Invest Like the Best with Patrick O'Shaughnessy
· Gavin Baker
[00:00:00] S1: 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. One, they have a partnership with Intel, which is very important because they're getting access to 50 years of institutional knowledge that's just 9 months, a few quarters, 12 months — 3 to 5 quarters behind the front. That's an advantage. It's also an advantage that the Terafab is going to get attention from the A-teams of all the semi-cap equipment companies. One big reason Taiwan Semi caught up is ASML and KLA-Tencor and Lam Research and Applied Materials — they wanted them to catch up. They don't like having a monopsony. The A-teams were in Taiwan working. Intel made some mistakes, and presto. So the A-teams will be here because of Elon's reputation in hardware engineering. 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 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. He's doing Starlink, which is amazing for the world. He's creating all these blue-collar manufacturing jobs, which is a goal of a lot of liberals and good for America. 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.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Put them in space! [00:00:00] S1: It's sad, but he is a living deity in China, Taiwan, South Korea, and Japan. Having watched him for a long time, what he's going to do is recruit the best people, because the best engineers want to work for Elon, especially in hardware engineering. Next to the Terafab, there'll be a Taiwantown. "Oh, 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 Japantown — same thing. We're going to have Koreatown. We're going to have all these things exactly dialed to recruit the best engineers. That's just not the way that the people who run Intel and Samsung 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 Taiwan Semi. [00:00:00] S0: And these have long lead times, right? Terafab is going to be pumping out whatever GPUs, whatever chips — quite a long time from now. [00:00:00] S1: We'll see. Elon tends to do things differently. Everybody else has taken 3 years to build a data center; he built one in 122 days. 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 and building.
· Gavin Baker, Patrick O'Shaughnessy
[00:00:00] S1: We'll see. Elon tends to do things differently. Everybody else has taken 3 years to build a data center; he built one in 122 days. 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 and building. [00:00:00] S0: Are you surprised by — you mentioned DeepSeek earlier. The simple reaction to that was, "OK, these models are just going to get 95% as effective for some tiny fraction of the cost. Distilled 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, 2 years from now, there's no reason I have to spend $1 million a year in my small little firm on tokens or something." But the actual reality seems quite different than this, and I'm curious why there's that dissonance in your mind.
· Gavin Baker
[00:00:00] S1: 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 of them have been at the frontier, which is surprising to me. I think it's been surprising to a lot of people. 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? It is surprising. I remember when Gemini 3.1 Pro came out. It was mind-blowing to me. It was so good. Today, it's intolerable. There's probably a little bit of a dynamic where companies prototype with frontiers and then when they put something into production, you're hearing a lot of people 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. Whether or not it continues 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. I do use Grok 4.3 a lot. It is on the Pareto frontier. The companies that are on the Pareto frontier — and this is, by the way, a big change and a consequence of what we talked about last time — Google losing their per-token cost leadership as a result of making very conservative design decisions with TPU v8 to try to 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. This is the most important thing to look at to analyze AI labs. Google dominated that 9 months ago. Every point on the Pareto frontier — OpenAI, xAI, and Anthropic were inside of them. Now the Pareto frontier is dominated by Anthropic and 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 onto the Pareto frontier. If I were to bet, I would bet that they're subsidizing that out of pride. A violation of Richard Sutton's bitter lesson is for sure the biggest risk to this trade, to all of AI. The closer someone is to AI, the more skeptical they are this will occur.
· Gavin Baker
One thing that contributed to weakness in March was a much more stupid version of DeepSeek — 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 SK Hynix to sign some LTA that would lock in really high prices for a long time, they released this. What people do is always more important than what they say. They publicized it on X, and it goes viral. "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 — more compute will always outperform human algorithmic ingenuity. More compute and data, Chinchilla optimal, beyond Chinchilla optimal — I guess what people increasingly do today. That's a real risk. 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. Maybe we get a temporary period where, 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. The bitter lesson literally 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, Gavin Baker
[00:00:00] S0: 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'd enter. What do you think about the role of those two? [00:00:00] S1: We've done a lot with memory through these harnesses. It turns out that harness engineering is not as important as the model, but it really matters. These harnesses and these models are increasingly being co-developed. One of the big things a harness does — which you can usually just think of as a runtime that the model operates in — it knows where the tools are, it creates context, memory, state. It has very specific prompts or instructions. [00:00:00] S0: Makes a huge difference, even simple versions.