"The Cognitive Revolution"
01:12:32 · Marc Bhargava, Nathan Labenz
[01:12:32] S1: Yeah. We're not really focused on the savings, to be honest. So we're focused on grinding up. No. If you [01:12:37] S0: flip it and say, how much more can you do with the same inputs? What Yeah. Average. [01:12:40] S1: There we there we have an objective. I think ideally in a three year period, we would like folks to be with the 20% cost increase, be able to double your revenue. So that would be like a broad target. But in three years, if we come in, we spend 20% more than you spend today to make sure you have the right software, the right engineers, the right AI tools, increase the cost 20%, can we increase the revenue a 100% in a three year period? And if we can underwrite, yes, we think that is a plausible case, then we think buying that company could be a good investment. But we are not looking at AI as a cost saving even though it of course can be that. That kind of limits your upside. We're looking at it much more how do we grow and have venture like returns and fast growth and scale by AI enabling our workforce to take on more revenue. So that's... So maybe our mindset is a little
01:13:28 · Nathan Labenz
[01:13:28] S0: bit different on that metric specifically. Is there anything that big tech could do that you are afraid of? I mean, one that I always point to is, and they swear they'll never do it right up until maybe they do it, is not allowing... You know, basically, using their best models for their own products first. Right? Like, so far, we've had this sort of generally even playing field of ChatGPT as a model and the API gets it roughly the same time, maybe even a little before, whatever, but it's, like, pretty much parity. But you can imagine a world where Claude five and Grok five and GPT five are all proprietary products only for some significant period of time, if not indefinitely. And that could potentially really shift the power. Now from a consumer surplus side, like, they probably still can only charge me so much a month because I've got four or five of those options. Of course. Yeah. How do I... How does that play into, like, the app layer? It seems like it could be a real problem for the app layer.
01:14:30 · Marc Bhargava
[01:14:30] S1: I think that's true if there were two or three players. So even two years ago, you could argue OpenAI in the lead followed by maybe Google that kind of fumbled the back there they were the OG with the transformer paper, but still credible and Anthropic, which had got many of the early OpenAI best folks. And, you know, there were two or three players. But then x AI came in and enter stage left DeepSeek and Alibaba and all these other folks. Now I don't think there's a world where, you know, because you have seven, eight pretty top quality model providers, obviously, Facebook with Llama is doing a whole refresh on talent, which is in the news. But there are just so many players now that the idea that all of them are creating closed models and shutting the door is extremely unlikely, and customers can always go and use deepseq. The Chinese labs are also quite good, and there's a very good technology coming out of there. So there are just too many players at this point who are open source, and it was really like a shortage of researchers. Two or three years ago you could kinda count who had worked on these projects because they were really Google OpenAI and starting to be anthropic. Today a lot of PhDs are being done in this space. There are a lot of other folks hiring Facebook, x AI, I would say both have kinda caught up as well to a certain degree. The companies out in China and Mistral in Europe. And so I think we're at a point now where there's a lot of kind of AI talent, AI researchers, AI companies that are creating these models for all of them to turn off and go close model. Maybe we can trade that on Polymarket or something, but I will put the odds at, like, sub 3% there. So
01:16:01 · Nathan Labenz, Marc Bhargava
[01:16:01] S0: Yeah. Okay. Yeah. I mean, the dynamics do change a lot from one to two to eight. [01:16:07] S1: Exactly that. [01:16:09] S0: Because, I mean, some other interesting factors around just like safety, reliability. I mean, there's a whole... There could be some other factors that could make it shift, but I do think I I tend to agree on a purely competitive market basis. There is a strong incentive to try to win the API market, and so there's always a... Even if you have something, you know, better in reserve, there's always a incentive to leapfrog your competitor and, you know, try to win share. Why are you telling this story publicly? You know, one first rule of having a great investment strategy is keep it to yourself. Right? Like Yeah. You know, coming here and talking to me, you're just talking to a bunch of AI obsessives. So maybe there's like a talent angle, but like what... I've also seen you on CNBC. How do you think about the public comms that you're doing?
01:16:54 · Marc Bhargava
[01:16:54] S1: Yeah. We've decided to go public on it for two major reasons. The first is to continue to attract amazing founders. Like we want people who are leaving Applied AI programs or research labs and thinking about building a company to consider working with General Catalyst and to think about we are the firm that can help you go to market in the most different ways. Like for us, we're just hyper focused on how do we work with the best people. And we think that's really the thesis that'll win out. And we need to get the message to the best people. And a lot of them are watching this show or watching CNBC or other places. And so we definitely, the talent angle is it. Like... And... But I wouldn't minimize it. It's the most important part. Like we can't incubate and build these companies and do the strategy without the right people. And so that's really, I think, one part of it. And the second part of it is to push back on kind of the doomsday AI scenario or the private equity consolidation play and tell people who may be wanting to sell their company that there is a third way here as well, which is you could sell to a AI enabled roll up that believes in compounding, that doesn't want a three to five year flip, but really wants to give AI to your workforce, take home more customers, have you own shares in what we're doing, go public, and be compounders like TransDigm and Danaher. So I think a lot of the press is also honestly directed at the companies and the people who are thinking about maybe selling their business to a private equity firm or to a strategic. You know, we would just say, hey. Look. There's a third way here, and we'd love to tell you more about it and talk to you more. And at the end of the day, our secret sauce is essentially incubating companies with the best people and buying the best assets. So I think it makes a lot of sense to tell this story.
01:18:29 · Nathan Labenz
[01:18:29] S0: That could be a great place to end. Is there any other, aspect of a positive vision for the future? I always say the scarcest resource is a positive vision for the AI future. Any other aspects of your positive vision that we haven't touched on that you'd wanna share?