OpenAI president and co-founder Greg Brockman joins us to discuss OpenAI's newest model Spud aka GPT 5.5 and where it leaves OpenAI competitively. That's coming up right after this. Welcome to Big Technology Podcast. Today we have an emergency episode with OpenAI president and co-founder Greg Brockman all about GPT 5.5, the famous Spud model, looking at what it does and what it means for OpenAI. Greg, great to see you. Welcome back to the show.
Thank you for having me. Hope it's not too much of an emergency.
Well, I am definitely recording in a Vegas hotel room. So, more emergency than our last conversation, but we had some time to prepare. So, it's great to be on with you. So, let's just start with this. Can you confirm GPT 5.5 is Spud?
Well, it's an amazing model. 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, right? And all the different aspects of debugging and solving very hard, gnarly problems, just being very proactive and really being able to solve problems end to end with little instruction. But the thing that's to me most remarkable is not necessarily the fact that it got better at coding, like 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 2-year research process. So, was this planned two years ago? Is that how far back OpenAI plans?
I would say that 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 time scales. 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 end point. 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 I think will be very exciting. And we're just always thinking about how can we make what we're producing more useful for real world use for real users and real applications.
Can you share specifically what those aspects are that we should be looking out for over the next few months? If this is the beginning, what is it the beginning of?
Well, I think that the big vision we have and you can see it reflected in many things, not just the models but the kind of, you know, you 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 actually useful. That's actually an assistant that's out there trying to solve your goal that's really operating according to your instruction. And you can see right now Codex is becoming this app that's not just for the coders. It's really for anyone using a computer and that it's not perfect, right? That there are still some tasks where it should be able to do it and it doesn't quite get it right. Sometimes the personality isn't quite what you wanted, right? That it doesn't quite, you know, it's like extremely powerful and out there doing a lot of really amazing things, but the way it communicates back to you that 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 I think 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. And one thing to know internally is that we think a lot about the end application like that is one thing that changed for us over the past 12, you know, 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 actually make the model have not just the theoretical capability to help but has actually experienced those kinds of tasks that's actually 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 you know of this fleet of agents perhaps is the way to say it and that they are operating according to your goals. Now you are still accountable, right? You're still in the driver's seat. You're still the person who thinks about, well, is this what I actually wanted? Was this work up to standard? But that the details of exactly what buttons were clicked and exactly the kind of code that was written or exactly how the formula in 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.
Okay. Let me take my best guess as to what's happening and you tell me how close I am. I mean I'm thinking about this. This is like, you mentioned a culmination of two years of work. There's two different types of, I mean not to tell you know this but for our audience two different types of AI training. There's the pre-training or at least the ones that have been pertinent for these models. The pre-training where you just make the model generally smart by having it predict the next word and the reinforcement training where you have it like go out and actually take, you know, try to accomplish different tasks and you reward it when it does a good job with those tasks and effectively it sort of teaches or learns how to do those tasks. Is what you're saying basically that like this is the first result that we're seeing where OpenAI has just loaded a ton of reinforcement learning on task specific stuff into this model and that's what's producing the results you're talking about?
Well, I would actually say it a little differently. I would say that there's many steps in the pipeline, right? That there's pre-training, mid-training, reinforcement learning, there's, you know, the data collection. There's like a lot of these different things that all come together to produce the end result and the way in which it's connected to the world. That's also very key to making it useful. And the thing that I'm really saying is we have been investing on every single one of these and have a repeatable, we have like a team right that it'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. It's really about the overall effort of trying to like if you think about if you're building a car, right? That it's not just about do you have like a better engine, right? 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 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.
Um, you were on a media call earlier today with myself and a number of members of the press and, one of the interesting things that you said or basically I think you said this right off the bat is that the model more intuitively knows what you want and you don't have to spell it out exactly as you would in the past. Here's a tweet from Rune. There are early signs of 5.5 being a competent AI research partner. Several researchers let 5.5 run variations of experiments overnight given only a high level algorithmic idea waking up to find a completed sweep dashboards and samples never having touched the code or a terminal at all. Just to if we can answer briefly on this a two-parter how do you do 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, right? The models are becoming much more intuitive to use because they have deeper understanding of what it is you're asking of them, right? That 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, you know, to the second part, is prompt engineering dead? I actually think the prompt engineering in some ways may be even more vibrant than before. But you spend so much time right now trying to explain to your computer what you even want. You try to like pack in this context and be like, 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? Like the whole thing is the computer should be doing the work to help me. Like I don't want to have to be sort of, you know, 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 kind of provide feedback along the way, but I want it to be the driver of those 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. Think about how much more you could even get. And I think that we're just at the leading edge right now of seeing the ceiling of what even today's models are capable of.
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 now you're not saying how much money or computer used you've used to train this but I think we can be safe in assuming it was a lot. And there's been this pattern where these massive models come out they get distilled by open-source model makers and then open source is just a couple months behind the leading foundational models. And you know, I guess like when the investment was smaller, being a couple months ahead, mattered a lot. But I'm curious now that the investment is so big, and the models are the capabilities are increasing, you know, fairly dramatically as you go, how is this defensible in the long term if you're just going to have that pattern repeat over and over?
Well, I look at it a little differently. Like I think that the real investment that we are making is into that end-to-end co-design right of having a system and a system of people right who are producing this technology right a way of working together and some of this is about how you leverage these massive supercomputers to produce these models. Now it is also the case that it's not as simple as you can take the output of 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, you know, much more easy to serve in many ways. And of course, there's a lot of art behind distillation. There's a lot of great things there. But the point that I'm getting at is that the real thing that we are investing in is the machine that makes the machine. Now, at the deployment side, we think a lot about safeguards. We think a lot about mitigations and we do that for many 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 areas like cyber or thinking about that in areas like bio that we have a long-standing effort that you can see in our preparedness framework which is public about how we approach these kinds of uses of the model and how we try to maximize the benefits mitigate the risks. And so I think it's a real motion that every piece of what we do needs to connect to the question of how do we continue to make progress but also how do we make these models broadly available because that's something that we really believe in that we believe this technology empowers people and that we want it to benefit people and lift everyone up.
Yeah. But just to go back on that the pricing on this model is I think double the last model GPT 5.4. And so from an economics or a business standpoint, the question would be, you know, let's say you keep on progressing, but because there's been all this infrastructure that's been put towards training the models, if open-source can deliver not as good performance, but almost as good and do it cheaper, how do you handle that threat?
