OpenAI's Greg Brockman: Why Human Attention Is the New BottleneckOpenAI's

I mean, it's all very exciting. It shows you what technology can do.

I'm not sure if there's ever an official title, but I've been called many things. Let's just say 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.

No.

Yeah, definitely not.

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.

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.

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.

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?

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.

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.

Well, I think one of the amazing things about being at OpenAI is you do get to live in the future. You do get to really see the shape of what's emerging and we can co-design. 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. 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 codebase more maintainable? We want to make sure there's a human who is signing off to say. And that's, I think, that thoughtfulness of not just saying, 'Oh, just blindly use this,' or, 'Oh, we don't want to use this at all.' I think neither extreme is quite right. And then we are also going vertical by vertical within OpenAI 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 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, 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.'

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, 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. 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. To really make sure that, okay, 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? So, normally it's they go into the doc, 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 this source one. The source one's 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 it really changes where the bottlenecks are and what's hard.

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 we'll 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, 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. But a lot of how we run organizations right now, there's almost only one way to organize large groups of people where you have teams and 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-5.4 Pro to solve these unsolved math problems. Normally you need a math team and they're just doing it.

But see this is the question, right? If you look at something like AlphaGo, you know, move 37, this move that just 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'll be true for these other domains, too.

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 was working with my Codex. I asked it to install some package that someone had written, ran into an error. I was like, 'Oh, ping that person on Slack and ask them for help.' So, it 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 not just sitting around waiting to be told what to do. But on the other hand, maybe should have taken a little bit longer. Maybe 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. 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. 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, that's the most important thing to do.

Well, I think there's a couple levels to the answer. I do think that the internet has been a place where security has been just 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. 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 codebase. They can actually be used for end-to-end red teaming. 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? 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. 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 a 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.

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. Barrier to entry goes down means it's also much more easy to build value, 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. It's very different to hear AI described versus to use it. But the beautiful thing about AI is it's so intuitive. 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. It's doing work for you and it should be something where you ask it and it 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.

Yeah, I think at a values level, what OpenAI is about, we really want to put the power of AI in people's hands. 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. 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 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 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 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. And also when we release models to everyone, kind of the mitigations that we have and how we're going to tune those to really balance, to really try to bring these capabilities as broadly as possible while also making sure that the ones that are—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 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, a whole world effort to really get to where we need to be.

Well, people have probably seen the word 'focus' being applied to OpenAI quite a lot recently, possibly for the first time in a while. And it's hard because the field of AI is one of opportunity. Anything you can imagine is going to be great. No question. It's 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, maybe it's an 80/20 or just 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 agentic transition. And so products that are—and it's not just about enterprise versus consumer. So it's clear we are being very serious about enterprise, we're selling to big companies and building a whole muscle and sales motion there. But consumer, what consumer is is going to change. 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. 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. That you can go to it for advice and give you useful information, maybe health information or maybe about finances or about if you're trying to figure out what to do with your career. 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 that things that accrue to that singular vision of what we want to build you should expect us to pursue.

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. It's like you want to spend more time with your loved ones. You want to spend more time talking to people and coming up with brilliant visions or just 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? 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 going from having to write out things by hand with a quill or something to being able to just send a text message and have people go and working on your behalf on your goals.

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've been working on this for quite some time thought was totally impossible. Thought it was like maybe an unsolvable problem and it's pretty significant. It's real serious physicists who view this as a step towards really being able to get to some sort of answer for quantum gravity and all these things. Not there but it's a step that's much bigger than where we were just a couple months ago and so it makes you really wonder a year from now like how far will we have traveled. Now things like biology, they are different from physics and math. 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, programming competitions, that's not enough. You need something that's seen real world messy code bases, humans interrupting it different ways, this adversarial banging at it. And so I think that on science I expect we're going to see a real renaissance. Maybe we'll see some big results this year. Next year I think is going to be a totally wild time.

Fun, I mean same as anyone, like to watch movies, go on hikes, those kinds of things. 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.