The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:44:02 · Harry Stebbings, Harrison Chase
[00:44:02] S1: Seriously, not fear is justified. [00:44:04] S0: I don't really think that fear is justified. I think there's different concerns. I think one concern is that the data that it runs on will somehow go back to the Chinese labs. And again, if you're if you're using Fireworks or hosting it yourself, I think that's just ridiculous. I think there's other fears that the stuff that they've been trained on will cause it to respond in a way that's, you know, not consistent with the values of the enterprise or something like that. Think I that's where evals come in handy, I think that's largely unfounded. But, like, sure, maybe for some domains, there's there's some realness there. So I think it's probably largely unfounded, but for some domains, because of that kind of, like, response, like, I could see there being some some nuance there. [00:44:45] S1: Do you think in the next twelve months, we'll have comparable US open source models? [00:44:49] S0: I think in the next twelve months, we'll absolutely have comparable US open source models. I think you look at everything that NVIDIA is doing to kind of, like, work with the ecosystem. I think they they care about it tremendously. You look at... Reflection had a model launch recently. I think it was still a little bit behind some of the Chinese models on some benchmarks, but I think there is a tremendous amount of attention in that domain. And so I think absolutely this will catch up.
00:44:49 · Harrison Chase, Harry Stebbings
[00:44:49] S0: I think in the next twelve months, we'll absolutely have comparable US open source models. I think you look at everything that NVIDIA is doing to kind of, like, work with the ecosystem. I think they they care about it tremendously. You look at... Reflection had a model launch recently. I think it was still a little bit behind some of the Chinese models on some benchmarks, but I think there is a tremendous amount of attention in that domain. And so I think absolutely this will catch up. [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?
00:45:11 · 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?