Venture with Grace
00:41:04 · Grace Gong
[00:41:04] S1: I guess when you're evaluating the researchers, what does the researcher day to day kind of look like? And then how do you set up, like, I wouldn't say benchmark, like milestone for everybody's research? Since I feel like researchers, sounds like it's kind of like people are creating the solution for things and doing a bunch of tests, right? How long would it be too long for someone to design something new? And would it be like... Would you judge based off current... Certain feature? Or how do you benchmark... Not benchmark, but how do you know people are actually performing on things?
00:41:40 · Jerry Tworek
[00:41:40] S2: Yeah. Yeah. Like, in some way, the most important thing about the researcher performing in a lab or in a company like ours is just like, do they get stuck on things or not? This is like, what, probably the number one failure case of researchers. They just look at a problem. They don't really know how to solve it. And they keep doing things that don't move them forward. They are stuck in a place to learn without gaining any new knowledge, any new insights. Like, this is not going anywhere. And I've seen teams, groups of people just stuck in place and kind of like being on a treadmill, trying to run and not being able to move forward. A good researcher tries to understand pretty quickly, do I have a way how to at least advance my understanding of something? Are there questions that I can know? And even if the problem is very hard and kind of feels distant, usually, you need to have some way how to get to the next step and how to get to the step afterwards where you're sure you are making progress. That's kind of something that I think is very important for researchers. And to me, the question is, does the person doing their work increase their understanding of what they are working with periodically? And that's already pretty great. And the other ones is just not thinking too small. There are some discretion of which direction do we go today, which direction do we go and do we go tomorrow. Are they good at interpreting data? And are they good at being in some way skeptical enough of not being fooled? Randomness has fooled many people as well. It comes experience and it's a skill that you need to get to. And usually, if people combine those things, they end up being pretty successful. Machine learning is extremely fruitful place right now. If you are smart, if you have a few good colleagues around you and a number of GPUs, you almost surely can discover something that no one has discovered before that is fully valuable.
00:43:55 · Grace Gong, Jerry Tworek
[00:43:55] S1: Can we talk about, like, the talent landscape? So are the pace... Best researchers are at these, like, really big labs, or is there something that, like, people are kind of overlooked? Or do you guys straight up hiring people from all the top schools? How do you think about the experience versus academic experience as well? Academic experience as well as the work experience versus someone just have absolutely zero experience, they're really crack coder. [00:44:26] S2: Yeah. I don't think anyone has a monopoly on talent. There is an interesting thing You [00:44:32] S1: guys don't have a monopoly
00:44:33 · Jerry Tworek
[00:44:33] S2: on talent. Don't think we have monopoly on talent. I don't think anyone has. I think the talent has a natural ability to disperse, think, frontier labs, the Foldy at some moment where we're the absolute hotbed of the most amazing people. Sometimes you don't even know if this is like, those people are amazing or a good place at a good time. But definitely, people being there have built absolutely amazing things and had great achievements. But many people will agree with me when they say that throughout growth and throughout years, the talent density of these very big companies have slightly lower over time. But the most... I think the more damaging thing is that they've become slightly more bureaucratic in various ways, which just lowers the agency. When a company is smaller, it's easier for an individual to achieve tremendous outcome. The bigger the company is, the more the more the team's org structure there is. It's kind of harder to be able to achieve that. And that's why talent disperses a little bit. And there are many, many people starting startups and joining startups and trying to do things like that, which is I don't think there are one or two startups that have the best people. I think it's kind of the opposite. Like, almost every startup has a few really, really great people that have achieved things before and are clearly excellent. And they're building their own teams and their own groups of people trying to bet on the new generation of researchers that will drive what this startup will do. So I think in some way, I think on base levels, the talent is somewhat equally spread in the system with obviously various there are some companies that may be slightly less talent dense, some companies slightly more talent dense. But extremely, extremely talented people seek for the places where they can achieve the most with their time on earth and with the brainpower that they have. And that's kind of the optimization function for many.
00:46:37 · Grace Gong
[00:46:37] S1: Is there different milestones that you kind of set your mind to for your lab? Because I think the thing is Anthropic and OpenAI are the first of wave of labs that we can see that there's a potential for them to IPO. And obviously, it takes them a lot shorter than a lot of other companies. I wouldn't say shorter since I feel like OpenAI started seven, eight years ago. And now it is like, we have companies like Stripe that takes forever to IPO. And obviously, it's just a really different range of the timelines for people to achieve that status. And nowadays, I believe it was like $303,100,000,000 ARR or something like that to actually achieve that status. I'm sure in the next ten years it will shift. Shift. In your mind, like, is there any, like, different kind of, like, target time zone that you kind of, like, think, oh, like, we're gonna spend three years on, like, x, like, which is, developing, like, I don't know, like, the best, like, auto and, lab and refining the technology and maybe the next three years on GKM, whether it's creating a consumer version of the product or something like that. So do you have a general really concrete plan? Or do you feel like it's more towards, like, you implementing the day to day now? I mean, you will see what's happening. I'm sure there's also a company probably starting trying to buy you guys at some point slash, like, maybe assertive. I don't know. But yeah.
00:48:12 · Jerry Tworek, Grace Gong
[00:48:12] S2: Yeah. Yeah. No. No. I I think, like, you know, we we have internal road map, pretty precise, but at the same time, it's not something we really talk about publicly when we are when we are doing right now and we want to achieve by the by the end of this year. But those things we have set and we know where we are going. In terms of building companies, I think Anthropic has achieved this incredible achievement of building company in five years. And many are saying right now if we're looking for their investor material, they are probably the most efficient scaling company in the history of capitalism, which is something and definitely an achievement. But I think we live in exponential curve of acceleration. So I do believe there will be a next company that will be even more efficient and that will scale even faster. I believe that there will be a next company, probably already has been started, the next company that will become a trillion dollar company in three years. [00:49:09] S1: Do you know who these people are? Which? I mean, the next generation of these companies. [00:49:15] S2: Who's some bad? [00:49:16] S1: Who's that Core that? Automation? [00:49:17] S2: Who's that? [00:49:25] S1: So I wanna be mindful of time. Maybe we'll do, like, one real question and one on fire round. [00:49:29] S2: Yeah. [00:49:29] S1: So one real question would be, given today's, like, how much do you know about AI? Like, what is, like, the most, like, undervalued technology that you you think in the AI arena?