Greg Brockman (Part 1)

Greg Brockman is a cofounder and president of OpenAI, the company behind ChatGPT. ------ Thank you to the sponsors that fuel our podcast and our team: AG1 https://DrinkAG1.com/tetra ------ Athletic Nicotine https://www. AthleticNicotine.com/tetra Use code 'TETRA' ------ LMNT Electrolytes https://DrinkLMNT.com/tetra Use code 'TETRA' ------ Sign up to receive Tetragrammaton Transmissions https://www.tetragrammaton.com/join-newsletter

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Rick Rubin?

  Tetragrammaton.

• 0:23Speaker 01

  One thing that's really changed over the course of 2025 was people started to use ChatGPT for much more personal, very intimate applications. For example, so my wife has complex medical conditions, including hypermobile airless stimulus syndrome, which took many years for us to get diagnosed. And as we put those symptoms into ChatGPT, it would be able to figure out pretty immediately. But the thing is that every doctor has their own specialty rather than one doctor who can see across everything. And she uses ChatGPT to manage her health all the time.

• 1:00Rick Rubin?

  Great.

• 1:00Speaker 01

  For me, the funny thing is I'm actually a late adopter of our own technologies usually. And I usually test them and I stress test them in all sorts of ways. And it's funny for early versions of our models, I would usually like try to break some of their filters and so I would swear at them and yell at them. And my wife is always like, he's just kidding, you know, telling me to be nice to the AI. And I think that for me, I actually have been someone who's almost very set in my ways. And the first time this has changed is very recently with Codex and really starting in December. Like I've been a curmudgeon, I've been, I've got my way of doing things. I use my terminal, I use my Emacs, like all these like tools that I grew up with.

• 1:43Speaker 01

  And I've just abandoned all of that now.

• 1:46Rick Rubin?

  Wow.

• 1:46Speaker 01

  I'm just using Codex.

• 1:47Rick Rubin?

  That's revolutionary.

• 1:49Speaker 01

  It really is.

• 1:49Rick Rubin?

  You sound like you were set in your ways.

• 1:51Speaker 01

  I really was, yes.

• 1:53Rick Rubin?

  What changes would each new model?

• 1:56Speaker 01

  Everything. And the way to think about it is that we, from the outside, perception is, oh, you're just scaling up the models, you're just doing this kind of dumb thing. On the inside, every single part of the process, we are always up-leveling. Like, the thing that works in machine learning, and that machine learning rewards is attention to detail. And so you really want to make sure that all of the scaling is right. You want to make sure that the systems, for example, GPUs failing, that happens. And so how do you detect if you have a run of 100,000 GPUs? How do you detect which GPU is the broken one? It's not easy, right? You can't just be like that one, right? So you need this, almost, there's a physical process to it, there's the software process

• 2:38Speaker 01

  to it, there's understanding if your data's any good, and that there's so much reward to just actually looking at the data to understand what's in there and to make sure that you formatted it correctly and it's tokenized properly. And so there's just every single part of the input, we are constantly improving. And I pay attention a lot to the output, too. We're trying to see how does improvement here connect to an eval shifting? And one observation I have across many years of OpenAI is that if we have some signs of life, some application that kind of works right now, one year from now, you should expect it to be excellent. And so we are on this exponential and able to make these like very, very sophisticated improvements over time.

• 3:19Rick Rubin?

  Tell me about your co-founder, Sam. He seems to generate strong reactions out in the world.

• 3:25Speaker 01

  I think Sam is very misunderstood by the world. And I think that the way I think about Sam is that he is a very good person, and that that goodness is something that gets held against him. And it's very much no good deed goes unpunished. And I think that some of the critics, I run through my mental filter of every accusation is a confession, right? That I think that people project onto him.

• 3:50Rick Rubin?

  Yeah.

• 3:50Speaker 01

  Things that they see in themselves, they're insecure about, or that they want on their own. And I think he's very resilient. He's been through a lot, and that I'm very grateful for him, honestly, being at the center of so much attention and to continue going, because he's been very critical to OpenAI becoming what it is today. And I think that there is no one who I think could have kind of filled that role as well as he has today.

• 4:17Rick Rubin?

  Yeah, it's also nice that he can take all the arrows so you could continue doing the work.

• 4:21Speaker 01

  I wasn't gonna say it that way, but it is true. It is something I'm deeply grateful for.

• 4:25Rick Rubin?

  Tell me the story of the firing. It was a big story.

• 4:29Speaker 01

  It was a much bigger story than I would have ever expected.

• 4:33Rick Rubin?

  What led to it and what happened?

• 4:36Speaker 01

  I would say for the year, maybe a year and a half leading up to the firing, I think that one thing we did wrong was that we let conflict brew. And of lessons that I have throughout the course of OpenAI, it is that having the hard conversation and actually really addressing when there are conflicts, people disagree, that's normal, that happens. But if you let it fester, it is always going to be more painful down the road. And that's something that time and time again, I think that we've seen. And that I think that the firing was the most salient one of those examples. And that's one lesson that I really take away from the whole experience.

• 5:13Rick Rubin?

  Anytime anything grows so quickly, there's a lot of confusion and complication. Comes with the territory.

• 5:22Speaker 01

  I think that's true, yes.

• 5:23Rick Rubin?

  So I imagine in the explosion that was going on at the company, the rate that you were growing, there are bound to be difficulties.

• 5:33Speaker 01

  Yeah, I think that there were, I think at multiple levels, right? Just conflict between different people, different ways of operating, just decisions. Maybe there's some fundamental ones around was ChatGPT a good one or not? You know, that two of our board members have spoken about. And I think that this led ultimately to that event in November. And

• 5:58Rick Rubin?

  Describe it. Tell me what the event was. How did it happen?

• 6:01Speaker 01

  I was coding, I was coding away. I was preparing this change that I'd been working on for some time. I was very excited to get it merged and shipped. And I got a message asking me to hop on a video call. So I go onto the video call on the Google Meet. I preview screen shows who's in there and I noticed it was the board except for Sam. It's very surprised, so I click join and then they tell me the news.

• 6:30Rick Rubin?

  What do they tell you?

• 6:31Speaker 01

  Well, they tell me essentially the same information that was in the public press release, saying that Sam's been fired, saying that Mira's the interim CEO and saying that I've been removed from the board. And they tell me that I'm very important to the company, that I am someone who can get things done and that they want me to stay at the company.

• 6:54Rick Rubin?

  You're off the board, but they want you to stay?

• 6:55Speaker 01

  That's that's accurate. Yeah. And it wasn't even really framed in a way of will you stay or not stay? It was just saying, this is your new, your new role. And for me, I knew in that moment, it wasn't right.

• 7:09Rick Rubin?

  Did it come out of the blue?

• 7:10Speaker 01

  Yes.

• 7:11Rick Rubin?

  Wow. Were you shocked?

• 7:13Speaker 01

  Yes.

• 7:14Rick Rubin?

  I would imagine.

• 7:15Speaker 01

  Yes. But it was one of those moments where because I know all the people, I know all the dynamics, I know the conflicts that have been brewing. For me, it was, okay, I see what happened. It's sad that it came to this, but I understand.

• 7:31Rick Rubin?

  Yeah. It could have been handled differently earlier if somebody was focused on that.

• 7:38Speaker 01

  Could have been handled differently earlier for certain. I think I would expect everyone involved would say the same at this moment. And I remember asking for more information. They wouldn't share it at the time. And again, I understood, okay, this is, this is the way that that things are going. And after hanging up the call, I told my wife and said, we have to leave. And she said, yes, we do. And I said, just so you know, we should assume that all of the equity that we have to date, if you're not sold anything, will go away. All right, the board will be adversarial in some way. We just need to be prepared for that.

• 8:21Speaker 01

  I told her the amount that it was valued at. And I said, you just assume all of this goes to zero.

• 8:27Rick Rubin?

  Yeah.

• 8:27Speaker 01

  Still want to do it. And she said, yes.

• 8:31Rick Rubin?

  And this is something that you co-founded from the beginning and have spent at that time, how many years? Eight years?

• 8:38Speaker 01

  Eight years.

• 8:39Rick Rubin?

  Eight years totally devoted to.

• 8:42Speaker 01

  Totally devoted to it, yes. We had put off children so I could really focus on this. It was like, really, I think that OpenAI had been such a big part of our life.

• 8:52Rick Rubin?

  Yeah.

• 8:53Speaker 01

  And that I think we both just believe in the mission so much. And it's something that the potential for AI to help humans, and my wife loves animals, and to really help them, like, just every living being, like, that's something we really care about. And just realizing that it wasn't right what had happened. And that you could see a different world where you try to take advantage of it in some way, but it didn't even occur to me that the thing that I just felt was this was wrong.

• 9:20Rick Rubin?

  Yeah.

• 9:21Speaker 01

  And so I called Sam, asked him what he was planning on doing. And he said, "Well, I'm gonna go start another company, I guess." I said, "No, Sam. We are gonna start a new company." And so that day, I quit. And I think that in the narrative that people remember, people forget that moment. It all kind of blurs together.

• 9:43Rick Rubin?

  I didn't know that part of the story.

• 9:46Speaker 01

  The thing that happened was, prior to then, I think that it looks like there's a firing, something terrible must have happened. And the thing that changed it was when I quit. I wrote a very short message saying that when I heard the news today...

• 10:07Rick Rubin?

  Were they not expecting you to quit?

• 10:09Speaker 01

  I do not think they expected it.

• 10:12Rick Rubin?

  Was the decision made by the board?

• 10:14Speaker 01

  Yes.

• 10:15Rick Rubin?

  How many people were on the board?

• 10:16Speaker 01

  So at the time, so one of the things that we had let fester was that we had been losing board members that year for reasonable reasons, each of them. One wanted to run for president, other reasons for each. And so left on the board were six members, including Sam and myself.

• 10:35Speaker 01

  And I think that for me, just really trying to operate through this, and this is part of what the board said to me during that call is that of people who could really get things done with an OpenAI, that I was uniquely capable there.

• 10:51Rick Rubin?

  Yeah.

• 10:51Speaker 01

  And so a lot of what I always focused on is what do we need to do? How do we do it? How do we organize the people? How do we bring them together? And that that motion sometimes could cause conflict itself, right? But it's always because.

• 11:04Rick Rubin?

  You're doing what you need to do to push it forward.

• 11:06Speaker 01

  Yes.

• 11:06Rick Rubin?

  Makes sense. And then what happened next?

• 11:09Speaker 01

  Well, so we started to get a lot of calls from people. And it was truly surprising to me, the number of people who reached out saying, I don't know if you're planning on doing something next, but I want to go with you. It's not something.

• 11:22Rick Rubin?

  In the company or outside of the company?

• 11:23Speaker 01

  Within the company.

• 11:24Rick Rubin?

  Really?

• 11:24Speaker 01

  Yeah. It was truly humbling. It was not something I ever would have asked or expected.

• 11:31Rick Rubin?

  Yeah.

• 11:32Speaker 01

  But it was something that that really happened. And I remember saying that day, there is a 10% chance that we get the company back.

• 11:43Rick Rubin?

  Really?

• 11:43Speaker 01

  Not zero, but not more than 10%.

• 11:46Rick Rubin?

  Yeah, yeah.

• 11:47Speaker 01

  Because the board has all the cards. They need to decide that that's the direction they think is right for the company, for the mission. And part of why I left is because, to me, the mission is bigger than the corporate entity.

• 11:59Rick Rubin?

  Yes.

• 11:59Speaker 01

  Right? The mission is something I'm pursuing, that I care about, and I want to pursue in the form that I believe can most accomplish it. So that day, some of my close collaborators, Jakob, Shimon, Alexander, quit as well. So we had a small ban.

• 12:14Rick Rubin?

  It's like a mutiny.

• 12:15Speaker 01

  A little bit. It was the people who were just like, all right, like, we see what happened. This is not right. We need to go do something different.

• 12:23Rick Rubin?

  Yeah.

• 12:24Speaker 01

  And then we just started thinking about, okay, well, what's the new company? It was great energy, honestly.

• 12:29Rick Rubin?

  Yeah.

• 12:30Speaker 01

  Because we had this blank slate where we had a whole vision of what we wanted to accomplish. And we were charting out a whiteboard and thinking about all the ways it could fit together. And there were all these people reaching out. And so I set up a meeting the next day at Sam's house, where I said, come, let's all meet, we'll show everyone the vision, we'll talk through things, we'll brainstorm how it's all going to work.

• 12:51Rick Rubin?

  It's amazing that it's the next day. It's like

• 12:53Speaker 01

  Yeah.

• 12:54Rick Rubin?

  Amazing.

• 12:55Speaker 01

  There's no time. We all had energy. We were so jazzed about this because it was an opportunity to really anything that we wanted to rethink. Now was the moment. And we had all these reflections on the conflicts we let brew. So one of the values that I actually came back with was have the hard conversation.

• 13:12Rick Rubin?

  Yeah.

• 13:12Speaker 01

  And that was one very clear takeaway from the whole thing. So next day, and by the way, no one's really sleeping. Like people are staying up super late. It's just a lot of caffeine energy, so to speak.

• 13:24Rick Rubin?

  Yeah.

• 13:24Speaker 01

  And next day, we have the meeting around 1:00 PM. That day was, again, amazing energy. We spent a lot of time on charting out what the new company would be. That night, the executive team came over and so it was a very nice reunion as we all kind of felt like we're in this together. That we're all going to to try to get to a good outcome together. Next day, more conversations with the board, more trying to figure out how to get through.

• 13:52Rick Rubin?

  Wow.

• 13:53Speaker 01

  I cannot capture the intensity of this time because it was this company that we had built, that we had poured our heart and soul into.

• 14:00Rick Rubin?

  Yeah.

• 14:00Speaker 01

  That there was this crucible moment of is it going to survive? And it was right before Thanksgiving. People packed the office. People canceled their flights home, came back. We're just there. It wasn't that they felt they could do anything active, but they just wanted to be there showing support and to try to just like, just be together.

• 14:22Rick Rubin?

  It's a wild story.

• 14:24Speaker 01

  It's absolutely wild. And so we were all up on the on the top floor and every so often someone would go down the stairs to like go get a water or something. And everyone would be like, any news, any news, you know, or there'd be applause. It was just like, really, like, everyone just was just really wanted to be a part of it. And I think that is something that no one's really talked about, just like how much the support of the people mattered in this in this time. And that night, that Sunday night, the board decided that, hey, we're going to replace Neura as interim CEO with a different interim CEO. And the company went wild and just rejected it and said, this is absolutely wrong. And at this point, It was chaos and the people started to just leave the office, just streaming out of

• 15:08Speaker 01

  the office. And it was just like a thing. It was almost like, you know, the new CEO is coming. Everyone's like, we just need to get out of here and be gone. Yeah, there are all these stories of people went over to different, there were a couple of different houses and there were like hundreds of people packed into backyards and people were making speeches. And there were a bunch of crazy things that happened that night. Everyone was like trying to figure out, is this company somewhere I want to be? Like, there were lots of the competitors swirling around, making offers, saying that we'll make offers to anyone at OpenAI, all these things.

• 15:39Rick Rubin?

  Yeah.

• 15:40Speaker 01

  And then meanwhile, we were trying to set up a lifeboat for people. And we already had this plan for a company. It was like a small life raft. We rapidly extended it to be a full, like, you know, full size for all the 770 people.

• 15:52Rick Rubin?

  In a day.

• 15:53Speaker 01

  In a day. And this was with partnership with Microsoft. Microsoft says, yes, we will take everyone. Microsoft was there ultimately to support. Like, if that's what we wanted to do, that they were there to help. Sam and I and Jakob and the others who had quit, that we were trying to figure out, yeah, what is the structure we want? Do we want an independent company? Do we want to be funded by Microsoft? Do we want to be actually part of that corporate entity? Those kinds of things. And so it's really our decision to figure out how to carry the mission forward. And I remember I went to sleep very late because I was comforting all the people who were, you know, trying to figure out what was going on and saying, we have a plan, here's how it looks. And that night, there

• 16:33Speaker 01

  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.

• 16:49Rick Rubin?

  Yeah.

• 16:50Speaker 01

  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.

• 16:56Rick Rubin?

  It sounds like a revolution.

• 16:57Speaker 01

  It really was. It had.

• 16:59Rick Rubin?

  That's what you're describing.

• 17:00Speaker 01

  That's how it felt. That's 100% how it felt. And that morning, I remember waking up 5:00 AM or so. And I check Twitter and I see a post from Ilya.

• 17:13Speaker 01

  And for me, I feel emotional even just talking about this.

• 17:17Rick Rubin?

  Yeah.

• 17:17Speaker 01

  I remember when the firing happened, I just felt this distance, right? I just like, it was like this emotional thing and you just feel this like.

• 17:26Rick Rubin?

  How can you do this to me?

• 17:27Speaker 01

  Exactly, hundred percent.

• 17:28Rick Rubin?

  Yeah.

• 17:28Speaker 01

  And in that moment, I felt forgiveness, right? I felt like, okay.

• 17:34Rick Rubin?

  Beautiful.

• 17:36Speaker 01

  Yeah, it was.

• 17:36Rick Rubin?

  So it's forty eight hours, the whole story?

• 17:39Speaker 01

  A little bit longer, that was probably seventy two or so.

• 17:41Rick Rubin?

  Still, a tiny amount of time.

• 17:43Speaker 01

  We got two more days to figure out how to get back into the company, right? Because, yeah, and I think that those next two days, we were really trying to figure out what is the right configuration because we clearly needed.

• 17:54Rick Rubin?

  It needed to be different.

• 17:55Speaker 01

  It needed to be different in some way.

• 17:56Rick Rubin?

  Because we don't want this to happen again.

• 17:58Speaker 01

  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.

• 18:10Rick Rubin?

  How would you say the company is different since that event?

• 18:13Speaker 01

  Extremely.

• 18:14Rick Rubin?

  In what way?

• 18:16Speaker 01

  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.

• 18:25Rick Rubin?

  Yeah.

• 18:25Speaker 01

  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. 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.

• 19:04Rick Rubin?

  Like the human component.

• 19:05Speaker 01

  Yes.

• 19:06Rick Rubin?

  Because would you say the discontent was about were human things? It wasn't technical, it wasn't what the business was doing.

• 19:13Speaker 01

  Yes.

• 19:13Rick Rubin?

  It was just human stuff.

• 19:14Speaker 01

  Always human stuff.

• 19:15Rick Rubin?

  Like in a marriage.

• 19:16Speaker 01

  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.

• 19:41Rick Rubin?

  Yeah.

• 19:42Speaker 01

  And so I think since then, you can never take it for granted.

• 19:45Rick Rubin?

  And do you feel like you have a good understanding of what happened now?

• 19:48Speaker 01

  I do.

• 19:49Rick Rubin?

  You do.

• 19:51Speaker 01

  Part of the deal for coming back was to say, let's do an independent review of all of that. All right. So there's someone, some law firm is 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.

• 20:14Rick Rubin?

  Yeah.

• 20:15Speaker 01

  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.

• 20:30Rick Rubin?

  Yeah.

• 20:31Speaker 01

  One of my approaches throughout OpenAI has been, I had this mantra of keep the band together.

• 20:35Rick Rubin?

  Yeah.

• 20:36Speaker 01

  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 they're 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 have, feel like I do at OpenAI is try to figure out, we have this grand mission, but how do we do it?

• 21:00Rick Rubin?

  Yeah.

• 21:01Speaker 01

  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?

• 21:19Rick Rubin?

  Yeah.

• 21:20Speaker 01

  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 have, who are pushing on the company, we want other people to be able to own their story.

• 21:43Rick Rubin?

  Yeah.

• 21:44Speaker 01

  But then sometimes people use that to kind of whack us.

• 21:46Rick Rubin?

  Yeah. Tell me what you remember about our David and the life conversation.

• 21:52Speaker 01

  Huh.

• 21:53Rick Rubin?

  Because I feel like this relates.

• 21:54Speaker 01

  Yeah, we did, we did, 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.

• 22:21Rick Rubin?

  Upstart.

• 22:21Speaker 01

  That's right. And somehow, I think that because ChatGPT has been so successful, that people then think of us, well, because we're the category leader, therefore, we're the Goliath, we're the establishment. And where I sit, I say, the AGI, that's the real prize. And that 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 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?

• 23:05Speaker 01

  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. Ilya and I spent so much effort to try to make it work with him. And we were very transparent.

• 23:27Rick Rubin?

  What was the difference in vision?

• 23:29Speaker 01

  So, control. The reason that we could not get to a deal with him, because we all agreed that we're gonna 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.

• 23:47Rick Rubin?

  And he agreed with that as well?

• 23:48Speaker 01

  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.

• 23:55Rick Rubin?

  Okay.

• 23:57Speaker 01

  We all got that this was the only way forward. And then the negotiation over the details began. And he needed majority equity. He needed absolute initial control. 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 fold? 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?

• 24:42Rick Rubin?

  Too much.

• 24:43Speaker 01

  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...

• 24:55Rick Rubin?

  Did it end on bad terms right in that moment when the deal wasn't made?

• 24:59Speaker 01

  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 this, 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 two more board seats. It would be, you know, Ilya Sam Gregg, Elon, Elon, Elon. 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.

• 25:42Speaker 01

  And we said, we're, we were 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 gotta 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 cash Tesla, the cash cow was the term that was used. And that one, we had no desire to do. Like, we really thought about it. We were just like, just doesn't seem right.

• 26:13Rick Rubin?

  Just a different business?

• 26:15Speaker 01

  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. 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.

• 26:56Speaker 01

  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, he was on board. And we spent a lot of time thinking about, okay, let's 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. 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're going to talk about this like for-profit entity. Again, all these ideas were just swirling around and different, different things we could try.

• 27:40Speaker 01

  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 the team. 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 and 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

• 28:22Speaker 01

  and the wolves aren't having safety, then there's no point, referring to Google. So, you know, it's like that kind of tone. And so then we began over the next year to figure out, yeah, how do we actually raise these large amounts of capital?

• 28:38Rick Rubin?

  Was it all cool with Elon at that point?

• 28:41Speaker 01

  For sure, yes. I mean, I think it was very explicit that-

• 28:45Rick Rubin?

  Everyone understood this was what was happening moving forward.

• 28:48Speaker 01

  Yes.

• 28:49Rick Rubin?

  Okay. And what was the next step?

• 28:54Speaker 01

  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, not a 1%, I wish it were different, but that's true.

• 29:37Speaker 01

  This is going to require billions of dollars per year, even hundreds of millions of dollars, which is what we told them we were planning on raising, isn't enough. And I remember I was on vacation and I just was like, kind of knocked out for that whole day. Yeah, it was very clear that he thought that we were destined to fail.

• 29:56Rick Rubin?

  Yeah, I don't think he would have sent it if he didn't think you were destined to fail, so.

• 30:00Speaker 01

  Yeah, I agree. That'd 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.

• 30:15Rick Rubin?

  When's the last time you spoke to him?

• 30:17Speaker 01

  Couple of months ago.

• 30:19Rick Rubin?

  Pleasant?

• 30:20Speaker 01

  Very pleasant. It's always pleasant. Yeah.

• 30:27Rick Rubin?

  How did you end up getting into business with Microsoft?

• 30:31Speaker 01

  So Microsoft was an early partner. We got compute donated from them for our Dota project. So 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.

• 31:01Rick Rubin?

  And when did they actually become partners? Did they end up funding the LLC?

• 31:08Speaker 01

  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 of capital at the time. Different now, but at that time.

• 31:27Rick Rubin?

  Yeah, yeah, yeah.

• 31:28Speaker 01

  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, 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.

• 31:57Rick Rubin?

  How did your business change when you had the influx of cash?

• 32:02Speaker 01

  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. And we were very eager to build great supercomputers for deep learning.

• 32:46Speaker 01

  And so 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.

• 33:05Rick Rubin?

  And it sounds like you were aligned.

• 33:07Speaker 01

  Yes.

• 33:08Rick Rubin?

  It made sense.

• 33:09Speaker 01

  Yes.

• 33:10Rick Rubin?

  Does ChatGPT believe in God?

• 33:12Speaker 01

  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, you know, sort of agents that that can be shaped in different form.

• 33:40Rick Rubin?

  Does it have emotions?

• 33:44Speaker 01

  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? 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 that 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?

• 34:25Rick Rubin?

  Does it sleep?

• 34:28Speaker 01

  ChatGPT never sleeps.

• 34:31Speaker 01

  Which is part of what makes it great. Right? It's a 24/7 in your pocket. It can be a doctor, it can be a teacher.

• 34:39Rick Rubin?

  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?

• 34:49Speaker 01

  The exponential continues.

• 34:52Speaker 01

  And so the doubling period is the same, but it feels like the rate of breakthroughs has actually been fairly constant.

• 35:02Rick Rubin?

  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?

• 35:11Speaker 01

  For sure. Yes. 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, that was a real exciting things to see. And I think where I'm excited to see the models get great, but I don't feel they are yet.

• 35:47Rick Rubin?

  Yeah.

• 35:48Speaker 01

  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. And can you have models that help negotiate people 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.

• 36:25Rick Rubin?

  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?

• 36:33Speaker 01

  I think we just really need to figure out how to train them for those tasks.

• 36:37Rick Rubin?

  Is AI best with current information, historical information, or theoretical information?

• 36:45Speaker 01

  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. Like, 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.

• 37:27Rick Rubin?

  Are you still coding?

• 37:29Speaker 01

  I am.

• 37:30Rick Rubin?

  It's unusual for someone who is in your position to continue coding, no?

• 37:35Speaker 01

  Yes.

• 37:36Rick Rubin?

  Do you just feel a connection to it?

• 37:38Speaker 01

  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.

• 37:52Rick Rubin?

  Yes.

• 37:53Speaker 01

  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, 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.

• 38:36Speaker 01

  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 three, which was the 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. 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, 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 like intense grind of building this thing from nothing and getting the first beta users.

• 39:20Speaker 01

  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. The second was get a use case that we would all use internally every single day.

• 39:31Rick Rubin?

  Yeah.

• 39:31Speaker 01

  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.

• 39:53Rick Rubin?

  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?

• 40:07Speaker 01

  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.

• 40:42Rick Rubin?

  How did it feel?

• 40:44Speaker 01

  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 forward, backward, and an optimizer step. 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.

• 41:19Rick Rubin?

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

• 41:31Speaker 01

  At the time, we trained GPT-4.

• 41:33Rick Rubin?

  Yeah.

• 41:33Speaker 01

  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 raising ChatGPT 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. And just making it accessible and available, that that's something that would then cause it to click.

• 42:12Rick Rubin?

  Because for you, it was already kind of

• 42:14Speaker 01

  Old hat.

• 42:14Rick Rubin?

  Wow.

• 42:16Speaker 01

  Yes. And that's one of the amazing things about working at OpenAI is you live in the future.

• 42:24Rick Rubin?

  Tell me about Sora.

• 42:26Speaker 01

  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 create tools to do it, but we don't have something that works quite in Sora. 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? 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 you really feel it when it makes a mistake,

• 43:09Speaker 01

  like dropping this 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. 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.

• 43:54Speaker 01

  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, yeah, anything else in the physical world. Having such models can be extremely helpful for that.

• 44:12Rick Rubin?

  How did it learn the physical world?

• 44:14Speaker 01

  Same way that our text models learn. The way that it learns is through observing video, right? Through observing the world and trying to predict what will happen next.

• 44:26Rick Rubin?

  Do you see a mystical side to AI?

• 44:29Speaker 01

  I do.

• 44:30Rick Rubin?

  Tell me about it.

• 44:32Speaker 01

  Well, so there's this idea of scaling laws, which are an empirical observation of as we pour more compute into models that are, their sizes increase in 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, you know, 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. That, that is difficult. But to me, there is some fundamental scientific discovery that we are making.

• 45:17Speaker 01

  This is an exploration into the unknown. This is a frontier. And you can see it because we're bringing like deep scientific principles and mathematical approaches, and you're seeing the empirical results. And so it's almost like, like the way that Yakup, our, our chief scientist, likes to describe it, is that building a bigger model is like building a bigger rocket, where it's the tolerances all become more tight. And, you know, you're still constrained by the same rocket equation and the same gravitational pull and all those things. 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.

• 45:57Rick Rubin?

  How many employees are there at OpenAI now?

• 45:59Speaker 01

  We're about 5,000 people.

• 46:00Rick Rubin?

  And what do they do?

• 46:02Speaker 01

  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. We have. A number of people, maybe it's 500 people, maybe another thousand, who are focused on deployment. So this is on the applications and actually bringing products like ChatGPT, Codex to life. There's deep partnership across all of these. Like, we cannot do this alone.

• 46:43Speaker 01

  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.

• 47:01Rick Rubin?

  How did you learn to lead such a big company?

• 47:04Speaker 01

  It is new to me. It is new. And it's not what I set out to do either.

• 47:09Rick Rubin?

  Yeah.

• 47:10Speaker 01

  Being under Dunbar's number and having a tight connection across everyone, knowing everyone, like that was something that appealed to many of us.

• 47:16Rick Rubin?

  Yeah.

• 47:17Speaker 01

  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 like the insanely competitive market that we are in. 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,

• 48:02Speaker 01

  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.

• 48:14Rick Rubin?

  Yeah.

• 48:14Speaker 01

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

• 48:27Rick Rubin?

  Of all the things to focus on in AI, how do you decide where your time goes?

• 48:32Speaker 01

  This is always a very tough question.

• 48:34Rick Rubin?

  Yeah.

• 48:34Speaker 01

  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. Again, I have great leaders helping me with this and, and great engineers who make it all possible. And I'm also helping out with Codex and spending a lot of time really trying to think about what is the right future shape?

• 49:17Speaker 01

  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, as fast as we can and as well as we can. And that's usually marshalling 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.

• 49:40Rick Rubin?

  Would you say every few months or every year, you're focused on something completely different than what you were before?

• 49:45Speaker 01

  Yes.

• 49:46Rick Rubin?

  Do you like that? Is that fun?

• 49:47Speaker 01

  I do. Yes. It's never boring.

• 49:50Rick Rubin?

  Where do you see the company in five years and then in 10 years?

• 49:54Speaker 01

  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. And 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.

• 50:18Rick Rubin?

  And that's new. That's just been in the past year?

• 50:20Speaker 01

  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. And to be fair, I think December basically turned into the, the agent moment. And so here we are. The second thing is scientific discovery. I think we're going to start seeing scientific discovery revolutionize 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? Like, how do you build a company with all of these tools? They're so accelerative.

• 51:03Speaker 01

  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 that just accrues to the incumbents. 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.

• 51:47Speaker 01

  For example, I spent some time working with the Art 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. You can ask it to determine if a particular protein sequence is valid or not, like really any biological tasks. 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 are going to head into a renaissance

• 52:29Speaker 01

  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.

• 52:43Rick Rubin?

  What is reasoning as it relates to AI?

• 52:46Speaker 01

  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 ChatGPT that you'd ask.

• 52:55Rick Rubin?

  Export.

• 52:55Speaker 01

  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.

• 53:05Rick Rubin?

  Yeah.

• 53:05Speaker 01

  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. 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 have a very fluid interaction back and forth.

• 53:47Speaker 01

  And I think we now have the technology to do this and that we need to pursue the productization of it.

• 53:52Rick Rubin?

  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?

• 54:06Speaker 01

  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.

• 54:19Rick Rubin?

  Yeah, often probably.

• 54:20Speaker 01

  Often, very often, right? And that people use ChatGPT 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.

• 54:39Rick Rubin?

  Or someone to bounce things off of and second opinion in a way.

• 54:43Speaker 01

  Yes.

• 54:47Rick Rubin?

  Tell me about the financial side of OpenAI. Is it profitable yet or it's still in growth mode?

• 54:52Speaker 01

  Still in growth mode, definitely not profitable.

• 54:54Rick Rubin?

  Is there a vision to profitability?

• 54:57Speaker 01

  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.

• 55:07Rick Rubin?

  Just because it can't be made faster, is that the issue?

• 55:10Speaker 01

  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.

• 55:51Speaker 01

  Like right now, you use ChatGPT 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 codecs 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. 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

• 56:36Speaker 01

  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.

• 56:51Rick Rubin?

  Will there be some breakthrough in that area where it won't be a problem in the future?

• 56:55Speaker 01

  No, because the way that this works, there's Jefin's paradox, or if you know Jefin's 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 a thousand times more. And you're going to want, rather than this level of intelligence, you're going to want the 10x or 100x intelligence. 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.

• 57:36Rick Rubin?

  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.

• 57:44Speaker 01

  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.

• 58:10Rick Rubin?

  Tell me something you believe now that you didn't believe when you were young.

• 58:14Speaker 01

  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 deep value of this, like, honesty and sort of, you know, faithfully carrying out duties. And I think one thing I have learned is that you can't just take what people say at face value.

• 58:58Speaker 01

  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. Like, I like to be straightforward and direct in everything that I'm doing.

• 59:09Rick Rubin?

  I'd be shocked if a Tory guy had made up the story they were telling.

• 59:12Speaker 01

  I know, yeah. It turns out.

• 59:14Rick Rubin?

  Hard to believe.

• 59:15Speaker 01

  It turns out, yes. It makes you wonder what else, you know, is out there that you don't know about.

• 59:19Rick Rubin?

  How does your company function different than how Stripe functioned?

• 59:24Speaker 01

  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.

• 59:52Rick Rubin?

  Yeah.

• 59:52Speaker 01

  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.

• 1:00:03Rick Rubin?

  Building this machine.

• 1:00:04Speaker 01

  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.

• 1:00:13Rick Rubin?

  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?

• 1:00:24Speaker 01

  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?

• 1:00:36Rick Rubin?

  Understood.

• 1:00:37Speaker 01

  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.

• 1:00:44Rick Rubin?

  Yeah.

• 1:00:45Speaker 01

  But I think it's pretty hard for me to be disappointed with where we are.

• 1:00:50Rick Rubin?

  Do you think setting it up as a nonprofit to start with was the only way to do it?

• 1:00:55Speaker 01

  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 OpenAI equity. And that that is something I think is very important. It's 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.

• 1:01:32Rick Rubin?

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

• 1:01:41Speaker 01

  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.

• 1:02:06Rick Rubin?

  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?

• 1:02:12Speaker 01

  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.

• 1:02:58Speaker 01

  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? That 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 of these things.

• 1:03:39Speaker 01

  And that we really configure ourselves around a technology because it's so worthwhile and so beneficial and that there's a

• 1:03:49Speaker 01

  Deep resilience that goes into that so that there's not just one way of doing it. This has been our picture all along is that if you want everyone to have access, how do you actually make sure that society 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. And I think the diversity and the resilience of the world is an important objective and something that we really aim to support.

• 1:04:28Rick Rubin?

  Well, it seems like also that in different parts of the world, different things are thought of as acceptable or not.

• 1:04:35Speaker 01

  Yes.

• 1:04:35Rick Rubin?

  So if it has one point of view, it can't be for everybody.

• 1:04:40Speaker 01

  That's true.

• 1:04:41Rick Rubin?

  It's a diverse place.

• 1:04:42Speaker 01

  Yes.

• 1:04:43Rick Rubin?

  What is the unsupervised sentiment neuron?

• 1:04:46Speaker 01

  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 because if you say something like, this product was great, but it was totally broken and I hated it.

• 1:05:30Rick Rubin?

  What's the sentiment?

• 1:05:31Speaker 01

  Exactly, right?

• 1:05:31Rick Rubin?

  Complicated.

• 1:05:32Speaker 01

  Complicated. So you need some understanding. And through this process, we learned a state of the art sentiment analysis classifier. So it's the best, 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 it was so clear that it was going to happen.

• 1:05:57Rick Rubin?

  Have linguistics changed since AI? Considering the way that it works. It's so much based on understanding of linguistics.

• 1:06:07Speaker 01

  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.

• 1:06:21Rick Rubin?

  That's probably like math and nature, though. It's probably like that.

• 1:06:24Speaker 01

  Yes, it is like that. It is like that.

• 1:06:25Rick Rubin?

  It's a system.

• 1:06:26Speaker 01

  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.

• 1:06:41Rick Rubin?

  Does AI understand sarcasm, double onchondra, things like that?

• 1:06:46Speaker 01

  Yes, you can test it on ChatGPT. It does a great job.

• 1:06:50Rick Rubin?

  Can it understand poetry?

• 1:06:51Speaker 01

  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.

• 1:06:56Rick Rubin?

  Yeah.

• 1:06:57Speaker 01

  The way to think about what we can do right now. So we build these machines that learned all the the underlying rules that generate language, so it really understands a lot. It 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? 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.

• 1:07:40Speaker 01

  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.

• 1:07:57Rick Rubin?

  Yeah.

• 1:07:57Speaker 01

  But I think that one day we'll get there, too.

• 1:08:00Rick Rubin?

  I'd like to see it.

• 1:08:02Speaker 01

  Me too.

• 1:08:02Rick Rubin?

  Yeah.

• 1:08:04Speaker 01

  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 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 a 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? And at a deep level, that is what these models are doing, is they are machines that deeply, deeply understand

• 1:08:49Speaker 01

  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 you know the processes, you know the the lessons, you know, the reasons that things were done.

• 1:09:04Rick Rubin?

  And sometimes, even when you get something wrong, a mismemory of the past sometimes leads you to a deeper truth.

• 1:09:14Speaker 01

  Yes.

• 1:09:15Rick Rubin?

  We're not so accurate.

• 1:09:16Speaker 01

  Yes, and I think this is the key.

• 1:09:17Rick Rubin?

  We're storing machines.

• 1:09:19Speaker 01

  Exactly, and you're like a connection machine, right? You're trying to figure out how is everything connected?

• 1:09:22Rick Rubin?

  Exactly.

• 1:09:23Speaker 01

  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.

• 1:09:49Rick Rubin?

  Yeah.

• 1:09:50Speaker 01

  To really see it implemented and working, like, that is something that is unprecedented.

• 1:09:55Rick Rubin?

  So if you keep asking it the same question, might you get different answers?

• 1:10:00Speaker 01

  You do. 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 could think of it as as a distribution. Like, 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 like the correct answer to it? And it puts more probability mass on the answers that are better.

• 1:10:27Rick Rubin?

  Would you say AI is the biggest destructor of the status quo that we've ever seen?

• 1:10:32Speaker 01

  I think it could be. I think that AI will be in many ways like the printing press. That it enables and unleashes creativity, productivity, expression in ways that are sometimes heretical, right? Sometimes unthinkable, sometimes very disruptive to how people have expected things to be. But no one would want to take away the printing press.

• 1:10:58Rick Rubin?

  Yeah. Have you gotten much pushback, though, the fact that it can be viewed as heretical?

• 1:11:03Speaker 01

  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. And I think that that is one of our objectives, is for people to really feel it.

• 1:11:37Rick Rubin?

  Tell me about your experience at MIT.

• 1:11:40Speaker 01

  So I started at Harvard and

• 1:11:44Rick Rubin?

  Undergraduate.

• 1:11:45Speaker 01

  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 would do maybe chemistry. And for these interests, that 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. 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.

• 1:12:30Speaker 01

  And I was like, I'm a sophomore. I'm not ready for this yet. I still want to learn.

• 1:12:33Rick Rubin?

  Yeah.

• 1:12:34Speaker 01

  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 that I knew I could learn from. And so I started spending all of my time there. I realized I'm probably in the wrong place. So I transferred.

• 1:12:50Rick Rubin?

  After how many years was this?

• 1:12:52Speaker 01

  Year and a half.

• 1:12:52Rick Rubin?

  Year and a half.

• 1:12:53Speaker 01

  Year and a half, yes. And it was a tough decision. It was very, very difficult.

• 1:12:56Rick Rubin?

  Absolutely.

• 1:12:56Speaker 01

  Like, my college roommate 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.

• 1:13:12Rick Rubin?

  Yeah.

• 1:13:12Speaker 01

  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? If it actually is as great as I think at MIT, I'd be able to learn all these things. 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 Harvard.

• 1:13:40Rick Rubin?

  Yeah, yeah.

• 1:13:41Speaker 01

  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.

• 1:13:51Rick Rubin?

  Wow.

• 1:13:52Speaker 01

  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 of 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, like, imagine a place that experienced computers and internet technologies 20 years before everyone else did. And then, there is 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.

• 1:14:34Speaker 01

  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. You don't ask them, what's so funny? You pull out your laptop, you go on Zephyr. Right, so there's this kind of mixed like virtual and physical reality that people existed in.

• 1:15:15Speaker 01

  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? It's like these little things that just you could just do there that you couldn't do anywhere else.

• 1:15:50Rick Rubin?

  Yeah.

• 1:15:50Speaker 01

  I've never experienced quite such a hackers playground as MIT.

• 1:15:54Rick Rubin?

  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.

• 1:15:59Speaker 01

  It was not an easy one at all, and it was one that-

• 1:16:02Rick Rubin?

  But clearly the right one.

• 1:16:03Speaker 01

  Thank you, yes.

• 1:16:04Rick Rubin?

  Yeah.

• 1:16:04Speaker 01

  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 gotta be tough on them.

• 1:16:12Rick Rubin?

  Were they supportive?

• 1:16:14Speaker 01

  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.

• 1:16:21Rick Rubin?

  Yeah.

• 1:16:22Speaker 01

  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.

• 1:16:35Rick Rubin?

  Yeah, yeah, yeah.

• 1:16:35Speaker 01

  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.

• 1:16:46Rick Rubin?

  Tell me about your parents.

• 1:16:48Speaker 01

  So they are both doctors. My mom is a psychiatrist. My dad is an ophthalmologist and I grew up in North Dakota.

• 1:16:59Speaker 01

  So, Earth to God, many people have heard of Fargo because of the movie, the TV show.

• 1:17:03Rick Rubin?

  Yep.

• 1:17:04Speaker 01

  If you go an hour north, you get to a town called Grand Forks, which when I was growing up was about sixty thousand people. If you then go eight miles outside of that, approximately, you're at Thompson, which is a town of about a thousand when I was growing up. And then you go about mile, half mile outside of that, and you're at my house. So.

• 1:17:22Rick Rubin?

  So would you say rural?

• 1:17:23Speaker 01

  It was, it was rural, yes.

• 1:17:25Rick Rubin?

  And the thousand-person town was your closest?

• 1:17:28Speaker 01

  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, in Grand Forks. And that we were eligible for open enrollment into the Grand Forks Public Schools. Because Thompson is very small, there weren't that many different opportunities academically.

• 1:17:53Rick Rubin?

  Yeah.

• 1:17:54Speaker 01

  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. And seventh grade is the first time that you have two tracks. You have the advanced math, 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, into algebra? And the teacher looked at us with this most condescending look and was like, every parent believes that their child is special. I can guarantee you that your child will be plenty challenged in my class. Couple of weeks later, I had been paying no attention, just playing calculator games in the back of the room.

• 1:18:39Speaker 01

  The teacher would try to trip me up and call on me and say, what's the answer? I'd look at the board and be like, two X, and then go back to the games. And she was like, all right, there's nothing for me to teach your child. You should go into, into the algebra class. And so seventh grade, I was 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-cal, algebra two, all in that one year. And so next year, I'm at the high school and I was in calculus. So I was this scrawny freshman, you know, with the seniors, but I was cool. Don't worry.

• 1:19:20Rick Rubin?

  Yeah.

• 1:19:20Speaker 01

  They were actually really nice. It was, it was, 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? Like, I didn't have a, 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. And that was my high school education.

• 1:20:01Rick Rubin?

  Awesome.

• 1:20:02Speaker 01

  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.

• 1:20:20Rick Rubin?

  That's amazing. So lucky that the school system was supportive of letting you do your thing.

• 1:20:25Speaker 01

  That's right. And the interesting thing is, it was 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. 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.

• 1:21:00Rick Rubin?

  Yeah, that's a great argument against standardized systems in general.

• 1:21:04Speaker 01

  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.

• 1:21:28Rick Rubin?

  Yeah.

• 1:21:29Speaker 01

  And I think that's very much how I've lived my life.

• 1:21:32Rick Rubin?

  I read that you were interested in acting when you were a kid.

• 1:21:35Speaker 01

  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.

• 1:21:50Rick Rubin?

  Yeah.

• 1:21:50Speaker 01

  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 you can make things happen. And so I really enjoy humor. My my wife would say that I enjoy making dumb jokes a lot. I appreciate that she puts up with it. And that for me, acting was an expression of this this value of 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.

• 1:22:35Speaker 01

  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 Manheim Steamroller was coming to Grand Forks, North Dakota for a concert. 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 to make in one sitting. Like, that'd be so great.

• 1:23:15Speaker 01

  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're all better 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 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 relaxing. And afterwards, they called up the people that they were selecting. 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.

• 1:23:57Speaker 01

  And so they instead gave me the part of being a gingerbread man. And so I got the job. I got paid the hundred dollars and it was really through that, that, you know, different audition approach.

• 1:24:10Rick Rubin?

  Do you think that there's a relationship between improv and AI?

• 1:24:15Speaker 01

  I do.

• 1:24:16Rick Rubin?

  Like the yes and?

• 1:24:18Speaker 01

  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.

• 1:24:27Rick Rubin?

  Yeah.

• 1:24:28Speaker 01

  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.

• 1:24:57Speaker 02

  Part two of the conversation with Greg Brockman is available now on the Tetragrammatin Podcast Network.

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