Entrepreneurial Thought Leaders (ETL)
00:13:56 · Carina Hong
[00:13:56] S2: Right. It's not the end of the day to be rejected. I think that is a very, very crucial lesson. A lot of things can look very grim at the moment. I think this is one thing is I think entrepreneurship is for those who are daring. Like... And and I also have a lot of respect always when I look at founders who like the company went nowhere because there's really not a lot of, I think, reason why a company would work and another company would fail. Like, execution is something that has a lot of randomness. And like every day, there are days where it just feels like this, like, local, like, minimum, like, things are just going so badly. And that definitely got amplified a lot. The feeling is very intense during the fundraise. We started getting a lot of rejections, right, in the first initial round. One thing that you asked about whether there was an approach that I sort of had that was a change. When I realized that I don't have to die over this hill, I said like this one thing is not what I'm gaming for, then I sort of look from a zoomed out perspective. There are a lot of other ventures that are also really great firms. And I think that after I realized that, it was a lot both, like, less stressful. I was more carefree. And it also, I think, sends this sort of, like, energy of, know, the company is really valuable. And I think that that sort of confidence coming from within. So I think that's one thing. But despair is something that is, I think, on the other hand, a bit less beneficial to a process.
00:15:25 · Ravi Belani
[00:15:25] S1: Yes. Yes. Okay. I wanna move to talking about AI. It is an industry that I've never seen move as quickly as as... It's the fastest moving industry I've ever seen in my life. And I'm curious about how you plan for what to focus on in an industry that is moving and changing so quickly. Can you share your thoughts about that?
00:15:44 · Carina Hong
[00:15:44] S2: Yes, I think this is a great question. AI, I think is just changing so rapidly, and then when you set goals, you need to make sure you're not setting an ambitious goal for the next quarter, you're setting an ambitious goal for the year. If you don't plan it in that way, you will be late, you will be like behind the curve, because the frontier is updating very fast, right? So I remember back in March, we were saying, is it possible that we do this formalization of bounded gaps in primes? It's a very cute result, which is there are infinite many pairs of primes that are bounded by a finite number. And what that finite number is keeps being pushed to lower. When you get to two, you get the two prime conjecture, congratulations. But in 2013, when it was 70,000,000 by Itau and Zhang, it's the first time we had to find that number. The world record has kind of stayed in like 2014 for 246. And there was a human mathematician, Julia Stalman, who pushed it to 240. That was very recent. And we're actually in a pretty sort of short, like time crunch race with her in the end. But when we wanted to formalize the result of 246, the result from 2014, most people in the company believed that was not possible in the sense that it will just take way too much work. And they didn't want to start the school. We have to kind of like be like, well, look, at the end of the day, we have to do something and we have to make an end out there. So I think that's one really big takeaway I have is we need to set ambitious goal and end up like at the time of boundary gaps in plans, people consider that natural that we are able to do that. The second thing I think is to be it's a dichotomy. You need to be both want to do everything and okay to let it go and use other stuff. I So think that's kind of a bit, it sounds paradoxical. When I say that you need to want to be doing anything is we take a full stack approach. Like we have our really big provider data set, we don't have data vendors. We figure out data strategy ourselves. We have a model training team, they do intense mid training and post training. We build our harness, we have our harness infra, like we have our versioning control. Almost nothing on the stack are like outsourced to others, except like we don't do pre training. That's sort of the scale of the ambition being full stack.
00:18:04 · Carina Hong, Ravi Belani
And the benefit for that is we should be prepared that for some parts of the stack, we will be beaten by others. And so it's okay to use other stuff when needed, but you kind of need to like cast out your net widely. And I think it's also like today, you might not be at the frontier. In three quarters, you might be. There are a lot of other companies like Harvey saying how they build a frontier model, a frontier model training lab after actually the company has reached significant revenue at scale milestones. So I think that's such an example. It's like, it's not a sprint, it's a marathon. And the reason I say I'm specifically thinking about companies that are like not adaptive enough to advances. So for example, when Jeff came out, we tried that classification model day one, like and then like immediately, I think by the evening, data warehouse, a lot of classification tasks are using Jeff. We're just kind of very interested in trying everyone's stuff without a sense of defensiveness in the sense that we could be building the same stuff, not in that specific example, but in other cases we have seen. [00:19:10] S1: So you're you're you're open... You're you're putting a lot of bets, sounds like. You're open to being wrong on certain ones, but you have enough bets where you have confidence that you'll still be relevant and and need
00:19:10 · Ravi Belani, Carina Hong
[00:19:10] S1: So you're you're you're open... You're you're putting a lot of bets, sounds like. You're open to being wrong on certain ones, but you have enough bets where you have confidence that you'll still be relevant and and need [00:19:19] S2: Yes, and you also have focus. So there are things that you're like, wow, like data, for example, that's my big focus. We think that the self improvement aspect comes from the ability to generate data synthetically. And so these are things that we're very sort of... We hold it tight to our chest. [00:19:35] S1: And do you think you need proprietary data to sustain as an AI company? [00:19:40] S2: I think so. I think it's kind of one of data and benchmarks are the definition of model, I think. [00:19:45] S1: Okay. Data and benchmarks. And do you think most people right now are building for the wrong AI stack?
00:19:45 · Ravi Belani, Carina Hong
[00:19:45] S1: Okay. Data and benchmarks. And do you think most people right now are building for the wrong AI stack? [00:19:51] S2: That's a question that we touched on. There's this old word where it's a hard coded harness calling a bunch of specialized models. Yeah. We think that the word is going to be... At least right now, we see it as a de minimis harness. It was an agenda or orchestrator model. Mhmm. It can call itself recursively. It can call, like, other hard coded tools that look more like even the hard coded harness is actor. So that's the word we see. And a lot of people are building infrastructure for the old stack and we're like, we probably should always be two steps ahead because look, even when you lose on the capability layer, you earn the right to play as infrastructure layer. But if you're not like two or three steps ahead, then you're kind of in a zombie position. [00:20:36] S1: Okay. So you're in the stratosphere. I just want people to really feel this of rarified group of companies. You raised a lot of capital. You're growing now [00:20:43] S2: New lab. [00:20:44] S1: With high ambitions. [00:20:44] S2: New lab. [00:20:45] S1: New lab. [00:20:46] S2: Yeah. This is what they call it. I think there is a... DD DAS has this like 50 companies, new lab list, and we're definitely one of them.