How I Invest with David Weisburd
00:51:39 · Matthew Harris
[00:51:39] S0: Yeah. And I actually think the the wrong question is like, how can Hertz and Avis run their business better? The broader question we see and we see this on the private equity side of bank capital, and there's this beautiful interplay between venture and private equity now around it isn't merely how can you run a relatively old school company better? It's what can you now do? Like the idea that you could build a beautiful software product as Hertz or the idea that you could actually launch your own insurance subsidiary versus partnering with AXA. It's sort of laughable like that. We don't we don't make software and we don't manufacture insurance products. We don't do any of those things. We don't know how to deal with the regulators and all the paper they require us to have. We don't have the actuaries in house to do that. We don't know how to price insurance. But then, you know, somebody like you would walk in that room and say like, well, don't you? Have there ever been accidents in Hertz's cars that you don't know about? Or do you have a comprehensive data set of every accident that's ever happened in a Hertz car? Oh yeah, we do. We actually know all that. And we know to the penny how much it cost to fix it. And we know who the driver was and how long the booking was and who that person worked for. We know all that. It's like, aren't you perfectly situated to launch your own insurance business? And just the fact that you have to file reports with an insurance commissioner in 50 states and another 30 countries, that's like trivially easy with AI.
00:53:09 · David Weisburd, Matthew Harris
[00:53:09] S1: Also could be solved. [00:53:10] S0: Yeah. These things. It's like just take your blinders off and say, like, what are we unfairly positioned to do amazingly well? And relax the constraints that you've historically had about what is it feasible for us to do. And so I think this actually, for kind of old school companies is it's a really wonderful moment. And it actually gives me confidence that when you pick Hertz and Avis, you did so. I think it's a it's a telling example. At first analysis, you'd say like, right now they're using AI to like figure out where the cars should be and manage their customer care costs. And their relatively few programmers are getting more efficient or they're pulling things from a BPO and whatever. So yeah, they could do that with open weight models like probably today. But as the models get more performant and more capable, if they have talented management, they'll start quickly thinking, how do we use these models to do things that we never even contemplated? [00:54:12] S1: Or asking their models what business should we be in?
00:54:12 · David Weisburd, Matthew Harris
[00:54:12] S1: Or asking their models what business should we be in? [00:54:15] S0: Correct. All of it. This is what people I mean, I remember going back quite a ways and I was learning how to use these agents and just trying to like by the quite a ways meaning like Q1 of this year. But I was like pushing myself in the deep end to like, get good at this. And the guy I was working with would always every time I asked him a question, he's like, Why are you asking me the question? Ask Claude the question. Like, I am a crutch for you. Like, ask everything that you need to know is in the model. And anytime you pick your head up and ask me a question, it's just a mistake. You don't know how to connect this to the box drive? Ask it how to do it. And it will tell you. And it was... It's just an incredibly liberating thing to realize that you don't need a coach. You don't need advice. You don't need training. You actually have it all there. You watch, like, a 15 year old use it, and you can see they just never pick their head up from it because that is everything they need. [00:55:15] S1: Going back to the 50,000,000 or so engineers in the world, up until 2026, coding was in code. Today, it's in every language. So in theory, up to 8,000,000,000 people can now code versus a year ago, 50,000,000 people could be code. That's another factor that no one's really factored in, which is what happens when the number of engineers go up by a factor of a 100 or 500 x.
00:55:15 · David Weisburd, Matthew Harris
[00:55:15] S1: Going back to the 50,000,000 or so engineers in the world, up until 2026, coding was in code. Today, it's in every language. So in theory, up to 8,000,000,000 people can now code versus a year ago, 50,000,000 people could be code. That's another factor that no one's really factored in, which is what happens when the number of engineers go up by a factor of a 100 or 500 x. [00:55:43] S0: We're gonna get a lot of useless software, but we're gonna get a lot of useful software too. I think it's amazing. I mean, this is why, like, the dystopian stuff. I just don't have time for it. I I... Like, nothing I can do about it. And I prefer to revel in the possibilities because I think they're so abundant, frankly. [00:56:01] S1: Speaking of dystopian possibilities, we earlier talked about this concept of a bubble and how if you keep on overpaying and everybody keeps on chasing momentum, at some point, you're overpaying. And at some point, the bubble bursts. Today, if I use analogy of a baseball game, what inning would you put us in terms of the bubble of AI?
00:56:01 · David Weisburd, Matthew Harris
[00:56:01] S1: Speaking of dystopian possibilities, we earlier talked about this concept of a bubble and how if you keep on overpaying and everybody keeps on chasing momentum, at some point, you're overpaying. And at some point, the bubble bursts. Today, if I use analogy of a baseball game, what inning would you put us in terms of the bubble of AI? [00:56:24] S0: It's probably a disappointing answer for podcast, but I just have to plead ignorance. And I I actually go further and say nobody knows. No one has ever seen anything like this. I'll answer one way, which is to say if this is like other technology waves, we're in the eighth inning. And all it would take is one misstep to sort of blow the top off this bubble, like, you know, November 2021. If this is just like any like the SaaS transformation, which I would say sort of started twenty twelve, twenty thirteen and then ended in extraordinary fashion in November 2021, aided by COVID, by rates going down, all sorts of factors. And then it was an excruciating eighteen months after that. If AI turns out to be like SAS, which is to say yet another chapter in the book of technology, but not a game changer per se. That would probably won't be November 2026, but we are definitely like months and quarters away, you know, in terms of my, like, pattern recognition about [00:57:36] S1: that. Do you put the odds of that at? [00:57:37] S0: And percent? I mean, I I just think that's
00:57:37 · Matthew Harris, David Weisburd
[00:57:37] S0: And percent? I mean, I I just think that's [00:57:39] S1: How how would it even be possible? Because unlike other types of technologies, I feel like... And this is a... Another question I ask people, which is if everyone's getting so much more productive, why hasn't it showed up in GDP? So we'll we'll handle that in a second. But assuming that I'm not myself hallucinating, How is it possible that I'm getting all these productivity gains? I'm getting better, doing more episodes, doing more investments, not using as many resources in terms of human capital, and and at the same time, we're in a bubble. How's that even humanly possible? [00:58:15] S0: I don't think it is. That's why I'm at, you know, this tiny percentage. [00:58:17] S1: Percent on the high end. [00:58:18] S0: Yeah. I think... I mean, now are people systematically overpaying because of this power law chasing dynamic in a... [00:58:28] S1: So it might be a bubble from an investment side, not necessarily from adoption side.