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…I definitely agree that continual learning is really the thing. It's really the thing that we're building, but I don't really think this is like a problem that's ignorant and off the path of what we're doing currently. I think it is what we're working towards. What are the other research areas within alignment that you're paying attention to or that you think are promising? A lot of the like longer-term challenge with alignment is about generalization. What are the values that the model falls back on? What are the things that you need to figure out to be able to really make models work well in some of these other spaces? I come back to this reality of Ilya Sutskever is the chief scientist of OpenAI. I think literally one of the most important people on the planet. And today on Unsupervised Learning, I got to ask him literally everything that I've been thinking about, and I know a bunch of people in the ecosystem have too. We talked a lot about model progress, what's required to make long-running agents work, as well as the really interesting work OpenAI has done in the AI for science world, and the progress he sees in that over the next years. We talked a lot about how companies should be thinking about model building in this moment, when they should be doing reinforcement learning, how they should be thinking about the evolution of harnesses and the impact that will have. We hit on a lot of his really interesting research, including the work he's done around alignment, the work that OpenAI broadly has done around math competitions. And we also talked about this focusing moment at OpenAI, and what it means for the research organization, and how he runs his team. Literally just such an awesome opportunity to talk to someone who is driving so much of the change that has revolutionized the space in the world. I hope folks enjoy this wide-ranging conversation as much as I did. I feel like you are the perfect person to talk to you about questions everyone has in the ecosystem. Uh what's, you know, happening with model progress. A lot of companies are thinking about how they should be building things based on what's happening with the models. A lot of people at a societal level are thinking about the impact AI's going to have on science and broader society. Uh and you've been at the forefront of the space for pretty much every generation of of improvement uh these past years. And so, really excited to have you on the podcast. Happy to be here. I think I'll start with one of the most the juiciest things you said, which is, you know, 4 months ago I think you and the OpenAI team talked about aiming for a system with research level intern capabilities by September of this year. So, coming up. I think that's what six months from now. And then a more 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 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.…

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