Venture with Grace
00:03:19 · Grace Gong, Jerry Tworek
[00:03:19] S1: Wait. Say more. So basically, I feel like nowadays, most amount of people come out of our podcast talking about open weight models. So essentially, you download stuff and then running stuff within your own database. But how do you guys kind of different from that? [00:03:36] S2: First and foremost, we use different algorithms. The problem with open weight models is that fine tuning is hard and efficient. It requires a lot of people, and the results are not really guaranteed and not really very good. [00:03:49] S1: So you guarantee results? [00:03:50] S2: Yes. And we want to, through a different architecture, we can provide a lot of the things that are just hard to do with fine tuning transformers. On one hand, you have a neural network. On the other, you have data. And there's a question, how do you combine those things together? Fine tuning is one way to do it, which arguably is just not very good. And we are building alternative to that that we think will be much, much better. [00:04:18] S1: Okay. So I wanna talk about... Which part do you wanna talk about more? Like, the people or the business logic? Because I feel like, obviously... Let's talk about the people because, like, you know, you guys obviously hire like the chief people officer, like the former chief people officer of OpenAI, who like, obviously have been at Hudson Trading for over fifteen years, who have like, I'm sure like she have like, recruited like multiple, like, a ton of, like, amazing talents. And then last time I tracked on your team is, you know, like, 16, 20 people, something like...
00:04:18 · Grace Gong, Jerry Tworek
[00:04:18] S1: Okay. So I wanna talk about... Which part do you wanna talk about more? Like, the people or the business logic? Because I feel like, obviously... Let's talk about the people because, like, you know, you guys obviously hire like the chief people officer, like the former chief people officer of OpenAI, who like, obviously have been at Hudson Trading for over fifteen years, who have like, I'm sure like she have like, recruited like multiple, like, a ton of, like, amazing talents. And then last time I tracked on your team is, you know, like, 16, 20 people, something like... [00:04:53] S2: I'm only seven at this time. [00:04:54] S1: Okay. Wow. I [00:04:54] S2: remember our girl. [00:04:55] S1: Grew seven since, like, the last time Internet has updated. So I have so many questions in the AI talent space. So obviously, one of the things is like, talent nowadays, like, worth a lot more than the past. But nowadays, like, so many decisions are actually made by AI. Like, why is some people more valuable than others? And when you guys are thinking about talent planning, what is considered a good talent? So you come from the trading era or like trading... Not era, but like the trading squad. So why is that a better, significantly better AI people than people who was studying computer science?
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?