Latent Space: The AI Engineer Podcast
00:57:43 · Jamin Ball, Shawn Wang
[00:57:43] S0: Yeah. Exactly. [00:57:44] S1: Yeah. And then Gemini has been the most... Like, we will explore everything. And so, like, we appreciate them because if they didn't try it, no one else would. Yeah. Right? Like, they have the funds for it. Yeah. But, like, I think often about Anthropic in terms of, like, well, okay, enterprise encoding. And they... They're first to it. They nailed it. They still continue to nail it. Right? Everyone's chasing after them. Yep. Is that the story going forward? Is is is it just more of the same, or, like, how... What should we expect? [00:58:10] S0: I think it's... I think a lot of this boils down to, like, a new type of innovator's dilemma, which I almost call, like, the compute innovator's dilemma. Like, let's say you're OpenAI, and you have Sora. You have many different model families. You have image models. You have everything. Well, the reality is is how do you choose what to serve? Maybe SORA demand explodes. Are you gonna allocate a lot of your codex compute [00:58:36] S1: To slot videos? [00:58:37] S0: Yeah. Because you probably will have to. Like, if you actually wanna make [00:58:40] S1: I mean, that's exactly why they shut down SORA.
00:58:40 · Shawn Wang, Jamin Ball
[00:58:40] S1: I mean, that's exactly why they shut down SORA. [00:58:41] S0: Yeah. Experience good. You need to. And at the end of the day, what a lot of people have decided is, well, guess what? A lot of the highest ROI, most economically valuable work, has been in coding. So it's hard to prioritize anything else other than that. It's why there's probably opportunities for other companies and startups to build models and build products in those domains because guess what? What is open ad, Anthropic chasing? They're not chasing the incremental $10,000,000,000 market cap. They're chasing the next trillion. And so the reality is for a startup or a neolab, great. You could go build a company, build a product, build a model where you exit for $10,000,000,000 and it's a home run. Grand slam. That's awesome. And simultaneously, it's too small for OpenAI and Anthropic to matter. And so like, what is Anthropic done just a bang up job at? They said coding is our thing. We're gonna be great at coding. We're gonna build the best coding harness in Claude Code, and we're gonna continue to nail it. I think everyone is kind of starting to ask themselves, well, when do we... Maybe you asked a version of this earlier. When do we tap out the coding TAM? I think one point of view is [00:59:41] S1: I have asked very many times this question.
00:59:41 · Shawn Wang, Jamin Ball
[00:59:41] S1: I have asked very many times this question. [00:59:43] S0: But I think one point of view is, hey, coding is actually the best generalizing And model and that can be applied. You think of it as coding, but when I use Claude code, I'm not coding, but I'm using Claude code. When you use Codex, are you coding? No. They're generalizing, and coding is a good framework for generalizing knowledge work. I do think you're seeing a big push by both OpenAI and Anthropic into life sciences and drug discovery. I think they've both been quite public about their interests to kind of like capture that domain as as a new field. And I do think those are just different. [01:00:24] S1: And it would seem like Anthropic is investing more than OpenAI on I specifically the live science stuff.
01:00:24 · Shawn Wang, Jamin Ball
[01:00:24] S1: And it would seem like Anthropic is investing more than OpenAI on I specifically the live science stuff. [01:00:28] S0: Yeah. I'd I'd agree. I'd agree. But now I can't remember the question you asked. But I but I do think you're starting to see these companies evolve the scope of what they want to go after, and it has been very targeted on coding, given it's been the best generalizing... You you want to generalize knowledge work. These models have generalized the best as they've been stronger in coding. But it also can give it some... A soft underbelly to other areas. Maybe it's visual thinking and reasoning. You're seeing great models there like Allurion. Maybe it's decision models like JEV and TypeSafe. Right? Like, there's there's a lot of different soft underbellies they may have, which is great. It's opportunities. Like, you'd love to see better cheaper models in other domains. You know, you don't need to drive the Ferrari engine to the grocery store. Hey. Maybe you... A Toyota will do just fine. Yeah. And I think that will continue. [01:01:18] S1: And good to Lauren shout out. We just interviewed Andrew Oh, no kidding. On his benchmark and and you... And glimpse, I think. Glimpse. Are you guys invested? [01:01:25] S0: Or We are.
01:01:25 · Jamin Ball, Shawn Wang
[01:01:25] S0: Or We are. [01:01:25] S1: Oh, yeah. Oh, wow. Promotion. Wow. Okay. Alright. Yes. It's it's it's weird to... Like, this is a whole different discussion, but, ultimately, I don't get as, like, well, you will lead the giant rounds in, like, you know, the Anthropic of the world, but you also care about Elorean. Yeah. It's, like, way smaller and, like Totally. You know, probably worth, like, a minute of your time. [01:01:43] S0: Yeah. Well, no. Like like, worth worth a ton of my time. I mean, I would say, like, we barbell. I mean, we're life cycle investors. We invest early, we invest late, and we invest in the public markets. And I think there's a lot of value. Like, the way we think about it is some of the best founders today, they need late stage capabilities early on. And late stage capabilities isn't just capital, it's relationships with execs, it's relationships with public companies. I mean, companies are built on hyper mode now. Right? It's not let me go raise a round and let me, you know, add 10,000,000 of ARR in eighteen months, and I'll go raise my next round [01:02:15] S1: and then [01:02:15] S0: two years later. It's like, you're you're growing to a 100,000,000 super fast. You're you're raising 9 figures of of money super quickly, and it's and it's hard. I mean, in many ways, like, you have to, like, break the laws of physics to how, like, cultures can be maintained. Lawyers are [01:02:28] S1: not set up for this. They're like It's hard. What do mean when we come here on a weekend to... Because, like, Monday might be different.
01:02:28 · Shawn Wang, Jamin Ball
[01:02:28] S1: not set up for this. They're like It's hard. What do mean when we come here on a weekend to... Because, like, Monday might be different. [01:02:33] S0: Yeah. Exactly. Exactly. Exactly. I mean, it's... You you used to think about as a board member and investor, hey. Let me think ahead. [01:02:40] S1: Like, next next quarter. [01:02:41] S0: Let me make sure we hire the VP of sales and the CFO, you know, six months before before we need to. So we're not behind. Now it's like, hey, six months before you need to may have been before the company was even incorporated, you know? And so how do you how do you do that? Like, when do you hire the right execs so you're not doing it too late? How do you hire ahead, but not too ahead? I mean, it's... I feel like the pressure of building companies has never been greater. Now the size of the prize has never been greater, but it is... It's just... You know, it might appear as easy as ever to build a company, but I actually think it's the opposite. It's almost as hard as ever to build a good company, and maybe more importantly to build a durable company, which is I think what what everyone aspires to do. [01:03:17] S1: Yeah. I'll also shout out the... On, you know, on the model factory thing. We also interviewed Poolside, [01:03:21] S0: which was [01:03:22] S1: acquired by NVIDIA. Yes. They were pitching this model factory really hard. And I I I do think, like, the business that builds the business is kind of like the the the the general [01:03:29] S0: Mhmm.