OpenAI President Greg Brockman on GPT-5.5 “Spud,” AI Model Moats, and a 'Compute Powered Economy'

OpenAI released its long-awaited GPT-5.5, or “Spud,” model on Thursday. The model is “a beginning point,” OpenAI President Greg Brockman told me shortly after release, setting up a series built to take action on your behalf and accomplish your goals. GPT-5.5, Brockman said, is adept at coding and computer control, able to act without arduous amounts of instruction.

“In many ways, it is a step towards a new way of getting work done with a computer,” Brockman said. “Being very proactive and really being able to solve problems end to end with a little instruction.”

In an ‘emergency’ episode of Big Technology Podcast, I spoke with Brockman about GPT-5.5’s capabilities, whether model commoditization is coming, how Spud plays in the ongoing cybersecurity conversation, and what he means when he says we’re heading toward a “Compute-powered economy.”

You can read our conversation, edited lightly for length and clarity, below. Or listen on Apple Podcasts, Spotify, or your podcast app of choice.

Alex Kantrowitz: Welcome Greg. First off, can you confirm GPT-5.5 is indeed ‘Spud?’

Greg Brockman: Yes

Okay, so what is GPT-5.5?

I think in many ways, it is a step towards a new way of getting work done with a computer. It’s a new class of intelligence. It’s extremely useful at things like programming, and all the different aspects of debugging and solving very hard and gnarly problems, just being very proactive and really being able to solve problems end to end with a little instruction.

But the thing that’s most remarkable is not necessarily the fact that it got better at coding. That, I think, is what everyone kind of expects.. But the fact that it’s now really crossed the threshold of usefulness for general kinds of applications. And so it’s much better at creating slides, spreadsheets, much better at computer use, using your browser, being able to kind of click through applications that are otherwise hard to have an AI operate. And so I think that we’re really seeing the emergence of this new way of using a computer, and it starts with this kind of intelligence at the core.

When we spoke last you mentioned that this was effectively the culmination of a two-year research process. This was planned two-years ago?

Yes, we do have very long horizons for how we plan now. One note is that we stack together many research ideas and bets on a variety of timescales. And so the way to think about it is that we are making constant progress across every single part of the stack.

And so what GPT-5.5 represents is not an endpoint in many ways. It’s a beginning point. It’s really a step towards the kinds of models that we see coming over even just upcoming months. And I think that you should expect that we are going to have even larger improvements in the capability across a wide variety of these aspects of what the model can do, and that’s something that I think will be very exciting.

Can you share specifically what those aspects are? If this is the beginning, what is it the beginning of?

Think about the models as the brain. You can think about the systems and the harnesses like Codex and the applications, like the Super App, as almost the body around it to make it into a useful AI. And that’s really what’s happening, is a shift from language models being the thing that is produced by labs like ourselves to an AI that’s useful, that’s an assistant that’s out there trying to solve your goal, that’s really operating according to your instruction.

And you can see now, Codex is becoming this app that’s not just for the coders. It’s really for anyone using a computer. And it’s not perfect. There are still some tasks where that it should be able to do it and it doesn’t quite get it right. Sometimes the personality isn’t quite what you wanted. It’s extremely powerful and out there doing a lot of really amazing things, but the way it communicates back to you — you have to still spend some time really trying to read through, okay, exactly how did it solve this problem?

And so these aspects, we know exactly how to make them much better. And we’ve already had a pretty remarkable improvement from 5.4 to 5.5. I think we’re going to have even more remarkable improvements across every single aspect of what makes these models useful.

Internally, we think a lot about the end application. That is one thing that changed for us over the past 12, 18 months, something like that, is that we used to really just be focused on — Let’s improve on the benchmarks. Let’s make these models more cerebrally capable. But we now are really focused on — Let’s bring them to real-world application. Let’s think about finance, sales, marketing, every single function that someone uses a computer. How can we help with their computer work? How can we make the model have not just theoretical capability to help but is experienced? Those kinds of tasks, that’s been able to see what good looks like.

And I think that the place we’re going is one where you as a person doing work, that you are the overseer, you are the CEO of almost this autonomous corporation, or, this fleet of agents, perhaps, is more, is the way to say it, and that they are operating according to your goals. Now you are still accountable You’re still in the driver’s seat. You’re still the person who thinks about, well, is this what I wanted, was this work up to standard, but that the details of exactly what buttons were clicked and exactly the code that was written, or exactly how the formula and the spreadsheet works, that you can abstract yourself from those if they’re not important to the evaluation of whether or not something was what you wanted. And so I think it’s like increasing leverage for every worker.

If this is a culmination of two-years of work, is it right to think of it as the moment reinforcement learning training methods overtook pretraining in importance?

I would say it a little differently. There’s many steps to the pipeline. There’s pre-training, mid-training, reinforcement learning, there’s data collection, there’s a lot of these different things that all come together to produce the end result. And the way in which is connected to the world, that’s also very key to making it useful.

The thing that I’m really saying is, we have been investing on every single one of these. We have a team that’s not just about individuals working on these pieces, but a team that really comes together and looks across the whole stack to say, how do we make this more useful for real-world applications?

And so it’s not really any one thing that we do. If you’re building a car— it’s not just about, do you have a better engine. You can build a great engine, but if the rest of the car is not up to the quality level of the engine, it’s not going to matter. And so I think that is the real innovation. It’s really the end-to-end, co-design and all coming together in a repeatable fashion to make these models better and better for our users.

You’ve said the model more intuitively knows what you want. How do you get it to know that? And does that mean prompt engineering is dead?

Number one, I think it really comes down to when we say there’s a new class of capability, a new class of intelligence, that’s really what we mean.. The models are becoming much more intuitive to use because they have deeper understanding of what it is you’re asking of them. They really look at the context, try to understand and puzzle out what am I being asked to do.

And it really makes you realize, to the second part is prompt engineering dead, which I think that prompt engineering, in some ways, may be even more vibrant than before, but you spend so much time now, trying to explain to your computer what you even want. You try to pack in this context and be well, here’s what’s going on, here’s the situation, here’s the thing I want from you. And you’re like — Why do I have to explain this to my computer? Right? The whole thing is the computer should be doing the work to help me. I don’t want to have to be, breaking down the task, trying to explain to it step-by-step how to do things. I want to point it in a direction, and I want it to be able to take care of the details and to get me the result again in a way that I can observe and provide feedback along the way. But I want it to be the driver of the of those like low level execution.

And so I think that in some ways, where prompt engineering is going to go is — it’s going to be about you can get so much more out of these models with so much less effort, but with the same amount of effort, you still have a multiplier.

Okay, let me briefly speak with you about the economics of building a model like this. There’s been this pattern where these big, massive models getting distilled by open source model makers, and then open source is just a couple months behind the leading foundational models. How is this defensible in the long term?

It’s not as simple as you can take the output to these models and distill and you have exactly the model the same capability, it’s just smaller and can run fast. If that were the case, we would just do that, and then we would also have a model that would be much more easy to serve in many ways. And of course, there’s a lot of art behind distillation.

Now the at the deployment side, we think a lot about safeguards. We think a lot about mitigations, and we do that for many, different aspects of how these models could be misused in real situations. And that’s something that we have been investing in for many years, and we think about that across are