Greg Brockman on Astra and the Future of OpenAI

Doing great. Thank you for having me.

Well, it's always a huge day in AI and modern scientific progress, I would say. Today we announced that our model had solved the Navier-Stokes problem, that we found a counterexample or a sort of proof that these theoretical equations do have a singularity or kind of break down under certain circumstances. And this is a problem that has been open for a very long time. It's one of the seven Millennium Problems, which are kind of some of the deepest, most important problems in mathematics. I think that the problem itself is important, right? This is new knowledge for humanity, that the proof itself is actually very elegant and beautiful, and I think that there's a lot to learn from it. The equations have lots of application in fluid dynamics and other areas, but to me what's even more important is about what this represents about where we are in terms of model capabilities and the fact that we can actually generate new knowledge, that we can learn from these models and have them help us solve problems that are otherwise outside of reach or would take us a very long time to solve.

Yeah, yeah. Well, look, I think that there's, first of all, the applications of this specific result or the equations themselves, right, which are things that let us better understand phenomena from ocean currents to airflow around aircraft, around turbulence and things like that. But it's really about the broader insights and methods that can help scientists and mathematicians further accelerate their research. And I think that, again, representative of if we have models that can help solve this kind of problem, then what happens from here? What other problems are immediately applicable? And I think that talking about curing diseases and new medicines we're going to be able to develop, all of that starts to become much more real when you have models that are at this level of assistance and capability. And I do think that there are going to be real changes to think about in terms of we can have so much more ambition with the kinds of challenges that we can hope to tackle now.

Well, first of all, I've just been blown away by the community reaction to Astra. It's been really amazing and very humbling, honestly, to see all the creativity and the different ways that people have been applying the model. And I think that it's very clear that we've reached a new threshold of computer use. So, this model is able to really work with different kinds of applications in a way that was not previously possible, and people are taking full advantage of that fact and really thinking about how to create. Lots of people showing off 3D creations and mapping out physical locations and turning them into these 3D models and thinking about, can you use this for design of physical parts? Someone talked about how they were designing some mechanic for catching hair in a shower drain and that they were able to—super cool, right? That they're now able to actually manufacture that.

Exactly. And I think there's a core there that's very important, which is that we are talking about these grand challenges sometimes or very esoteric applications, but really the everyday—the number of problems that you have in your life that you would love to solve—it's now possible, right? We're really trying to empower the individual to make it so that you can have superpowers, you can accomplish more. And really trying to benefit people, empower people, build tools that can really help you in your daily life, that's all part of what we're working on.

Well, I think you're exactly right that we really want to shift these tools from requiring kind of low-level access or guidance to really having the human be able to fly, right? To really be empowered for you to be able to set the goals and the objectives, and that you still should feel like you can get into those details and you can understand them. You can provide that oversight because ultimately you should feel accountable for the outcomes, but you have this absolute amplifier, right? Like a trampoline or like a rocket ship for the mind, however you want to analogize it. And I think what that means at a practical level, first of all, this year we've been really seeing this shift from just pure chat use cases to agentic use cases. But I would also keep in mind that chat is alive and well. We're now well over a billion users every week, and you can see that the market share of ChatGPT is starting to climb once again because we've been investing so hard in so many use cases that are important for people in education and health and a variety of other areas. And then at the same time, these productivity, deep knowledge work use cases, those are really taking off. We've had this almost vertical wall of agentic adoption since we launched ChatGPT Work. And I think that the fact that these are two distinct modes is actually a point in time. That is something that we're continuing to unify and merge, and we're starting to see that there's a new emerging form factor for how people want to consume AI. And it's almost like the promise of AI has always been that you have something that you can talk to and really delegate work to that's proactive and persistent. And what we were promised if you were to rewind 5, 10 years ago was never a low-level language model where you have to think about context windows and you have to select thinking strength and you have to select different models. None of that is the future. And so I think that we're moving towards real amplification, real giving you time back, real having computers that are able to operate according to your goals, to your desires. And I think that that is a core of it. We're developing this safely—that's one of the core commitments that we make and how we think about this. But we really see the power of these tools starting to really increase in terms of what people are capable of, and that under the hood utilizing tools like Blender, so that that is almost a detail that fades into the background, is absolutely the direction of travel.

Yeah, we think about this a lot, and I think that there's this discovery problem that we as a field need to really encounter in a first-class way. We haven't done it fully yet, but I think we have a real shot at solving it better than any product before because the thing that right now we kind of rely on—you think about ChatGPT and ChatGPT Work. These are both text boxes, right? And it's like, well, this new text box is way more powerful than the old text box, but there are some reasons that you still want to use the old text box. It's far too confusing. People just want something that can help them solve their problem. The whole point is to get your time back, not for you to have to go and become an expert in all these internal details. But at the same time, we also have a model that understands what you're trying to accomplish, right? That you're explaining to it, 'Here's what I want.' It has a lot of context on you. And so it should also be able to proactively say to you, 'Hey, actually, if you ask me this other way, or if you added this connector, or if you authorize me to do this, or if you hook up your credentials in this way, I can go and do this other thing for you.' So we're thinking a lot about that self-knowledge, that onboarding process. And I think that is a huge opportunity. I think that there is both the disadvantage that you cite of people tried it, they formed an impression, and it's changed, it's something new, but there's also an advantage. ChatGPT has over a billion users every week—that is unique. No one has that kind of use on these models. And I think that the number of people who have tried ChatGPT before, I think it's another billion, billion and a half, something like that. And so that's a huge opportunity as well for us to go back to those users and say, 'Hey, we can now solve the problem for you. We can now help you in ways that you didn't see before.' And I think that it's true, it's real. You look at how many people use chat for health: 300 million people every single week with health queries, right? And that's really making a difference in people's lives and that of their loved ones. And so we have such opportunity.

Yes, this has actually been a real sort of realization or just something that I have really come to over the course of this year. And if you look at even, for example, we just announced ChatGPT Images 2.5. And if you look at the launch video there, the thing that I love about it is it shows, here's someone creating an image and here's a bunch of different variations of it, and then here's them taking their favorite one and having it in the world. Someone said, 'Here's a cool little sketch of a candle holder.' You see an awesome visualization of it, and then you see the physical candle holder and you're just like, that's what you want, right? It speaks to you immediately. And I think that really showing people, 'Here's a use case.' And the thing that was also a little surprising to me is that we've sometimes highlighted esoteric use cases, something that appeals to someone in particular and it's amazing for that person, but people don't then say, 'Oh, because it's this powerful, I can also do this other powerful thing I've been waiting on.' That connection is something that is less easy to make than I'd realized, and it makes sense. What you want is for there to be use cases where people say, 'I actually want that particular thing. Now let me go try it myself.' And then from there you start exploring and you start to find, 'I actually do have this powerful use case that I didn't even realize was tip at the time.'