Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Gavin Baker
[00:00:00] S0: All of a sudden, you've got an assistant. [00:00:00] S1: Yeah. And you can just imagine ... Everybody talks about that, but you can just imagine it's on your phone. I think that's pretty near-term, but some big companies that are very tech-forward, 50%-plus of customer support is already done by AI, and that's a $400 billion industry. Then what AI is great about is persuasion. That's sales and customer support. Of the functions of a company, if you think about them, they're to make stuff, sell stuff, and then support the customers. Right now, maybe in late '26, you're going to be pretty good at two of them. I do think it's going to have a big impact on media. I think robotics, like we talked about the last time, are going to finally start to be real. There's an explosion in exciting robotic startups. I do still think that the main battle's going to be between Tesla's Optimus and the Chinese, because it's easy to make prototypes, it's hard to mass-produce them. Then it goes back to that, what Andrej Karpathy said, about AI can automate anything that can be verified. Any function where there's a right or wrong answer or right or wrong outcome, you can apply reinforcement learning and make the AI really good at that. [00:00:00] S0: What are your favorite examples of that so far, or theoretically?
· 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?