On the recruitment wars, I mean, this got a lot of attention clearly, and it looked like Meta was quite aggressive. What exactly does this tit for tat look like? What stage are we at?
Yeah, I mean there is a pool of talent, right? And everyone kind of knows who they are. And I think many companies have realized that one of the key ingredients, not the only important ingredient, but one of the key ingredients to building a great AI lab is to get the best talent. And I think not a surprise that Meta has been aggressively employing this strategy.
You know, we haven't sat back idly, and I actually want to tell this story from OpenAI's point of view. I think that a lot has been made in the media of, oh, you know, there's this unidirectional flow of talent over to Meta. But the way that I've seen it is, you know, Meta, they've gone after a lot of people quite unsuccessfully. So just to give you context, right, within my staff, within my direct reports, before they hired anyone from OpenAI, I think they went after half of my direct reports and they all declined. And of course, you know, if they have something like $10 billion of capital per year to deploy towards talent, they're going to get someone. So I actually feel like we've been fairly good about protecting our top talent. And, you know, it's been kind of interesting and fun to see it escalate over time. You know, some interesting stories here are Zuck actually went and hand-delivered soup to people that he was trying to recruit from us.
Like, just to show how far he would...
Yeah, I think he hand-cooked the soup. And, you know, it was shocking to me at the time, but over time I've kind of updated towards these things can be effective in their own way, right? And, you know, I've also delivered soup to people that we've been recruiting from Meta.
You're doing a soup counting.
I've thought of, you know, if I had an offsite, the next offsite for my staff, I'm going to take them to a cooking class.
And yeah, I mean, it's just been... Yeah, but I do think, you know, there's something I've learned about recruiting.
Uh, it's better if you get like Michelin star soup. You know what I mean?
Yeah. No, no, no. I think Dejo is very, very good and probably better than any soup I could cook.
But yeah, I do think there is something I've learned about, you know, just how to go aggressively after top talent. And I think the thing I've been actually very inspired by is that at OpenAI, even among people who have left for Meta, I haven't heard anyone say AGI is going to be developed at Meta first. Everyone is very confident in the research program at OpenAI. And one thing that I've made very clear to my staff, to the whole research org, is we don't counter dollar for dollar with Meta. And the multiples below what Meta is offering that people are very happy to stay at OpenAI gives me so much conviction that, you know, people really believe in the upside and believe that we're going to do it.
Well, and you and Alex... Alex... he used to be one of the math competitions...
I'm sure you guys hung out.
Yeah. I mean, I have hung out with Alex a handful of times, but we don't do much anymore. Yeah. I mean, yeah.
Why did soup become the thing? It was just...
I don't know. You know, there's been soup, there's been flowers, there's been anything you can think of under the sun. But I don't know. I think, you know, life's an adventure. I play into the meme.
Yeah. Yeah. Is there any poker strategy to employ like as you're thinking?
Well, again, I think it really goes back to what I've said about the media narrative. The game is not to retain every single person in the org. It's to trust in this pipeline that we have for developing talent and to understand who the key people we need to keep are and to keep those. And I think we've done a phenomenal job at that.
We have a special treat today. I'm excited. Mark Chen is here from OpenAI. He's the chief research officer. He's somebody I've gotten to know over the last couple of years. Thank you so much for coming.
No, it's been great to know you for so long.
I feel like, you know, there's a handful of people in this world working on this very important project, and I mean, you're right at the top of it. So it's so cool to have a chance to chat.
Yeah. Thanks for having me on.
It's my pleasure. And I mean, there's a bunch of things that I want to talk to you about because I've gotten to know you, like we said, over those last couple of years. I want people to know a bit more about your biography, but I also know there's going to be AI enthusiasts who want us to go deep on a couple of things there. So we'll try to do everything. I wanted to start just by giving people a feel for your job, which in my head, and you, I mean, just correct me if I get any of this wrong, but you're, you know, Sam has been, he's really into research. He's the boss. He's kind of at the top of the food chain. But then you and Jakub are working together to shape OpenAI's research direction. And then you're in this additional part of this role is actually deciding which compute goes where onto these projects. So you kind of have to chart where OpenAI is heading and then the mechanics of how you're going to get there. And this always strikes me as a horrible job because I picture people doing everything in their power to get GPUs from you.
It's true. People are very creative in the ways that they try to make backroom deals to get the GPUs they need. But yeah, I mean, it is a big part of the job, right? Figuring out the priorities for the research org and also being accountable for execution. So really to that first point, you know, there's this exercise that Jakub and I do every one to two months where we take stock of all the projects at OpenAI. And it's this big spreadsheet, about 300 projects, and we go and try to deeply understand each one as best as we can and really rank them. And I think for a company of 500 people, it's important for people to understand what the core priorities are and for those to be communicated clearly, explicitly, verbally, and also through the way that we allocate compute.
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Okay. So, and then in that, when you're talking about this 300 projects, I imagine, I mean, obviously some of those are the giant frontier models and then some are probably experiments that people are working on. And so like, how do you possibly keep track of all that and then come to some sort of conclusion about what merits GPUs and what doesn't.
Absolutely. So I think it is very important when doing this exercise to keep the core roadmap in focus. And one thing that differentiates, I think, OpenAI with other big labs out there is OpenAI has always had core exploratory research at its core. We are not in the business of replicating the results of other labs, of kind of catching up to other labs in terms of benchmarks. That isn't really our bread and butter. We're always trying to figure out what that next paradigm is, and we're willing to invest the resources to make sure that, you know, we find that right. And I think most people might be surprised at this, but more compute goes into that endeavor of doing exploration than it is to training the actual artifact.
It must be. It's still got to be, how do you stop yourself from being persuaded by someone? Because everybody's going to put, you know, like when I think about this sometimes, I picture when I was at the New York Times, you would have this page one meeting where everybody wants to be on page one. Everybody thinks their story is the most important story. They're all doing their very best job to tell you why this thing is so important. Everybody in that room has worked weeks, months on whatever they're pitching. And so it feels like life and death. And that, I mean, it just, it seems so difficult for me.
Yeah. No, it is also a difficult process. And I think the hardest calls you have to make are, you know, this is a project that we just can't fund right now. But I also think that's good leadership. You need to clearly communicate that, hey, these are the priorities. This is what we're going to talk about. These are the types of results that we think move the research program. And, you know, there can be other things, but those have to be clearly number two.
And when you, like you were talking about not being reactive to your competitors. When I was looking through my notes, I don't know if I could go to the line quick enough, but I mean, this was like a point of pride that I saw that you feel like some of the other companies are, well, you know, you guys were in this position where you were ahead and setting the bar for others, and so they were reactive to what you had coming out. We happen to be doing this interview a few days after Gemini 3 came out, and, you know, there is a degree to which your rivals, at times, like, yeah, I mean, there's this back and forth going on. And I know the benchmarks are sort of controversial, how valuable they are, but people go ahead on these things. So how do you also, as time has gone on, maintain that luxury or that intellectual position where you feel like we're just going to do what we're going to do?
Yeah, I think AI research today, the landscape is just much more competitive than it's ever been. And the important thing is to not get caught up in that competitive dynamic because you can always say, hey, you know, I'm going to ship an incremental update that puts me in front of my competitor for, you know, a couple weeks or a couple months. And I don't think that's the long-term sustainable way to do research because if you crack that next paradigm, that's just going to matter so much more, right? You're going to shape the evolution of it. You're going to understand kind of all the side research directions around that sphere of ideas. And so when you think about kind of our reasoning program as an example of this, right, we bet more than two years ago that we're really going to crack reasoning on language models. And this was a very unpopular bet at the time. You know, right now it seems obvious, but back then the environment was, hey, you know, there's this pre-training machine that's working great. There's this post-training machine that's working great. Why invest in something else? And I think today everyone would totally tell you, you know, thinking in language models, it's just a primitive you can't live without. And so we're really there to make these bold bets and to figure out how we can scale and build the algorithms to really scale to orders of magnitude more compute than we have today.
It's just, you know, I mean, and I get that intellectually, in my, you know, it gets harder as you guys started as like basically a pure research company. When you look at OpenAI today, I mean, you have product lines. There's parts of OpenAI that look much more familiar to a mature Microsoft or a Google where you have product lines, you've got all these different things that you have to serve. Typically, I feel like you guys are still young enough, so maybe you don't have these exact pressures yet, but, you know, as those companies go on, it always becomes, well, we're more focused on the things that are serving the bottom line than spending a ton of money on research always seems to get dwindled down over time.
Yeah. And I think that's really one of the most special things about OpenAI. At its core, we're a pure AI research company. And I don't think you can say that of many other companies out there. And, you know, we were founded as a nonprofit, and I joined during that era. And I think the spirit is, you know, build AGI, advance AGI research at all costs, and do it in a safe way, of course. But yeah, I actually do think that's the best head fake to really creating value, right? If you focus and you win at the research, the value is easy to create. So I think there's a trap of getting too lost into like, oh, you know, let's drive up the bottom line. When in reality, if you do the best research, that part of the picture is very easy.
And you started in 2018, and so you feel like that soul, that core culture and that core nucleus, it's really persisted.
What does Elon say? What does he say? We shouldn't call any of you guys researchers. It's just engineering, right?
Yeah. No, I think, yeah, we... No, it's true because I feel like once you have this hierarchy and you elevate, let's say, research science as a thing beyond engineering, you've completely already lost the game. Because, you know, when you're building a big model, so much is in the practice of optimizing all of those, you know, little percentages of, you know, how do you make your kernels that much faster? How do you make sure the numerics all work? And that's a deep engineering practice. And if you don't have that part of the picture, you can't scale to the number of GPUs we use today.
So, because I think there, well, okay, but there is like a mystique that surrounds a researcher versus an engineer, you know what I mean? So do you feel like it is better to kind of stay levelheaded on that? Is that kind of what you're saying?
Well, I just feel like researchers, they come in so many different shapes, you know? Some of our best researchers, they're the type that, you know, they come up with a billion ideas, right? And many of them are not good. But, you know, just when you're about to be like, ah, is this person really worth it? They come up with some, you know, phenomenal idea. Some of them are just, you know, so good at kind of executing on the clear path ahead. And so there's just so many different shapes of researchers, and I think it's hard to just lump it into one stereotypical type that works.
That makes sense. Okay, I won't belabor you with too many competitive, like, rival questions. It's just since Gemini 3 did come out, I did wonder what happens with you personally or the team when one of your rivals puts it, like, does everybody go and look and see what it can do? Is there like a prompt or a question that you often throw at these new models to see what they can do?
Yeah. Yeah. So, to speak to Gemini 3 specifically, you know, it's a pretty good model. And I think one thing we do is try to build consensus. You know, the benchmarks only tell you so much. And just looking purely at the benchmarks, you know, we actually felt quite confident. You know, we have models internally that perform at the level of Gemini 3, and we're pretty confident that we will release them soon and we can release successor models that are even better. But yeah, again, kind of the benchmarks only tell you so much. And I, you know, I think everyone probes the models in their own way. There is this math problem I like to give the models. I think so far none of them has quite cracked it, even the thinking models. So yeah, I'll wait for that.
Is this like a secret math problem?
Oh, no, no. Well, if I announce it here, maybe it gets trained on. But yeah, I do think it's one of the nice puzzles of last year. It's this 42 problem. So you want to create this random number generator mod 42, and you have access to a bunch of primitives which are random number generators modulo primes less than 42. You want to make as few calls on expectation to these sub-generators as possible. So it's a very cute puzzle, but the language models, they get pretty close to the optimal solution, but I haven't seen one quite crack it.
Okay, this is, we're heading down a direction I want to ask you about. But then just before we get there, so I know you're, I've seen you, you're very competitive. You've also told me, I think I found, I love competition. I hate to lose somewhere.
I really hate losing. I hate losing.
Yeah. So I'm picturing, I'm just curious if this is at all right. I mean, if we know Gemini 3 or whatever is coming out on a Thursday, I mean, are you up at like midnight throwing that problem at it? Or is it not quite that drastic?
Um, no. I mean, I think it's in long arcs, right? And any endeavor, like, I'm kind of a person who has obsessions. I think any endeavor you have to play the long game. And, you know, we've actually been focusing on pre-training, specifically supercharging our pre-training efforts for the last half year. And I think it's a result of some of those efforts, together with Jakub, focusing and building that muscle of pre-training at OpenAI. You know, crafting a really superstar team around it, making sure that all of the important areas and aspects of pre-training are emphasized. That's what creates the artifacts today that feels like we can go head-to-head with Gemini 3 easily on pre-training.
Okay. And I want to ask about the pre-training stuff because I've been talking to all you guys about this a lot. But okay. But so you're saying that you're less obsessed about lobbing problems at these new models just when they appear and more at this long journey.
Yeah. Okay. Hey, the reason I want to talk about sort of the puzzle that you were at, I mean, I, you know, I first met Jakub before OpenAI ever started when he was doing a coding competition and I got super into coding competitions for a while. There's this guy, Kennedy, I don't know if he's still famous, but he was like the Michael Jordan of these coding competitions. And so I went to watch one at Facebook used to, I don't know if they still do, but they had an annual Hacker Cup. And that's where I saw Jakub for the first time. And then I know you, I think, did math competitions in high school.
Like probably grade school through high school. And then you also did, you also do IOI?
So I got into coding really late in life. It was a roommate in college that convinced me to take my first coding class. And I had all the hubris of a mathematician at that time, whereas like, you know, math is the purest and hardest science and that's where you really prove your worth. I mean, I think I was probably too into the competition back then. But yeah, I mean, it became this super rewarding endeavor. And it started out as purely a way to keep in touch with my friends from college.
Yeah, I went to MIT. You know, I graduated and every weekend we would just log on and do these contests just to keep in touch with each other. And over time I found myself having a talent for it. You know, I started competing fairly well and then writing problems for contests like the USA Computing Olympiad. Eventually started coaching that team and yeah, it's been a great community where I've met people like Scott that you know.
Yeah. Yeah. Okay. So, I think lots of people might be familiar with like math competitions because they probably see kids going through that. The IOI and these coding competitions are a little bit different. I mean, it's, I mean, you'll know it so much better, but when I saw them, I mean, it looks like a, it's almost like a word problem that's a puzzle, and you're trying to kind of find the most efficient and correct way to solve that, and you're in this race against everybody and everybody's like writing code on their computer and then some people try to get there really fast, but then their thing kind of doesn't solve the problem, right? And then, you know, there's like this trade-off, right?
And so you actually were on the MIT team.
No, no, it's something I did after college.
After college. Okay. But today you are like the coach of the US national.
Yeah. One of the coaches.
One of the coaches. Okay. And was it last year or the year before? Like the US, like we hadn't won one in a long, long time, right?
Yeah. Yeah. Yeah. So, yeah. I mean, the team, I mean, it's, you know, you can never predict what the makeup of top talent looks like every year. But we had a very spiky team, I think, two years ago. And yeah, I believe they won the Olympiad.
Because I feel like usually it's like China or Russia or like Belarus and Poland. I mean, right? Yeah. And so this competition takes place in a different country every year. What does it look like? How many people show up?
Yeah. Yeah. So they take the top four students from every single country. It is as much of a competition as it is a social event, you know. This is a tight-knit community. They all do go on to do phenomenal things. And yeah, it's this intense two-day contest where each day you get just three problems, five hours to solve them, and you can really feel the adrenaline and all the pressure in the room. But it's also great fun. I think people settle down and they make, you know, lifetime friends through it.
What do you, and like as coach, I mean, you're so freaking busy, man. How much time do you spend on this? What does that look like?
Honestly, the kids are so self-motivated. Sometimes it's really about just managing their performance and strategy. I think, you know, you're going to have good days, you're going to have bad days, you're going to have good hours within the contest, bad hours, and you can't let that get into your head. There's a lot of similarities between managing contestants and managing researchers. It's like on a much longer time scale, but, you know, researchers have good months, bad months. You know, you can't really let those strings of failures get into your head because that's just the nature of research, right? And I think a lot of it's morale management at a certain point. Yeah, I think one other interesting thing that contests have helped me realize lately is when you put the models and deploy them towards solving these contest problems, which they're quite good at these days.
Yeah, I was going to ask you about that.
They work in a very different way from humans. You know, we typically think of these machines as, you know, they're very good at pattern recognition. You can take any problem if it maps to a previous problem, it's probably going to be able to solve it. But what I've noticed is in some of the previous IOIs, there's this problem like, messages, very ad hoc. I didn't think the models would solve it at all, but actually one of the easier problems for the AI. So yeah, I mean, this has given me the sense that AI plus humans in frontier research, it's going to do something amazing just because the AI has a different intuition for what's easy and what's not.
So, okay. Is it vaguely, you know, when DeepMind did the whole AlphaGo thing, you know, there was that moment where it was doing things humans hadn't played before. So, kind of like vaguely similar to that or...?
I think so. I think so. I think really with GPT-5 Pro, right, there's been an inflection point in frontier research. And one of the best anecdotes I have for this is, you know, I think three days after the launch, I met up with a friend who was a physicist. And, you know, he had been playing around with the models, felt like, you know, they were cute but not super useful. And I challenged him with the pro model, just try something ambitious. And, you know, he put in his latest paper, it thought for 30 minutes and just got it. And I would say that reaction in that moment, it was kind of like seeing Lee Sedol during that, you know, move 37, move 38. And I just think that is just going to keep happening more and more for frontier mathematics, for science, for biology, material science. The models have really gotten to that point.
I was going to ask you this question, which is not very original because I think we've been doing this ever since kind of Big Blue and all the chess stuff. But yeah, just as somebody who had followed all these competitions, if, I don't know, there's a sadness when you start seeing these models solving these things that were like the height of achievement for these very unique human minds.
Well, yes and no. I mean, I was good at competitive programming. I was never at the absolute top. And maybe this is a way to get revenge. No, but I do think, no, there's certainly a moment for myself, right? We tracked coding contest performance while we were developing reasoning models for a while. And, you know, at the start of the program, you know, they were not super great, you know, at the level of any average competitor going into the contest. And yeah, over time they just started creeping up and up in terms of capability. And you still remember that moment when you walk into the meeting and they have where your performance is, and then the models exceeded that. Man, that was also a shock to me. It's just like, wow, we've automated to this level of capability so fast. And of course, you know, Jakub was there still a bit smug, but within like one or two months, it was also surpassing him. So, yeah, no, the models are at the frontier today, right? It's so clear by even through the results we've done this summer at Codeforces, right, top optimization competitive programmers in the world, I think it achieves second place there. And so really it's jumped from, you know, hundredth place last year to top five this year.
And like, do you think we'll still be doing these competitions in 10 years?
I think so. I mean, they're just fun. I mean, certainly a bunch of people who use it to pad their resume are going to drop off from doing it, but I think the people who've always excelled at it the most are people who just do it for the fun of it. And I don't think that'll go away.
When I was doing this story, I mean, they were telling me that like if you're from Russia or I don't know which countries, that you basically get like an automatic free ride to any university that you want. I mean, I see the guys on the US team go to like Harvard and MIT, so they seem to be doing okay, but it doesn't seem like the US has a...
Yeah. I mean, don't you think it's going to, yeah. I mean, interviews, right? They're going to be kind of broken going forward. And everyone's seeing this a little bit. And, you know, even college exams or college homework, it's all broken at this point, right? And I do think we're going to need new ways of assessing and gauging, you know, who's performing well, who's learned the material.
Where somebody's actually at.
Yeah. Yeah. So I mean, I've had this idea here where maybe for our interviews we should just have candidates talk to ChatGPT. And, you know, it's a special kind of ChatGPT where the model is trying to gauge whether you know the material or whether you're at the capability level to work at OpenAI. And, you know, you have to have this conversation with it that convinces it deeply you belong at OpenAI. And of course, you know, you can't be allowed to jailbreak it, but we look at the transcript after. But maybe like tests like this will more accurately reflect in the future whether, you know...
So, you don't do that yet, but you're thinking about...
Yeah. Yeah. Just creative ways to revamp the interviews.
Yeah. Yeah. Well, I mean, Silicon Valley is famous for doing the like brain teasers during the interviews and everything. Yeah. So you, I mean, we talked, you were very good at math growing up. And I think you were, you born on the east coast?
Uh, yeah, born on the east coast.
And then you lived on the west coast too.
And then you lived in Taiwan for like, for like grade school to high school.
Okay. Your parents worked at Bell Labs.
So you come from like engineering stock. I mean, it's a really interesting background just because you kind of got like a flavor for all these innovation hubs and especially with your parents being at Bell Labs and most of...
I mean, yeah, I just grew up in a very scientific environment, you know, dinner table talk was puzzles and things like that. And I also got kind of the more traditional, you know, Bell Labs east coast experience. On the west coast, my dad came to do a startup, so a little bit of that kind of new company, got exposed to that when I was young as well. And of course the big jump to Taiwan, right? And I think it's a huge culture shock. You wear uniforms, you're in a school, it has barbed wire around the school, right? And also getting exposed to kind of that level of rigor. I think it was just a number of really great experiences growing up.
So, like the schools were harder or...?
Well, I would say it was just much more kind of, you know, it's just there's a little bit less flexibility and freedom in the school system, but I think it also teaches you something.
Yeah. Okay. Since day one, the Core Memory podcast has been supported by the fine people at E1 Ventures. They are a young and ambitious VC firm in Silicon Valley, investing in young and ambitious companies and people. Thank you so much to E1 Ventures for all your support.
And you knew you wanted to come back to the US for college.
Okay. And then, okay, so you're at MIT. You're kind of like in this interesting group. I guess MIT probably always has an interesting...
Oh, man. Yeah. 2012 was such a great group.
Yeah. Like, who is there, sort of like an all-star list?
Oh, I mean, it was a great year. Like, I don't know if you knew, like, Jacob Steinhardt, you know, he's doing Transluce now. He and I used to do projects together in computer science class. There was Paul Christiano who's, um, a bunch of really phenomenal... He worked at OpenAI. A bunch of kind of big names in AI came from that year.
And then we were talking about the competitive coding, like Scott...
Woo, who's at Cognition. I mean, he's kind of like famous now as a meme on X for his math abilities, but you just got to know him through the coding competition.
Oh, yeah. Through the coding community.
Okay. Okay. And then now I see the competitive end of you guys. The output of this to me looked like poker these days. I think we were at an event which I think we have to keep secret or something, the specifics on this event. But I think I'm okay to talk about this part, which is like I'm walking by this table, there's you, Scott, I think Sham from Palantir, and then a handful of other people in a fairly intense poker game. So you guys, this is where you've applied your math and competitive skills now.
Yeah, I mean, poker is a really fun game and you know I've talked about my life in terms of a series of obsessions. Poker was definitely one of these obsessions in the past and I think the big revelation for me in poker was, you know, it's so much more a mathematical game than a game of reading people and bluffing. And I think the more you learn about poker, the more you update in that direction, right? I used to be a terrible bluffer. And when you know it's mathematically correct to bluff, then it's so easy, right? It's like you don't feel any nervousness around it. And yeah, it's just so interesting that you have a game that I think is perceived as so human, but the underlying mechanics and how to win are so deeply mathematical. And yeah, I kind of thought about this the other day where, you know, there's something about that in language modeling too, right? You have this deeply human process of generating language, but there's this mathematical machine that can really do it as well as we can.
I think about that part all the time as a writer and then I did all this philosophy back in college with Wittgenstein and all these guys thinking about these things. Yeah. Well, how do you find like an edge? If you and Scott both strike me as supernaturally good at math, I don't understand how one of you is outcalculating the other person.
Um, well, no, I mean, it is mostly a forum for us to just kind of hang out and catch up with each other. And you know, today we don't take it as... I think there is an element to taking something like poker very seriously that takes the fun out of it. And so, you know, my obsession with poker I think has ended more than a decade ago and now it's just fun.
You're just saying this 'cause I saw Scott win both days. I think...
You might be right about that.
He was taking it quite seriously. And so like coming out of college, I mean you had in some sense...
Oh, I beat him on the plane though.
You beat him on the plane right home. Okay. All right. All right. So was it just you versus him or was it like a group thing?
And maybe three or four people.
Okay. I feel like a lot of... I feel like there's three... I don't think I'm overgeneralizing too far, especially among like if you cut back to the 2018 sort of time frame. As far as like people who were in AI at a high level, I mean a lot had academic backgrounds. A lot were math prodigies or had gone on to sort of take their math background and get into robotics or something physics like that. And then there's this other bucket which is people who had gone to Wall Street and done high frequency trading and quants and things like that. So that was the first path that you took was you went straight from MIT to Wall Street.
Yeah. I mean I don't wear that badge with too much pride to be honest. Like, you know, it was a path that was fairly common for very quantitatively oriented kids at MIT. And I mean it was certainly a very meritocratic system, right? You could apply intelligence and there's a very concrete reward function, the amount of kind of profit that you would make. But I think culturally it was hard for me. It was a place where when you discover something, your first instinct is to just keep it away from as many people as possible because your knowledge is what gives you your worth. And so it felt like you would have out of this an outgrowth of just even internally at a company like these competitive dynamics and people weren't very trusting of each other. I think it also felt like such a closed ecosystem, right? I think we today don't feel too much like, you know, when someone in HFT finds a breakthrough that makes their algorithm a little bit faster, no one else feels it, right? And I over time just kind of felt like, you know, I woke up after four or five years, we're competing against the same exact set of players. Everyone was a little bit faster but had the world really changed that much for it? And it felt like time to do something else, right? It just a bunch of things lined up back then. You know, there's the AlphaGo match which I think was a huge inspiration for a lot of people at OpenAI.
I did not, but I think the sense in which, you know, the model was able to do something creative, I really wanted to understand what was going on behind that.
So you're watching that happen and had you been at all reading AI research papers and things like that or...
To be honest no. And then I saw that event, it was really inspiring and that's when I started doing my deep dives into AI. So one of my goals after seeing that was reproduce the DQN results. This is a network that was able to play a lot of Atari games effectively at a superhuman level and going from there, you know, that's how I got my start in AI.
And you were doing that like on the side as you... so you just work all day and then go back and try to...
Yeah. Okay. I mean it is weird. I remember I was interviewing George Hotz like it might have been roughly 2018. Maybe a little before that, you know, and he had just done this thing building a self-driving car on his own in his garage. And then, you know, I mean, it's George, so he says large statements sometimes that may or may not be exact or spot on or apply to other people. But he's like, AI is still so young. You can basically learn the whole field if you read I don't know what the number was, 10, 20, 30 research papers. I mean it is fascinating to me that it's like old in many ways stretching back decades but this particular moment...
It's very shallow. I always give this advice to people who are intimidated by getting into AI. So shallow, like just spend three to six months picking some project like maybe reproduce DQN and you can get to the frontier very quickly. The last couple years has added a little bit of depth but it's not anything like theoretical math or physics.
Do you think is this a field where... I asked Jaan Tallinn this the other day, I don't know why I'm obsessed with this but you know like in mathematics you see people tend to do their best work in their 20s or to have the big breakthrough and then it's very hard as they get older to have that same kind of moment. Like what you're saying, you know, are we dependent on young people reading these papers and then having some insight or is this something where you can keep going throughout your whole career?
I mean, I think you can keep going. I mean, OpenAI itself does have a pretty young culture, but I don't think you need to be young to do good research. I think there is something about being young and having less priors about this is the way it's done. I think over time you may develop your own vision, which is a good thing, right? But it also locks you into a frame of mind of like, oh, you know, this is how research is done, this is how good results come out. And I think younger researchers tend to have a little bit more plasticity around that concept.
Yeah. Yeah. So as your career at OpenAI is funny because it seems like you walked in the door and had a very important large position from the get-go. But when you first got there in 2018, it must have been what, like 50 people?
Oh, no, it was much closer to 20.
