Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Gavin Baker
[00:00:00] S1: It makes an incredible difference. The last time I was on here, or one of the other times, I 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 Claude Code or Codex, and you need to be on an enterprise plan. The reason for this, and this is another dynamic enabled by Google losing their cost leadership, is these AI models just shifted to usage-based pricing. If you were on that $250 or $300 or $280 a month plan or whatever it is, you were getting severely rate limited. You were getting a lobotomized version of the AI because, like we talked about, Claude now produces 70% less 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. By the way, this is so bullish for AI. If we go back to '05 to '07, 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. 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 the shift to usage-based pricing is probably why you will see OpenAI and Anthropic exceed well over $200 billion in ARR this year. Not only is more compute going to come online, but they're going to be able to push frontier token pricing with these usage-based enterprise models. 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. And it's going to throw off a lot of investors' intuitive sense of the capabilities of AI. But yeah, continual learning — if we solve that…
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: How do you conceptualize that? [00:00:00] S1: AI is constantly updating its weights. It may end up being something different. There are so many mysteries about the human mind. We're such sample-efficient learners relative to AI, by many orders of magnitude. We have a crude variant of continual learning today when something is verifiable, and that's just reinforcement learning during training. Continual learning is a model that dynamically adjusts its weights or adjusts in some way, in real time. As a human — the first time I put my hand in a fire, I 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 people are working on really smart techniques beyond this. If we get that, then we have a really fast takeoff. People seem confident that continual learning is just around the corner. 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?
· Patrick O'Shaughnessy
[00:00:00] S0: What is the role of new chip companies in all of this? We talked a lot about Nvidia and their relationship with TSMC, and Intel, and all these sorts of things. There's a thousand flowers blooming — I think 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, and what role it'll play.