The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:49:01 · Harry Stebbings, Harrison Chase
[00:49:01] S1: good layer. Any specific verticals? [00:49:03] S0: I don't know if there's any kind of like verticals that I would overweight so much. I think the important parts are having like domain specific kind of, like, point of view and and really diving deep there. I think Patrick Collison said this on a recent podcast, I think with Amjad from Replit when he was talking about, like, what the next startup he would build. It probably wouldn't be, like, a sexy area. It would be some area where, like, technology maybe or at least AI penetration is low, but there's a lot of domain expertise built up there and kind of going in there and and really transforming that. The key part is basically just having some domain expertise that can either be captured in data or logic that goes in the harness and eventually, maybe the model weights, but but I wouldn't start there. Having having some domain expertise that can be captured there and and then putting it to work. I think, by the way, there there there are maybe like three things that I would look for in all of these, and I think are prereqs. If you look at all of these teams that that are succeeding, one, I think, like, the talent that they have is tremendously strong. Like like, Modal has an incredibly strong kind of, like, talent team. Lin and Fireworks. Right? They wrote PyTorch. Like, how cool is that? Right?
00:49:03 · Harrison Chase, Harry Stebbings
[00:49:03] S0: I don't know if there's any kind of like verticals that I would overweight so much. I think the important parts are having like domain specific kind of, like, point of view and and really diving deep there. I think Patrick Collison said this on a recent podcast, I think with Amjad from Replit when he was talking about, like, what the next startup he would build. It probably wouldn't be, like, a sexy area. It would be some area where, like, technology maybe or at least AI penetration is low, but there's a lot of domain expertise built up there and kind of going in there and and really transforming that. The key part is basically just having some domain expertise that can either be captured in data or logic that goes in the harness and eventually, maybe the model weights, but but I wouldn't start there. Having having some domain expertise that can be captured there and and then putting it to work. I think, by the way, there there there are maybe like three things that I would look for in all of these, and I think are prereqs. If you look at all of these teams that that are succeeding, one, I think, like, the talent that they have is tremendously strong. Like like, Modal has an incredibly strong kind of, like, talent team. Lin and Fireworks. Right? They wrote PyTorch. Like, how cool is that? Right? [00:50:07] S1: When we met them, you know, when you're kind of meeting founders, the last time you do the brief intro, it's like, I built PyTorch. Yeah. Oh, well, that that that works. Yeah. Yeah. A good tick in that box.
00:50:15 · Harrison Chase
[00:50:15] S0: So so I think to build, like, especially this really deep infrastructure, you have to be, like, super technically strong. And there's just not that many people that can do that. Two, you have to go, like, really, really fast. Like, this this... Like, being able to iterate quickly, I think, is incredibly important. The market just changes so rapidly. I think I think speed and execution speed... When when I think about kind of, like, our advantages at Linkchain, I we think do it really, really quickly, and I think that's one of the advantages we we have over other companies. And then the last thing would be brand. I think like a lot of companies that made it in this AI area AI era basically caught lightning in a bottle and then, like, rode that wave and captured that. You look at, like, Lane Chain early on. Like, we went very viral and and have executed against that. Cursor, a great example. They went very viral, executed against that. JEV, even Claude Code, ChatGPT. Like, it seems like the space is moving so fast. And because there are so many competitors... And maybe this is even because of it. Because there are so many competitors, there there were, like, a 100 different frameworks that came out in the the two months after Langcheng came out. But I think we had a brand advantage that was built up, and I think that was a durable advantage that we kind of, like, rode. And so Instinct, another example of this, is probably a bunch of personal assistants. I think if there is a... In terms of, like... It would probably be at, like, maybe higher valuations than is rational. But in terms of some of these funding rounds that are getting done, where there's a breakout company with a really strong technical roster that has proven that they can execute quickly, I think you just don't look at the price and you just invest.
00:51:40 · Harry Stebbings, Harrison Chase
[00:51:40] S1: Is being first more important than ever before? [00:51:42] S0: I think so. Absolutely. Yeah. You look at... Mean, it's possible to catch up. I think OpenAI's an incredible job of catching up with Codex. But being first, Anthropic and Cloud Code, that... Wow. What an advantage that was. [00:51:53] S1: But to your point, yeah, Langhain comes out. And then months after, more and more come out, but Instinct, same, same. But actually, the durable brand monopoly that comes from being the haloed first is very real. [00:52:05] S0: I think that is super real. I also think that you have to execute incredibly well after that. Counter example to what I just said, like, just because you're first doesn't mean that you're guaranteed success. AutoGPT. I don't know if you remember the AutoGPT project, but it came out a few months after LangChain, was the fastest growing GitHub project of all time. I haven't heard much from them since. And that's not a knock, because it's very hard to kind of like execute with speed. But I think like just because you're first in an area doesn't mean that you'll you'll kind of like survive. [00:52:32] S1: I never like to knock people, so I find it like helpful to like praise them instead in some respects. If you were to write a memo joining the firm for why we should invest in $9,000,000,000 JEV round, what would your bull case be for JEV?
00:52:32 · Harry Stebbings, Harrison Chase
[00:52:32] S1: I never like to knock people, so I find it like helpful to like praise them instead in some respects. If you were to write a memo joining the firm for why we should invest in $9,000,000,000 JEV round, what would your bull case be for JEV? [00:52:45] S0: Bull case for JEV is that there's a new type of inference and new type of model, and the model category is literally named after them. And so there's... They're gonna ride that wave, and they're a very talented team that can execute on that. I don't know the team super well. I think that would have to be the assumption for for a round at that value. From everything that I've seen, I I... That seems to be the case, but but that would be the bull case. They basically invented a new model category, are associated with it, and have a talented team that can execute on that. [00:53:11] S1: Does taking money from Benchmark change the trajectory of a company?
00:53:11 · Harry Stebbings, Harrison Chase
[00:53:11] S1: Does taking money from Benchmark change the trajectory of a company? [00:53:15] S0: A 100%. I I think Benchmark... When Miles and really the whole Benchmark partnership invested in us, it was me and Ankush. We were two people. We really thought of them as kind of like a third cofounder. You know, when when I think about company building from from two people to where we are now today, which is like 400 people, they have been kind of like our our most consistent kind of like partner in that in that journey for sure. You know, I am I am not a VC. I've never done VC. There's probably also other market dynamics where great, Benchmark's involved and that signals thing. And we're very lucky to have Sequoia join us after, like, two months, three months after the Benchmark round, and they've been a tremendous partner as well. [00:53:52] S1: Who was it from Sequoia? [00:53:53] S0: Sonya from Sequoia, who is one of the [00:53:55] S1: most plugged in people in the spaces. How do their styles differ?