Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: What are your favorite examples of that so far, or theoretically? [00:00:00] S1: I mean, just does the model balance. They'll be really good at making models. Do all the books globally reconcile? They'll be really good at accounting. Double-entry bookkeeping, it has to balance. There's a verifiable. You got it right or wrong. Support or sale. Did you make the sale or not? That's just like AlphaGo. Did you win or did you lose? Did the guy convert or not? Did the customer ask for an escalation during customer support or not? Its most important functions are important because they can be verified. I think if all of this starts to happen and starts to happen in '26, there'll be an ROI on Blackwell and then all this will continue, and then we'll have Rubin, and then that'll be another big quantum of spend, Rubin and the MI450 and the TPU v9. Then I do think just the most interesting question is what are the economic returns to artificial superintelligence? Because all of these companies in this great game, they've been in a prisoner's dilemma. They're terrified that if they slow down ... [00:00:00] S0: Gone forever. [00:00:00] S1: ... and their competitors don't, it's an existential risk. Microsoft blinked for like six weeks earlier this year. [00:00:00] S0: Yeah.
· Gavin Baker
[00:00:00] S1: I think they would say they regret that. With Blackwell, and for sure with Rubin, the economics are going to dominate the prisoner's dilemma from a decision-making and spending perspective, just because the numbers are so big. This goes to the ROI on AI question. The ROI on AI has empirically, factually, unambiguously, been positive. I just always find it strange that there's any debate about this, because the largest spender just on GPUs are public companies. They report something called audited quarterly financials, and you can use those things to calculate something called a return on invested capital. If you do that calculation, the ROIC of the big public spenders on GPUs is higher than it was before they ramped spinning. You could say, "Well, part of that is OpEx savings." Well, at some level, that is part of what you expect the ROI to be from AI. Then you say, "Well, a lot of it is actually just applying TPUs, moving the big recommender systems that power the advertising and the recommendation systems from CPUs to GPUs, and you've had massive efficiency gains. That's why all the revenue growth at these companies has accelerated." It's like, so what? The ROI has been there. It is interesting. Every big internet company, the people who are responsible for the revenue are intensely annoyed at the amount of GPUs that are being given to the researchers. It's a very linear equation. "If you give me more GPUs, I will drive more revenue." "Give me those GPUs. We'll have more revenue, more gross profit, and then we can spend money." It's this constant fight at every company. One of the factors in the prisoner's dilemma is everybody has this religious belief that we're going to get to ASI. At the end of the day, what do they all want? Almost all of them want to live forever. They think that ASI is going to help them with that.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: Right. Good return. [00:00:00] S1: It's a good return, but we don't know. If, as humans, we have pushed the boundaries of physics, biology and chemistry, the natural laws that govern the universe, then maybe the economic returns to ASI aren't that high. [00:00:00] S0: I'm very curious about your favorite throw cold water on this stuff-type takes that you think about sometimes. One would be I'm curious what you think, the things that would cause this demand for compute to change or even the trajectory of it to change. [00:00:00] S1: There's one really obvious bear case and it is just edge AI, and it's connected to the economic returns to ASI. In three years, on a bigger and bulkier phone to fit the amount of DRAM necessary, and the battery won't probably last as long, you will be able to probably run a pruned-down version of something like Gemini 5 or Grok 4, Grok 4.1 or ChatGPT at 30, 60 tokens per second, and that's free. This is clearly Apple's strategy. It's just, "We're going to be a distributor of AI, and we're going to make it privacy-safe and run on the phone. Then you can call one of the big models, the God models in the cloud, whenever you have a question." If that happens, if 30, 60 tokens a second at a 115 IQ is good enough, I think that's ... [00:00:00] S0: A bear case.
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: A bear case. [00:00:00] S1: Other than just these scaling laws break. In terms of if we assume scaling laws continue, and we now know they're going to continue for pre-training for at least one more generation ... and we're very early in the two new scaling laws for post-training, mid-training, RLVR, whatever people want to call it, and then test-time computed inference ... we're so early in those, and we're getting so much better at helping the models hold more and more context in their minds as they do this test-time compute. That's really powerful, because everybody's like, "Well, how's the model going to know this?" Well, eventually, if you can hold enough context, you can just hold every Slack message and Outlook message and company manual in a company, in your context, and then you can compute the new task and compare it with your knowledge of the world, what you think, what the model thinks, all this context. It may be that just really, really long context windows are the solution to a lot of the current limitations. That's enabled by all these cool tricks like KV cache offload and stuff. I do think, other than scaling laws slowing down, other than there being low economic returns to ASI, edge AI is to me by far the most plausible and scariest bear case.
· Patrick O'Shaughnessy
[00:00:00] S0: I like to visualize different S-curves. You invested through the iPhone, and I love to see the visual of the iPhone models as it went from this clunky, bricky thing up to what we have now, where each one's a little bit ... obviously we've sort of petered out on its form factor. If you picture something similar for the frontier models themselves, does it feel like it's at a certain part of that natural technology paradigm progression?
· Gavin Baker, Patrick O'Shaughnessy
[00:00:00] S1: If you're paying for Gemini Ultra or SuperGrok and you're getting the good AI, it's hard to see differences. I have to go really deep on something like, "Do you think PCI express or ethernet is a better protocol for scale-up networking, and why? Show me the scientific papers." If you shift between models and you ask a question like that where you know it really deeply, then you see differences. I do play fantasy football, winnings are donated to charity, but it is like these new models are quite a bit better at helping who should I play. They think in much more sophisticated ways. If you're a historically good fantasy football player and you're having a bad season, this is why. This is why, because you're not using it. I think we'll see that in more and more domains, but I do think they're already at a level where, unless you are a true expert or just have an intellect that is beyond mine, it's hard to see the progress. That's why I do think we need to shift from getting more intelligent to more useful, unless more intelligence starts leading to these massive scientific breakthroughs and we're curing cancer in '26 and '27. I don't know that we're going to be curing cancer, but I do think from almost an ROI S-curve, we need to hand off from intelligence to usefulness, and then usefulness will then have to hand off to scientific breakthrough just that creates whole new industries. [00:00:00] S0: What are the building blocks of usefulness, in your mind?