An Interview with OpenAI President Greg Brockman About Astra and Alignment

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Listen to this post: Good morning, This week’s Stratechery interview is with OpenAI President and co-founder Greg Brockman. Brockman dropped out of college in 2010 to join Stripe, and rose to become the company’s CTO; he left in 2015 and co-founded OpenAI, where he also served as CTO. Today, after an interesting few years, Brockman is President of OpenAI, and is the face of yesterday’s announcement of Astra, OpenAI’s newest model. In this interview, recorded before the Astra announcement, we discuss Brockman’s background, his time at Stripe, and the early years of OpenAI. We touch on the ChatGPT launch and the drama of 2023, and whether or not having a billion users is actually a disadvantage. We also touch on OpenAI’s place on the value chain, and their competition with companies closer to consumers, like Microsoft, and their suppliers, like Nvidia. We also talk about Astra and OpenAI’s stated commitment to alignment, and debate whether or not OpenAI took security seriously in the run-up to the Hugging Face incident. As a reminder, all Stratechery content, including interviews, is available as a podcast; click the link at the top of this email to add Stratechery to your podcast player. On to the Interview: An Interview with OpenAI President Greg Brockman About Astra and Alignment This interview is lightly edited for clarity. Topics: Background | Stripe | OpenAI | OpenAI and the Turing Test | ChatGPT and OpenAI Drama | Productivity and AGI | Astra | The AI Value Chain | Cybersecurity Background Greg Brockman, welcome to Stratechery. Greg Brockman: Thank you for having me. Excited to be here. So we obviously have a massive amount of news to get to, but given this is the first time we have talked, I don’t want to pass up the usual Stratechery biography question I ask anyone. I do want to ask — do you define North Dakota as being a part of the Midwest? GB: I do. All right, well, a fellow Midwesterner, of course I have to spend some time there. You went to school in Boston, as they say, but before we get to there — you have an amazing resume even before you get to school, like the International Olympiad, but in chemistry. Where did the computers come in, or were computers a part of your life from the beginning? GB: Well, computers were always in the background for me growing up. I loved to play computer games, but I was really into math, I was into science. I actually thought that I was going to potentially be an actor all the way through ninth grade — I was very into acting and performing as well, and dabbled a little bit in philosophy. There’s a curveball! I did not know that was coming, but I’m going to figure out what the connection is between what you do now and acting, but let’s continue. GB: Well, I felt like in ninth grade, I’d been the star of, or the male lead in, a play or two in my middle school, high school. And I was thinking about, I wanted to double down on something, I felt like I could be best in the world and really try to move the needle in a field, and I felt like I either had to pick a more cerebral, hard sciences approach or more of the arts and acting direction and creative route. I ended up picking the hard sciences one, because I felt like maybe that was an area where I could most make a difference in the world. And why did you think you could most make a difference in the world by going in that direction? GB: I guess for me it felt like — one of the things I loved about acting was actually the group aspect of it. I loved improv, you’re just creatively thinking about things, you’re bouncing ideas back and forth, but it also really requires being part of a team that works together super well, and that’s something that’s not always guaranteed. That’s a hard thing to accomplish and find. The thing that I really liked about the more cerebral route is it feels like you sharpen your own skills. One thing I did learn, actually, was that even if you’re great at writing code, that’s not enough. It is actually about also bringing in that great team, and that’s part of, I think, what my career has been about — really helping to build and shape environments and culture that are actually able to deliver great results. One thing that’s interesting, there’s an aspect here about environment also shaping some of these things. You mentioned you were always the male lead in plays and dramas. I just recognize from my kids going through this era — my daughter was very into musicals and stuff for a while — there’s intense competition for the female lead, and usually if there’s just a competent male who’s willing to volunteer, he gets the role every time. Was there a lot of competition for the male leads, or were you one of one? GB: (laughing) I think that might explain it. I’ll tell you a story, though. What about the flip side too? Being in North Dakota, were there not a lot of people super intense at the hard sciences, and so did that almost seem like a rarer thing by the same token? GB: Well, I’ll tell you two stories. So one on the acting front. My first ever paid job was an acting gig. Mannheim Steamroller was in town in Grand Forks, North Dakota. Are you familiar with Mannheim Steamroller? I am. Yes, absolutely. GB: So they were putting on a concert, and they needed extras. They needed people to be these tin soldiers to walk around, because it was a Christmas holiday concert. I went to the audition, and I was this scrawny ninth grader, and there were all these big college students there. What they told everyone to do is, “Okay, everyone march in that direction”, and then the casting people would compare notes and you’d have some downtime, then they’d say, “Okay, march in the other direction”. What I noticed is that all these college students during that downtime were talking to each other, hanging out, and I was like, this job has one requirement, which is you’re going to be six hours at attention the whole time walking around this concert. So during that downtime, I was there standing at attention, just being in character. At the end, they said, “Okay, we’re selecting this person, this person, this person. Everyone else can leave”. I was not picked. But on the way out, they said, “Actually, we thought you were amazing. We loved seeing how much you were dedicated to this, so we’re going to actually make a new role for you”. And so I got to be a gingerbread man, and they gave me a whole costume. And that was my first job. So it was a little bit of trying to be out of the box in order to try to get the job done — not always defaulting into the role, but trying to find creative ways to get it. But I think that when it came to growing up in North Dakota, one of the things that was really great was that it was possible for me to really excel and be the best in the state at different areas that I put my mind to. So I was advanced in math. I ended up going to University of North Dakota starting in 10th grade and taking a bunch of courses there. I got very into math competitions, and then I’d go to the national competition, I’d go to the national math camp, and there I would meet the best in the country. These people were so amazing, and actually, one thing that’s been a real privilege and honor is that many of the people that I really looked up to at math camp now work at OpenAI. So I’ve gotten to see them in this new field, this new light. That’s amazing. GB: But it was both that I could really chart my own course. I started doing math research, and I know that if I’d gone to some of these high-powered high schools or these much more competitive states, I think I wouldn’t have stood out. I would have had to be in the standard track. So it was by being in this area that I was able to explore my interest and really march to my own tune. So when people say they went to school in Boston, they usually mean Harvard, which is where you started. Then you switched to MIT. And then at some point you’re working for Stripe. What’s the sequence there? When did you meet the Collisons? What happened in Boston? GB: Well, after high school, I took a year off, and I actually started working on a chemistry textbook, because I’d gotten very into chemistry in high school, competitive chemistry, and come up with a unique way of thinking about it. Very first principles, very mathematical, rather than memorization. I wanted to teach that, I wanted to propagate that. So I actually wrote 100 pages. It’s on my website right now. I haven’t finished it. I’ve been intending to get back to it. That’s like a retirement project, I love it. GB: Exactly. Well, at this point, I think you can just ask Astra, and it’ll do a great job. But I was trying to figure out how do I get this thing published, so I asked one of my friends who had done something similar in math, and he said, “Well, you don’t have a PhD, so no one’s going to publish it. So you can either self-publish” — and I was like, oh, that’s a lot of work, a lot of capital — “or you can make a website and try to promote the ideas that way”. And I said, “I guess I’m going to learn how to code”. So I went on W3Schools. Do you remember W3Schools? Have you ever seen that website? No, I don’t think so. GB: Okay, so this is the classic — they have an HTML tutorial, JavaScript, CSS, PHP. I read through them and I was like, “I should test this out”. I remember I built a little first widget: you could click a table column, and it would sort the rows accordingly. It was the coolest feeling ever. I had this thing in my head that I was picturing, now it’s in the world, and anyone can benefit from it. They don’t need to understand the details behind it, it just works. I remember the very first thing I built that had users, it was actually a competitive chatbot game, and I got 1,500 hits from StumbleUpon one day. It was the most glorious feeling, there were 1,500 people who had played with my game, had hopefully enjoyed it, but they stuck around enough to play it. I was just like, “This is what I want, I want to help people, I want to benefit people and build for them”. So that’s what I showed up thinking I was going to do at Harvard, that really had changed for me. I thought I was going to do these more eclectic interests, and instead I was like, “I just want to build”. Freshman year at Harvard, I was in this computer club, there were these two seniors who would have obscure technical debates every single time. We would all listen and say, “One day we’ll understand, one day that will be us”. But then they graduated, and sophomore year came. So did you just switch over to MIT because you realized you had this focus on coding and building? GB: That’s basically right. Because sophomore year, I was running the club. I was supposed to be having the obscure technical debates, and I was like, “I’m not ready. I have so much to learn. I need to be around people who are so much better than me”. Got it. GB: And so I spent so much time down at MIT, and I was like, it just makes sense to transfer. Stripe Got it. So when did you meet the Collisons then? GB: So I met them in 2010, late 2010. We had a lot of mutual friends, because John had gone to Harvard, Patrick had gone to MIT, and they were poking around. That team was poking around trying to find who’s into computers at either of these schools, and my name kept coming up. Right. GB: So I got a reach out from the team, and I flew out. And I remember meeting Patrick, really, for me, was the moment I was like, “All right, this is someone I want to work with, I feel like we could build something great together”. So my next question was what attracted you to Stripe, but it sounds like you just answered it. What did you learn there? You were pretty early on the team, progressed very rapidly. By the time you left, you were CTO. What was the takeaway for you from Stripe itself and Stripe scaling, but also yourself growing so rapidly inside this company that is itself growing rapidly? GB: First of all, for me, it’s always been about the people. I knew that these were people that I wanted to work with, that it felt like we could learn together, we could accomplish something great, and so that was a real key. And by the way, dropping out of school twice is something that I do not wish on anyone’s parents. It definitely was something that was difficult for mine, but they actually were very supportive in the end. No, we’re getting the idea, you take things to the extreme, right? Most founders drop out once, you had to do it twice. We’re getting the drift. GB: Exactly, exactly right. I remember a lot of the early days of Stripe was really about first principles thinking. We were in a domain, this credit card industry, that is very opaque, very Byzantine, it’s been built up over many decades and has so much complexity that the card networks themselves run on ISO 8583, the spec from the ’80s. It’s a byte-oriented format, the whole thing. And we were trying to figure out, “How do we make this simple, dead simple, for the Internet era?”. So a lot of this is about deep understanding of a domain that you have no familiarity with. None of us really grew up as payments experts, but you just want to really deeply understand how it works so that you can expose the right primitives and the right APIs and abstractions. That to me was actually the core skill, and something that has been very transferable between Stripe and OpenAI. They’re very similar in some ways, where you go and you have to scientifically learn about a domain. AI versus payments, obviously different in terms of the specifics of those domains — one is much more about natural science, the other is almost this system that has been built up of complexity — but they are fundamentally about understanding the underlying why of how something works and exposing it in a way that’s simple and easy. I spent a lot of time on recruiting, a lot of time on culture, I think one thing I found is that I love coding, I love that feeling of flow state and just building and creating. Right, you’re legendary for these hours-long flow states and just coding endlessly. What’s the longest coding session slash flow state you had while building Stripe? GB: Oh, it all blurs together. I couldn’t possibly say, but I would just say that for me, there was this 24-hour sprint that was how we actually got onto the credit card networks. That was just this really great time that all of us were there together in the office, none of us slept, and it was supposed to be an integration that was going to take nine months, we got it done overnight, and if we had missed it by a day, it would have been another month. As a startup, every day matters, so that kind of accomplishing what seems impossible otherwise, I love it. That is something that was incredibly exciting. When you became CTO, you wrote a post saying how you were talking to other CTOs, you thought it was more of an architectural job, none of them did that, and you’re like, “I feel like I lose my feedback loops, I’m not connected to the product, I need to code again, I’m going to become a coder”. I’m curious, you wrote that towards the beginning of being a CTO, how long did that last? How long did you stay in touch with coding? GB: Well, I would say probably for almost another decade. One area that I think I’ve grown on and that I’ve really learned is how to stay in touch and really help move forward a team and bring together a team, even if you yourself are not hands on keyboard. And by the way, I will say that this is something that I think is actually an important lesson almost for every software engineer now, because the act of what coding is has changed so significantly over the course of the year. I think over the next year it’s going to change even more. We are all moving away from having to be the one who knows exactly which library to use and is able to craft the syntax and where the semicolons go. We are all moving to being these higher-level managers, these directors, the source of the inspiration, the vision, the judgment, the feedback. I think that shift, it’s been something for me that was difficult, because you had to let go of something that I was used to and valued and really loved. But I’ve actually replaced it with something I love even more. I know I’m talking to a smart guy because you stole my foreshadowing. I was going to circle back to that in a little bit, but yes, that’s exactly where we’re going. OpenAI and the Turing Test Let’s get to OpenAI. You’re a part of the OpenAI founding team. What’s your version of the story? I’m sure this could be a whole hour-long podcast, but what drew you to this space and got you guys started? GB: Well, I have been excited about the idea of AI for a long time. I remember when I was first getting into programming, I read Alan Turing’s 1950 paper on the Turing test. It’s this really interesting paper, it’s like 70 pages or something. It starts out by saying, “Well, what does it mean for a machine to be intelligent? I don’t know what that means. Intelligent is not well-defined. So let’s have a well-defined version of it”. I’m going to ask you what is AGI in a little bit. So it sounds like it’s still unsettled, right? GB: Well, there you go, yes. So Turing very smartly sidestepped the question and said, “Let’s just have an operational definition of this, where if you can have a test where a human can’t tell the difference between an AI speaking to them and another human, we’ll define that machine as intelligent”. The thing that was very interesting, though, that gets much less airtime, is he said, “Well, how are you ever going to solve this? It’s just too hard to program an answer to it. You cannot write down all the rules for how to respond to questions. Instead, what if you could build a machine that learns? What if you could build what he called a child machine?”. And then you teach it — you have a teacher who gives it rewards and punishments, and then you’re able to actually give it intelligence and help it be able to pass this test. I remember being so struck by this idea, because as a programmer, you only make progress by deeply understanding the solution to something. There are so many problems I don’t know the solution to, you don’t know the solution to, no person has ever come up with a solution to, we’re never going to be able to program the answer. But what if you could have a machine that could understand problems that we cannot, that could understand solutions that we could not? This isn’t just about image recognition, though of course it has applied to that. This is also about questions of how do we get along better as a society? How do we structure the world? How do we ensure that the benefits of what we’re creating end up lifting up everyone? These are super hard questions that humanity is not necessarily the best positioned to solve. But if a machine could understand, could look through more data, could have a deeper, richer understanding of many different fields all coming together, maybe it could solve them in ways that we could not.= So I was so inspired by that idea. But it was an idea. I remember showing up at Harvard, asking my professors, “Hey, could I do some AI research?”, and they showed me what the natural language processing state of the art was at the time. It was so clear to me, I was like, this is not what Turing was talking about. It’s much more hard-coded, parse trees, all of those things, this is not going to scale to AGI. But something in the early 2010s changed, and I was watching from the outside. 2012 was AlexNet, then there was a series of other papers, and the thing that I would just keep seeing on Hacker News, it felt like every day there was a new “deep learning for X”, I was just like, “What is deep learning?” — I remember going to deeplearning.org, and it just said, “Deep learning is a new approach to artificial intelligence”. I’m like, I have no idea what this means, I actually knew one person in the field, I went to them and asked them to introduce me to more people in the field, and I just kept getting reintroduced to a bunch of my smartest friends from college. I was like, “Wait, that’s interesting, these people are working on this, that’s actually a very strong signal”. So by 2015, it felt to me like there was something real happening. I was spending a lot of time as well really thinking about AI safety, thinking about the long-term future of this kind of technology, what it means to get it right, and it was more philosophy at the time. There were various writings you could find online that were very cool thought experiments, I ran a reading group at Stripe where we would talk about these things every week. So it’s something I deeply cared about, thinking about if there’s any way that I could help AI go slightly better than it would without me, that would be the best thing I could do with my career. That was all leading up to 2015, and I felt like I’d reached a milestone at Stripe where the company was going to work with or without me, it was kind of a question of, “Do I want to go the manager route?”, which is what you need to get to the next phase, or, “Do I want to go start a new company?”, That was something that had always motivated me, and so I decided that’s what I wanted to do. As I was about to leave, Patrick said, “Why don’t you go talk to Sam [Altman]“, who he had introduced me to a couple years before. He said, “He’s seen a lot of young people in similar situations, maybe he can give you some advice” — kind of hoping that Sam would convince me to stay. It didn’t quite play that way. (laughing) Yeah. GB: I met up with Sam, and three minutes in, he’s like, “Okay, you’re clear you’re out, what are you thinking about doing next?”, I said, “Well, I’m thinking about doing something in AI”, he said, “I’m also thinking about doing something in AI”. And that was the start. Were you on board with the whole non-profit thing? What were your thoughts on that when you set that up? GB: Well, the idea of being a non-profit is something that Sam had proposed, and I think that there are actually a lot of very important properties, and you see ones that have really rung true to today. The technology we are building, it’s just bigger than anything that’s been created, it’s bigger than the traditional structures and systems. There is no one corporate structure that exists that actually fully encapsulates the mission and the work we need to do. So I think starting that way made perfect sense, and there was always a question of what is it going to take to actually operationalize the mission? That’s something we spent a long time really thinking about. I think we’ve been the company that’s been the most innovative in really thinking about can you build a structure around all of the different aspects of both the commercial development that needs to happen, the distribution of benefits that needs to happen, the practical way of actually bringing forth this compute-powered economy, and doing all of that at once. So I think it’s been an important element, it remains a critical element to what we do. But again, I think we’ve innovated so much on corporate structure around the core of this mission, and that mission is invariant. Yeah, innovate is one way to put it. You mentioned the credit card networks, right? It’s super opaque, lots of stuff from the ’80s, a massive amount of path dependency that gave Stripe an opportunity, because you could abstract that all away and just be, for everyone else, “Here’s an API, it’ll work, don’t ask questions”. Now when you look back at OpenAI — it’s hard to believe it’s been over a decade now — could OpenAI have come about in any other way? Is there that sort of path dependency in there, or is there a, “If I went back to first principles, me, Greg Brockman, which I like to do, I would have structured this a lot differently”? GB: I don’t see any other way that we could have gotten to where we are, and I think where this mission needs us to be. ChatGPT and OpenAI Drama Tell me about the ChatGPT launch, because you have the turning point where you realize you need to scale, you partner with Microsoft, add the for-profit bit, and then ChatGPT comes out and it’s huge. Did you have any expectations it would be as big as it was? GB: So the thing that surprised me, my prediction error, was GPT-3.5 being something that people would really love and want. We had GPT-4 at the time, it had finished training in August or so, and we launched ChatGPT at the very end of November of ’22. The thing that always happens when we have a new model is that we just latch onto it. The old one looks terrible. GB: All we see is just flaws in the previous one. We’re just like, “Ah, this previous one is so bad, I can’t imagine anyone would ever want to use it”. We had like 200 testers who we’d been paying to use the pre-release ChatGPT, and again, we had to pay them to use it rather than the other way around. So there were some signs of product-market fit if you really dug in and were close to the details, but if you zoomed out, it really didn’t look like we had it. But the way that we thought about it was GPT-4 clearly was going to change the world, we knew that, it was very obvious from the first moment we talked to it. I remember for that first week after GPT-4 came out of training, just feeling the reality of it. We’d been dreaming of AGI, thinking about AGI, thinking about what it might be like. But the first time you have a technology that really you can ask any question and it can give you pretty sensible answers, that got a 5 on AP Bio — all of those things, to me, felt like, okay, something is going to be different. It may not transform the world tomorrow, but over upcoming years, this technology absolutely will, and it’s real now. That was very clear. So you look at the ChatGPT launch, the way I thought about it was we just need to get the infrastructure out first, so that we can have LLM-serving infrastructure that’s battle-tested, that we’ve put our reps in. Then in March, when we launched GPT-4 — which, if you remember, we did the six-month delay between completing it and actually launching it — then we’ll already have the infrastructure ready to go. But I didn’t expect it to quite take off in that form, even though I expected it to do so in the future. What was it like at that time? Was it just all hands on deck to keep the servers from melting? GB: Oh, absolutely. So we launched into what we called a low-key research preview, and of course, it was just the full exponential, every single system you can imagine breaking, broke. Our login system became a big bottleneck, we had to do so much work to improve the login system, and you’re scratching your head saying, we’re building this magic AI technology, and the thing that is your bottleneck is, “Does your login actually scale?”. I remember that we had a fairly inefficient set of inference kernels that were rolled out to production, and I’d actually written some more efficient things, or we had some more efficient things on the research side, and one of the big pieces of work was, “Let’s actually take those optimizations, let’s move them over”, so a bunch of people swarmed on that problem, got it done. I think this was the general flavor of it for that first day, for that first week, for that first month, it was just scaling every system and trying to really keep up with this wave after wave of demand. What happened in November 2023? GB: Very complicated answer. Where do you want to start? I don’t know, I feel like I have to ask you about it. They’re tied into — you took a sabbatical not too long after, was there a link between those two things? GB: Look, I would say the way to think about it is that at the highest level, I think that 2023 really showed that there were tensions that had built up, really interpersonal tensions that had built up, that we had not sufficiently gotten ahead of. To me, that’s one of the most important lessons of OpenAI, the fact that we’re building technology, but it’s always about the people, in good and bad ways. It means that really managing people dynamics, that is one of the most important things that we do, and if we don’t get ahead of it, if we don’t have the hard conversation, then that is actually where things can become much rougher. So I’m happy to drill into more details, but I think that a lot of it, if you really get there, it’s not the more interesting technological things. How much of that is tied to ChatGPT being this massive, huge hit you weren’t necessarily expecting? Was there a link between those things, or do you think these tensions would have come to a head regardless? GB: I don’t think that there’s a direct causal link, at least not in my view. I think that to some extent, there maybe is an underlying theme of, as our technology has progressed, everyone feels the weight of the world on them, feels the stakes on them. Actually, one of the things that’s hardest is how do you just move forward? To me, the thing that I always remark upon is that the day-to-day activities that we do almost look the same as at every other company. You’re still debugging some low-level issue, someone’s upset at someone else because they said something, or they didn’t include them in the meeting, whatever it is. It’s just the human factors, the human work. But of course, the stakes are so massive. So I think that there is something that has been very important at OpenAI, and actually one of the big things that I have focused on, is really trying to not put people in positions where they feel that weight of the world and feel like they’re alone in it. Really doing it together as a team, that’s the critical thing, and that I think is maybe the way in which I would say that there is something — and it’s not really specific to those events, but it is a consistent theme over the course of OpenAI — which is really keeping that feeling