Invest Like the Best with Patrick O'Shaughnessy
00:20:37 · Gavin Baker
[00:20:37] S2: Yeah, well that's just-- That has been the current- Risk all month in response to these narratives that factually, except for credit, aren't true, and the work we've done makes me think that credit just isn't gonna matter as this reprices. Let's just say you do need credit to like build the flops we need. Well, if credit's not there, it just means the flops that are there are going to be even more valuable, and then eventually that will improve the metrics, and then it's like credit is there, so as long- As long as we're in a compute shortage, which I'm just like desperately trying to find a single sign that we're not in one and that it's not actually getting worse almost by the day, it's almost like the problem becomes the solution. And then this company, Black Forest Labs, I think that's their name, I hope I got it right, 'cause there is an interesting essay that got sent to me, you know, I think we've talked before about Mike Mowson's theory that breakdown in diversity is kind of what leads bubbles and crashes, and essentially everyone I know The public equity investment business, whether retail or institutional, every piece of news gets fed into Claude, and Claude, Claude code, sometimes a Claude agent, and it's probabilistic. There's probably not that much variation in the way it's interpreting this news. It's, it's almost like we're back to, um, in stock market terms, there's never really been this way in the stock market before, but people talk about the fragmentation of media and how- It used to be like Walter Cronkite, only voice of truth, and now we don't have that anymore. It's like Claude, it's kind of Walter Cronkite for the stock market, and everybody just believes Whatever it says. Whatever it says. And by the way, it's really smart, but it's not always right. Its interpretation isn't always correct. And with the stock market, you are fundamentally dealing about, you know, a probabilistic Bayesian interpretation of the future. It feels like, in the market, here's this piece of news, it gets fed through Claude, Claude interprets it this way, a huge chunk of people trade on Claude's view. And so you've seen stuff, there's this guy- TBU, he's like, uh, part of the anonymous semiconductor mafia on X. Yeah. Actually, very smart guy, I know him in real life. But he posted this amazing chart of Japanese capacitor stocks.
00:23:02 · Gavin Baker
And he said, "We've had an entire capacitor cycle in six weeks. " And it's true, you know, the stock's like, whether they double, triple, or quadruple, I don't know, but like, vertical, and then whoosh! Like the actual fundamentals haven't even hit, and yet you've already had what probably would have normally been a three-year cycle in like six weeks.
00:23:26 · Patrick O'Shaughnessy
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00:25:17 · Gavin Baker, Patrick O'Shaughnessy
[00:25:17] S2: Yeah, I am very curious. A lot of people seem to feel Like they are very close to solving continual learning and sample efficient learning, which we've talked about before, and it is possible that if those are solved, that Could that be like a temporary discontinuity in demand if instead of I was trained on effectively twenty billion tokens, and then it's like these models are trained on three hundred trillion tokens? And if, you know, you can train something on ten trillion tokens and then let it out into the world and learn sample efficiently, that doesn't sound good for training demand, but like training has a percentage of semiconductor demand to compute is gonna asymptote to something not approaching Zero, but very small. But I would say that is the most interesting, and, you know, who knows if it's long horizon or short horizon. You know, SSI says that they're gonna come out with their model in August. There's this whole generation of new labs that are focused on this [00:26:20] S0: And this would be good for the world. [00:26:21] S2: This would be amazing for the world. To be clear, yeah, this would be awesome for the [00:26:24] S0: world. We all want, we want this. Yeah, we want this. It
00:26:24 · Patrick O'Shaughnessy, Gavin Baker
[00:26:24] S0: world. We all want, we want this. Yeah, we want this. It [00:26:26] S2: would be amazing for the world, and it's just, it's hard for me to believe that that would actually be negative for AI infrastructure demand. But again, trying to be really, really open-minded I would say that was probably like the biggest scientific or technical takeaway. You know, it's also-- Or you still know. Well, yeah, and also like Nvidia is heavily involved with All of these startups. [00:26:52] S0: If you were just forced to come up with the set of circumstances that would really switch you around and get you really scared, would it just be that this operating cash flow thing doesn't play out and therefore we just need to definance this whole thing? Yeah, if [00:27:03] S2: the operating cash flow does not continue to accelerate, that would be negative, and that to some degree is gonna be a function of how Anthropic, OpenAI, Grok, Cursor, and Open Source do. If there was a pretty dramatic contraction in GPU prices that was kind of sustained, the market would react to that instantly. That would be worrisome if it started to get to be really easy to get GPUs. I mean, have you heard anyone say they have too many GPUs? Like, not, not a single person. No, no, in fact, [00:27:38] S0: it's the opposite. It sounds like a drug market or something. Yeah, it really
00:27:40 · Gavin Baker
[00:27:40] S2: does. It's just wild. But yeah, I mean, I think there's a long list of pretty obvious things. If the sum of these labs plateaus or starts to decline, that's really negative, unless it's just because open source tokens are net growing the pie and taking share. And I do really think the future is multimodal, particularly for the AI natives, they're going to want to take an open source model, it's got all these inference clouds have gotten really good at supervised fine tuning and reinforcement learning, so you can take your data, custom- As an open source model, and then get something that you can put behind a router, and the router routes it to, often first, your model, and then Claude, Frontier model, whatever Claude Grok, checks it, and you can, in a lot of cases, get slightly better outcomes At half the cost. But again, that half the cost, I think a lot of people hear that, they're like, "That's bad for AI demand." It's actually not at all, because the cost the user pays is just a function of the margin on the tokens, and you're literally just shifting tokens from really expensive tokens with like ninety percent gross margins to tokens with maybe, let's call it a thirty percent gross margin. That's where the savings are coming from, but the tokens cost the same amount of compute To produce. And then also all these things are kind of happening on different cycle times. All these big public companies are like, "Oh my God, my AI spend is twenty X, I've burned my budget in three months," so they set up a router, and that actually cuts their AI spend, but it doesn't really impact, it may actually increase the amount of tokens that they are generating just by shifting them to these cheaper open source tokens, and that's just more compute. So a company getting smarter about which model to use for which task, that may lead to a stabilization in their spend or even a decline, but it actually has nothing to do with the amount of GPU compute hours they are effectively consuming behind these model layers of this router. The GPU compute hours probably are going up as you shift to these cheaper tokens you can use more of.