00:39:25 · Thariq Shihipar
[00:39:25] S0: Yeah. I I think there's both there. Like like like, you know, there's definitely like an interest I have in particular. I think it's both we have to improve the u... Agent UX, and I think that teaching people is very valuable, you know. And so there's a lot of work to do in both ways. I I think that like I am... Like, I think there is some fatigue from people in like, oh, like, I need to learn this new thing all the time, you know, and I think that kind of sucks. And I think ideally, should be Claude's job to do things for you and proactively and know exactly the right amount of things. It's a hard problem. Right? Because like let's say that I have a request and Boris has a request for example. Like Boris knows the code base like in in and out. If I'm like, hey, could you make this change? Maybe Claude should be like, okay, well, do you know that this change would have this follow on effect? You know what I mean? And I'd be like, no, I didn't actually. But like Boris might be like, actually, yeah, did. Why are you asking me? You know what I mean? And so I think there's this like... The model is very complicated. It's not just model and harness. It's model harness user and like the world as well. Right? Like what is possible? What code base are you operating in? What does the user know? And how does the user like model like both the world and the harness? And so it it... Yeah. I think this is, one of the biggest problems in AI right now.
00:40:42 · Aditya Agarwal
[00:40:42] S1: Yeah. So we have ten minutes left, and I'm gonna sneak this in because we were chatting about it on text. Because I think a lot of people are familiar with, the the iOS Sora notion. But because we have somebody who is at Sora, it's worth asking. And obviously, that was... I mean, admittedly, last time we were up here talking about it, it was kind of a very clickbaity type phrase. There's a lot that you can build that's still very interesting, and most can be built. But, like, just a couple comments on from the inside, and you don't have to like share specifics. How would you advise a friend, your friends, who are here at SBC, of navigating what is coming with models, how to actually build in a way that takes advantage of models versus getting steamrolled, specific areas where like, you you can be like, I don't know, buddy. Like, not a good one.
00:41:29 · Thariq Shihipar
[00:41:29] S0: To start, I I think AI is one of these things that is like... It... It's unpredictable for all of us. You know what mean? And and at Anthropic, like Dario's talked about how, for example, we have to pre allocate how much compute we need to buy years ahead of time. You know what I mean? And it's very risky. Right? Like, because we need to buy billions and billions of dollars of compute and we need to hope that we will make that revenue. The way I think of it is that this is like kind of an age of exploration. You know, like, think everyone including the frontier models labs are like figuring out what is possible now. And it is like really exciting. There are so many unknown unknowns about, yeah, where is the direction going? How do you hire? How do you build? How do you organize a team? And it's like, I think some people look at you when you're making a startup and be like, this is like, why are you doing this? You know? Like, it's like extremely like, everything's changing all the time. Like, how do you know it's going to work? Like, what do you know? Like, Well, isn't Anthropic conducive? Is an OpenAI gonna do this? So I I think at a high level, I think you just have to be excited about the exploration and uncertainty and like be like, no, I can do it. You know, like, really believe that I can do it and have that sort of like internal confidence I think is the most important thing. Because it's not, I think, the safe thing to do anymore and it was for a long time at Silicon Valley or the high status thing to do. So I think that's one. I think specific areas of like how to think about it. Again, like, dude, I have a I'm
00:42:57 · Aditya Agarwal
[00:42:57] S1: just gonna get steamrolled. Scroll the beans.
00:42:59 · Thariq Shihipar
[00:42:59] S0: Look, I'm not the... No one's an expert. You know what mean? Like, I I I haven't made a successful startup. You know what I mean? I I think that probably one way to think about it is, can you be complementary to like the ModelLab thing? So for example, like, so much of the world is not meant for agents right now. So agents can't access it. Like an agent could not access your health data, your, you know, like your banks, whatever. Right? Like, can you make that more legible to the agents? There might be different payment mechanisms that need to happen there. But like, the more you can take this like locked data source and turn it into something that, you know, the agents can use, that's like extremely, I think, helpful and valuable. How do you, like, disperse AI into, like, specific niches that may be more regulated or, like, you know, like need... Like, have some, like, regulatory capture or something, I I think is, like, extremely helpful. I think thinking about boiling the ocean. So instead of like putting AI in your product, can you use the AI product capabilities to do things that have... Like, were just too ambitious to do before? You know, like, I I think that one of my... Cheng Lou, who made pretext a little, and it's this custom text rendering library that he used to work on the React team, he had all these problems with text rendering on the browser. And then he was like, wait, I would never have been able to do this before, but now I can just do it. And I think that this idea of what's the thing where if you could write... What was the problem that needed 20,000,000 lines of code or 500,000,000 lines of code or something, And can you solve that? Yeah.
00:44:35 · Aditya Agarwal, Thariq Shihipar
[00:44:35] S1: Your first point is resonant, I think, with one of the things we were saying, which is like when building is cheap, kind of the moats are found in a lot of the human problems and trying to access data is a human problem. It's like how do you convince somebody to give you access and like going through all the red tape. And that's... I do think that is, like, one one area of exploration to explore where you can be a little bit out of the, quote, unquote, eye. Right? Because it's still gonna require somebody with, like, a lot of tenacity who would just go and sell it and get in there and access whatever the data and the like these kind of legacy workflows that need to be transformed. [00:45:13] S0: Yeah. Definitely. But I mean, you you never know. You could maybe make a coding harness that's better than us. You know? Like I... Like you just have to believe in it. You know? So... [00:45:21] S1: Yeah. I used to use one example. Like, let's say, I use Conductor a lot. I think it's pretty popular. Just raise the big series a. Just not speaking on behalf of Anthropic, but just as a person who looks at that. Would you be like, in a previous world, you... As a dev tool, you're like, that's interesting. There's a lot of pickup. People are liking this. Let's just go run after that. There's time you can kinda figure out how to build a big business. But now in this day and age,