"The Cognitive Revolution"
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?
01:18:44 · Marc Bhargava, Nathan Labenz
[01:18:44] S1: No. I think that's generally it. I think, you know, there's a good question and a fair question around there's just so much investment here being done in the space. Will it really pay off? And maybe we haven't seen that yet. And even last summer, so about a year ago, it sort of felt like a lot of the models were flatlining. I'm sure you were covering that as well, but we weren't really getting a lot of interesting updates. And then I think a lot of that changed honestly at the end of last year, when the reasoning models and there was kind of more context and we've seen now the deep research product from Google and OpenAI and Anthropic releasing their own versions. So I would just say kind of on one maybe positive note is, you know, for a while it did feel like there was a lot of spend and maybe less results even in the productivity of the models. Today, we're seeing a reacceleration of improvements, which kind of you referred to as well. And so I think that's really exciting. And so it's a very, very cool trend to be a part of, and I'm really appreciative of folks like you for covering it. And especially as engineers and students in university or in computer science or thinking what to do next. I think having more and more people look into AI and especially applied AI, which I'm so focused on, is extremely exciting. [01:19:49] S0: This has been excellent. I really appreciate your time and, all the deep dive answers. Marc Bhargava from General Catalyst, thank you for being part of the Cognitive Revolution. [01:19:57] S1: Thanks for having me on.
01:19:57 · Marc Bhargava, Nathan Labenz
[01:19:57] S1: Thanks for having me on. [01:19:59] S0: If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guest and topic suggestions, and sponsorship inquiries either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of a sixteen z, where experts talk technology, business, economics, geopolitics, culture, and more. We're produced by AI Podcasting. If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at aipodcast.ing. And thank you to everyone who listens for being part of the Cognitive Revolution.