So, Greg, thank you for coming back here. I don't think we ever charge you for rent, so maybe we'll send you an invoice later. But Greg, you've been part of two really spectacular companies: Stripe as employee number four and then the first CTO. I just recently heard that they process 1.6% of the global GDP. You must be proud of that. That's amazing. You must be even more proud of the fact that OpenAI has almost a billion or maybe more than a billion in weekly active users at this point.
I mean, it's all very exciting. It shows you what technology can do.
And you're not just co-founder and president, but you're also chief builder at OpenAI. I heard that that was one of your titles.
I'm not sure if there's ever an official title, but I've been called many things. Let's just say that.
Well, you have an audience of great builders here. So, we'll start from all the way at the bottom of the stack. OpenAI has multiple stacks to the business, one of which is compute. And you guys have been very aggressive on securing compute. Why is that?
Well, in many ways, we have a very simple business. We buy, rent, build compute, and we resell it at a margin. That's it. As long as the margin's positive, then you want to scale it because the demand for solving problems, the demand for intelligence, that's unlimited. And the AIs that we have right now really are able to rise to the challenge of effectively any kind of problem that you want to throw at them.
Do you have enough compute?
I was just with Matt Garman and he says the GPU compute availability in 2026 rounds to zero. Don't you guys have all of it?
I mean, we would love more. We're constantly out there hunting for more, honestly. And I'll tell you, when we first launched ChatGPT, I remember being on a call with my team and they were like, 'All right, how much compute should we buy?' And I said, 'All of it.' And they're like, 'No, no, no, seriously, how much should we buy?' I'm like, 'No matter how fast we try to ramp compute, I guarantee we're not going to be able to keep up with demand.' And that has been true ever since.
That's fascinating. Moving up from compute, since I don't know if much of this audience can help you with securing more compute because most of them are founders of startups, about architecture and scaling laws: where are we in the scaling laws? Are they still doubling each year? Are you changing architecture? What are you guys pushing on the frontier on the research side?
Well, I would say first of all the scaling laws are a deep and very beautiful mystery. They feel deeply fundamental. It's like this scientific truth that just like you think about physics and Newton's laws, there's somehow this truth of the universe. And they're empirical. We don't necessarily have all the theory to explain exactly why it works. But to me, the most beautiful thing is that neural networks were really designed in the 1940s before there were computers. And somehow we've been able to take the exact ideas that were developed back then and apply increasing amounts of computation. And as you pour more compute into the models, they get correspondingly more capable and it just keeps going. There's no wall. And I think that's a beautiful thing.
That's pretty beautiful. Are there more research or more algorithms that are in the works? Because in the past we had neural networks in the 1940s but we didn't have the compute for it. Now that we have the compute for it, are we just pushing the same things or are there new architectures and new ideas coming out?
Yeah. So I would think of it as we absolutely have new ideas that are constantly powering what we do. It's very simplified to say, 'Well, let's take a neural network from the 1940s and put it in a gigawatt data center.' We have made tons of innovations and we constantly are improving things. And sometimes these are micro tweaks like you just realize that the way you've been formatting data was not quite right and that can actually be a very big deal. Sometimes it's larger. You think about the shift from the LSTM to the transformer and I don't think the transformer is, you know, everyone's moved past the transformer as described in the 2017 paper. So there's constant innovation happening. And I think of places that have been perhaps the most invested in long-term research on how to improve the architectures, how to improve the fundamental algorithms and how to get the paradigm shifts. I think OpenAI has been leading the pack there and that's something we continue to invest in and I see lots of fruit on the horizon.
Got it. And on the models, does OpenAI have a formal definition for AGI? Are we close? Are we not close? Pat and Sonia published this thing that we are at AGI functionally. Do you agree with that? Do you not agree with that?
Well, we do have a formal definition, but to some extent, one thing I have learned is that everyone has their own intuitions about what AGI is. And maybe you can view it as, according to my view of where we are, I think we're about 80% of the way there in that we have models that are smart. They're very capable. They are able to, if you give it all the context, then yes, I think that they are. They're just so capable. It's really remarkable. Like, does anyone here feel better at writing software than GPT-5.4?
Oh, all right. Writing kernels.
So even there we're seeing massive gains. Exactly. And for some of our internal results, if you have the right setup for your problem, then you're able to get really massive results out of very low-level tasks. And just to give you one example of how things have been trending, one of my systems engineers similarly was like, 'Hey, I haven't been able to get value out of the models for GPT-5, for 5.1, for 5.2, as well for 5.3.' He, on a lark, had prepared this design document for a very complicated systems optimization he was about to do. He handed it over to the model, went to sleep, waking up intending to give this to his team to work on for the next week. And when he woke up, it was done. The model had actually implemented the initial spec, had seen that it was slow, had added instrumentation, had actually run the code, used a profiler to figure out where things were slow, and iterated multiple times until it got to an optimized result. And that is incredible. That's where we are.
And so what would you advise all startups here to do because the models keep getting more and more capable? I've asked this when Sam was here in the past: what if you're building today, do you need to rebuild in two years when a new model comes out because all the functionality and all the capabilities all change around you? Do you need to make sure that you're not in OpenAI's way because you're just going to run over startups because the models are so much more capable? How would you recommend a set of startup founders to build in this environment?
Well, first of all, I would say to lean in. The tools right now have become incredibly useful. And if you look even over the course of December, I think that we went from these agentic coding tools being like, they're writing 20% of your code to writing 80% of your code, which means they go from being kind of a sideshow to being the main thing that you're doing. And I think we're doing that across all of the work that people do with computers, all computer work this year. And you can look at the recent progress on Codex, really changing from a tool for software engineers to a tool for anyone who's doing work with a computer. And just over the past week, we've released a bunch of features that just make it so much more powerful and capable. And one thing we just announced today is a new tool called Chronicle that plugs into Codex where it actually can see everything you're doing with your computer and can form memories of what's going on. And so you ask it a question, it instantly knows what you're talking about. You're like, 'Huh, what was I doing five minutes ago?' It knows. You're like, 'Oh, what was this person talking about?' It knows. To me it was this real wakeup call to realize you spend so much of your effort right now just explaining to your computer what's going on. Like, why are you explaining to your computer what's going on? That makes no sense. And so, I think what's going to happen over upcoming years is the models are going to get much more capable. We'll have better harnesses. We'll be able to solve harder and harder problems, come up with new knowledge, all of these things. But there is a one-time shift that's happening now, which is really about context. It's really about: is your AI able to—you have all these meetings, you didn't include the AI, that's not very nice to the AI. Like you're asking it to help you with things and it has no information. So I think really leaning into how do you make sure the AI even has enough information in theory to solve the problem and then trust the models are going to really get there and improve. So I think it will be a constant cycle of improvement and iteration and leaning into the tools and talking to your friends and figuring out how they are using it. But there is this investment that's a one-time investment that now is the time to make.
And in terms of, let's say you set that all up. How is OpenAI using Codex differently than you think everybody else outside is using it?
