How I Invest with David Weisburd
00:43:11 · Matthew Harris, David Weisburd
[00:43:11] S0: I'd say I... The two countervailing things in my mind when I hear that. One is vehement agreement. And at a certain point, increasingly in our companies, there aren't even human users of AI. They're just agents doing stuff. And so it's not 8,000,000,000 that you're going, you know, that 8,000,000,000 humans getting more productive. It's actually 80 or 800,000,000,000 new entities being created. [00:43:38] S1: And then they're doing deals with each other. [00:43:40] S0: And they're doing and they're using tools [00:43:41] S1: and creating new agents.
00:43:43 · Matthew Harris
[00:43:43] S0: Yeah. Right. So I actually think that, you know, this this artificial cap of 8,000,000,000 as we think about market size is for the first time lifted because of agents that have proven they can do long running tasks with great efficacy. So I agree with Lucas and I would amplify it. And then the one caveat is open weight models. So intelligence is becoming more abundant. It's also becoming cheaper. Those who are related, it's abundance and people's desire and ability to access it gets greater as the price comes down. So there is this beautiful expansionary quality to open weight models and even just the token cost of old anthropic models being so much less expensive than new anthropic models. But that mix shift and exactly how that mix shift happens for what applications is the $2,000,000,000,000 question. I think there's no doubt that demand is going to expand and that will have a benefit to pricing as demand expands. It helps pricing, but supply is massively expanding as well. And so the value of a frontier model is what we don't know. If the 2027 frontier models are as transformational relative to the 26 models as the 26 models were to 2025, then I think 2,000,000,000,000 is surely just a starting point. We basically went in '25 until about November, December 2025. You know, it's still chatbot territory and kind of like finish your line of code but not independent long running reasoning agents doing their own coding. That was really sort of winter twenty five, twenty six And that led to SaaSpocalypse and that led to all of these advances in agentic workflows the course of 2026. So I don't know what the next leg up is. I don't know when it comes and if it's as dramatic as chatbot to agent. But if it is, then open weight models that don't have the frontier capabilities yet will be a pale substitute for those high priced tokens.
00:46:06 · David Weisburd, Matthew Harris
[00:46:06] S1: It's interesting because we live in such a meme world. So many people, even with podcasts, they just consume this fifteen second clip, the the thirty second clip. So everyone tries to reduce these paradigms into these very simple binaries. But in reality, there's so many factors. Some are negative. Some are positive. Some play on each other. Some counteract each other if they start to compound to a certain level. And then there's all these actors and all their game theory, and as one person makes a move, another person makes a move. Almost feel like you need an AI model to tell you what everybody's going to do and what all the AI models will do. It becomes complex quite quickly. [00:46:43] S0: Just today, was talking with Claude about data center CapEx. And the question I had was how much of the call it $5,000,000,000,000, which I think is sort of consensus CapEx spend next five years, how much of that, by the it could be seven. How much of that will be spent on expanding access? So more and more people using more and more A. I. Versus expanding actual functionality. So training models so that A. I. Could do things that at this point it simply can't do. And at least Claude and it's funny in the in the caveat to the answer, it says, you have to remember, I was made by Anthropic and that surely influences my answer. So that was a brilliant disclaimer. Yeah, disclaiming very specifically. [00:47:30] S1: I think it's in the IPO quiet period.
00:47:30 · David Weisburd, Matthew Harris
[00:47:30] S1: I think it's in the IPO quiet period. [00:47:32] S0: That's exactly right. The chatbots in the in the quiet period. But it was it came down squarely in the on the functionality side of things. It said that, you know, it's a mix, but that the preponderance, the reason the number is $5,000,000,000,000 and not 2,000,000,000,000 or some other lower number is actually because of the intensity of training models to do unprecedented things, which was my intuition that this isn't just about the spread of AI and the scope of AI. It is actually about what AI can do. And that's what's behind these CapEx budgets. And that's scary and just incredibly exciting. [00:48:17] S1: I wanna bring up a contrarian thesis. There's now this meme in the market that AI has advanced so much that you could use old models to do virtually all of tasks. All the important, all the day to day tasks. A lot of people are are are, saying this meme. You said it earlier that even the old anthropic models could do a lot of these tasks. I hear that. And I think also open source has a great role to play there. But at the same time, it conflicts with what you previously said and what is true about capitalism, which is this winner take all markets. The number one player gets 70%. Number two player gets 20%. So are we overestimating these old models' capacity, do these tasks, or is now all these industries game theory and that if Hertz is using Anthropic 7.0, now Avis needs to use Anthropic 7.0 as well, and now the old models are no longer have value.
00:49:21 · Matthew Harris
[00:49:21] S0: It really depends on this question we were just discussing, which is what can the new models do? So I I don't think it's as simple as, you know, tit for tat with competitors having to keep up with the Joneses. I think it actually has to do with the functionality. So if you told me that Anthropics twenty twenty seven family of models could do two month long reasoning tasks reliably, then I don't think you have a choice for many of the things that are being attempted right now. I don't think you have a choice but to use those models because they can accomplish things in drug discovery and in insurance underwriting and in all sorts of other historically human fields of endeavor that an open weight model will struggle to do in smaller chunks and will still be, you know, arming the human to do better versus actually replacing what has historically been exclusively human tasks. So if you told me that the leap forward in 2027 was massive in terms of functionality, then I would completely agree that for anything AI is doing today, a year from now, they'll be using cheaper, you know, open weight or old models to do. But what will AI enable us to do tomorrow? And that tomorrow task will always be a frontier by definition, always be the frontier models. And so what's that mix of the value? Have we plateaued? Have we gotten the most out of it that we're going to get and it's all incremental from here? That's true. 2,000,000,000,000 is way too high. If the pace doesn't even have to steepen, if it just continues on its trajectory, the twenty five to twenty six trajectory, if that pace continues, then we're... Then absolutely both hurts. And even the car rental companies with their relatively modest reasoning tat.
00:51:28 · David Weisburd
[00:51:28] S1: I use those two companies because it's not the leading cutting edge companies, but they have a lot of data. They have insurance and all those factors that they are data intensive.