OpenAI COO Brad Lightcap: GPT-5's Capabilities, Why It Matters, and Where AI Goes Next

As OpenAI released GPT-5, its new flagship model, I spoke with Brad Lightcap, the company’s chief operating officer, about what it’s learned building the new model, and how it hopes to apply it.

We had a fascinating conversation covering the state of AI research and scaling, what AI needs to be considered AGI, and how this technology will be useful to businesses.

Our full conversation is below, edited lightly for clarity and length. You can also listen to it on Apple Podcasts, Spotify, or your app of choice.

Alex Kantrowitz: Hi Brad, Tell us briefly what GPT-5 is, and how it improves on previous OpenAI models?

Brad Lightcap: GPT-5 is our next generation flagship model. It does something really interesting, which is it combines into one model the ability to dynamically choose whether to think hard about a problem and reason about it to give you an answer.

Previously you had to go deal with the model picker in ChatGPT — everyone's favorite thing. You had to select a model that you wanted to use for a given task. And then you'd run the process of asking a question, getting an answer. Sometimes you choose a thinking model, sometimes you wouldn't. And that was, I think, a confusing experience for users.

GPT-5 abstracts all of that. So it makes that decision for you. And it's actually a smarter model. So you're going to get a better answer in all cases, regardless of whether you're using the thinking mode or not. And it's vastly improved on things like writing, coding, health. It's much more accurate, it’s much faster, and so all around we think it’s a better experience.

Those of us following the hype around GPT-5 probably imagined you would lead a claim about an explosive increase in intelligence vs. a switcher that will route the query to reasoning or non-reasoning. Why lead with usability versus the intelligence increase?

Because intelligence really is a function of how much time the model is going to be thinking. Depending on how much you want to allocate thinking time to a problem, you're going to get a better answer. Typically, the longer it thinks, the better an answer it can give you. So when we test the model on certain benchmarks and evals and we allow it to think, it will dramatically outperform any of our existing models by far. Even though if you don't allow any thinking time, you still get a typically net better answer than you would for one of our non-thinking models like GPT-4.1.

So it is a dramatic improvement in intelligence. It should be, I think, a better quality model across pretty much all dimensions. But that reasoning time — and being able to use the reasoning time dynamically to think — we think actually is the important part. It makes it for a much better user experience.

Would you say that this model is an exponential increase in capabilities or an incremental increase in capabilities?

It's hard to measure it that way. I think we're now kind of into this regime of having to measure intelligence across a lot of different dimensions, which isn't a way to dodge the question so much as it is to explain why GPT-5 is such a special model. Obviously it's better at the core things that you'd expect it to be better at. It scores better on things like SWEBench. It scores better on all the kind of academic evals that we put it through.

This one in particular, we actually made a real emphasis to have it score better on certain health benchmarks. So it's better at medical reasoning and other health related things. But there's a lot of things that go into what makes a model good now, because you have a lot of dimensions to play with depending on how that model is trained and how it can think about problems. So if it's faster, for example, we think that's actually indicative of it being better. If it can give you a better answer per unit of time thinking, we think that's an improvement that’s an important vector to measure also.

If it can do things like structured thinking, problem solving, tool use, all these things are things we actually measure and they're kind of invisible to users. If you're just using ChatGPT, you don't necessarily appreciate each of these things happening under the hood, but all those things are better for GPT-5 than they were for our previous models.

The reason why I'm asking is because the leaps between GPT to GPT-2, GPT-3, and GPT-4 showed general increase in capabilities across the board. There were no caveats of there's intelligence increases in this place and that place. It was, I believe, we trained a bigger model and it's better across the board. So have things changed?

They've changed, yeah, from a technical perspective...