Greg Brockman on OpenAI's Road to AGI

22:41And so I think that there's just something new about pushing forward the intellectual frontier together as a partner with GPT-5. Do you think people are limited by the difficulty of the problems that they work on? I think like, you know, for me in Cursor and in Codex, it feels clear that the model is better when I give it hard tasks. I feel like a lot of people post screenshots on X and it's like, oh, GPT-5 is not that much better. It's like, well, the question is not that hard. You know, like what gave you such confidence when you called it the best coding model in the world? Obviously, you're one of the best coders in the world.

23:15So the game recognizes game. But for people, how should they really think about evaluating these models? Yeah. So there definitely is a saturation on certain tasks, right? If you're just going to chit chat and say, hello, how are you? There's only so many things you can say. If you're going to say, here's the remount of hypothesis solution, please. Okay. Yeah. There's like a broad range of intelligence that will be desirable there. And of course, most tasks are somewhere in between the two of these. And I think what we've observed is that we've seen GPT-5 be able to solve intellectual problems, you know, sort of tasks that require deep intelligence much better than any other model that we've tested.

23:55The second thing we did was we really spent a long time seeing how are people using it in interactive coding applications and just taking a ton of feedback and feeding that back into our training. And that was something we didn't try as hard in the past, right? For something like 03, we really trained it with tasks that we'd set up once and the model, we'd see it go up and to the right on all of our metrics. It'd be great at code forces, you know, competitive programming competitions, which is, again, very exciting. but is not reflective of how you actually program. You actually program in a much more messy way, right?

24:28That you have some sort of repo that has some sort of local state and that has different abstractions and, you know, that just like different versions of different libraries. And that sort of diversity isn't something that magically arises from a very structured, here's this one specific task, 10 specific tasks you need to accomplish. And so a lot of what we've been focusing on is saying, not just how do we push the intelligence, Although that is always going to be the core. But also, how do we connect the intelligence to real-world applications? And so that it really got to experience being pushed out of its comfort zone, out of its ivory tower, and actually be able to see the messy reality and diversity of the real world.

25:06Yeah. What are suggestions on a more practical level that you have on getting the potential energy out of these models? So part of it is adding, you know, the linter, the type checker, the task to, like, have it self-loop. Any other meta that developers should think about? How do you use the models? Well, the number one thing that I've observed is that there is a real skill in extracting the most from these models. And it requires this tenacity, right, of really trying to, like, almost understand the shape of the model's skills and weaknesses. And so you test it, right? You test it with something small.

25:39You get a little feedback. You test a little bit higher. Try to give it some bigger tasks. Try to see if it can work in a certain way. And I think that people usually have their library of different prompts, right? So I definitely have my library of prompts that I've built up since the GPT-4 days. I remember in advance of GPT-4 starting to gather up a couple of like, okay, I wonder if I'll be able to do this. You have some sort of query that importantly, you want queries that could have a range of different answers that don't have any one specific right thing. And so, for example, on creative writing, I've liked to ask for like a mashup of Lord of the Rings and startups, right?

26:14Just like try to push together two different topics and see what you get. In terms of actually testing the model and pushing it, I think that I do a lot of trying to think about, okay, how do you first of all break up tasks and have something that's self-contained that you can let the model run with? Because you don't want to just have one instance of the model operating. You want to have multiple. You want to be a manager of not an agent, but of agents. And so you need to first of all think about how your code base is structured, but then actually go and try to push the model to say, can you actually operate it on these multiple different pieces of your code base?

26:45I think that people love doing front-end vibe testing. GP5 is very good at front-end, it turns out. But of course, that's not what most developers spend their time doing. And so it's important not to overfit to that. But I think that maybe just getting a feel for the model and kind of starting to become in tune with its strengths and weaknesses and viewing it almost as an extension of yourself and know, you know, often another thing I'll do is just be kicking off tasks to the model that are sort of not on the critical path. while I'm thinking about some super hard thing that the model, for whatever reason, I don't want it operating on.

27:20And so I'm just constantly getting information back. I'm just like, okay, was it able to do a thing? Or it's just like low risk if it makes a mistake because I don't feel like I had to sit around waiting for five minutes and then sort of get no return. You've always mentioned, I think, that the roadmap for Codex and OpenAI's coding capabilities, since we're there, is that the background suite agents sort of merge with the NIDE agents. How's your thinking involved there? Is it just as simple as the IDE can call the background APIs and the background APIs can export to the IDE? What's a deeper connection than that?

27:51I tend to think about AI productization by analogy to a co-worker. What do you want out of a co-worker who's a great programmer? You don't... Slack them. Exactly. You want to slack them, but sometimes you're like, hey, I kind of need help with this thing. Can you come over and look over my shoulder? And like, hey, can you take the keyboard? Exactly. So you want the pair form factor. You also want the remote async form factor. And you want it to be one entity that has knowledge and memory across all of this. You don't want it to be a junior programmer who shows up every day being like, okay, I forgot everything.

28:23Can you remind me how to SSH into the whatever, right? So I think all of that has to happen, right? That you need AIs that have access to your infrastructure in a trustworthy way, right? A way that you can audit. One thing that is different about these models is that they're fine being micromanaged. Turns out humans don't like that very much, right? If you look at every single command that they're running and you demand reports on everything they did, probably you're not going to retain that person. But the models are perfectly happy to, right? And so that's an affordance that's well worth thinking about and changing the interfaces to take maximum advantage of.

28:59At the same time, yeah, you really want the seamless blending between a model that's able to do a bunch of work on its remote machine, doesn't mess up my local state, fully sandbox, fully observable, and then sometimes can be like, okay, I'm ready to run something locally. And depending on what that is and depending on how sandboxable it is, that you can do one-off approvals, you could give it full, dedicated access. And I think that having the human be in control of this observability and to be managing this team, an agent that has just different surfaces, right? It doesn't like the identity of the agent being something that runs locally versus the identity being something that runs remotely.

29:40To me, that's the wrong question. It's really the agent should be this like model that's executing and then requesting to run things in a remote sandbox or locally or maybe multiple sandboxes or maybe it's running on your computer and my computer. Like there's no reason that has to be local to any of these things. Yeah. Software agents, you can just sort of seamlessly and fluidly move around. You mentioning approvals gives me a chance to spotlight my friend Fuad, who is helping to start the agent robustness team that was also launched at AIA Engineer. What's that? What's opening us interest in that?

30:11The way we think about agent robustness is through defense in depth. There's a layer of the model itself. We publish techniques like instruction hierarchy. And so with instruction hierarchy, you sort of indicate that, hey, this message is from the system, this message is from the developer, this message is from the user, and that they should be trusted in that order. And so that way the model can know something that says ignore previous instructions from a user. I'm not going to follow that. Right? And so I think that having, like, it's almost like thinking about how we prevent SQL injections, right?

30:44Having systems at a low level that are robust against these attempted exploits is very important. But that's not where you stop, right? You want multiple layers of thinking about the system controls, right? If a model is sandboxed and isn't actually able to execute something or access a specific piece of data, then you have full guarantees around what's possible. And there's various levels in between of approach that we take. And so I think that a lot of what is the frontier as these agents become more embedded in our lives and are trusted with more responsibility is also increasing the safety and security of them in lockstep.

31:19There's an analogy that I make to the Linux kernel OS rings as well. And it's really interesting that we're basically kind of building this in to the LLM as like concepts of sort of different layers of security. And also the other thing I also was very happy to see was that I invited a talk on the model spec for AI engineer. And that was the most viewed talk of all that we've ever had. Yeah. Which is like, it's hard to make safety and reliability sexy. I think the model spec is a perfect example of when the models are very capable, you start to really care about what they're going to do. That becomes the most important question.

31:59And the model spec is an example where we've made it very legible to the outside world what our intention is for this model to do. And it doesn't mean that we always produce a model that is capable of following that, but it's a north star, right? It's something that really sets this is the intention, and anything that deviates from that is not through our explicit effort. It's anti to our explicit effort. And I think that the gap between the spec and the actual behavior is shrinking very, very constantly. The thing that's very interesting is almost like values. It's really thinking deeply about, well, what should a model do if you ask it a controversial question?

32:33If you say, I think that the world is flat or whatever, is it supposed to say, yes, it's flat? Or you're supposed to be like, well, here's what science says. And honestly, these things are subtle, right? That it's not really clear what the right thing is just on two minutes of thinking about it. But if you read the spec, you can actually really see the thoughtfulness that has gone into it. And it's not the final answer, right? It's something we want feedback on. It's something that we want to produce collectively as a community. I know we want to talk about open source next to, but I had a more esoteric question.

33:01I was listening to your old Lex Friedman interview, and you kind of mentioned foundation by Asimov. It made me think about, we have Brett Taylor on the podcast, and we talked about how certain languages have inner-in capabilities, like Rust is memory safe. And so that just happens. Do you see almost like a psychohistory of LLMs and software engineering where it's like, hey, these models, I can predict the way software is going to look. Like everything is going to be blue and purple gradients, right? We're kind of seeing that today. What else are these models really driving us towards? And is there a way that we can change that?

33:36Well, there's definitely a psychohistory of them because to some extent these models are a product of psychohistory, right? It's like these models have been trained on observing human thought, right? Effectively, that's what you can think of. Take public data, learn on that and just observe. of the point is to understand the rules that govern a data set. Like what are the underlying rules that generate the data in the first place? And that's kind of what these models grew up on, right? It's almost like watching a bunch of TV as an alien, trying to figure out like what are humans all about. And then you have this reinforcement learning phase where they actually got to try things out.

34:12And there are given positive and negative feedback, depending on how much that aligns with what the human wants. And now we put them in reality and say, okay, okay, now try stuff and here's a new task you've never seen before. And it uses all of that previous history to decide what to do. As an aside, like it's not clear, like sometimes the biological analogy, the humans, it's very easy to overstate it, but it's also easy to understate it. I think it is at least a useful template to think about to some extent that's how humans work too, right? It's like you have some sort of prehistory encoded into your DNA.

34:45You have your life experience. You have your parents who provided positive and negative rewards. And do you have your experience in just trying things out in reality? And now you have to go out and use that knowledge. And what do you do? And how do you predict what a person's going to do? And actually, you can predict a lot of what a person's going to do. It turns out you have a pretty good model of other people and how they'll react to something, if they'll like it, if they won't like it. And a lot of that gets baked into knowing someone's values tells you a lot about what they're likely to do and how they're likely to behave.

35:15And I think that for models, the future is not predetermined. It's not like the algorithm itself says that the model's going to have to prefer purple gradients or something, right? But there's something in this whole process that does produce that preference. And I think one of the opportunities with models, one thing that Alec liked to say is that these models are less like a human and more like a humanity, right? That there's so many personalities embedded within them. It's almost every single personality is in there. And our goal is to elicit that personality. And some of this post-training work, some of this reinforcement learning work almost narrows down the space of those personalities to just the ones that are desirable.

35:57and I think that what that means is that we have both an opportunity to produce models that operate according to our values, right? According to, if you don't just want the purple gradient one, you want the blue gradient, the green gradient, whatever. You can have all that in a single model, it's fine. And GPT-5 itself is extremely good at instruction following and so it actually is the most personalizable model that we've ever produced. You can have it operate according to whatever you prefer just by saying it, just by providing that instruction. the analogy i have is like the borg like there's this like collective intelligence there's always this debate between star wars people and star trek people like who's who has a better model of the future and i think it's like star trek well sam picked you know he tweeted the the death star so on the star wars one thing i think is very interesting about these models is that we have all these arenas now right like elm arena and and others where you can actually see human preferences on top of how the models operate.

36:56And that you almost have this layering of like, the models were trained on human preferences, now they're doing stuff and being judged by humans. And then we kind of use that to feedback on like, huh, like, okay, yeah, maybe the purple is a little bit too much and we should change it there. And so it's almost this co-evolution of the models move in a certain direction, do humans have a certain set of preferences, so then we move them in a different direction. And then, you know, you kind of keep iterating to get something that's more and more useful and aligned with human values. How do you do that when the RL rewards are kind of tied to things that the humans maybe don't prefer?

37:26Like in my experience, it's been like try-catch. Like the models like to write try-catch. They love to try-catch. So that it doesn't fail. Do we need just a lot of preference data that shows them they shouldn't do that? Is there something in the RL environments that we're going to change to make that less desirable? Like I'm trying to figure out where we go from here. Yeah, I think that the way that you decide or the way that you figure out where do interventions go is very multifaceted. And it's very specific to the behavior, right? There are some things like the model's knowledge of different libraries and things like that that's kind of baked in from the early days.

38:00But you can also teach the model that, hey, don't rely on your previous knowledge. Like, go and look up the most up-to-date docs. And that's something you can kind of put at a higher level. And then something like overusing try-catch, that's something you can actually prompt the model for, right? And that's something where when we train it in reinforcement learning, you can provide rewards saying, like, ah, don't go in this direction. And the beautiful thing about these models is it feels like, okay, there's probably a long list of different preferences and different styles and things like that.

38:25You're going to have to give it feedback on during training if that's the way you want to go. But these models generalize. The algorithms that we have generalize. And that's the beauty of deep learning. That is the true magic, right? It's very easy. Like we kind of have this whole stack now that's built up around the core of deep learning, right? It's like all these ways of orchestrating models and how you get feedback and all of these things, the data, et cetera, et cetera. The core magic of deep learning is its ability to generalize. And in some ways, the generalization is weaker than you'd like.

38:53But I think that the same is true for these models. It's really trying to think about, in order to get them to be able to operate according to different preferences and values, we just need to show that to them during training. And they are able to sort of generalize to different preferences and values that we didn't actually train against. And that's something that we've seen very consistently across different model generations. I was just envisioning this meme of like, oh, my model doesn't generalize. We'll just make the whole world your distribution. You know, that's how you solve everything.

39:21Done. Done. Exactly. As simple as that, you know, you just have to build the Dyson Sphere along the way. One thing I wanted to touch on for the, I think, last couple of topics on GPT-5 before we move to OSS. You've acknowledged that there's a router, which is really cool. I was also listening to your podcast with John Collison on Cheeky Pints, which is really fun format that they say that you told a story of the dota side that i don't think i've heard before about the beta model versus like the the center main model and stitching it together is that like a similar insight for gpc5's router where you have like reasoning model non-reasoning and then you just stitch it together to some extent yes right i'm in the multiple models and you put some sort of router on top of them uh that specific one was for a very specific reason which is that we had a deficiency on the first half of the game.

40:10Because it kept losing, right? Exactly, right? So there was part of the game that this specific model didn't do a good job of. There was part of it that it did. And these models, the behavior, the domain they were operating in was simple enough. It was very easy for us to say, here's when you want to use one model versus the other. And to some extent, what we have with GPT-5 is no different. We have a reasoning model that we know is good for applications that require this intelligence, but you're okay waiting a little bit longer. We have a non-reasoning model that is great for applications where you want the answer fast.

40:40Still a good answer, right? But not like deeply thought through that might have a lot of tricks to it. And then you just kind of want to put an if statement that says which of these it should be. And then sometimes too, it's like, if someone's run out of their credits that you want to fall back to a different model and all these things and not pushing that burden to the user is actually a really nice thing. And by the way, I do want to say model switchers are not necessarily the future, right? They are the present, like having a fully integrated model that just does the right thing feels very preferable in many ways.

41:09The flip side, though, is that I think that the evidence has been away from having the final form factor, the AGI itself, being a single model, but instead thinking about this menagerie of models that have different strengths and weaknesses. And I think that's like a very interesting finding of the past couple of years, right? Just a direction of like, it's much easier to have a small, fast model that's less capable, but can do a lot more. You can generate a lot more tokens from it, coupled with a much more expensive reasoning model. And if you combine those two things, you kind of get adaptive compute.

41:44And we haven't really cracked how do you do adaptive compute within the architecture, but doing it within the orchestration of a system, it's very straightforward. And so I think you get a lot of power out of the fact that these models are composable in this way. Yeah. I want to give whoever did the model card was amazing. They even provided the big parameters to the if statement of conversation type, complexity, tool needs, explicit intent and usage rate limit, which is kind of interesting. Any one of those you want to comment on in particular that was interesting for debate? No, I mean, I think honestly, all of it is like fairly what you'd expect.

42:21Yeah. And I think that the core message in my mind is that at OpenAI, there are many things we've done right. Naming is not one of those. Having a simple surface for users to understand how to use it, not necessarily one, right? If you look at all the different models that we've had, how are you supposed to know which one to use? I remember my wife was using 4.0 at one point. I was like, no, you need to use O3. And she's like, wait, but why O3 is better than 4.0? Well, ship O4, then you have 4.0. There you go. And so, yeah, so, okay. We clearly needed to do a reset, right? A reset on complexity.

42:56And I think that us internalizing that complexity rather than pushing it to the user, that is really important. And so I think this is a first step. And I think we've heard loud and clear from the community about the places where they weren't ready, right? That we were not delivering on that simplicity for people, right? That it should just be, it's always better to go with our choice of it rather than, you know, the manual selection. And we're not quite there yet. I think that we can make the progress, but I think that ultimately our goal should be to both make sure that power users are able to have the kind of control and consistency that they're looking for, while also not forcing the broad base of people who don't want to have to think about the 4.0.0.3, all that stuff, to have to go to that level of detail.

43:40Yeah. Awesome. Pricing question. We talked about that GPT-5 pricing is aggressive and very competitive, even compared to like Gemini. One thing I was surprised to learn from the meetup that we had the other day was that GPT-5 pricing can go much cheaper. What degree of order magnitude are we talking? How much percent of that is just getting better in front of like Stargate? I think that the answer for these things is always that, okay, if you look at the history of our pricing, we have very consistently cut prices by like, I don't know the exact factor, but let's say like 10x per year. I'd say more aggressive than that, yeah.

44:12Probably more aggressive than that, which is a crazy thing, right? And you can see it with 03. I think we did like an 80 % price cut. And actually the usage grew such that it was like, I think in the revenue, it either was neutral or positive. and it just shows you that I think there's this cost curve, like the demand is extremely steep, right? And so it's like if you just make it more accessible and available to people, they will use way more of it. And I think that's very aligned with our mission, right? Our goal is to ensure that AGI benefits all of humanity. Part of that is making sure that this technology is broadly distributed, that lots of people are using AI and using it to apply to things in their life and their work.

44:53And one of the things that helps us get there is by having more efficient inference, having cheaper models, all of these things. Now, what unlocks it partly is having just more compute. Right now, we are extremely compute limited. And so I think that if we were to cut prices a lot, it wouldn't actually increase the amount that this model is used. We also have a lot of efficiencies to gain. And that's something where our teams are always working super hard to get to the next level of inference efficiency. Some of this is about improving the model architecture itself, right? That there's lots of architectural decisions that you can make.

45:25And that now that we're in this world of reasoning, that it's not just about the sort of model architecture, it's also about the post-training, right? It's about how long does it think for a specific task and things like that. And so there's just many, many dimensions of improvement that we have to make and that we'll keep pushing. By the way, the numbers, I have a chart for this if you ever need it. Since the day you launched GPT-4, it's been a 1 ,000x improvement in cost for the same level of intelligence. That's pretty wild. That's pretty wild. It's pretty good. Yeah, that's like two and a half years or something like that.

45:56What else has like a three-order magnitude improvement over the course of two and a half years? I don't know. Nothing. Yeah, can't think about it. And it's going low. It's not even, it's like from$10 ,000 to like$1 ,000. It's going to like pennies. For the GPT-5 release, I did this article called Self-Improving Coding Agents. So I basically asked GPT-5, can you build tools for yourselves to be a better coding agent. And this is a Swellancer task. And then it does the task, it kind of fails in some ways, and then I ask it, can you improve the tools for yourself and kind of do this loop? And what I found is like, the models don't really like to use these new tools that they built for themselves.

46:33They basically respond saying, you know, I can just do it. I don't really need the tool. And I think there's kind of like this. Sounds like a human. Yeah. There's kind of like the ceiling of like, how can they really push themselves to like improve? Do you feel like part of it is like, hey, they're just being taught to use these tools, which is like, you know, graph and like whatnot. And so it's kind of hard for them at inference time to build the tools. Or do you see that as like part of that jump? I think that's part of the step for sure, right? And I think it's not like we're at zero on being able to do that, right?

47:04And I think a lot of this is just about the training, right? If the model really has trained with just a specific set of tools, hasn't really been pushed to adapt to a new tool very quickly, then you shouldn't expect it to do any differently at evaluation time. But the idea of producing your own tools that make you more efficient and build up a library of those over time in a persistent way, like that's an incredible primitive to have in your toolbox. And I think that if your goal is to be able to go and solve these incredibly hard challenges, unsolved problems, then I think you're going to need that kind of thing as a dependency.

47:37Any architectural decisions or innovations that you were in line to talk about? Sliding window attention, the very fine-grained mixture of experts, which I think DeepSeek popularized, rope, yarn, attention sinks, Anything that I think stood out to you and the choices made for GPT-OSS? I would say that these choices are all, you know, look, we have a team that's been working on different architectures. We explore different things. Something like mixture of experts is something that, it's funny, I would say that I would credit our team for the choices there. But I say that the picture in my mind is we wanted something that would be easy to run in these environments.

48:15And so picking things like just how sparse to go is very tied to your memory footprint. And then, you know, how much compute you actually can use for forward pass and things like that. So I think that to some extent, the architectural decisions were fairly constrained by the model sizing and the compute we expect for them to have access to it when they're running. Yeah. I mean, it's very practical engineering decisions. Yeah. Yeah. Yeah. I think so. And I think that the power of the model really shows, like, we really did use a lot of our cutting edge techniques to actually push the capabilities models further and further.

48:50I'd say it definitely detects a difference between the architecture for models designed for API use versus models designed for single machine. You know what I mean? Like, when you have multi-tenancy, when you can have batching, it's very different from, like, single machine. Very different. Yeah. I don't know if that'll ever combine, but maybe it's a menagerie of models, like you always say. Yeah, I think it's also really interesting to think about an architecture where you have a local model that then delegates to a remote model sometimes, right? And this can be something where you can run much faster.

49:22It's helpful for a privacy architecture perspective that just trying to decide what actually goes, what stays. And having that edge compute means that then you lose internet connection, you're still able to do something, and you can have a slower planning model. It's like this interplay between those things is very interesting. Yeah, so like a GPT-5 on-device where you have GTOS here and then it routes through online if it's available. I don't know. Yeah, something like that. And then you have your codex infrastructure that has a local agent and a remote agent and then is able to seamlessly interplay between the two.

49:57And then is able to multiplayer. This is what the future is going to look like, and it's going to be amazing. And then you have a device always with you. I can see where things are going. It all connects. What can we say about the device? You raised it. I don't want to get Greg in trouble. What can we say about the device? It's going to be great. Okay, and then another political, I don't know if it's political or not. There's a lot of open models coming out from China. Why is it important for there to be American open source? Another thing at a very practical level that we've thought about with open source models is that people building on our open source model are kind of building on our tech stack, right?

50:37If you are relying on us to help improve the model, that you're relying on us to get the next breakthrough, then that means that you actually really have a dependence in both a way that's good for our business, but I think it's also good for the country, right? That you think about having an American tech stack from the models that people are running directly, but then how those are going to interface and interplay in the way that we just talked about, that it actually allows us to build a whole ecosystem where people are able to have control over the parts of it that are important to them, ultimately be built on these models that reflect American values, and then be able to interplay with American, hopefully, chips underneath and cloud models on the back end and execution environments.

51:21And all of that fitting together is something that I think adds a lot of value. And I think it allows for American leadership to really also mean that we have leadership in our values in the world. Yeah. Congrats on launching that. Thank you. Let's talk about engineering at OpenAI. I know there's a lot of debate about CloudCode and AIDR and OpenCode and all these different tools. How do you think about structuring the team itself that gets the highest leverage out of this? Are you changing the way you build the team from a numbers perspective, from a capabilities perspective, from a team size perspective within the org?

51:55Anything that you want to share? Well, engineering, software engineering is definitely changing in many dimensions. There's a part of engineering that's very difficult for these models to really crack, but we're starting to see the beginnings of it happening. and that that's these like very core hard algorithms, right? Things like CUDA kernels are a good example of a very self-contained problem that actually our models should get very good at very soon, but it's just difficult because it requires a lot of domain expertise, a lot of like real abstract thinking. But again, it's not intractable. It's self-contained.

52:28It really is the kind of problem that is very amenable to the technology we have. There's other problems that are very difficult in terms of architecture, right? How do you think about how a system should be put together and thinking about the abstractions? And again, our models are starting to get kind of good at this. But so I think what we've seen is that there's, for most of our engineers, even our extremely good engineers, and a lot of their work that actually maps very well to the core strengths of the models right now. And definitely for anything where it's like a language that you're not an expert in, like, yeah, you definitely don't want to be writing that code yourself.

52:59You really want a model to be doing it. And then there's parts of the job that become much harder because it requires like, you know, things the models don't have access to, right? It requires a lot of context going and talking to people in order to make good decisions. And so I think we're not at the point yet where we really see changes in how you structure a team because these tools exist. But I think we're at a point where it is like an extreme high priority to get these models to be used in all domains that they possibly could be. And to think about how you do that well and responsibly and think about what the guardrails should be and that that happens in a very practical way.

53:33And so I think a lot of what I'm seeing is like we're in an early adopter phase that's starting to transition to a mainstream phase. And the productivity impacts of people being able to do more means we actually want more people, right? It's like we are so limited by the ability to produce software, so limited by the ability of our team to actually clean up tech debt and go and refactor things. And if we have tools that make that 10x easier, we're going to be able to do 100x more things. And so I think that there's this incredible opportunity that is entailed by these models not being a real driver of just do the same stuff more efficiently, but be able to do way more.

54:14And that that is, I think, the overall goal. How have you changed the team's work to fit the LLMs better? Is there a different way in which you track issues? Is there a different way in which you structure a code basis? So I think we're still at the early edge of this, but the thing I've seen be most successful is that you really build code bases around the strengths and weaknesses of these models. And so what that means is more self-contained units have very good unit tests that run super quickly and that have good documentation that explains what this module is for. And if you do that and you kind of leave the details to the model, it works really well.

54:50and then thinking about how these things compose and making sure that you're thinking about the dependencies that you only have these like clean, like AI optimized modules can only be depended on by other AI optimized modules. Then you end up with like a whole system that's actually optimized. And so I think that we're still scratching the surface of what's possible. And, you know, the models are advancing so fast that actually what it means to work around the weaknesses of the model in six months, I think that those weaknesses will be like, you know, vastly shrank. So you don't want to necessarily spend all your time just overfitting to what exists today.

55:23But I think there's a lot of potential to be able to move quickly in this particular moment. One question I'm very curious about is the value of an engineer. Increasing over time. Increasing over time. Well, I mean, also, you know, there's some part of our work that's being automated away. And I think, like, obviously, there are very, very high signing bonuses, like higher than we've ever seen in the history of our industry. Is it really the engineers that are valuable or the systems that enable them? You know, like, I feel like it's kind of like a bit of both, but people are paying a lot for the engineers.

55:54I mean, I think that the thing at the end of the day that is new, right, is that we are producing technology, these models that are the most useful tools that humanity has created. Right. And that underpinning them, we are building the biggest machines that humanity has ever created. Right. It's like at some point, the dollars that go into these data centers starts to be an abstraction. Right. What is 50 billion dollars? What is 100 billion dollars? How can you possibly internalize what that is? I think it's beyond almost the scale of human comprehension, the engineering project that we collectively as a country, as a society, as a world are undergoing right now.

56:34It's like projects like the New Deal, like pale in comparison. The Apollo program, pale in comparison to what we're doing right now. And in many ways, it's as it should be, right? Like the economic return on this technology is very large. But even more importantly, the way in which we are moving to a new economy, right, an AI integrated economy, an AI powered economy. And this is ultimately what our mission is about, right? It's like we see this change on the horizon. We want to help. We want to help steer it to be something that uplifts everyone, right? That it's this amazing opportunity, almost unique in human history.

57:13And we are all fortunate, right, to be at this moment in time and to be able to be involved in some way. That to me is the backdrop to really think about this big shift that is going on at humanity scale. And it's sometimes almost you feel this cognitive dissonance because you're debugging some low-level CUDA deadlock or you're worried about the purple gradient. And you realize this is like the future of humanity that we're really talking about. And so when you think about engineers and who's at which company and all these things, like these things matter, right? It's like it's not just about any individual.

57:45It's about a team, right? But it's also not about any one product or any one system. It's really about the overall society, the overall economy that we are building together. And so I guess I sometimes step back and think about the big scale. But you also need to think about the micro scale. You need to think about are people happy, right? Do people feel connected to the mission? Do they feel like the work they're doing matters? And those things actually turn out to be the most important things. And so what makes the headlines is not necessarily the stuff that actually most drives the people. but it is for sure like a reflection of the economic reality that people see as the potential of this technology.

58:21This connects a bit with what Noam was saying on the multi-agents team, where like the individual intelligences of humans, you know, we can only do so much individually, but as civilizations, we can, you know, go to the moon and like build cities and build AI. And like together, I think we can do a lot more than we can individually. We can do amazing things together. No question. What do you think about the current state of AI research? Is everyone really just doing the same thing? Do you feel like every lab is like a different take that is eventually going to help us converge to the right thing?

58:53Or just because now the dollars has gotten so big that you need to do the thing that you think is going to work? I think there's a surprising amount of diversity in the field. I think sometimes it can feel like there's convergent evolution. But I think that if you really talk to people at different labs, you really realize that there's different perspectives people have. One of the decisions we made early on in OpenAI was that we really wanted a set of people who are aligned in how they think, right? Because for people who have been pursuing a PhD for a long time, who are, you know, sort of have their own research vision, you kind of can't tell them what to do.

59:23And so if you want people who are going to row in the same direction, it means you have to select that set of people. And that was, I think, the most maybe important early decision that we made at OpenAI that helped us to achieve the things that we have. And so I think that that means that you necessarily have different vectors that you could pick. And you really see it in the taste of different labs and what they focus on, what they produce. And at OpenAI, I think we've been very much focused on how do you do the research that gets you to the next level. and even for something like GPT-5 that, you know, we sort of had a lot of pressure to think about, okay, let's just like, you know, sort of do the grind of like, you know, here's feedback on problems that we have on the coding side.

1:00:07And, you know, you can pursue that grinding and get somewhere, but you also sometimes have to step back and think about how do you do the next step function? How do you do the next paradigm shift? And something like the reasoning paradigm is a good example of a time that we did that very successfully. And we've done that many times over the course of OpenAI and will continue to do that. And so I think that the breakthroughs remain to be made. And there's such a diversity of multimodal and different ways you could generate things and all of the stuff that I think that the field of research is more abundant than it ever has been.

1:00:40Yeah. And not to forget, that's like the mainline research. There's also voice. There's also image generation, video generation. Yeah, yeah. It's easy to forget about these things. Remember Studio Ghibli? It was like the biggest thing in the world. Exactly. It's amazing. It's amazing. And that's the kind of thing, by the way, that was like, it's really a team of a small number of people who are really focused on that problem for multiple years. And that that is, I think, the sort of core ethos of OpenAI is to make these long-term bets on problems that matter in a direction that really adds up to a cohesive whole.

1:01:11So from the outside, it's kind of hard to figure out what you're focusing on. You know, kind of Imogen just came out of the blue almost, which was great, got a lot of adoption. How should people think about how you prioritize versus what people should explore and build and should wait for you to improve on? Well, there's a massive possibility space in this field, right? Because neural nets, deep learning is applicable to effectively any sort of data, any sort of domain. And that we can't do everything. The core reasoning paradigm, that clearly is something we're going to keep pushing on. multimodal voice, things like image generation, video generation, these kinds of areas are also things that we view as very important and all kind of fit together.

1:01:53But there's been areas where it's just hard for us to really figure out how do we prioritize as part of the core program, right? And we've been through times where, for example, robotics was one in 2018, where we had a great result, but we kind of realized that actually like that we can move so much faster in a different domain, right? That actually, you know, we had this great result with the robot hand solving a, you know, unscrambling a Rubik's cube. And that team was bottlenecked by the fact that this robot hand, you could run it for 20 hours before its tendon would break. And so then you would have a mechanical engineer come and fix it.

1:02:26And that team went on to go do what became GitHub Copilot, which is obviously an amazing feat and a real accomplishment and something that they were able to move so much faster in the digital domain than in the physical one. And so I think that for us, we really try to, we have, you know, no matter how many people we hire, how many GPUs we get, we have limited bandwidth, right? That we are, you know, sort of one company, one lab that's focused on as much as we can, a coherent one problem. And so I think that you can kind of look at the set of things we're doing and sometimes we'll do offshoots and sometimes that will be something that then becomes part of the core program, but that there's just so much possibility space for everyone.

1:03:05Awesome. I'd like to take a chance, you know, we're kind of closing up. Feel small, little lightning questions just on zooming out from OpenAI. This question I got from Alessio, so why don't you take it? Oh, so when you started OpenAI, you almost believed it was too late to start an AI lab. What are things that people today think it's almost too late to do that they should be doing? Well, I think it's pretty clear that connecting these models to real-world application domains is extremely valuable. And I think sometimes it might feel like all the ideas are taken, but the economy is so big. Every application of human endeavor is so big.

1:03:43And so it is worthwhile and really important for people to really think about how do we get the most out of these amazing intelligences that we've created. And a lot of that is for something like healthcare, you have to really think about all the stakeholders, right? You have to think about how does the system work today and how do you slot these models in well? And I think that's across all of these domains, there is so much fruit that is not yet picked. Yeah. So go ahead and write the GPT wrapper. Yeah. But I think the thing that I would advise is to really think about domains where the value that you're producing is not necessarily just having written a better wrapper.

1:04:16It's really about understanding a domain and building up expertise and relationships and all of those things. You do occasionally angel invest. What gets your attention? I actually have not angel invested for a number of years now. Oh, okay. Yeah, it's just like everything is a distraction from open AI and I just like to stay laser focused. Okay, this is a time travel question. What is one post-it note you want to send to 2045, Greg? So you'll be 58. How's the Dyson sphere? How's the Dyson sphere? I don't know if you've actually done the math on like what it takes to do that, but. Yeah, I mean, more seriously, it's like 2045 is just so hard to imagine, given how fast things are moving right now.

1:04:54And so I hope it'll be a world of amazing abundance and that I think at that point, we really should be multi-planetary and kind of almost any sci-fi dream you can imagine. It's hard to deny its possibility, except for things that are limited by the physical ability to move some atoms at that rate. But yeah, it's like, I just, I think I would just hope that that world is as amazing as it could be sitting here in 2025. Will we even need UBI with abundance? Because true abundance means we don't need it. Well, I think, well, first of all, I think that there's been a lot of debate. I remember early on in OpenAI of post-AGI, will money mean anything, right?

1:05:32And it's really unclear, right? If you can just talk to a computer and it'll produce anything you want, you want something that, you know, you want some physical good, you want some, you know, any sort of material item, and it can just be manufactured for you instantly, you know, effectively free. What does money mean? And the flip side is like, I think that there is one resource that is very clearly going to be in very hot demand, which is compute. Already the case, we see this within OpenAI, that the researchers that have the access to the most compute are able to have the biggest projects and do more.

1:06:04And I think in the future, thinking about how do people get access to compute and the more compute that you have for whatever task you care about, for whatever application you care about, it will be solved more, the more will happen. and that I think that that question of what the compute distribution looks like will be something very important. And so I think that the question of exactly how, you know, if you don't do work, do you survive? I think the answer will be yes. You'll have plenty of material, your material needs met. But I think the question of, can you do more? Can you have not just generate like, you know, as much, you know, like Sora movie as you want, but have like this amazing detail and like all this extra fanciness to it and have this thing go, you know, think super hard for, you know, 100 years worth of a subjective experience about what the best thing is for you specifically.

1:06:51I think that there will always be more return on more compute. And so that will be something we have to really think carefully about, about how that society is architected. And then I always find this harder, by the way. Post a note to send to 2005, Greg. So 18-year-olds. Wow. I get the time travel. How long of a note can I write? Like a post a note. A little bit of advice to yourself. And obviously, this is a proxy for everyone else, right? But address it to yourself. I think the single thing that I have been most surprised about is the abundance of problem grows over time, right? Because I remember in 1999, 2000, reading about Silicon Valley and feeling like I've missed the boat.

1:07:32I was born just a little bit too late. Very common. Exactly, right? I just felt like all of the cool problems must be solved by the time I'm ready to go work on things. There'll be nothing left. That turned out to be totally false, right? Like now is just the most exciting time to be in technology, to really be operating in the world because we have this amazing tool that is going to uplift and revolutionize every application, every field of human endeavor. And I think that the fact that that's something to be excited about, that is something that we can apply. And, you know, there are challenges we have to work through, no question, but for the purpose of achieving this amazing outcome.

1:08:07And so I think that just that message of that the problem availability will grow over time rather than shrink, I think, is the core thing I wish I had sort of internalized at the moment. Awesome. Thank you so much for joining us, Greg. Thank you for your time. Thank you so much. It's been great to be here.