Sam Altman: commitment

Transcript context

…I mean, I think more than ever. The, there was like a time in our history when the right thing to do was just to scale up compute, and we saw that with conviction, and we had a spirit of like, We'll do whatever works, you know, like, we want to, we have this mission, we want to like, build, say, AGI, figure out how to share the benefits. If the answer is like, rack up GPUs, we'll do that. And right now, the answer is, again, really push on research. And I think you see this with O1, like, that is a giant research breakthrough that we were attacking from many vectors over a long period of time that came together in this really powerful way. We have many more giant research breakthroughs to come, but the thing that I think is most special about OpenAI is that we really deeply care about research and we understand how to do it. I think, it's easy to copy something you know works, and you know, I actually don't even mean that as a bad thing, like, when people copy OpenAI, I'm like, great, the world gets more AI? That's wonderful. But, to do something new for the first time, to like, really do research in the true sense of it, which is not like, you know, let's barely get soda out of this thing, or like, let's tweak this. But like, let's go find the new paradigm, and the one after that, and the one after that. That is what motivates us, and I think the thing that is special about us as an org. Besides the fact that we, you know, married product and research and all this other stuff together, is that we know how to run that kind of a culture that can go, that can go push back the frontier, and that's really hard. But we love it and that's, you know, I have to do that a few more times in a week at AGI. most surprised you? Yeah, the degree to which we have to just, like, follow the science, and let that determine what we go work on next, and what products we build, and everything else, is, I think, hard to get across. Like, we have guesses about where things are gonna go. Sometimes we're right, often we're not. But, if something starts working, or if something doesn't work that you thought was gonna work, our willingness to just say, we're gonna like, pivot everything, and do what the science allows, and you don't get to like, pick what the science allows? Yeah. That's surprising. Yeah I think it's true we have a different take on alignment than, like, maybe what people write about on whatever that, like, internet forum is. But we really do care a lot about building safe systems. We have an approach to do it that has been informed by our experience so far. And touch on that other question, which is you don't get to pick where the science goes. Of, we want to figure out how to make capable models that get safer and safer over time. And, you know, a couple of years ago, we didn't think the whole strawberry or the O1 paradigm was gonna work in the way that it's worked. how to make capable models that get safer and safer over time. And, you know, a couple of years ago, we didn't think the whole strawberry or the O1 paradigm was gonna work in the way that it's worked. And that brought a whole new set of safety challenges, but also safety opportunities. And, rather than kind of, like, plan to make theoretical ones, You know, superintelligence gets here, here's the like, 17 principles. We have an approach of, figure out where the capabilities are going, and then work to make that system safe. And, O1 is obviously our most capable model ever, but it's also our most aligned model ever, by a lot. And as, as these models get better intelligence, better reasoning, whatever you want to call it, the things that we can do to align them the things we can do to build really safe systems across the entire stack our tool set keeps increasing as well. So, we, we have to build models that are generally accepted as safe and robust to be able to put them in the world. And when we started OpenAI, what the picture of alignment looked like, and what we thought the problems that we needed to solve were going to be, turned out to be nothing like the problems that actually are in front of us and that we had to solve now. And also, when we made the first GPT 3 if you ask me for the techniques that would have worked for us to be able to now deploy. all of current systems as generally expected to be safe and robust. They would not have been the ones that turned out to work. So, by this idea of iterative deployment, which I think has been one of our most important safety stances ever and sort of confronting reality as it sits in front of us, we've made a lot of progress, and we expect to make more, and we keep finding new problems to solve, but we also keep finding new techniques to solve them. All of that said, I I think worrying about the sci fi ways this all goes wrong is also very important. We have people thinking about that. It's a little bit less clear, kind of, what to do there, and sometimes you end up backtracking a lot, but, but I don't think it's I also think it's fair to say we're only gonna work on the thing in front of us. We do have to think about where this is going, and we do that too. And I think if we keep approaching the problem from both ends like that, most of our thrust on the, like, okay, here's the next thing, we're gonna deploy this. What it needs to happen to get there. But also like, what happens if this curve just keeps going? That's been, that's been an effective strategy for us. agents off doing things in the world, that is going to become really, really important. As these systems get more complex and are acting over longer horizons the pressure testing from the whole outside world, like, really, really…

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