Venture with Grace
00:05:41 · Jerry Tworek
[00:05:41] S2: We definitely care a lot about talent in many ways, right? When you are starting an AI lab, talent is one of your most important resources. And the type of team we want to build is generally a small team of people that can move very quickly and build some of the frontier systems. We enable people very deeply with automation of agents, so we can build a lot with a pretty small team. But there's still a number of people that you'll need. And sometimes I am joking that for today's models, people serve as continually learning Meet proxies on top of them. And we obviously Julia has been a close friend for a while, and we basically decided to start this company together. And I cared about how do we build a team and the culture and how it work. This is what we are doing. And I think we are picking every person very specifically to fulfill a specific role. We are not just blanket hiring people that we think are nice and smart. We have a mission to do. We are building a team almost like you are drafting a football team, this person in this role, this person in this role. And that's how we operating. We are thinking that companies of the future generally will work very differently, that companies of today, with increased automation of AI being able to do a lot of mundane work and a lot of grinding that was pretty labor intensive times of the past. The question is how many maximally enabled humans do you need and how to structure the work of humans that could work much faster than ever before into a company that actually delivers a pretty audacious and a pretty, pretty difficult mission.
00:07:20 · Grace Gong, Jerry Tworek
[00:07:20] S1: You mentioned, like, the talent or, like, the company structure will be different. How different? Also, like, are you tired with, like, the two stores in the back? You need to have to start. [00:07:30] S2: Well, that's four more [00:07:30] S1: stairs. Just in case. [00:07:32] S2: Yeah. [00:07:33] S1: Okay. So how is the company in the future different from today? [00:07:37] S2: Yeah. I think company of the future surely is largely AI operated. AIs will be processing all the information flowing through the company. AI will be proposing all the decision making. AI will be operating lots of software and machinery. There are questions kind of where do the humans come to in that process. And the future that I like the most is where the humans generally give the objectives of what do we want to achieve with the company's resources. We should take advice from it. But I like the world where we are clearly in control and in the steering wheel of the increasingly automated systems that can generally fulfill what we want to do. That's kind of like I see there is a potential future where the whole company is just a state board meeting, where there is a decision, how do we allocate company resources? What do we want to do with those resources? And then AI goes and does it. And there's a next meeting where we see how did that go and what's the next thing to do. That still will take a few iterations to get there. But I do see how slowly but surely we are inching towards that.
00:07:37 · Jerry Tworek, Grace Gong
[00:07:37] S2: Yeah. I think company of the future surely is largely AI operated. AIs will be processing all the information flowing through the company. AI will be proposing all the decision making. AI will be operating lots of software and machinery. There are questions kind of where do the humans come to in that process. And the future that I like the most is where the humans generally give the objectives of what do we want to achieve with the company's resources. We should take advice from it. But I like the world where we are clearly in control and in the steering wheel of the increasingly automated systems that can generally fulfill what we want to do. That's kind of like I see there is a potential future where the whole company is just a state board meeting, where there is a decision, how do we allocate company resources? What do we want to do with those resources? And then AI goes and does it. And there's a next meeting where we see how did that go and what's the next thing to do. That still will take a few iterations to get there. But I do see how slowly but surely we are inching towards that. [00:08:48] S1: Okay. So another question I had was, I'm personally using... You name it, AI Lab, I've been probably using their product. So is that obviously you guys, since you guys are kind of stealthy? What do you use?... Out yet. Okay. So what do you guys use in house? Like, do you have your own, like, everything? Like, you [00:09:11] S2: you have are your own, like... Building more and more stuff ourselves. So we have to [00:09:14] S1: turn on the amazing code or no?
00:09:14 · Grace Gong, Jerry Tworek
[00:09:14] S1: turn on the amazing code or no? [00:09:16] S2: But we we we have started using codecs and we've been using it for a while. We are slowly migrating to our own solutions. In some ways, the state of the art models are very good, but the layer on top of models still has a lot to improve and a lot of how do we use AIs. I think, well, Evolve and surely does Evolve Codex is kind of a very general tool that can be useful for everyone, but actually doesn't have that much value on top of just the model. We've built things that we think are both more flexible, give individual more power, but also like tailor slightly more to what we are trying to achieve and how do we want to work together for us. But the models we are using largely are are still OpenAI models. [00:10:02] S1: What are something that you felt like you would take away, whether it's, like, you know, culture or how things have been done at OpenAI that you would take to your own company? And then what are some, like, you know, the new things that you guys kind of have been implementing?
00:10:16 · Jerry Tworek
[00:10:16] S2: The thing I care about the most, and I brought with myself when I started this new company, is being the innovator and daring. OpenAI, for a while, was a company that had very audacious dreams of building artificial general intelligence. It didn't have that much idea how to do it, but it had a lot of conviction and a lot of confidence in going in that mission. And for many years, OpenAI did breakthrough research year after year, going towards that goal and being very strategic about how to build compute towards that goal, how to build a team towards that goal, and what's going there. We wouldn't have AI that we have today without OpenAI being there. And I think all of us who think OpenAI is a positive force in the world, should be extremely grateful for that. So daring high, having high ambition, and actually focusing your forces on innovative research is what I want to really take into that account. And just because times are different and place are different, I don't think a company like OpenAI is possible even today. It was possible in the day it started, but the landscape landscape has shifted a little bit. I am trying to take the best of the research culture of OpenAI and trying to add a little bit more of our own, the place where we are, which things move very quickly, so we need more sense of urgency these days. We are a bit more of a counterculture and underdog because we are betting against the existing paradigm. And we obviously are much smaller and have less resources. So we need to be much, much scrappier than companies of the past.
00:11:53 · Grace Gong, Jerry Tworek
[00:11:53] S1: I mean, you have $5.20 no. 532,000,000 for seed. [00:11:58] S2: That that feels very small compared to the budgets [00:12:01] S1: of some the labs. Where where do people spend the budget? And also, I I believe NVIDIA is your investor as well. Yes. So, like, does it just go straight back to... [00:12:12] S2: It goes straight back to NVIDIA. Wanna wanna buy the best GPUs on the market, and we wanna buy a lot of them. [00:12:18] S1: Okay. So just straight up GPUs? [00:12:21] S2: Yes. [00:12:21] S1: Spending money on anything else? [00:12:25] S2: We spend a little on the office, but not that much. [00:12:28] S1: Okay. And snacks. And snacks on that We have pretty [00:12:31] S2: weird and fancy snacks. [00:12:33] S1: Okay. Like what? [00:12:36] S2: Salty salmon skin. [00:12:38] S1: Salty salmon skin. Okay. It's it's it's popular in Asia. Yeah. Probably. Yeah. [00:12:44] S2: I haven't had that before, but someone... We had an agent that automated ordering snacks, and that that distribution was getting weirder and weirder all [00:12:51] S1: the It's a it's a very important task to hand it to the agents. [00:12:55] S2: But most importantly, it's not something you really want humans to be spending a lot of time on.