Invest Like the Best with Patrick O'Shaughnessy
00:05:12 · Patrick O'Shaughnessy, Ben Thompson
[00:05:12] S0: these conversations. Put yourself in their shoes. Like what do you think the motivations are? Everyone [00:05:17] S2: can use a good bogeyman. I think from the AI trade perspective, nothing works better than we have to beat China. And I do think we need to beat China. We need to be competitive. I despair at the extent to which over the last few years in particular, so many of our responses, particularly from a political perspective, has been to like try to be like China. I think we should be going the other direction. More openness, more innovation, less top down control, less restrictions on speech and things along those lines. America succeeds by being on the leading edge and by leading into that. [00:05:54] S0: You said probably The US being purely dominant in AI is not the right end state for the world. What is your ideal equilibrium for how this goes worldwide?
00:06:03 · Ben Thompson
[00:06:03] S2: There's a bit where AI right now is kinda like the Taiwan situation. In that the current status quo actually doesn't seem so bad. The question is how sustainable is it? But maybe it's sustainable for longer than we think. The way I think about it right now is I think OpenAI and Anthropic are clearly on the frontier. Who knows what's happening with Google and then Grok and Meta are chasing them. Meanwhile, the Chinese are very capable, very smart, and also definitely distilling these models to sort of stay about six to nine months behind. And it feels like a pretty good equilibrium that I think is generally favorable to The US. Now the question is how long could it stay this way? And there's a lots of questions out there. Like, can the Chinese actually pull ahead? I'm still a little skeptical for various reasons, whether it from chips. Getting to the waiting edge, I think that last six to nine months is very difficult. It's gonna be instructive how Meta and Grok do in terms of actually catching up, especially as we get to the world of AI improving itself, using AI to make the AI better, which I think is definitely a real thing. I think you see a real acceleration from both OpenAI and Anthropic recently, which was sort of theorized. It seems to be coming true. And to the extent that's true, can you actually catch up? And I think the other question about this, by the way, is to what extent does that apply to cost to serve, to marginal costs? If you can apply AI to optimizing your stack, to figuring things out, to analyzing all the data, is your cost to serve structurally lower than anyone else? This is the thing about the open source models. The talk about them being free is bizarre to me because it's marginal costs. You still have to run inference like GLM or Kimi. Kimi is very expensive to serve. The cost per answer is significantly higher. Everyone referring to these as free. It feels like in the narrative, it's in people's head that free is free. Now I can use AI for free. No, you can't use AI for free. You're not paying necessarily the R and D to create the AI, but you're definitely paying the inference to sort of run it. So right now, I kind of like where we are and the pushback would be that's right now. It's not gonna stay that way, which I think is fair pushback, but I don't know. This way longer than we think.
00:08:16 · Patrick O'Shaughnessy, Ben Thompson
[00:08:16] S0: If you can know anything about the future of how this will go to be more confident in, like, where the equilibrium will end up, what is it? Is it like the length of the s curve? Like, how far up the s curve we are? At some point, these things presumably will level out, maybe not. What would be the thing you'd wanna know that would give you a better sense of what the future might look like? [00:08:34] S2: I am concerned that with the scare around people freaking out about mythos and this hugging face incident, that the actual implication of that is not that we reduce these dangers, but we just stop releasing stuff. We on the outside start to lose any sense of like where exactly What's [00:08:58] S0: actually the frontier, yeah.
00:08:58 · Patrick O'Shaughnessy, Ben Thompson
[00:08:58] S0: actually the frontier, yeah. [00:08:59] S2: What is actually the frontier and where it is. There becomes sort of a false sense of security because right now everyone's basing their understanding of mythos on fable. But how good is fable actually relative to mythos? That sort of gap is only going to, I think, increase over time. So I think that's a real question that I'm not sure about. This question of the recursiveness and AI sort of making itself better, does that lead to some sort of takeoff? And at the end of the day, there's timing questions in lots of different ways. I'm worried about the timing mismatch in terms of the actual return on investment producing enough revenue to fuel investment. We're working our way down the capital curve. We started with free cash flow. The speed with which the tech companies blew through the debt markets is kind of incredible. It took like a year. And now Google's issuing equity. Nvidia's putting together these [00:09:51] S0: This $500,000,000,000 thing. This $500,000,000,000
00:09:51 · Patrick O'Shaughnessy, Ben Thompson
[00:09:51] S0: This $500,000,000,000 thing. This $500,000,000,000 [00:09:53] S2: thing to tap into like pension funds and insurance floats and things like that. What's after that? Where's the money come after that? Well, ideally, we actually flip back to free cash flow funding this. But if there's a gap there, if we don't get there soon enough, then we could have a big blowup. But at the same time, even if we have this blowup, the AI is not going away. It's not going to stop improving. It's going to keep sort of progressing in a way that we look back on the .com era, or we look back on the railroad era, or we look back on whatever bubbles through history, ultimately immaterial in terms of the broad scope of humanity, even if they were very devastating. [00:10:34] S0: What can the railroads teach us, do think? It's not the last bigger build out, right, in terms of percent of GDP or getting I
00:10:34 · Patrick O'Shaughnessy, Ben Thompson
[00:10:34] S0: What can the railroads teach us, do think? It's not the last bigger build out, right, in terms of percent of GDP or getting I [00:10:41] S2: think we might be bigger at this point, or it's like it was the biggest. In the ballpark. The railroads had a real duration mismatch. To build a railroad and make money off it was a decade or multiple decades long endeavor. Whereas you had to issue money to pay for it in the short term. And the world ran out of money. Right? I think that is probably the aspect. I think that's why people reach for the railroads because everyone talks about, are we gonna have enough compute? Are we gonna have enough electricity? Maybe the nearest term questions, are we gonna have enough money? Which is kind of a bizarre thing to think about. That's what happened in the eighteen seventies. The world just ran out of money. The funny thing is the railroads kept operating and they expanded the West. Their contributions to GDP was astronomical. They're still contributing to GDP. Railroad money is what's going into Google right now for Berkshire Hathaway. Like like very funny. No. [00:11:39] S0: It's quite literal.