The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:47:27 · Harry Stebbings, Harrison Chase
[00:47:27] S1: Why would you not be investing today? Like, I... So I can give you some examples. I think the the customer support space is, like, wildly uninteresting. 20 providers all funded with over 100,000,000 in the last eighteen months. You got Salesforce and Atlassian and big incumbents there too. You got kind of mildly old ones like Intercom and Talkdesk doing it too. Okay. Cool. All like robotics, like, tape, but there's so much money going into robotics. We're massively far away from having a chat GBT moment. Humanoids, in particular, really struggle in form factor. I just think funding in that excitement exceeds the capability significantly would be my answer. [00:48:05] S0: I like to think I'm somewhat of an optimist, and so I I do think this technology is transformative. And so I think there will be ton of opportunity, and so I might answer that. And through that, we can back out what I wouldn't invest in. I like... I I I think inference, we already talked about, I think there's a tremendous there's tremendous Let's [00:48:22] S1: do this. You're you're you're my newest partner in Welcome. The It's so good to have you on your first day. Thank you. Number one, we should have inference. [00:48:29] S0: We should have inference. K. There's gonna be way more software written, so we should also invest in like lower level kind of like infrastructure primitives. Modal, incredible company. Temporal, incredible company. [00:48:40] S1: So they don't compete with [00:48:41] S0: Modal does on some things, yeah. But they also do more. They do sandboxes.
00:48:41 · Harrison Chase, Harry Stebbings
[00:48:41] S0: Modal does on some things, yeah. But they also do more. They do sandboxes. [00:48:47] S1: So we've got inference layer, one, the coding layer. [00:48:50] S0: I would say inference and then like infrastructure. So like low level infrastructure, temporal, modal, super base, convex, vertical agents. I think vertical agents are going to be a [00:49:01] S1: good layer. Any specific verticals?
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?