The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:45:11 · Harry Stebbings, Harrison Chase
[00:45:11] S1: When you look at the state of land, say, today in terms of open models, what does the world not see that you see very clearly given your position? [00:45:19] S0: I think when it comes to open models, I think there is a lot of usage of them, but I do think they're for more narrow tasks than people think. So I think we see a lot of people using more open source models and more post trained models than than I would have maybe expected a year or a year and a half ago. But I still think they're primarily for these simpler tasks like routing or guardrails or extraction or summarization. I don't think they're really driving the the core agentic loops for a lot of these main agents. And and so I think open source models are are kind of divisive cause you have some people on on one end who are like, great. They're they're good enough. Let's use them for everything. And and I don't think they're quite there. And then I think you have other people who are like, you know, the frontier models are just going to keep on getting better and better and cheaper and cheaper. And like, yes, that's true. But if you really know what you're going to do, so you have to have a high volume use case. And if there are a few components of that that are simple enough tasks, I think it's very good ROI to start kind of like post training models for for those use cases.
00:46:13 · Harry Stebbings, Harrison Chase
[00:46:13] S1: How do you see the inference market playing out? You mentioned that Fireworks very openly. We're a big investor in Fireworks, and I love Lynn and the team. But everyone's trying to do it. You you... We had Chase on the show from Crusoe who also have that as part of their business. You know, friends with the Nebius guys, they also do it as part of their... How does this business play out? And how do you think about the need to do all of the stack versus inference only providers like a Fireworks or a Base 10? [00:46:39] S0: I think when it comes to how things will play out with respect to inference, there's two things that I I think I really underestimated from the start of Lang change now. One is the demand for inference, and two is the demand for, like, coding agents. I think both of those markets are just tremendously bigger than than I thought three, four years ago. I think actually it's it's Eric Visserie at Benchmark who said this on either a tweet or podcast recently where I think he basically said, like, everything just works. Like, this this market is so big. I think Thomas Tungs as well had something around just, the market for inference is just so big that... And I think Eric said it about more broad stuff than just inference, but I think applying it to inference, I think any inference will work. That's like, you know, there's just so much demand for it. And I don't know if it's gonna get less anytime soon. And so, yeah, sure. Fireworks will work. AWS will work. Crusoe will work. I just think there's so much demand for it.
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.