Greg Brockman (Part 1)

16:33Rick Rubin:And that night, there was a petition to the board saying, hey, please undo this. And in the end, 95 % of employees signed. And it was all self-organized. people sending it around. So many people were editing that Google doc for the petition that actually crashed Google docs. And so then they had to have, some people were designated as the person to add to the Google doc. It was a whole crazy thing. It sounds like a revolution. It really was. It had - That's what you're describing. That's how it felt. That's a hundred percent how it felt. And that morning I remember waking up 5 a.m. or so and I checked Twitter and I see a post from Ilya where Ilya says, I deeply regret my actions and that I want to help put OpenAI together again.

17:19Rick Rubin:And for me, I feel emotional even just talking about this. I remember when the firing happened, I just felt this distance, right? It was like this emotional thing and you just feel this like - How can you do this to me? Exactly. 100%. And in that moment, I felt forgiveness, right? I felt like, okay. Beautiful. Yeah, it was. So it's 48 hours, the whole story? A little bit longer. That was probably 72 or so. Still a tiny amount of time. But we got two more days to figure out how to get back into the company, right? Because, yeah, and I think that those next few days, we were really trying to figure out what is the right configuration because we clearly needed.

18:01Rick Rubin:It needed to be different. It needed to be different in some way. Because we don't want this to happen again. And so finding a set of board members that worked for the old board, that worked for us, that that was a process. And together we had like new and old and were able to move forward. How would you say the company is different since that event? Extremely. In what way? It took us time to really live by the have the hard conversation and to really figure out how do you get ahead of conflict. I don't think we're perfect at it. There's still moments where I see it, but I think that the feeling that we came back with is this feeling of mortality, that this corporate entity is not destined, it's not preordained that it succeeds.

18:46Rick Rubin:It does not survive simply because of momentum. It survives and thrives because people work hard, because people make it be so. And so I think that that understanding that we need to work hard to achieve this and that the thing that is most likely to get in the way of success is tripping over ourselves. Like those are. Like the human component.

19:12Greg Brockman:Yes. Because would you say the discontent was about were human things? It wasn't technical. It wasn't what the business was doing. Yes. It was just human stuff. Always human stuff. Like in a marriage.

19:23Rick Rubin:Yes. Yes. Yes. And of course, you know, it plays out in all sorts of different ways and forms. But fundamentally, I think that for me, getting to a aligned board, leadership team, company that's moving in one direction, that has a vision and is executing on it, that's been the most important thing. And I felt like in that moment that we had our life flash before our eyes. And so I think since then, you can never take it for granted. And do you feel like you have a good understanding of what happened now? I do. You do. Part of the deal for coming back was to say, let's do an independent review of all events.

20:02Rick Rubin:Right. So there's someone, some law firm's going to come in, take a look at everything and say, like, is there something that needs to be different here? And a law firm did that and said that we understand why the board did this. It was within their right, but they definitely didn't have to. And so there are all these onlookers who then project their own explanation onto what happened. I think they've all been pretty disappointed so far in terms of what they've seen. And it's uncomfortable. It is uncomfortable because I think that it's a moment where our internal dynamics spilled out into the outside world.

20:37Rick Rubin:One of my approaches throughout OpenAI has been, I had this mantra of keep the band together. Yeah. And through various splits that we had, that was the approach I took is to say, hey, we've got all these people. We have different views. We're having conflict. We want to push in different directions. Different people think they should be in charge, whatever it is. But these are all smart people. And I'd love to distill all these views into consistent picture of what needs to happen. And that to me is the core thing that I feel like I do at OpenAI is try to figure out we have this grand mission, but how do we do it?

21:07Rick Rubin:And what is the right decision? And in each of those instances, and it's really not just for the firing, it's like looking back to our time with Elon, to our time with the Anthropic founders. For each of these moments, I have really gone through each of them trying to say, how can we all work together to get to a great outcome? In the end, we've ended up with departures, either because the other person decides they don't want to do it anymore. and that there's usually downstream pain. And that if you look at what happens, normally we let other people tell the narrative. Like we keep going. And partly I think we just don't want to, you know, if we're the ones who are pushing on the company, we want other people to be able to own their story.

21:50Rick Rubin:But then sometimes people use that to kind of whack us. Yeah. Tell me what you remember

21:56Greg Brockman:about our David and the Life conversation. Huh. Because I feel like this relates.

22:01Rick Rubin:Yeah. Yeah, we did talk about it. And I'm trying to remember exactly what context it was in. But I think that the core is that OpenAI, like if you look at the industry that we're in, right? There's Google, there's Meta, and these are established companies with very, very huge market caps and many people and all sorts of users and lots of compute. And we are the challenger. Like we are the ones who are just getting started. Upstart. That's right. And somehow, I think that because ChatsDBT has been so successful, that people then think of us, well, because we're the category leader, therefore, we're the glia, we're the establishment.

22:43Rick Rubin:And where I sit, I say, the AGI, that's the real prize. And this technology is something that I think could play out such that it really benefits these incumbents. and our ethos has always been the whole reason we started was to think about how can we steer this in a better direction for everyone? And that is something where I think we are very much, David, in an industry of Goliaths. And I think the same is true with Elon, right? That Elon Musk, you know, the richest man in the world, one of the most powerful men in the world, and that he's suing us, right? Saying that we tricked him. And when I look back at what we did, I spent so much effort, Billy and I spent so much effort to try to make it work with him.

23:32Rick Rubin:And we were very transparent. What was the difference in vision? So control. The reason that we could not get to a deal with him, because we all agreed that we're going to need far more capital than we can get through philanthropic means. So we're going to need a for-profit entity. and we negotiated over the terms. And he agreed with that as well. Oh, he agreed with that. Yeah, he told us the time is now to go. He said, this is the triggering event. Once we did our first big result, we all got that this was the only way forward. And then the negotiation over the details began. And he needed majority equity.

24:16He needed absolute initial control.

24:20Rick Rubin:And he needed to be CEO. and we could not give on the control. I remember Ilya and I were thinking about it. Do we fall? Do we give this to him? Do we give him the company? And then you start thinking about, well, imagine it actually works. Imagine that you actually achieve the mission. Imagine you actually build an AGI and there's one person who has absolute control over it. It doesn't matter who that person is. Do you feel good? Too much. Too much. And so we pushed hard for an override of some form. So if everyone else was voting against and we just couldn't get it done. And so we said no. And that.

25:02Rick Rubin:Did it end on bad terms right in that moment when the deal wasn't made? I thought it would. It was very, very tough. It was a very emotionally draining moment because we'd spent six weeks, five and a half, six weeks, not doing any work, just really negotiating these terms. We had these deep emotional conversations. and I thought that it was all blown up, but it wasn't. And that we still tried to find a path forward together. But the thing that he came back with was to say, hey, either commit to the nonprofit, which means giving Elon a few more board seats, that it would be, you know, Elias, Sam, Greg, Elon, Elon, Elon.

25:42Rick Rubin:You'd have to commit to a non-solicit and to not quitting for a year or two or something like that. So there were very specific terms for the commitment. And we said, we're going to think about that. The other thing that he came back with was he said, you know what? Why don't you just merge into Tesla? I don't have full control over Tesla. It solves your concern. Build the AGI here. Do it in secret. You got to do it because the shareholders won't like it. And that was the big thing he pushed on. So you get your for-profit. You get the Tesla at the cash cow was the term that was used. And that one we had no desire to do.

26:17Rick Rubin:Like we really thought about it. We were just like, just doesn't seem right. Just a different business? It's a different thing. It's a different thing. It doesn't feel like it's likely to succeed. And it's not like pushing on AGI in like this transparent way that's going to benefit people. And so we went through multiple iterations. We talked about, can we open up this for-profit negotiations again? And it was kind of like, yeah, maybe we could in the future. And then we actually came up with an idea. So we pursued a nonprofit fundraiser. So let's do this for now. But we need to see what we can actually raise.

26:46Rick Rubin:And along the way that we had an idea for maybe we could do an ICO and we could actually raise capital through doing some sort of crypto coin. And we had some ideas for how that would tie to the future compute or AGI that we produce. And he got so excited about that. I remember we had a conversation where he said, you've solved it. It's all great. We're going to raise$10 billion. He said that he was on board. and we spent a lot of time thinking about, okay, let's like really practically chart this out. Is this a good idea? Do we want to do it? We kind of started to get iffy on it. And then he sent an email saying, you know, I've decided I will not support the ICO.

27:24Rick Rubin:This all kind of played out in January of 2018. And then at that point he said, we really need a path to raising more money. And we told him that, hey, we have another idea that doesn't involve an ICO and it would still be, you know, We were going to talk about this for-profit entity. Again, all these ideas were just swirling around and different things we could try. And he said that he just didn't believe it was going to work. So he said he's out. His plan was to build AGI at Tesla and do that thing. He actually tried to recruit a bunch of our people. No one went with him. But on his way out, he did this all hands with a team.

28:01Rick Rubin:And in that all hands, he said that, hey, Greg, Sam, and Ilya see a path to raising billions of dollars. I don't think it's going to work. but if you see a path, you got to follow it. And so I support them. He said he'd even keep advising, he'd stay involved, he supports all of this, but he's just like, you know, this requires billions of dollars per year. He also then went to talk about that he's going to do Tesla AGI, that he wasn't going to work on safety because in his words, if the sheep are legislating safety and the wolves aren't having safety, then there's no point referring to Google.

28:35Rick Rubin:So, you know, it's like that kind of tone. And so then we began over the next year to figure out, you know, how do we actually raise these large funds of capital? Was it all cool with Elon at that point? For sure. Yes.

28:50Greg Brockman:I mean, I think it was very explicit. Everyone understood this was what was happening moving forward. Yes. Okay. And what was the next step?

29:00Rick Rubin:Well, we needed to figure out some structure that would actually let us raise more capital. And we thought about different ideas. we ended up implementing this entity called OpenAI LP. And in mid-2019, that we actually got started. And along the way, we kept Elon apprised of what was happening. We sent him the term sheet. There were a bunch of calls where Sam would talk to him about what we were doing. And I remember getting an email from him in December of 2018. It was devastating. He sent an email saying, the subject was something like, need I remind you? And the body was something like, OpenAI has a 0 % chance of success relative to Google without a dramatic change in resources and execution.

29:41Rick Rubin:Not a 1%. I wish it were different, but that's true. This is going to require billions of dollars per year, even hundreds of millions of dollars, which is what we told him we were planning on raising, isn't enough. And I remember I was on vacation and I just was like, I knocked out for that whole day. Yeah, it was very clear that he thought that we were destined to fail.

30:03Greg Brockman:yeah i don't think he would have sent it if he didn't think you were destined to fail so yeah

30:08Rick Rubin:yeah i agree that would have been his theme for that whole year and i think that things really changed as we started to be successful and that i think that now there's a very different tale that he's trying to tell when's the last time you spoke to him a couple months ago pleasant Very pleasant. It's always pleasant. Yeah.

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32:50How did you end up getting into business with Microsoft?

32:55Rick Rubin:So Microsoft was an early partner. We got compute donated from them for our Dota project. That was the very beginning of it. And over time, I think Sam had kept a good relationship with Satya, with Kevin Scott. And they really believed in what we were doing. and I think really saw the vision for AGI and building these powerful systems.

33:25Greg Brockman:And when did they actually become partners? Did they end up funding the LLC?

33:32Rick Rubin:Yeah, so they funded the billion dollars into the LP, and then they did a$2 billion investment, and they did a$10 billion investment. So that funding was, in fact, very significant, right? that no one else was putting in that, you know, that quantum capital at the time. Different now, but at that time they were doing it. So I was grateful for them really helping us further the mission and what we were doing. And along the way that of course, that we had licensing of our technology and we would partner and you know, that the goal was, hey, let's build this. And then if we can also have this technology be deployed within Microsoft and create value there for Microsoft, then, you know, again, I think it is the thing that ultimately unlocks our pursuit of the models of the mission.

34:20Greg Brockman:How did your business change when you had the influx of cash?

34:26Rick Rubin:I would say that my ethos is always to remember, we have not earned any of this. We are not a profitable company. And so we spend a ton on compute and that that's our goal. And so I think that the big thing that changed is that we could dream, it wasn't really about the cash in some ways, it was really about the supercomputers. And so we spent a lot of time designing supercomputers and we're dreaming of these like massive clusters and we had all sorts of design. And it was very interesting because one of their theses of the deal was, hey, we don't know if these models will be useful, but at the very least, OpenAI can help us figure out how to build supercomputers better for deep learning.

35:07Rick Rubin:And we were very eager to build great supercomputers for deep learning. And so that That was something that it became not just, you could kind of like be like, how are we going to get a data center? Because in 2017, it was very much like, how are we going to get a data center? And the numbers just don't add up. There's no way to get to the scales that we needed to. And then it started to become possible because of this partnership.

35:29Greg Brockman:And it sounds like you were aligned. Yes.

35:32Rick Rubin:It made sense. Yes. Does ChatGPT believe in God? I would say ChatGPT does not have a consistent personality. and the way we think about it is that you should be able to shape it to your own preferences. And so you can have one that has such a belief and you can have one that does not. To me, one thing that's important is to recognize that ChatGPT is not an entity, right? But it's almost this plurality of sort of agents that can be shaped in different form. Does it have emotions? I think that this is an open question. And there's a whole idea of what is called model welfare, right? Are these models, do they have emotions?

36:17Rick Rubin:Are they having a good time? And I think that we're going to have to encounter these questions as the models get smarter and smarter. And I think that right now we don't have a good theory of what it means to have emotions. Can you tell even another person does? You believe that they do because you yourself have them, but we don't have a scientific measurement of it. And so I think that we're going to have to pay some serious attention as time goes on to figuring out what is it that we're creating, not just functionally, but how is this model actually operating? Does it sleep? ChatGVT never sleeps, which is part of what makes it great.

36:57Rick Rubin:It's a 24-7 in your pocket. It can be a doctor. It can be a teacher.

37:03Greg Brockman:Would you say that the breakthroughs coming are coming faster and faster? Or is the rate of change the same as it's been from the beginning?

37:13Rick Rubin:The exponential continues. And so the doubling period is the same, but it feels like the rate of breakthroughs has actually been fairly constant.

37:25Greg Brockman:Is there something that you thought that AI would be good at that it's not? and is there something that you didn't think it would be good at that it is? For sure, yes.

37:36Rick Rubin:I'd say that the models have gotten so good at software, not just the coding part, but all of the debugging, the testing, the end-to-end of that. And it really felt like it transitioned very quickly for us between 5.1 and 5.2, night and day difference. and just that's in a month, two months worth of, it wasn't working. It was like kind of okay to, it's great. And that was a real exciting thing to see. And I think where I'm excited to see the models get great, but I don't feel they are yet, is more in, call it human judgment. Like the kind of thing I'm very excited about is the idea that you can have models that help us all be better versions of ourselves.

38:22Rick Rubin:And can you have models that help negotiate people who have differences? You know, maybe if we had this in earlier times at OpenAI, we would have different outcomes. But think about achieving outcomes like how do you have world peace, right? And how do you actually have better trade deals between different countries? Like being able to negotiate things at that level. I don't really see the models yet adding value. I don't think there's anything that stops them from getting there. But I think that we have some ways to go.

38:48Greg Brockman:Do you feel like they just haven't been trained up or tested enough in those realms? Is that why or because there's something missing? I think we just really need to figure out how to train them for those tasks. Is AI best with current information, historical information, or theoretical information?

39:09Rick Rubin:I think that AI is best with theoretical information in the sense that what we really want out of it is to be a reasoner. We want it to be able to think hard, solve new problems. and so any of the historical information, current information that's in there is incidental. So the way we train them, it does learn a lot about historical facts. A model that you've just trained has no access to current information. You need to hook it up to tools in order to be able to get that real time. But the whole point of this is you want something that's smart, right? You want it to be able to apply to a new discipline and be able to accomplish whatever it is that you care about in that discipline.

39:51Rick Rubin:Are you still coding?

39:53Greg Brockman:I am. It's unusual for someone who is in your position to continue coding, no? Yes. Do you just feel a connection to it?

40:02Rick Rubin:I feel that the way that I know the right direction to lead is by leading from the trenches, leading from the front. And the thing that I have found is that in this field, because things change so quickly, figuring out what to do, like what is the right decision? And sometimes it's a strategic decision for the company, but sometimes it's just very micro. Like what is the way that we should architect this API or this particular piece of software? You need to really feel it. You need to have tried things and have a sense of what works and what doesn't. And so a lot of how I have operated has been like, for example, when we wanted to create the API, which was our first product.

40:41Rick Rubin:We knew we needed to create a product in order to raise capital. But we thought about, well, what are the different things we could build? how do we actually go from nothing to something? And it was actually the hardest projects I've ever worked on because it felt totally backwards, right? The way that you're supposed to build a product is that you have a problem you want to solve. No one cares about the technology behind it. But in our case, we had GPT-3, which was a technology in search of a problem. And so we said, let's just make it accessible to businesses through an API and they can decide what to do with it.

41:17Rick Rubin:and it was a six-month grind. I remember January, I was driving around San Francisco trying to find companies that would be willing to try out this technology. Some of my friends took the meeting just as a favor, and we did a little bit of that in February, which was very good because in March, the whole world shut down for COVID, so there's no driving around, but it was just this intense grind of building this thing from nothing and getting the first beta users. And I remember telling our team that we had two goals. One was to get the first paying customer. So the first dollar that someone would give to us.

41:52Rick Rubin:The second was get a use case that we would all use internally every single day. And that first one we got very quickly. It actually took another two months or so. But that second one, we didn't really get till ChatGPT. So that took like another two years. So it was really a leap of faith. But the way that I did it was by building and really moving the technology in a way that I don't think that anyone else could have marshaled.

42:17Greg Brockman:Do you feel like if you took a year off from the building part, could you come back? Or is it something that if you're not always on top of it, is it moving so fast that it would run away?

42:31Rick Rubin:Well, I actually did this. So I have an answer. So 2025 was my first year where I was really full-time management. And at that point, I think I had like 25 direct reports and was really running a very diverse and complicated set of problems, including our data centers, including GPU infrastructure, including large training runs. And there were really, really excellent engineers that I needed to support. And this year, I've hired some really excellent managers to help me. And so I've actually been able to get back to writing code. How did it feel? It felt there was a mix of rustiness with familiarity because the problems in this field, in some ways, they actually do not change because we're still training neural nets and the neural nets still are a forward, a backward and an optimizer step.

43:24Rick Rubin:And so that the fundamental shape of what we're doing is the same. But in some ways, everything is different. And this year in particular, the tools are entirely different. Now, the one nice thing is they're different for everyone else too. And so everyone is learning together. And I don't feel like that everyone moves and that I'm discovering something that is old to everyone.

43:43Greg Brockman:Tell me about when ChatGPT first came out. What was it like seeing people experiencing talking to it? It was a totally new thing.

43:55Rick Rubin:At the time, we trained GPT-4. Yeah. It was very clear to me that GPT-4 was going to be a huge hit. And we should turn it into a chat system. and we had an earlier model, GPT 3.5, that we'd actually been having different beta testers try out. We were paying like hundreds of contractors to use it. It just didn't feel like it was great. I was very supportive of releasing chat GPT where it's like, let's get the infrastructure out for doing a chat thing and then we'll put in the real model and it'll be great. And so the real surprise was that we'd gotten so used to this next model that we had forgotten that the previous one was actually something people had never seen before.

44:31Rick Rubin:And just making it accessible and available that that's something that would then cause it to click. Because for you, it was already kind of... Old hat. Wow. Yes. And that's one of the amazing things about working at OpenAI, is you live in the future.

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46:22Rick Rubin:Tell me about Sora. Sora is a different branch of the tech tree. And the thing that's interesting is that humans do not generate videos, right? We do not directly output pixels. We can create tools to do it, but we don't have something that works quite sore. But maybe we do in our imagination, right? So maybe there is something that is deeply tied to human intelligence there. And there's definitely something very important, which is a world model. And I think that there are lots of debates about do the GPT text models have a world model? and if you ask them things about, well, there's a ball on the table and it rolls off the table, where is it now?

46:57Rick Rubin:They can answer it. So I think there's much more world knowledge in there than you'd expect. But for Sora, it's so front and center, right? That it has an understanding of physics and you really feel it when it makes a mistake, like dropping an iPhone and that if it doesn't shatter at the right moment, like early versions of Sora would get that totally wrong and it just breaks the experience entirely. But I think that what Sora represents in my mind is creative potential. And we saw very much with last year when we released our image gen and people started to turn photos of themselves and their family into like Studio Ghibli portraits.

47:34Rick Rubin:And it was this moment where the thing that I found most interesting was that people didn't really want to just generate images that were not connected to reality in some way, where there's no humanity in it. But as soon as there was an element of humanity, it's a picture of you and your family, it's not just some arbitrary people, suddenly people connect with it and it really matters. And so I think we're still at the very early days of what these models will be capable of. But I think for applications that require a world model, you think about robotics, you think about anything else in the physical world, having such models can be extremely helpful for that.

48:11Greg Brockman:How did it learn the physical world? Same way that our text models learn.

48:16Rick Rubin:the way that it learns is through observing video, right? Through observing the world and trying to predict what will happen next.

48:24Greg Brockman:Do you see a mystical side to AI? I do.

48:29Rick Rubin:Tell me about it. Well, so there's this idea of scaling laws, which are an empirical observation of as we pour more compute into models that their size is increased certain ways, you get the relationship between all these parameters, right? The size of the data sets. that there's some deterministic curve of how good those models are. And you can project it out very precisely. And there's something about the scaling laws. And you can go on public forums and see people saying, oh, scaling laws are dead, but they're wrong. They're absolutely wrong. Scaling laws continue unabated. The thing that's harder is the engineering to actually realize that.

49:08Rick Rubin:That is difficult. But to me, there is some fundamental scientific discovery that we are making. This is an exploration into the unknown. This is a frontier. And you can see it because we're bringing these like deep scientific principles and mathematical approaches and you're seeing the empirical results. And so it's almost like the way that Yakov, our chief scientist, likes to describe it is that building a bigger model is like building a bigger rocket, where the tolerances all become more tight. And you're still constrained by the same rocket equation and the same gravitational pull and all those things.

49:42Rick Rubin:But I think that what we're doing as we keep going is that we learn more, not just about the models, but we learn more about ourselves. We learn more about what intelligence is. And I think that that is something that is just very profound. How many employees are there at OpenAI now? We're about 5 ,000 people. And what do they do? A lot of different things. So we have about maybe 1 ,000, maybe 1 ,500 people that are in research. And then my organization, which is called Scaling. And so together, that part of the organization is really pushing forward what deep learning can do, trying to explore its limits, engineering into silicon, building the data centers, that whole apparatus.

50:25Rick Rubin:We have a number of people, maybe it's 500 people, maybe another thousand, who are focused on deployment. So this is on applications and actually bringing products like ChachiBT, Codex to life. And there's deep partnership across all of these. Like we cannot do this alone. It really requires the end-to-end of building, deploying, and learning from what we've deployed. That is really how we make progress. And then there's people who work on sales, finance, communications, legal, really a whole host of functions that all come together to make this apparatus work. How did he learn to lead such a big company?

51:02Rick Rubin:It is new to me. It is new. And it's not what I set out to do either.

51:08Greg Brockman:Yeah.

51:08Rick Rubin:being under Dunbar's number and having a tight connection across everyone, knowing everyone, like that was something that appealed to many of us. And I think that what's happened is that the mission is big, right? To really be able to deliver an AGI that's going to benefit everyone. You don't just do it with a small set of functions. You really need to figure out how do you build a large business? How do you get large revenue? Because you need that to fund compute and to be able to raise the capital and to be able to pay the employees, especially in the insanely competitive market that we are in.

51:42Rick Rubin:And so I think that the necessity of it has forced me to then adapt. And at every single stage of OpenAI, the way that I operate, I've learned and I've changed and I've grown. And I think that there's so much of how I work today that I look back at last year, me, I look back at five years ago, me, and I'm like, I knew nothing, like really nothing. And so I think that it's just through constant iteration and constant, I go back to that very beginning of having the humility to not believe that I know everything. And I think that a lot of how I operate is I ask a lot of questions and try to drive clarity and try to really understand why are we doing this?

52:20Rick Rubin:Does this make sense? How does this fit together? Who are the people who are working on this? And get into those details.

52:26Greg Brockman:Of all the things to focus on in AI, how do you decide where your time goes?

52:31Rick Rubin:This is always a very tough question. Yeah. And I think there's a combination of intuition. And I think that what I have successfully done over the years is kind of have a sense of when we have a breakthrough or the signs of life on a breakthrough, that this is the significant direction and that we really need to focus on this. And sometimes it means I focus on it personally. And sometimes it means it's just, we really need to get the company on it, but I am dedicated to a different problem, but that is how I operate. And so right now I'm spending a lot of my, my effort on the data centers, the ML engineering.

53:04Rick Rubin:Again, I have great leaders helping me with this and great engineers who make it all possible. And I'm also helping out with codecs and spending a lot of time really trying to think about what is the right future shape? Like what's the product that people have not yet realized is going to be the product everyone's going to be using in six months, 12 months, and make sure we're running at that as fast as we can and as well as we can. And that's usually marshaling people from across the company. But a lot of it is driven by where I see the models are, where they're going and mapping it to where is the value that we're trying to deliver.

53:38Greg Brockman:Would you say every few months or every year you're focused on something completely different than what you were before? Yes. Do you like that? Is that fun? I do. Yes. It's never boring.

53:49Rick Rubin:Where do you see the company in five years and then in 10 years? Well, in this field, those timelines are so hard to project because I think the world is going to be massively different in that time. I started saying this a year ago that 2025, 2026, 2027 are going to be these transformative moments. I think they're going to be these critical moments. And I think you can really see it. This year, we're going to have agents for knowledge work for every single function. And that's new. That's just been in the past year. That's just new. I was a little bit early on this prediction. I thought last year would be the year of agents, but it is very clearly this year.

54:25Rick Rubin:And to be fair, I think December basically turned into the agent moment. And so here we are. The second thing is scientific discovery. I think we're going to start seeing scientific discovery be revolutionized in a real way. And I think that there are going to be organizations that are early adopters that see this wave coming and really retool around it. And what we're doing with OpenAI is we're retooling around this. We want to be the most AI-forward organization, both because we think it's helpful for our operation, but because we want to figure out how do we bring this technology to everyone?

54:57Rick Rubin:Like, how do you build a company with all of these tools? They're so accelerative. And I think that what we're going to see in 2027 is these tools start to be diffused very, very widely. And it's very interesting to think about that the shape of what a company is is going to change because it's so easy to create companies now. You can run a company that's very effective and makes lots of revenue with a small number of people. And so we're going to have a Cambrian explosion of different companies doing different things. And this is one way that I think that it won't be a technology just to accrues the incumbents.

55:31Rick Rubin:And that's something I think is very important. So you fast forward five years, I think that we will be a very transformed company by AI. And that we will deploy lots of compute for very hard problems. AI is already starting to revolutionize biology. For example, I spent some time working with the ARC Institute to train DNA models. So you just take the exact same type of models that we train for language, and instead you put DNA sequences in. So rather than text, you put in ATGC, and it just learns to predict what's next. But because it learns the underlying rules, that you can actually apply it to downstream applications.

56:08Rick Rubin:You can ask it to determine if a particular protein sequence is valid or not, like really any biological task, because it learns something about biology. and that we're seeing great strides with being able to, you know, there's systems like AlphaFold that have revolutionized protein folding. And this is just scratching the surface. So I think we're going to head into a renaissance of how people are actually able to approach different diseases. And then you can also bring to bear the reasoning technology that we're creating. And we're seeing it already make massive breakthroughs in physics and strides in mathematics.

56:42Rick Rubin:What is reasoning as it relates to AI? I would think of reasoning AI as an AI that expends a lot of compute before coming back with an answer. Like if you think about the original chat CPT that you'd ask for it. Exactly. It would just give you the answer right away. And sometimes it would make a mistake where it would clearly say something was wrong. And then as it was writing, it'd be like, say something inconsistent with that. And so instead, you really want an AI that if it's a hard problem, it can pause, it can think, it can call tools, it can go look on the internet, it can run some experiments and then come back with an answer.

57:15Rick Rubin:So at the core, that is what the reasoning technology is. Now, I think all this is going to change in terms of how people use it. I think that what you really want is you want an always-on AI you can just talk to and that is not something you have to sit around waiting for answers. And it should be able to say, oh, by the way, you asked me this question five minutes ago and I gave you an answer, but I realized actually that was the wrong answer. Here's like the correct thing and here's why I made the mistake. And just like a human assistant, a human coworker, that you should have a very fluid interaction back and forth.

57:46Rick Rubin:And I think we now have the technology to do this and that we need to pursue the productization of it.

57:51Greg Brockman:In the way normal people use ChatGPT, they ask it a question, they get an answer. Do you think they want the right answer or do you think they just want an answer?

58:04Rick Rubin:I think it depends. I think users are more sophisticated than many people think. And so I think people do really care that the answer is accurate and trustworthy and useful. And so sometimes there is no right answer. Yeah, often probably. Often, very often, right? And that people use ChatGVT as a brainstorming partner to come up with new ideas, to come up with, you know, where do I want to go on date night? Like all of these kinds of things. So I think what people actually care about is having AI be an amplifier for their intent, for their goals. Or someone to bounce things off of

58:39Greg Brockman:a second opinion in a way. Yes.

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1:00:22Greg Brockman:Tell me about the financial side of open AI. Is it profitable yet or is it still in growth mode? Still in growth mode. Definitely not profitable.

1:00:30Rick Rubin:Is there a vision to profitability? There is. But I think the challenge that we have is that we are in a world where compute is going to be so scarce. Just because it can't be made faster? Is that the issue? Because it cannot. Yes, exactly. So there's a physical boundary for how much compute can be created right now. And if you go out into the market and you look at the supply chain, you look at TSMC, who does most of the actual wafers, so the actual silicon, you look at memory, you look at even hard drives. these are all basically sold out for sometimes 18 months sometimes longer but the supply chain is becoming incredibly crunched right now because everyone is trying to build everyone is trying to build compute and so it's every single piece of that supply chain and it's a limiter but the reason it's important is because we're moving to a compute powered economy like right now you use chat gpt to ask some questions people are starting to use codecs and tools like that to write code but one thing i have found is that if i want codecs to go do something hard like it's going to build a big application a big website something like that i want 10 parallel codexes that are breaking up the task into 10 sub pieces or i want 100 of them and that compute goes so fast And we have these applications internally where there are teams who want 100 GPUs or 1 ,000 GPUs each to write a very optimized piece of code for them.

1:02:03Rick Rubin:And the thing that we've seen within OpenAI is that the battles over compute have been intense. And I think that we're going to see the exact same thing in the economy, that people's productivity will be directly tied to how much compute they can harness. And so I think that the compute, like really becoming a basic human right is something I expect to see in the future because that will determine your own quality of life in many ways.

1:02:27Greg Brockman:Will there be some breakthrough in that area where it won't be a problem in the future?

1:02:31Rick Rubin:No, because the way that this works, there's Jevons paradox. Are you familiar with Jevons paradox? It's if you increase the efficiency of a technology, the demand for it can increase more than the efficiency gave you. And I think that's what we've seen is every single year, we make the models way more efficient. You want to hit the same level of intelligence. Yeah, no problem. You can do it for 10X cheaper, 100X cheaper sometimes. But you're going to want to use 1 ,000 times more. And you're going to want, rather than this level of intelligence, you're going to want the 10X or 100X intelligence.

1:03:02Rick Rubin:Because rather than solve your small website, why not build a big website? And so I think our ambitions, the problems we want to solve are endless. And therefore, the applications of AI have the same property.

1:03:12Greg Brockman:Yeah, it sounds like if you would have left at the time that you almost left, focusing on making more GPUs would have been a good thing to do.

1:03:20Rick Rubin:That's true. That's true. Yes, yes. And I think that this, by the way, is something that Sam, I think, has put a lot of effort in over the years to try to figure out how do we get the most compute possible. It's not just for OpenAI's competitive nature, and I think it's important for OpenAI's competitive posture for sure. But I think that the degree to which we're going to find that everyone wants compute and no one can find it, like that is going to be just a wild thing.

1:03:45Greg Brockman:Tell me something you believe now that you didn't believe when you were young.

1:03:50Rick Rubin:When I was young, I think I really believed that people would always directly carry out, honestly carry out whatever formal position they're in. For example, my sister was a tour guide at Yale. And I remember going on her tour and she said, all of these really interesting stories. Afterwards, I asked her, wow, that's so cool. How do you guys keep track of all these stories? And she said, oh, we just make them up. And it had never occurred to me that that would be possible. You're a tour guide. Therefore, you will tell the tour and you will tell the true stories of everything that happened. And I think I really had this perspective and really a deep value of this like honesty and sort of, you know, faithfully carrying out duties.

1:04:29Rick Rubin:And I think one thing I have learned is that you can't just take what people say at face value. You need to really look at the motivations, the reason that they're doing it. And that's been a tough realization, right? That's not how I like to operate. I like to be straightforward and direct in everything that I'm doing.

1:04:45Greg Brockman:I'd be shocked if the tour guide made up the story they were telling. I know. Yeah, it turns out. Hard to believe. It turns out, yes. It makes you wonder what else is out there that you don't know about. how does your company function different than how Stripe functioned?

1:05:00Rick Rubin:Because we started as a research company, we had a very different founding DNA. And I think one thing that is very different about a research company and a research company like ours is that we were always thinking about everything we've done right now will be obsolete in a year. It's like being a movie studio. It's like you have your hit. That's wonderful. good for you, but you already need to be thinking about your next hit. And just because you made this good hit doesn't mean that your next one is really going to be a winner. And I think that at Stripe, there was much more, in some sense, security in terms of, well, we had this idea of we want to build a payment process.

1:05:39Rick Rubin:Building this machine. Exactly. And you keep building the machine and the fact you have the machine, you have all this momentum and users and those kinds of things. And so I think that learning to operate in that world has been very, very different.

1:05:49Greg Brockman:Knowing what you know now, if you were going back to the beginning of OpenAI, how might you have set it up differently or would you do everything exactly the same way?

1:06:00Rick Rubin:I'm very happy with the position that we are in. And I feel that we made principled, reasoned decisions at every step. And so it's hard for me to think about the counterfactual. Like, is there anything that I would do differently?

1:06:12Greg Brockman:I understand.

1:06:13Rick Rubin:And I think in the micro, yes. Like I do wish, like there are so many mistakes that I've made and learned from and grown and all those things. Yeah. But I think it's pretty hard for me to be disappointed with where we are.

1:06:26Greg Brockman:Do you think setting it up as a nonprofit to start with was the only way to do it?

1:06:31Rick Rubin:Well, it's not obvious to me the set of choices we could have made for the beginning. I think that what I'll say is that people forget that we are still a nonprofit, right? That the nonprofit has over$100 billion of open AI equity. and that that is something I think is very important and something I think will have a very large impact. Now, we're not profitable yet, right? But our operation, our ability to actually bring to bear both a business that is self-sustaining and also a philanthropic arm that is unprecedented, like that is something I'm extremely excited about.

1:07:08Greg Brockman:Do you see the AI landscape changing over the next few years in any way, either with mergers or companies going out of business?

1:07:17Rick Rubin:I think it's going to be a very dynamic landscape. And I think that all sorts of surprising things are going to happen. Like, I think that the world that we're signed up for is one of an unprecedented nature within AI and I think more broadly in the industry. And so I expect that there will be way more companies trying to get involved. I think these tools are going to diffuse widely. I think there may be surprising mergers, but it's hard for me to predict exactly what.

1:07:41Greg Brockman:and do you think there'll be many winners in the AI space or do you think there will be one big winner or two big winners?

1:07:48Rick Rubin:Well, I hope that there will be many winners and I think that the technology deserves it. My belief is that knowledge work, the economy, lifting up everyone, robots, digital space, like there's just so much surface area to cover that I think that the distribution of different companies being able to push on different aspects of this is very important. One vision that we have for how to make this really good for humanity, for humans, is to focus on what we call resilience. We thought a lot about safety. How do you make sure that AI is aligned with humans? And that there's one vision of this technology where you say you want the centralized AI that is the most powerful thing, and you make sure it's aligned with humanity's values, and then good things will happen.

1:08:33Rick Rubin:But I think that story is dissatisfying because, first of all, there is no humanity's values. You're really talking about someone's values. And that's a scary thing, right? Humanity and really life itself, I think, survives through diversity, through not being a monoculture, through there being the possibility of heresy. And I think that what we need instead is a system of resilience. so just like you think about how the steam engine has changed society right we've built a lot of the world around the fact that we have cars and so we build roads in a certain way we develop safety standards in terms of seat belts to make them you know we have crash testing all these things and that we really configure ourself around a technology because it's so worthwhile and so beneficial and that there's a deep resilience that goes into that so that there's not just one way of doing it.

1:09:29Rick Rubin:And this has been our picture all along is that if you want everyone to have access, how do you actually make sure that this idea is stronger as a result rather than more fragile? Like what do you do if someone does want to do something bad? And that you think about when it comes to all the different bad things, how do you make sure that there are people who can stop it? For example, if you have AIs that might be able to hack into things, maybe you should spend a bunch of effort trying to secure things and trying to have AIs that go and find security vulnerabilities and provide patches. And that's the kind of thing that's happening right now.

1:09:57Rick Rubin:And I think the diversity and the resilience of the world is an important objective and something that we really aim to support.

1:10:04Greg Brockman:Well, it seems like also that in different parts of the world, different things are thought of as acceptable or not. Yes. So if it has one point of view, it can't be for everybody. That's true. It's a diverse place. Yes. What is the unsupervised sentiment neuron?

1:10:22Rick Rubin:Ah, the unsupervised sentiment neuron was a paper in 2017 that for me was one of those crucible moments where you realize this technology is going to work, that there is a moment of transition about to occur. This was the first result where we trained a language model that learned not just syntax. So it didn't just learn where the commas go and the nouns and those kinds of things. It learned semantics because we trained it on Amazon reviews to predict the next character. And it learned a state-of-the-art sentiment analysis classifier. so it could tell you if a review was positive or negative sentiment, which is harder than it sounds.

1:10:58Rick Rubin:Because if you say something like, this product was great, but it was totally broken and I hated it. What's the sentiment? Exactly, right? Complicated. Complicated. So you need some understanding. And through this process, we learned a state-of-the-art sentiment analysis classifier. Just like that AlexNet was the best image classifier, this is the best way of doing sentiment analysis. And so you're just like, we need to scale this up. And so so much of that system changed, but the idea of language modeling leading to intelligent language systems, like that was the moment that was so clear that it was going to happen.

1:11:33Greg Brockman:Have linguistics changed since AI? Considering the way that it works, it's so much based on understanding of linguistics.

1:11:43Rick Rubin:Yes. And because the thing about it is that linguistics to me has always felt like it's like this approximation of how language works. Because language is this living thing, right? And you think about how grammar works. There's all these exceptions that probably that we all split infinitives and stuff.

1:11:57Greg Brockman:That's probably like math in nature, though. It's probably like that. Yes, it is like that.

1:12:01Rick Rubin:It is like that. It's a system. Exactly. And so it's like we're trying to encode the rules for something that's just too diverse to encode the rules for. Whereas these models, they actually capture it. They capture the true underlying rules that we use to generate language.

1:12:17Greg Brockman:Does AI understand sarcasm, double on Chandra, things like that?

1:12:22Rick Rubin:Yes. You can test it on ChatGPT. It does a great job. Can it understand poetry? I think so. Yeah, I think it does a great job. What we're not good at yet is writing jokes. That's something we need to work harder on.

1:12:32Greg Brockman:Yeah.

1:12:33Rick Rubin:The way to think about what we can do right now. So we build these machines that learn all of the underlying rules that generate language that really understands a lot and has a lot of world knowledge within it, knows a lot of facts. And then we do this reinforcement learning process where we give rewards and punishments based on how well it does when actually trying to do something. So anything you can grade, we can now teach the model. And so we started with the most simple thing to grade, which is math problems, things like that that have a deterministic answer. We can just check, is it right or wrong?

1:13:06Rick Rubin:Something like a joke, how do you grade how good that is? But we've been expanding the space of what we can grade because the models themselves can be good graders, right? They can be smart. And so the way to think about how progress has been made is just back to that original plan of gradually learning more complicated things. It's exactly what we're doing. We're expanding what can be graded. And I think that jokes are something that we probably could do. We haven't focused on it because we're focused on knowledge work. But I think that one day we'll get there too. I'd like to see it. Me too.

1:13:38Greg Brockman:Yeah.

1:13:39Rick Rubin:The point is we want something to generate something new. And so what's going on is that when these models learn, again, they're learning the underlying rules, the underlying compression of just like what is the underlying reason that this thing existed. And so if you think about jokes, if you think about scientific paper, it's really trying to just like, okay, actually, it's very similar to when I was writing my chemistry textbook. My whole goal was to not give you here's all of the facts. Here's all of the different chemicals. My goal was to say, how do you think for yourself? What are the underlying things you kind of need to know to then derive everything else?

1:14:16Rick Rubin:And at a deep level, that is what these models are doing, is they are machines that deeply, deeply understand whatever is put in front of them. And if you think about how you as a human learn, it's not that different, right? You don't really remember most of the things that you've been shown. You don't remember most of the things you learned in school, but you know the processes, you know the lessons, you know the reasons that things were done.

1:14:40Greg Brockman:And sometimes even when you get something wrong, a missed memory of the past sometimes leads you to a deeper truth. Yes. We're not so accurate. Yes. And I think this is the key. We're storing machines. Exactly.

1:14:55Rick Rubin:And you're like a connection machine, right? You're trying to figure out how is everything connected? Exactly. And you're constantly trying to figure that out. And that is exactly how these models operate is they're constantly trying to figure out, I've got these parameters. I'm trying to do a better job of this data coming through, predicting what's next, which I've never seen before. And so the only way to do that is to build better and better connections between all these different concepts and to form those somewhere. It's one of these things that if you had said this was how it was going to work 10 years ago, most people would have said that's impossible.

1:15:26Rick Rubin:To really see it implemented and working, like that is something that is unprecedented.

1:15:31Greg Brockman:So if you keep asking it the same question, might you get different answers? You do.

1:15:37Rick Rubin:Because what these machines are trying to do is not give you one particular answer. They want to give you the whole range of possibilities, right? And that we can think of it as a distribution. At a technical level, we think of what these models do is they learn a probability distribution. And so it kind of learns over all possible answers, how likely is this to be the correct answer to it? And it puts more probability mass on the answers that are better.

1:16:02Greg Brockman:Would you say AI is the biggest disruptor of the status quo that we've ever seen? I think it could be. I think that AI will be in many ways like the printing press,

1:16:14Rick Rubin:that it enables and unleashes creativity, productivity, expression in ways that are sometimes heretical, sometimes unthinkable, sometimes very disruptive to how people have expected things to be, but no one would want to take away the printing press.

1:16:34Greg Brockman:Yeah.

1:16:34Rick Rubin:Have you gotten much pushback though? The fact that it can be viewed as heretical? I think that there is a moment right now where people are trying to decide and understand how AI fits in. Like, are they benefiting from AI? And to me, this is one of the most important things that we can do this year is to really show people that AI is something that helps them, that they can build businesses, they can make money off of it. It's something that helps them in their personal life and sometimes financially, like helping them save money, helps with their health, helps with education. All of these things, I think, are the reason that you should have AI and want AI.

1:17:08Rick Rubin:And I think that that is one of our objectives is for people to really feel it. Tell me about your experience at MIT. So I started at Harvard and - Undergraduate. Undergraduate. And when I was choosing where to go, I wasn't into programming at the time. that I thought I was going to do mathematics, I thought I was going to do philosophy, I thought I'd do maybe chemistry. And for these interests, Harvard seemed like a pretty good place. But by the time I got there, I was addicted to building and to building for other people. And so I just started being at the Computer Society, was working on some startups.

1:17:47Rick Rubin:And I remember that my first year at the Computer Society, there were these two seniors who would spend every meeting, have an obscure technical debate. And the rest of us would listen in thinking like one day that will be us. And then they graduated and suddenly I was running the club and I was supposed to have the obscure technical debates. And I was like, I'm a sophomore. I'm not ready for this yet. I still want to learn. And I kept looking down the street at MIT where I knew a number of people who were far better at programming than I was. And I knew I could learn from. And so I started spending all of my time there.

1:18:22Rick Rubin:I realized I'm probably in the wrong place. So I transferred. After how many years was this? Year and a half. Year and a half. Year and a half. Yes. And it was a tough decision. It was very, very difficult. Absolutely. Like my college roommates started to maintain this weather report of how likely Greg was to transfer to MIT any given day. And so over the course of a month, it went from 99 % to 1%, 50%. And in the end, I decided to do it. And the reason I did, and looking back, I've realized that many of my life decisions are made by me dreaming of the upside, of thinking of, well, what if this really works, right?

1:19:00If it actually is as great as I think at MIT, I'd be able to learn all these things.

1:19:05Rick Rubin:And then thinking about, well, what is the downside? What's the worst case scenario? I mean, the worst case is I come back to Harvard. Harvard, actually, when I went to tell them I was dropping out, they said, sounds like a leave of absence to me. Like they do not let you drop out of hardware. And so it felt like, okay, this is actually a great opportunity. I can try it and see what happens. And it wasn't as good as I hoped. It was even better perhaps.

1:19:27Greg Brockman:Wow.

1:19:28Rick Rubin:Because I really got to spend that time just constantly in their computing club. I built all these different services, learned a lot. I interned at this startup that was built by people from that computing club. and from all of them, I just learned all these deep technical ideas. And there's this culture at MIT that is hard to describe and I'll do my best, but it's like, imagine a place that experienced computers and internet technologies 20 years before everyone else did. And then there's like a lot of little cultural decisions and ways that people talk and operate that fall from it. And even very minor things you wouldn't think about.

1:20:10Rick Rubin:For example, if you're sitting over someone's shoulder, you know, doing something together and they have to type in their password, you look away. Right? Just like little things like that that you just notice culturally, this is how people do it. No one says it. It's just what you do. And there was a system called Zephyr, which was a chat system that the whole university was on. And this had existed. This is far before Facebook. Right? This had existed for probably two decades, maybe more. and that often if you go into a room, you see a bunch of people out with their laptops, they all start laughing.

1:20:41Rick Rubin:You don't ask them what's so funny. You pull out your laptop, you go on Zephyr, right? So there was this kind of mixed like virtual and physical reality that people existed in. And I remember that the IT staff were deeply technical, that there was all sorts of funny things. Like for example, that MIT, because they were so early on the internet, They actually own one 256th of all of the IP addresses. And so then the rest of the world, you don't experience what it means to be IP rich, right? To have far more IPs than you could ever use. And so every single light had its own IP. So you could actually program every single light bulb, right?

1:21:21Rick Rubin:It's like these little things that you could just do there that you couldn't do anywhere else. I've never experienced quite such a hacker's playground as MIT.

1:21:30Greg Brockman:That's great. It's great. you had the opportunity to do it and you made the choice because it does seem like a bold choice

1:21:34Rick Rubin:it was not an easy one at all and it was one that but clearly the right one thank you yes yeah i remember i remember talking to my parents and no matter what you tell your parents you're going to go do if it involves leaving harvard it's got to be tough on them were they supportive i think that at the time it was hard for them to understand and they said that but they said it is your decision you have more information than us. Sitting where we're sitting, the idea of leaving Harvard, that seems like a tough pill, but we do trust you. And so in the end, I'd say that they were supportive, but they didn't make it easy.

1:22:10Rick Rubin:Yeah, yeah, yeah. But in retrospect, I think that having that forcing function to really think it through and really understand what it was that I was signing up for is something I think helped me even get more out of the opportunity. Tell me about your parents. so they are both doctors my mom is a psychiatrist my dad is an ophthalmologist and i grew up in north dakota so north dakota many people have heard of fargo because of the movie the tv show if you go an hour north you get to a town called grand forks which when i was growing up was about 60 000 people if you then go eight miles outside of that approximately you're at thompson which is town of about a thousand when I was growing up.

1:22:52Rick Rubin:And then you go about mile, half mile outside of that and you were at my house. So would you say rural? It was rural, yes. And the thousand person town was your closest. That's right. Yes. And so I went to elementary school through fourth grade in Thompson. And I was lucky in that my parents worked in Grand Forks and that we were eligible for open enrollment into the Grand Forks public schools. Because Thompson was very small. There weren't that many different opportunities academically. And so for fifth grade onwards, I was in Grand Forks. And one of the big things that unlocked a lot of my future learning and development was in sixth grade, my dad taught me some algebra.

1:23:43Rick Rubin:And seventh grade is the first time that you have two tracks. You have the advanced math, a pre-algebra in seventh grade, and then the normal seventh grade math. And my mom and I went to the math teacher ahead of the school year and said, hey, can he just skip into algebra? And the teacher looked at us with this most condescending look. And I was like, every parent believes that their child is special. I can guarantee you that your child will be plenty challenged in my class. A couple of weeks later, I had been paying no attention, just playing calculator games in the back of the room. The teacher would try to trip me up and call on me and say, what's the answer?

1:24:18Rick Rubin:I'd look at the board and be like, 2X. And I'd go back to the games. And she was like, all right, there's nothing for me to teach your child. You should go into the algebra class. And so seventh grade, that one year advanced. But then eighth grade, no more math at my middle school. I didn't have a car, so I couldn't go to the high school. So instead, I did an online independent study. And in that year, I just got super into it. And I did three years worth of math in that one year. So I did geometry, pre-calc, algebra two, all in that one year. And so next year, I'm at the high school and I was in calculus.

1:24:51Rick Rubin:So I was this scrawny freshman with the seniors, but I was cool. Don't worry. They were actually really nice. It was fun. And I also asked the school, hey, could I also just skip the ninth grade science and do chemistry? And they were accommodating. That was very nice of them. So I just really got to explore my own interests. And then 10th grade, I was considering, what do I do now? I didn't have any more math in the high school. I did have a car now because it's North Dakota. You can get a car pretty early. And I had the University of North Dakota right there. And so I worked out a deal with my high school where I could take any classes I wanted at the university as long as I was taking just three classes a semester at the high school.

1:25:35Rick Rubin:And that was my high school education. It was amazing. I did research with a professor named Ryan Zier. He was fantastic. We got a published paper out of it. I studied a bunch of chemistry, did philosophy, just really got to explore and just take my academic interest in whatever direction I wanted.

1:25:56Greg Brockman:That's amazing. So lucky that the school system was supportive of letting you do your thing.

1:26:00Rick Rubin:That's right. And the interesting thing is very unusual. And if I was just slightly to the east in Minnesota, right, so we're right on the border with Minnesota, I would have had a very different experience. Because there, they actually have a standardized program for how high schoolers go to college. And you wouldn't be able to take so many classes because it would be, if you're a junior, you get like one class or something like that. Senior, maybe you can have two classes. But this idea of like really just being embedded into university, because you have the standard system, it would have been impossible.

1:26:29Rick Rubin:And so there's something about this because there is no program, but you have supportive educators. Then I was able to really do it.

1:26:36Greg Brockman:Yeah, that's a great argument against standardized systems in general.

1:26:40Rick Rubin:Yes, yes, because then you stop the exception. And for me, it's always been like I remember in elementary school or so, we had to write like a little essay as you would in third grade, not the most sophisticated essay, but, you know, the Frost poem of Two Paths in the Woods. And the question that you're supposed to answer was, which of these paths would you take, the one that's trodden or not? And I was like, I wouldn't take either of them. I would glaze my own path through the woods. That seems way better.

1:27:04Greg Brockman:Yeah.

1:27:05Rick Rubin:And I think that's very much how I've lived my life. I read that you were interested in acting when you were a kid. That is true. So first of all, I was the male lead in my middle school play. So I was very good. Thank you. And the thing I really liked about acting, like I really loved improv because it's about seeing the connection between things. And you have this interplay with another person or people and you're just imagining and you're really just constrained, not by reality in any way, but just by your wits and how quickly you can make things happen. And so I really enjoy humor. My wife would say that I enjoy making dumb jokes a lot.

1:27:46Rick Rubin:I appreciate that she puts up with it. And that for me, acting was an expression of this value of really feeling the imagination turning into reality while connecting across different concepts. And so I, in ninth grade, faced what I felt was a choice between going the more math science route or going the acting route, because I wanted to be great. I wanted to really dedicate myself to the craft. And I ended up going the math route, but I always kind of wondered. And my first paid job was actually an acting gig. So this was ninth grade. I was a small high schooler and the Mannheim steamroller was coming to Grand Forks, North Dakota for a concert.

1:28:33Rick Rubin:And we were told that they're looking for extras to act as tin soldiers for the show. And I was like, this sounds great. You get paid like a hundred dollars for the whole night. That'd be like the most money I could have imagined to make in one sitting. Like that'd be so great. and I show up and it's all these college students and I'm like oh man like I'm gonna be in trouble like clearly they are all that are typecast for this role and so the way that they had the audition go was they had everyone stand at attention walk in a direction and then the the casting people would all compare notes and in the meanwhile all the college kids would hang out with each other and talk but I was like there's one job is to stand for the whole concert at attention and so I spent the whole audition just standing at attention when everyone else was was relaxing And afterwards, they called up, you know, the people that they were selecting.

1:29:25Rick Rubin:I didn't get called. I was about to leave. They said, hey, no, actually, we thought you were just so amazing. You're just too small, so we can't give you that role. So we're going to create something new for you. And so they instead gave me the part of being a gingerbread man. And so I got the job. I got paid the$100. And it was really through that, you know, different audition approach.

1:29:45Greg Brockman:Do you think that there's a relationship between improv and AI? I do. Like the yes and?

1:29:54Rick Rubin:Yes. Yeah. I think that AI in many ways is the ultimate improv partner. And if you take away the post-training and you look at just the pre-trained model, that is its job. Its job is to say, I'm in this situation. I need to get oriented. Here's some text. Here's some scenario. Here's some images. Here's some DNA sequence, whatever the input data is. And its job is just to figure out what comes next. and it's never seen this before. It's a total mystery and it needs to orient itself and figure out what's a plausible thing and then do the right thing. And come on, like that's improv.

1:30:33Greg Brockman:Part two of the conversation with Greg Brockman will be available this Friday on the Tetragrammatin Podcast Network.

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