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…fully automated AI researcher by March 2028. And so I guess you know, checking in four months later, how are you feeling about those timelines? Yeah, I think you know, over I think over over the last months I think like the change that's really happened is we've seen this explosive growth of coding tools. >> Yeah. It's an understatement. Yeah, we've definitely like really kind of gone to a place in OpenAI where we use Codex for the um for the majority of you know, actual coding. Um and so I think I think for most people like the kind of the act of programming has has has changed quite a bit. Um So, I definitely see this as a signal that like you know, something here is on track. The other kind of like very interesting update over the last few months to me has been the progress on the math research capabilities. Also, the results we've kind of seen in physics and other fields. I think I think this kind of level of capabilities that was like ability to provide insight when combined with ability to access infrastructure, ability to use maybe uh more computer test time that's something that Codex isn't currently. Uh and very strong improvement in general level intelligence which I also expect over over the next couple of months. Yeah, it's something we're still very much planning for and very focused on. And how do you like know when you've you've gotten there? Like what's like a a workflow you might look to to say, hey okay, I think we've got these, you know, research intern level capabilities. The way I would distinguish you know, a research intern from from full automated researcher is the kind of span of time that that you would have it work mostly autonomously or the kind of like specificity of the task that has to be given. So, I don't expect you know, we'll have systems where you kind of just tell them all like you know, go improve your molecular ability, go solve alignment and they will do it. Not this year, you know, I think we might get there at some point. But I think for like more specific technical ideas like I I have this particular idea of how to improve the models, how to like you know, run this evaluation differently. I think I think we have the pieces that we mostly just need to put together. Andre Karpathy released, you know, a pretty viral version of of using some of these models to you know, improve some of his you know, obviously way less complex models than what you guys are building here. But did that feel like generally in this you know, in the spirit of some of what these tools might look like? Yeah, I think it's in the spirit. Yeah, I mean I I expect it to look like a pretty continual evolution from kind of where Codex is now. I think towards a bit more autonomy running for a longer time. Um But yeah, I I think I think we'll see a lot of this sort of application. I think we'll in general we'll see we'll see like more autonomous and higher compute use of these models for different things. You mentioned kind of like the math and physics side and obviously you've had these really impressive breakthroughs in math on you know, some interesting like different kinds of competition you know, problems. oned kind of like the math and physics side and obviously you've had these really impressive breakthroughs in math on you know, some interesting like different kinds of competition you know, problems. Maybe you know, I think for our listeners it like intuitively makes sense how progress in coding directly translates to something like you know, helping with AI research. How does like math and physics progress like also tie into this? The the the biggest role that like you know, focusing on this math benchmarks has played for us as as general yeah, like benchmark and and and and and North Star for like how to improve this technology, right? Math is very measurable, right? It's much easier to tell whether you've actually solved the math problem than like whether you've even like produced a good you know, piece of software. And also it can get very hard, right? So, you can have things where like it's very definite whether you've solved them, but it can be like arbitrarily pretty much hard to to actually solve them. You know, I would say like up until not too long ago, like um you know, my perspective has been like, "Well, okay, like we you know, our models are not you know, maybe able to solve like simple math problems. Okay, our models are able to solve simple math problems, but are not able to solve like IMO level problems." So, clearly there is just like a gap in just like this uh you know, intelligence of these models of like that that is very measurable, very v- you know, v- very easy to run at. It's very clear what we need to do. And you know, and this has become kind of our North Star for like reasoning models and so forth. Now, of course, um that is changing quite a bit, right? And we are um you know, we have kind of reached these milestones that we've been working towards of like, "Yeah, IMO goals level, solving IMO problem six." And you know, and making progress in in in in research-level mathematics. Um and you know, from this one, I think I think there still is uh you know, there's a definite still is utility like continuing to measure progress on this. I think there's also like, you know, there's definitely like transfer that that you can get from like getting better at mathematical reasoning to getting better at AI research. You know, a lot of our uh best researchers uh are uh you know, mathematicians we're training or from other kind of theoretical fields. But definitely we are uh you know, we we are very much uh changing how we think about you know, these North Stars, and we are very focused on how the models, the next models that we're producing are actually useful in the real world, you know, useful you know, especially for AI research, but also for other kind of economically valuable activities and for other uh fields of science. Uh and especially maybe more applied sciences. And the reason for this shift is because we believe the models are now capable enough, not as smart as people in all ways, but capable enough to actually materially change the economy, change how things are done. And so, uh yeah, we feel a lot of urgency about that. In the early days, uh uh picking a domain like math that is so uh le enough to actually materially change the economy, change how things are done. And so, uh yeah, we feel a lot of urgency about that. In the early days, uh uh picking a domain like math that is so uh hard to solve, but then easy to verify whether you did it, like it's kind of the the perfect place to get started. And I think code obviously shares a lot of attributes to that, you know, uh possible to check uh and verify and and great for reinforcement learning. I think one question that a lot of people are are thinking about is, okay, we've seen reinforcement learning work incredibly well in these domains where you can verify it rather easily. A lot of, you know, valuable tasks in the world, medicine and law, finance, you know, there's some level of of the ability to do that, but it's certainly not to the same extent that math and code are. And so, I think a lot of people are trying to figure out, you know, are we going to see similar improvements? You know, obviously code and math, the the rates of improvement have been so astronomical and shocking. Yeah, I definitely expect so. Um I think an interesting duality that we think about a lot is um you know, for this more general tasks, for this tasks that are kind of harder to evaluate, they share a lot of a lot of common uh commonalities with um just longer horizon tasks, right? Because if you think about even like a very well-specified math or coding problem, again, like if it's it's something that you need to work on for like a year, then uh you know, even if it's very clear what the criteria of success are, in the long term, like what to do on your first day of working on it is a pretty open-ended problem. Yeah, and so, I I kind of believe this these difficulties coincide and they're very clearly the next the next frontier uh for for for for for our systems to develop. And I think we definitely see very encouraging signs both on just like our ability to scale RL on these more general domains. And I I I think also like we we can we can scale um I don't know if it's that that that that that that showed a lot of promise. In these other domains, it feels like one of the hardest things to know is just what was success in a task, right? And you can imagine, you know, there's going to be, you know, whatever the problems you are that are facing code and math that are short-term tasks and then longer-term tasks. Feels like it'll be amplified in the space that is, you know, outside of those, right? Where a short-term uh legal task or medical task may be harder to run thousands of iterations on, right? And figure out, you know, was that done correctly? And then those longer-term tasks, like even harder. I'm curious like how you even conceptualize that research challenge. Like what are the things that need to be that you need to figure out to be able to to really make models work well in some of these other spaces. Yeah, I think I think I I come back to this while I will tell you of like how do we make the models work for very long time and how do we teach them to evaluate kind of partial progress? Yeah. I mean I I think if you if you look at like even outside of RL…

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