Cerebras IPO. It’s not going to be a problem. But it took Cerebras three generations of chips to get it right. And it’s really hard. Andrew Feldman, the CEO—you can just see how hard it was, and that whole team did to get where they are today. And they need to have the grit to do that, the resilience. This first chip is a failure. It happens. Can you come back and make a second chip? But one last thing on this topic: this is going to be amazing for the useful lives of GPUs and may single-handedly save private credit.

49:00-50:00

Patrick O’Shaughnessy: Say more about that. What do you mean by private credit?

Gavin Baker: Well, just, you know, private credit—they’re in pain from these SaaS loans, and however much they’re marked down, they probably need to be marked down more, because if the public companies are struggling to adapt, how’s a debt-laden company going to adapt and invest in what is a very different margin structure business? But there’s a lot of private credit in GPUs too. They were underwriting that to, I think, three or four years. And the disaggregation of inference means that I think these GPUs are going to have 10- or 15-year lives. The AI skeptics are like, “Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because of the rapid technological change.” No. What rapid technological change has done with the disaggregation of pre-fill and inference is mean that you can put a Cerebras system or Groq LPUs that Nvidia acquired effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for pre-fill, and extend the useful life of that GPU until it melts. Now, they do melt, so they have a time limit, but maybe you don’t have to run them as fast. This is going to be really good for the whole private credit industry.

50:00-51:13

Gavin Baker: It’s going to help finance the AI buildout, because if you can start to finance GPUs at more like, you know, 5% or 6% instead of—I think CoreWeave’s lowest financing was like low sevens—that actually mathematically changes the cost to finance this buildout. We had this technological innovation that’s going to lower the cost of financing, extend the useful life of compute on Earth. And then, I do think the one last thing that’s interesting about that is my friend Jamin from Coatue just did a podcast, and Coatue had a deck, and they talked about, hey, you know, the sellers of shortage are doing so much better than the buyers of shortage. Buyers of shortage being, you know, the hyperscalers. But if you own a giant installed base of what is currently in shortage, that’s also a very, very good place to be. And we’re hearing, you know, CPUs are way more important than they were in an agentic world. They do all these things around orchestration, tool calls, etc. The biggest CPU fleets in the world sit at the hyperscalers. So, I think some of these hyperscalers may catch up a little bit to the sellers of shortage.

51:13-52:00

Patrick O’Shaughnessy: I want to talk about this idea of “different and hard” applied outside of the infrastructure piece of this. So, now you’re starting to interact with new founders, existing CEOs and founders that have to adjust to this new world. What are you seeing? The most AI-native founders that aren’t building chips or infrastructure or models, but just people using this technology to build other stuff—how do they feel the most different to you, if you’ve observed differences?

Gavin Baker: Well, one, I do think this is just for chip design. To me, it’s always been a fundamental question for venture. There are different ideas that are obvious to everyone on planet Earth as soon as they hear it. And if that’s where you are in venture, if it’s not hard to do, if it becomes obvious to the world before you have built scale—scale is the ultimate advantage—you’re in trouble. And the great thing Amazon had was, you know, I think it was obvious to a lot of people, but it wasn’t obvious to the retail CEOs. And Amazon, they were very smart. Any e-commerce company that VCs invested in, they would destroy. They’d be like, “Oh, that’s so cute. We’re going to take our margins of that to negative 10,000%.” And that’s what, like, the guys at Wayfair—they did something hard, and Amazon tried to kill them and they failed. Those are tough, operationally really competent CEOs. For me in venture, I always look: is this going to be obvious to the world before this...

52:39-53:19

Patrick O’Shaughnessy: Does it have to be obvious to the world before this company could build scale, or is this both not obvious, different, and really hard to do? I think a lot of founders are really struggling with this in AI. I think people are becoming worried. Today, in Jensen’s five-layer cake of AI, the profits are accruing to energy, data centers, chips, and models. They’re not really accruing to the applications. Cursor and Cognition got to a scale—you know, they focused on coding. Eighteen months ago, people were focusing on coding. OpenAI was doing everything under the sun. The people focused on coding were Cursor, Cognition, and Anthropic, and it was really right to focus on code.

Gavin Baker: I’m MSAD, the founder of Replit, tweeted something that I thought was so smart. It was something like, “Bitter lesson adjacent is the fact that coding might be the shortest path to ASI and useful AI,” because if you’re really good at coding, you can write yourself code to do anything. So I think it was really smart of those companies to focus intensely on coding, and I think they all probably got to a scale where they have a place. I think Cognition is doing something really, really different, but I think a lot of founders are really struggling—man, they’re really struggling.

53:19-54:22

Gavin Baker: And you know, I think they’re trying to get confidence that in nichier areas, they can get to them and get a data moat before the model companies get to that niche, or that it’s a small enough niche that the model companies won’t do it themselves, but it can still produce a venture outcome.

Patrick O’Shaughnessy: Is this related to what you would call the token path? I know you’ve used that phrase with me before.

Gavin Baker: Yeah, it comes from a guy at Altimeter, Jamon Ball. He just said, if you’re a software company or an AI company of any kind, you have to be in the token path. So Databricks, that’s in the token path. Comparable companies are in the token path. If you’re not in the token path and you’re not in some really niche thing, life may be hard. And even for these vertical niches, I think if you talk to the people at the model companies, they’re even skeptical of some of these because all of the data that’s being generated in these niches comes from humans. But then you’re betting that you’re able to use that proprietary data in this narrow vertical to train a model that’s lower cost than the frontier labs can ever get to. And maybe that’s a good bet, but I just think you have to be very, very careful.

54:22-55:29

Gavin Baker: Now, on the other hand, if the returns to these frontier tokens relative to other tokens come down, there’s going to be an explosion in value creation at the application layer. And I think another really important point is I have a belief that whenever he wants, Jensen can probably get pretty close to the frontier with his own model.

Patrick O’Shaughnessy: With his own model?

Gavin Baker: Yeah, with his own model. They’re doing some really cool things. Neimatronics—commoditize your complement, as they say. I don’t think he wants to do that. That is what OpenAI and Anthropic are kind of trying to do to him, unsuccessfully. But he’s a very logical thinker. This is the logical counter move, and I think you will see that. Open source frontier, which today consists of Chinese models with stolen American tokens—somebody told me that DeepSeek, the latest one or maybe the original one, was only 150,000 reasoning traces. There are many ways to launder this if you’re a Chinese company. You can hit all these different APIs. You can make it hard. Now, the American labs are working really hard on anti-distillation technology. But I just think Chinese open source is doing really impressive things in a very resource-constrained way. But there’s a lot of distillation.

55:29-56:17

Gavin Baker: And this is why I think, in addition to there not being enough compute to serve Mythos, they did not want it to be distilled. They wanted to use Mythos, distill it themselves, use it to RL their next model, whatever it is. And then I think what they—and eventually, I think if OpenAI gets to economics they feel good about, anyone on the frontier will just say, “There’s going to be some very interesting game theory,” because it’s a new kind of prisoner’s dilemma. We talked about the old prisoner’s dilemma being just around, “Hey, you’re in a prisoner’s dilemma where you have to spend.” The new prisoner’s dilemma is going to be: if you are at the frontier, do you release that model via API or not?

Patrick O’Shaughnessy: And if everyone at the frontier agrees not to do that, then Chinese open source is quickly—

Gavin Baker: If one person defects, they’re going to have the best model. They’re going to have a lot of revenue and cash flow, and then, of course, resources equal intelligence. So they’ll start to pull ahead, and then that will lead to everybody else releasing it. So it’s a new game theory. It’s kind of the same game theory that you have with TSMC, Samsung, and Intel. The reality is, if a company like Nvidia or AMD were to ever really use one of these other foundries, that foundry would get better really quickly.

56:17-57:19

Gavin Baker: So I do think Jensen is going to keep open source a certain time frame behind the frontier. I think that’s going to be a very interesting thing to watch. And then, by the way, open source gets monetized. There’s this misnomer that open source is free. Open source tokens—they cost energy to produce. You need to make up on GPUs, and the open source model companies almost always get a revenue share.

Patrick O’Shaughnessy: How are you preparing or trading for the world of Mythos 3, Mythos 4?

Gavin Baker: We’re just trying to overinvest in cybersecurity. Something I’ve said in multiple forums, and I really believe, is everybody needs to have a safe word. Everybody needs to go leave your digital devices behind. Literally go to the ocean and have a family safe word or a company safe word. And it can’t be one that can be socially engineered. This is just to avoid cybercrime, where what looks like your son or your daughter or your grandparents or your parents or whatever FaceTimes you—it’s an utterly accurate simulation of them. They know everything and can extrapolate based on what they’ve said, what they’re likely to say, and say, “Wire me a million bucks.”

57:19-59:00

Patrick O’Shaughnessy: That’s defensive. What about what will you still be able to do that it won’t be able to do, I guess, on the analytical side?

Gavin Baker: It’s a good question. I just watched The Last Samurai, and I asked people at my firm to watch it. The Last Samurai—if you haven’t seen it, I highly recommend watching it. It’s actually a movie that’s aged really well. Tom Cruise movie from 20 years ago. The conceit is Tom Cruise is this bitter, washed-up Civil War veteran who’s actually a very good soldier. He’s bitter and washed up because he feels like he participated in negative actions against the Native Americans. He’s hired by Japan to train—just during the Meiji Restoration—and he’s hired by the modern elements of the Japanese government to train an army of peasants how to fight the samurai. There’s a first battle. Of course, the samurai win...

1:00:01-1:01:03

Gavin Baker: First battle. Of course, the samurai win, even though they don’t have guns. He fights valiantly, so the samurai decide not to kill him and take him to their village. He becomes a samurai. It feels like the Civil War to him, so he fights on the side of the samurai. And at the end, he’s massacred by a peasant with a machine gun. The machine gun is here, and if we do not all become masters of the machine gun, we’re going to get mastered. So I am trying to become a master of the machine gun. And then, you know, I’m optimistic. There’s a long period of time where, just like if you were a 50-year-old samurai, a veteran of many wars—I’ve fought many wars, mastered my craft—you will have advantages using the machine gun. And I’m optimistic, as a lifelong student of investing, that I’m going to be able to master the machine gun, this new technology, integrate it into my own process, integrate it into our firm’s process in ways that let me contribute value as a human being for a long time. But, you know, like everyone, I have agents running all the time now.

Patrick O’Shaughnessy: What’s your most useful agent?

1:01:03-1:02:24

Gavin Baker: The most useful agent, honestly—and I think I told you this, and I don’t want to hurt your business—but my single most useful agent is a really good summary of the points that would be interesting to me from podcasts. There’s like six hours a day of stuff that I feel like it’s in my job description to watch. Every time somebody from OpenAI, xAI, Google, Cursor, Fireworks, Binh, not to mention Jensen, Elon, Dario— I feel compelled to watch, and I just don’t have that much time. And there’s some real needles in haystacks. There’s a set of things I always like to see. Like, I’m very sensitive to management compensation. What are they incented to do? Do they just have stupid RSUs, or do they have PSUs? And if they have PSUs, what are those PSUs incenting them to do? I think systems that do a very good first pass at that save people a lot of time. It frees them up for more creative work than, you know, going through the proxy, pulling the PSU thing, looking at how it’s changed versus all the proxies—because there’s signal in that, and that’s very labor-intensive. That’s so good for an AI. And there’s obviously all sorts of similar things within investing. This is the most exciting, thrilling time to be an investor.

Patrick O’Shaughnessy: And it is. I am a little—I’m getting a little bit worried...

1:02:24-1:03:39

Patrick O’Shaughnessy: ...about the diversity breakdown thing.

Gavin Baker: Yeah, I’m getting—

Patrick O’Shaughnessy: Say just a little bit more about the kinds of people that are—

Gavin Baker: I don’t know anyone like me who’s not really bullish on DRAM.

Patrick O’Shaughnessy: No one.

Gavin Baker: No one. There’s all these interesting things happening with AI right now. So, one is, cross-sectionally, the valuations do not make sense. They just flat out do not make sense. They cannot all be true. You have semicap equipment companies trading at 40 times next quarter’s annualized earnings and DRAM companies trading at mid-single digits. At the peak of the last cycle, that was like five versus twelve. At one point, it was like three versus forty-five. Those can’t both be true. And yes, semiconductor capex business models have improved more than the memory business models. We don’t know how much HBM is going to improve memory business models yet. Yes, they have some element of recurring revenue with parts and maintenance, but it’s not worth a thousand percent multiple gap. I think it’s hard to square the valuation of something like Nvidia, which is still—in early April—was essentially as cheap as it gets relative to the market in the last ten or twelve years, or whatever it is, and very cheap in absolute terms. It’s very hard to square that valuation with something like GE Vernova’s valuation.

Patrick O’Shaughnessy: Because it builds in an unfathomable amount of share loss for Nvidia.

1:03:39-1:05:31

Gavin Baker: So valuations cross-sectionally are really different because we are in shortages. The lowest quality companies are doing the best. So if you’re an oil and gas investor, or a mining investor, natural resources investor, and you’re well-versed in thinking of costs, this is very intuitive to you. In a real bull market for a commodity, the commodity suppliers with the highest costs go up the most because it’s the most beneficial to them. They go from on the verge of bankruptcy to gushing cash. And this is, I think, one reason commodity investing is really, really hard—because quality outperforms during the cycles, but you get all of the outperformance during the downturns, when the high-cost guys that moon during the shortages and the commodity bull markets go bankrupt or whatever. You’re seeing that happen in every industry. The lowest quality players in these different industries that are hated and detested by the hyperscalers and the buyers—because they have high costs, they’re unreliable, the parts fail at a high rate, etc.—they’re sold out and raising prices. And then that activity gets the interest of these retail accounts on X, and these stocks get bid to the moon, whereas some of the higher quality expressions have actually really underperformed. And as an investor, it’s hard, because you know within a shadow of a doubt that that thing that’s moved 10x in three months or six months is going to go right back down, subject to what they do with all the cash. But these low quality...

1:05:33-1:06:22

Gavin Baker: But these low-quality companies really do smart stuff with cash. And so it worries me a little bit that people who were very skeptical a year ago are no longer skeptical. But then I just contrast that with the valuations of these high-quality companies, which are just not extended, and it makes me feel better. But it does kind of feel like—you know, I always thought it was funny in ‘24 and ‘25 that anyone asked about an AI bubble or talked about it, because it’s like you have this nuclear bubble and this quantum bubble right here, right in front of you. What are we talking about? This is so real. Some of that nuclear/quantum silliness has maybe spread into more speculative, lower-quality, smaller-cap names, where if you have a big presence on X or Reddit, it’s easy to move them. And that frightens me a little bit, but I just wish there were more AI bears. Like, I wish there were more memory bears.

1:06:22-1:07:13

Gavin Baker: You know, one reason—Astera is a stock I’ve been close to for a long time. There’s a lot of bears on that. I love that. Great. You know, I first invested in the Series C. Good luck thinking you’re going to price that differentially from me. Good luck thinking that’s a copper loser. And then there’s also—you can feel the baskets in the market and the leverage baskets, and what baskets you’re in is really important. You know, copper, optical, DRAM, NAND. And a very interesting thing that’s happened this year is, in ‘24 and ‘25, the AI trade traded together. So, like, you could be long GPU compute, scale-up networking, and optical scale across, and short power. That trade worked from a risk management sense, because—you know, I’m very factor-aware. That all blew out in January of this year. It’s like scale-up networking would go crazy while scale-out was going down, or DRAM massively underperforming NAND and HDDs, which had not happened. So these cross-sectional correlations within AI really fell apart, and you had to get very fine-grained. You couldn’t hedge your memory anymore with some semicap equipment or NAND. Everything cross-sectionally really changed in a very interesting way in January.

1:07:13-1:08:07

Gavin Baker: And I think maybe one reason for that was, you know, the AI got to a quality where it was all of a sudden really easy for a bunch of people to get really smart on these different subsectors, start trading them, and then they get put into baskets, and those baskets—

Patrick O’Shaughnessy: Yeah, creating price efficiency.

Gavin Baker: Yeah, exactly. And then it’s like—I think some of the biggest opportunities outside of these higher-quality names that I think can compound for a long time, and they’re safe, unlike these low-quality names, which are terrifying, is in names that are miscategorized. Like, Astera was in a lot of copper loser baskets. Astera—their biggest product is going to be a switch. You use both copper and optics to connect switches to accelerators. And so, definitionally, if you’re a switch company or an accelerator company, you cannot be a copper loser because you’re going to be on the other side of that connection.

Patrick O’Shaughnessy: I wonder if you could riff just for a sentence or two on each of the major companies. I feel like I always forget to ask you—like Google, Microsoft, Amazon, you know, the major players that are public that all the conversation is centered around. These exciting new companies...

1:08:07-1:11:13

Gavin Baker: Yeah. So, Google—it was incredible last year because they had that TPU advantage, which is now gone. The reason I think they’re still in a great position is just they have the most compute of everyone. We talked about the value of installed bases being higher as a result of shortages.

Patrick O’Shaughnessy: They have the biggest installed base of compute.

Gavin Baker: Yeah. I am a little surprised by their inability—and Google I/O is this week—and if they don’t release something that even slightly leapfrogs OpenAI and/or Claude, that’s interesting. It’s not a disaster for Google, it’s just interesting, and it just means this Nvidia effect we discussed is even more powerful than maybe I’d imagined. But I’m very curious to see what the Paro frontier looks like literally in five days after Google’s announced its new stuff. This is a big card for them. But Google, you know, between the amount of data they have—and the YouTube data is actually really genuinely valuable. It is valuable in a world of robotics. The amount of compute they have, and the search business they have—Google’s never not going to be in a good position. And then you see that with GCP going crazy.

You’ve got to give Zuckerberg immense credit. What he’s done in terms of making Meta an AI-first company internally—I do think he is the only one of those true internet giants to have done that. And I give him a lot of credit for that. I give him a lot of credit for paying up when he did for all those billion-dollar contracts, that talent. And Muse, I think, was a really big upside surprise. It was the first model from MSL, and it’s not on the Paro frontier with XAI, Google’s one entrant, and then OpenAI and Claude, but it’s pretty close. That was very impressive to me. So I think Meta is in a better position—still not as strong of an absolute position as Google, but their better position and rates of change matter more than level as you...

1:11:15-1:12:04

Gavin Baker: Change matters more than level, as you know, in markets—particularly over short, like three-year time frames. Over long time frames, level of competitive advantages tends to dominate. But even within that, the changes really matter. Amazon, I think, is in a really strong position because of Tranium. You’re going to see real P&L efficiencies from robotics over the next 18 months in their retail business. I actually think Nova, their internal models, are not where Muse is, but they’re better than they get credit for. Microsoft—I think Satya is a really brilliant man, but, you know, in investor conversations, people just don’t talk about him the way that they did. I like Satya. I admire him. I think he’s an exceptional CEO, and I give him a lot of credit for the decisions he’s made. But, you know, he did go from “we’re going to make Google dance” to being the product manager of Copilot in like three years. I would love to know, during the coup attempt against OpenAI, does Satya regret his decisions?

1:12:04-1:12:40

Gavin Baker: Does Satya wish that he had supported Ilya instead of Sam, and that Ilya and Mira were really running OpenAI today? In his heart of hearts, I would love to know, because I think the Microsoft-OpenAI partnership might look very different in that world. I think that’s a very interesting question that we’ll never know the answer to. But I give him a lot of credit. What he is doing now—he’s taking risk so they could earn. You know, this goes to the decisions you have to make in that cone of uncertainty: not only how much you spend, but what you’re going to spend it on. I think Microsoft flinched for a moment in early ‘25. You know, they have this algorithm: we spend this much capex dollars, we get this return. That algorithm was kind of off, and if you flinch, you lose position.

1:12:40-1:13:15

Gavin Baker: You lose all these allocations, and it’s difficult to get it back. So they flinched, and now the decision Satya is making—which the market has punished him for, but I think is the right decision—is: we’re going to use our compute rather than making... I mean, who knows how fast Azure could be growing if they were willing to just sell GPUs to OpenAI. We’re going to use our compute internally to make our own products better. You know, one reason Copilot is so bad, or has been so bad, is just not enough compute available. They’re fixing that. He’s the product manager of Copilot. I do think he’s a great CEO, and they’re trying to use their compute to train their own models. I am a little skeptical that they have the right team to succeed there, but, you know, they can certainly—just like Meta—they can afford to hire maybe a different team. But I think he’s making good decisions that are risky decisions to position Microsoft for this world where frontier models are no longer API accessible.

1:13:15-1:14:07

Gavin Baker: And I think it’s a really courageous decision that I give him a lot of credit for. He is foregoing—Microsoft would probably be an $800 stock today if they were using their GPUs to serve solely OpenAI and Anthropic’s capacity, instead of using them for their own products. So I give him a lot of credit for making a great decision. What’s really interesting is the degree to which these companies are outward-facing in their decisions. The two companies who are the most deeply engaged with startups are Amazon and Nvidia by a mile. Then there’s a really intense engagement with Google; they’re next most intense. Broadcom is engaged in a different way—they’re just, you know, everybody’s favorite ASIC supplier. If you’re a startup, it’s considered like a level up if you get to work with Broadcom for your second-gen chip, and it’s considered mana from heaven if Broadcom works with you for their first-gen chip. And then you see essentially zero engagement with startups from AMD, Microsoft, and Meta.

1:14:07-1:15:13

Gavin Baker: And I just—I mean, when I say zero, it’s a little... And I just wonder about that decision, because some of the best teams are no longer at big public companies. They’re at these smaller startups. And I think it’s going to end up being a pretty big advantage for Nvidia, with AMD and Google right behind them, to have this engagement that you just don’t see from these other hyperscalers.

Patrick O’Shaughnessy: As we wrap up, I’m curious for you to riff on any other out-there knock-on effects that you’ve started to think about for this giant trend. We’ve talked about the specific companies in a lot of detail that this most impacts. We talked a little bit about the application layer and what would have to happen for there to be more value occurring to that layer of the stack. I’m curious—any other just fun knock-on things that you’ve been thinking about as this world changes so quickly?

1:15:13-1:16:12

Gavin Baker: Yeah. And it is wild. I mean, at the application layer, forget value accruing—just value has been destroyed.

Patrick O’Shaughnessy: AI has net destroyed—even if you count Cursor, Cognition, the most successful AI natives—value has been... trillions of dollars of value has been destroyed by AI at the application layer.

Gavin Baker: And just in this context, I do think it’s something we need to be aware of. The companies that are doing the best today, that are seeing their values increase the most, that are creating economic value, are the companies with the highest effective ratio of utilized GPUs per human.

Patrick O’Shaughnessy: And, you know, maybe this just means that every human’s going to get a lot of GPUs, but I think that’s an interesting...

1:16:57-1:19:34

Gavin Baker: GPUs, but I think that’s an interesting fact that we need to be cognizant of. I will just say—and maybe this is a little dark—I am more and more worried about personal safety. I worry about this a lot more for people who have a much bigger public presence and are much more associated with AI, but I really worry about personal safety. I hope nothing tragic happens, but there is this upsurge in political violence here in America, and as AI increasingly becomes political, I worry that’s going to get directed at more and more AI political leaders. Whatever we can agree on—whatever I may think or may not think of OpenAI—I think it is terrible that someone threw Molotov cocktails at Sam Altman’s house. I am worried that we are headed into a higher variance, higher beta, higher risk world because of AI. And that’s for me as an individual, and then for people who are big players on the chessboard.

Think about what it means geopolitically. We’re watching the Ukrainians really start to win. And the reason they’re winning, I think, is not really because they have better drones—though I think they do, that’s part of it. I think the reason Ukraine is really winning is they have the best battlefield AI outside of probably America and Israel. And as China and our adversaries begin to process that, how do they respond? If the United States, because of its edge in AI—it’s great if you’re America, but it is destabilizing for the rest of the world.

Something I think a lot about is creating a charity to educate the world on how awesome the West has been. Slavery was endemic to essentially almost every civilization, and slavery was really ended by the British Empire. Tell that story. But America, after 1945, we had the nuclear bomb—no one else had it. We could have controlled the world forever. Instead, we rebuilt Germany and Japan, and now they’re America’s most reliable allies—Israel, South Korea, Japan. That’s a testament to the American spirit in our country. We didn’t take over the world. There were these fears, documented at the time, that the American generals—MacArthur was a little bit of an American emperor in Japan—but we didn’t just take over the world. And they could have, and they didn’t. They came home, we demilitarized, and then you had this period of great global stability. It was scary, but you had the Pax Americana.

Patrick O’Shaughnessy: So maybe it’s not destabilizing. Maybe it leads to another Pax Americana, informed by our AI dominance. And I’m so optimistic that AI is going to be amazing for the world. There’s someone like me whose daughter was diagnosed with a very rare mutation—there’s no cure. He was able to assemble a lot of resources, get a lot of compute from the labs. We were made aware of what was happening, spun up an immense amount of agents, and came up using AI with a drug on the market that can actually impact his daughter’s disease, and then has spun up a company to cure it. And her life is already immeasurably different because of AI.

So I’m an AI optimist, maximalist, but I also just acknowledge it’s like an event horizon. It for sure, I think, is going to be a discontinuity we need to navigate as a society. I think the elites are going to be wrong, but we need to be really thoughtful in how we address their concerns. We need to make sure that it’s good for everyone. It is a little dystopian that now the best AI is only available to people with a lot of money. We need to solve that. We need to approach this with humility, recognize there’s a lot of uncertainty, and be thoughtful.

1:20:50-1:21:02

Patrick O’Shaughnessy: When I do this with you, I tell people afterwards, “May you find something that you love as much as Gavin loves markets and companies and capitalism and history on display today, as always.” Gavin, thanks so much for your time.

Gavin Baker: Thank you. Thanks, Patrick.

1:21:08-1:22:09

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