OpenAI 总裁布罗克曼：算力永远不够，人类注意力成了新瓶颈

So Greg, thank you for coming back here. I don't think we ever charge you for rent, so maybe I'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 billion, sorry, 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 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 of the business, one of which is compute. And you guys have been very aggressive, 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 is 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? No. Really? Yeah, definitely not. I was just with Matt Garman. 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 were like, no, no, no, seriously. Like, come on. 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 the scientific truth that just like you think about physics and Newton's laws and things like that, there's somehow this truth of the universe. And they're empirical. Like, 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 like in the 1940s, before they 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 that's, 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 to your point 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 up? 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, right? 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, like everyone's moved past the transformer as described in the other 2018 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. I see lots of fruit on the horizon. Got it. 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 are very capable. They are able to, if you give- Are they smarter than you? I mean, they're certainly more capable than I am at writing software. If you give it all the context, then yes. I think that they are just so capable. It's really remarkable. Does anyone here feel better at writing software than GPT 5.4? Oh! Oh, it's got one. It's got one. It's got one. All right, writing kernels. So even there, we're seeing massive gains from- Exactly. And for some of our internal results, there, we're really seeing if you pour the right kinds of- If you have the right setup for your problem, then you're able to get really massive results out of very low-level, even low-level tasks. And just to give you one example of how things have been trending, one of my systems engineers also very similar was like, hey, I haven't been able to get value out of the models for GPT 5 or 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. That 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 into an optimized result. And like, 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. They're kind of, I've asked this when Sam was here in the past, and you know, 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 open AI's way because you're going to roll, 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, you know, they're like 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 codecs. It's 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 the codecs, 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. You just instantly knows what you're talking about. You're like, huh, what was I doing five minutes ago? It knows, right? You're like, oh, what was this person talking about? It knows. To me, it was this real wake up call to realize you spend so much of your effort right now. Just explain 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. You know, 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 kind of talking to your friends if you're how they are using it. But that there is this investment that's a one-time investment that now is the time to make. And in terms of like, let's say you set that all up, how is OpenAI using codecs differently than you think everybody else outside is using it? Well, I think one of the amazing things about being at OpenAI is you do get to live in the future, right? You do get to really see the shape of what's emerging and we can co-design, right? We can really change the models, the harness, everything together in order to better serve the needs that we see. And a lot of the approach we've been taking is, so we started with software engineering. And we set some clear guidelines for example saying that we still want a human to be accountable for all code that gets merged, right? So at the end of the day, is it a good thing to merge this piece of code? Is it well structured? Is it going to make our code base more maintainable? We want to make sure there's a human who is signing off to say yes. And that's, I think that thoughtfulness of not just saying, okay, let's just blindly use this. Or, you know, oh, we don't want to use this at all. Like I think neither extreme is quite right. And then we are also going vertical by vertical with an open AI to adopt these tools within finance, within sales, within IT. And there we have a small dedicated team who's really deeply understanding the domain, working with the people who are the experts in it in order to build skills, in order to modify the codex UI, whatever it is that is needed in order to get it to be good. And then that's something we can then, once we have it in good shape, we will externalize and that we're able to ship that to all of you. And so we are starting to work with certain customers as well. So for people who want to be very AI forward and want to be part of defining this revolution, that there's a place for that. And I'd love to talk afterwards. But yeah, I think that just this desire to say, hey, we really want to be AI forward, really live in the future and experience what it will be like for everyone else, one year, two years, three years down the road. Do you guys structure your company differently or the engineering teams differently because of the living in the future? I mean, if you have to go way back, when my father learned computer science, he was just himself. And then we had these long software releases that became waterfall. And then when the web happened and the cloud happened, we had these two pieces of teams and we had scrum. Now that we have these coding agents, how do you structure around everything differently? I think we're still figuring it out. And there's certain places where you really see it. For example, the cost of building a prototype is cheap now. It's so cheap. And if you want to build a dashboard that used to be like, oh, it would take someone like a week to do it. And you just do it now. And so actually a lot of the bottleneck has shifted to things like sharing. Like how do you, and so we actually have some internal work on this as well that again, we will be externalizing of how do you make it really easy for anyone in your enterprise to build a dashboard, a widget, a bot, whatever the thing is, and then share it with others. And then that starts to really put pressure on having good governance. Like you want your IT organization to be able to see all these different threads of execution that are happening, all the little things that are being shared around, have some control over data provenance, right, to really make sure that, okay, like a good example of this is I think people are now starting to take their internal knowledge dumps, turn them into wikis. We have a really cool one of these internally. And the thing you immediately think about is, well, if someone has a document in the internal knowledge base that was accidentally permissioned incorrectly, and they realize, oh, no, I didn't want this information to be accessible, how do they fix that, right? So normally it's they go into the dock, they change the permissions, but now there's these derived artifacts. And so you need to make sure you have some way of tracking through the system to say, well, this output document came from the source one, the source one is no longer accessible to this audience, let's go and invalidate that as well. And so you have to start really building your technical architecture with awareness of the way that people are going to use this information. And it really changes how teams relate to each other because you can just, it really changes where the bottlenecks are and what's hard. Do you think team size is going to be a lot smaller? Are we going to have still human software engineers in a decade? Well, a decade is a long time from now. And the ceiling on this technology is really hard to internalize. I think that it is clear that what a company is will change in a lot of ways. I think that we're going to have this ability for solopreneurs to build very incredible businesses. And so anyone who has a vision, I think, will be able to realize it. I think the jobs that you all have will become way easier in a lot of ways, way more fun. Now it might be more competitive too, right? Because everyone's going to have these amazing tools. And so really figuring out what is your niche, what is your unique angle is probably going to become kind of the most important core. And so I think that's a lot of how we run organizations right now. And there's almost only one way to organize large groups of people where you have teams, you have management structures, and you have scopes, and you have these hierarchies and all these things. Maybe that can change. Maybe you can be much more flat, small teams that can really just do incredible things. Like we're seeing it right now in mathematics where these individuals on the internet are using GPT-54 Pro to solve these unsolved math problems. Normally you need a math team and they're just doing it. Yeah. My son's a math nerd. I just told him that maybe we should be studying something else besides math. But I, well, let's see, this is the question, right? If you look at something like AlphaGo, you know, Move 37, this move that just like changed humanity's understanding of the game. But the thing that was surprising is it made the game more interesting and important for humans. And maybe that will be true for these other domains too. True. What about common failure modes when you're building with production agendic workflows? What do you see as the common things that founders get wrong and they're building incorrectly these days? Well, I think that these models, they have such power and really understanding how to operate them well takes thought. And so we've been investing a lot in primitives, security primitives, observability, having, again, good governance, things like that. But just to give you one anecdote that I think is evocative, I asked, so I was working with my codex, I asked it to install some package that someone had opened AI had written, ran into an error. I was like, oh, ping that person on Slack and ask them for help. So pinged the person on Slack. Two minutes later it said, this is taking too long. I've escalated to the person's manager. And it actually pinged the person's manager. And you realize it's like, on the one hand, it's kind of a reasonable thing for the model to do. It's being proactive. It's trying to solve my problem. It's like, you know, not just sitting around waiting to be told what to do. But on the other hand, like, you know, maybe it should have taken a little bit longer. Maybe it should have checked with me. And so I think that really thinking about these questions where we're still building up the EQ of the model and that in some places it's getting very good. For example, clicking approve, approve, approve is kind of where we've been. And humans are not very good at that either, right? They just default. They just default. And so now we're starting to have AIs that can actually take care of flagging. Is this a high risk action? Hey, this one should be escalated. This one's okay to auto approve. And it really makes you realize that human attention is going to be this incredibly scarce resource, right? The doing of things now is easy. The is this a good thing? Is this what I wanted? Is this aligned with my values, with my desires? That is going to become the single most important bottleneck. And so I think building systems that take that into account and really think about the human factor, like that's the most important thing to do now. Another human factor is security. How would you advise people to think about security in this world of AI? And I just heard about breaches left and right with Vercel recently. And then these models are incredibly powerful at finding security holes. So how would you recommend people here use the models to find those security issues? Well, I think there's a couple levels to the answer. I do think that this is, I think that the internet has been a place where security has been just like a ratcheting important concern over time. You think about where it started going through the 90s with viruses and worms and malware and those things. And we've moved past that. And so I think we are also moving now to a much more ultimately secure regime. But it does require kind of an internet-wide effort to get there. And so a lot of this honestly is just again leaning into the technology, having these models. They can scan your code base. They can actually be used for end-to-end red teaming. Like there's a lot that can be done with them. And a lot of how we're thinking about further models and improvements there is really leaning into how do we actually sort of leverage trusted access programs? How do we leverage the community of people who really care about being defenders and making the internet more secure? And I think that's something where everyone has a role to play and can participate. But the number one thing is just sort of recognizing that these models are very powerful. But they're not magic, right? That they are just like a part of the overall resilience ecosystem. And I think that we as a society and I think every company again really contributes to this. Have something to build in terms of how do we incorporate these in a way that results in more assurance and more sort of certainty on the impacts of whether it's this particular patch that you're taking. Whether it's thinking about how do you make sure that you're just sort of rolling in updates quickly as they're being released. So I think that there's a lot of work to be done. But I have a lot of optimism for where this is going. Let's switch to speed. Seems like things are moving faster and faster and faster. We're in the world of accelerating change. We were talking about it when you were walking up here around how you're trying to keep up with things. How do you keep up with all the accelerating change? How would you recommend everybody here keep up with everything that's changing? Well, I think this is the new normal. And I think to some extent it's not really because of AI. I think it's just been the trend of technology for the past two decades. There's more people doing things. It's easier to do things than ever. Barrett to entry goes down. It means it's also much more easy to build value, right? To have great successes. And so I think that really trying to keep your ear to the ground and understand what's changing. And to some extent it always starts with the same thing, which is play with the technology yourself. Like it's very different to hear AI described versus to use it. But the beautiful thing about AI is it's so intuitive. Like that's the whole point is that rather than have the machine be something you have to contort yourself to, the machine contorts itself to you, right? It's doing work for you. And it should be something where you ask it and does something. And so I think that just really trying to just get your finger on the pulse of what's changing, what's possible, where the models lag. That is I think the core skill that is going to really determine a lot of the success of companies in the future. And then on the flip side of that, you guys have held back models to work with security agents. So it's like the opposite of like going as fast as possible. So you're doing things responsibly too. So how do you like think about the balance? Because you're in a competitive environment. You want to ship as quickly as possible. And yet you're trying to do the right thing as well. Yeah, I think at a values level, like what OpenAI is about, like we really want to put the power of AI in people's hands. Like we believe that people can, we want to empower people to build the future with the tools that are being created. But we need to do that in a thoughtful way, right? That we really think about both sides of here are the benefits, here's the risks. How do you maximize the benefits? How do you mitigate those risks? And I think that in cybersecurity and in biosecurity, those are areas where we're very thoughtful. We've been building, we've been working on these kinds of both mitigations and trusted access programs for quite a long time. And that what we see coming is models that are going to be increasingly powerful and capable in a continuous way across all dimensions of capability. And the, you know, we announced last week the expansion of our trusted access for cyber program. By the way, has anyone here applied? No one? Oh, I see one hand, two hands. Okay, more of you should apply. It's great. We really need help because, and it's very important that people who are trustworthy and responsible and really want to push these models are participating in this because that is how, that's going to pay dividends for everyone. We're going to have more to announce over upcoming weeks on how we're expanding the program. But, and also when we release models to everyone, kind of the mitigations that we have and how we're going to tune those to be both, to really balance, right? To really try to bring these capabilities as broadly as possible while also making sure that the ones that are, you know, that we're thinking about the risks and able to have some observability over them and to ensure that this is maximally positive in terms of deployment. So, I think the short answer is like, it's core to our mission. We care a lot about the impacts of what we're doing, not just building the technology in isolation, but it is a whole community and a whole world effort to really get to where we need to be. On, now moving up from the models to the application layer, which is what a lot of people here are building. How do you, how does OpenAI decide what in the application layer you're going to build and what you're going to leave out? Well, people have probably seen the word focus being applied to OpenAI quite a lot recently, possibly for the first time in a while. It's been applied to her too. And it's, it's hard because the field of AI is one of opportunity, right? It's like anything you're going, you can imagine is going to be great. No question is going to be great. And we, as a company, as a single company, no matter how much compute we build, no matter how many people we have, are only going to be able to do so much. And so a lot of where we've been, how we've been thinking about things is what is the sort of most focused strategy that covers the parts of the space? You know, maybe it's an 80-20 or just like the parts of the space that we think we can have most impact on. And I think there it's very clear, right now we're going through this identity transition. And so products that are, and it's not just about enterprise versus consumer, right? So it's like clear we are being very serious about enterprise. Like we're selling to big companies and building a whole muscle and sales motion there. But consumer, what consumer is, is going to change, right? It's kind of a very broad term that buckets in multiple things. But the slice of consumer that's about not just productivity but about goals, about achieving your goal, about even knowing what is your goal, being able to elicit that and having an AI that can proactively do that, it's all kind of the same thing. Like in the end we're trying to build an AGI that you can talk to that has all this context that you can use in your personal life, your work life, that's trustworthy, right? That you can go to it for advice and give you useful information, maybe health information or maybe about finances or, you know, about if you're trying to figure out what to do with your career. Like all of these things, they all kind of ladder into one thing. And it's meant we had to make some very painful decisions about what not to do. But I think I would just say that that's the aperture that we look at things through and the things that accrue to that singular vision of what we want to build, you should expect us to pursue. Got it. Do you think we'll be coding with command lines and agents in a few years or is it going to be completely changed? I mean, I think that we're in a very unnatural state right now for how we work. Like we all sit behind this box and kind of type away and it's very clear our bodies were not designed for this. We got our carpal tunnel and our hunched shoulders and all these things. And I don't think we want that. I don't think any of us wanted that. Like I think that we want more free time. But it's not even about free time necessarily, right? It's like you want to spend more time with your loved ones. Yes. Yes. You want to spend more time like talking to people and like coming up with like brilliant visions or just like what you're excited about or just understanding yourself. So it's kind of like do you want to be a CEO of an organization of like 100,000 agents? Like that actually seems pretty good. And I think that we're all going to be able to get so much more done. But the mechanics of it are going to feel as different as like going from having to write out things with, you know, by hand with a quill or something to being able to, you know, just send a text message and have people go in and, you know, working on your behalf on your goals. All right. We talked about compute. We talked about model and security and agents and app layer. Let's talk about frontier. When are the models going to be good enough to push the frontiers of science, physical AI? Seems like we had Jen found here. It seems like LMS have been a great scaling law for digital intelligence. It hasn't been as strong for robotics, for physical intelligence, for aspects of biology and science where the problems are probably a lot harder to verify or takes a long time to verify. Well, how are you keeping track of science and physical AI in the world? Well, science is one domain that we're really leaning into and we see line of sight to really incredible progress. And we're starting to have some signs of life. And I think it's always important to ground in what is happening today when trying to predict what will happen six months, a year from now. So, for example, we had a physics result where our AI came up with this very beautiful formula that physicists who have been working on this for quite some time thought was totally impossible. Thought it was like maybe an unsolvable problem. And like it's pretty significant, right? It's like real serious physicists who really view this as a step towards really being able to get to some sort of answer for quantum gravity and all these things. It's not there, but it's a step. That's much bigger than where we were just a couple of months ago. And so it makes you really wonder a year from now, like how far will we have traveled? Now things like biology, that they are different from physics and math, right? That they are, you got to leave your beautiful simulated world and deal with messy reality. But I think we've been learning how to deal with messy reality in other domains. Software engineering is a perfect example where we've really realized that just building the thing that solves competition, you know, programming competitions, like that's not enough. Like you need something that's seen real world messy code bases, humans interrupting in different ways, like this adversarial banging at it. And so I think that on science, I expect we're going to see a real renaissance. You know, maybe we'll see some big results this year. Next year, I think it's going to be a totally wild, wild time. We live in interesting times. I promise that I get you out on time because you're a busy man. Before we let you leave, we got one minute on the shot clock. What since you have no time, but soon you will have lots of time. What do you and Anna do for fun? Fun? I mean, same as anyone, like to watch movies, go on hikes, those kinds of things. You know, not as much time for it as maybe we'll hopefully have post-AGI. But you got to kind of enjoy the ride along the way. Thank you, Greg, for joining us. Thank you, everyone. Thank you, everyone.