AI investors should look for expertise, speed and brand

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 48:05

   very good ROI to start kind of like post-training models for those use cases. How do you see the inference market playing out? You mentioned that Fireworks very openly were a big investor in Fireworks and I love Lin and the team, but everyone's trying to do it. We had Chase on the show from Crusoe, who also have that as part of their business. Friends with the Nebius guys, they also do it as part of it.

2. 48:26

   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? I think when it comes to how things will play out with respect to inference, there's two things that I think I really underestimated from the start of LangChain to now. One is the demand for inference, and two is the demand for coding agents. I think both of those markets are just tremendously bigger than I thought three, four years ago.

3. 48:51

   I think actually it's Eric Vichery at Benchmark who said this on either a tweet or podcast recently where I think he basically said, everything just works. This market is so big. I think Thomas Tungs as well had something around just the market for inference is just so big that 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.

4. 49:13

   There's just so much demand for it. And I don't know if it's going to 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. Why would you not be investing today? Like, I can give you some examples. I think the customer support space is like wildly uninteresting. 20 providers, all funded with over 100 million in the last 18 months.

5. 49:35

   You've got Salesforce and Atlassian and big incumbents there too. You've got kind of mildly old ones like Intercom and TalkDesk doing it too. Okay, cool. all like robotics like bold tape but there's so much money going into robotics but massively far away from having a chat gpt moment humanoids in particular really struggling form factor I just think the funding and the excitement exceeds the capabilities significantly would be my answer.

6. 50:01

   I like to think I'm somewhat of an optimist. And so I do think this technology is transformative. And so I think there will be a ton of opportunity. And so I might answer that. And then through that, we can back out what I wouldn't invest in. I think inference we already talked about. I think there's a tremendous... Let's do this. You're my nearest partner in the fund. Welcome. It's so good to have you on your first day.

7. 50:23

   Thank you. Number one, we should have inference. We should have inference. There's going to 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. Do they not compete with fireworks? Modal does on some things, yeah. Okay. But they also do more. They do sandboxes. So we've got inference layer, one, the coding layer.

8. 50:46

   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 good layer. Any specific verticals? 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 really diving deep there.

9. 51:10

   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 technology, maybe, or at least AI penetration is low, but there's a lot of domain expertise built up there and going in there 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 I wouldn't start there, having some domain expertise that can be captured there and then putting it to work. I think, by the way, there are maybe like three things that I would look for in all of these, and I think our prereqs, if you look at all of these teams that are succeeding, one, I think

10. 51:53

    the talent that they have is tremendously strong. Modal has an incredibly strong kind of talent team. Lin and Fireworks, right? They wrote PyTorch. How cool is that, right? When we met them, you know when you're kind of meeting founders the last time you do the brief interest, like, I built PyTorch. Yeah. Oh, that works. Yeah, yeah. We've got a tick in that box. So I think to build, especially this really deep infrastructure, you have to be super technically strong.

11. 52:16

    And there's just not that many people that can do that. Two, you have to go really, really fast. Being able to iterate quickly, I think, is incredibly important. The market just changes so rapidly. I think speed and execution speed. When I think about our advantages at LinkedIn, I think we iterate really, really quickly. And I think that's one of the advantages we have over other companies. And then the last thing would be brand. I think a lot of companies that made it in this AI era basically caught lightning in a bottle and then rode that wave and captured that. You look at Langchain early on. We went very viral and have executed against that. Cursor, a great example, they went very viral, executed against that. Jev, even Cloud Code, ChatGPT. It seems like the space is moving so fast. And because there are so many competitors, and maybe this is even because of it,

12. 53:02

    But because there are so many competitors, there were like 100 different frameworks that came out in the two months after LinkedIn 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 rode. And so Instinct, another example of this is probably a bunch of personal assistants. I think if there is, in terms of... It would probably be at 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 x you quickly i think you just don't look at the price and you just invest is being first more important than ever before i think so absolutely yeah you look at i mean it's possible to catch up i think open ai is an incredible job of catching

13. 54:45

    up with with codex but being first anthropic and cloud code that wow what an advantage that was but to your point yeah lang chain 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. 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, just because you're first doesn't mean that you're guaranteed success.

14. 55:12

    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 enough because it's very hard to execute with speed. But I think like just because you're first in an area doesn't mean that you'll kind of like survive. I never like to knock people.