Codex and the future of coding with AI — the OpenAI Podcast Ep. 6

Hello, I'm Andrew Main, and this is the OpenAI Podcast. In this episode, we're going to speak with OpenAI co-founder and president Greg Brockman, and Codex engineering lead Thibault Sottiaux, and we're going to talk about agentic coding, GPT-5 Codex, and where things might be heading in 2030.

Just bet that the greater intelligence will pan out in the, in the long run.

Uh, and it's just really optimized for, you know, what people are using, uh, GPT-5 within Codex for.

How do you make sure that that AI is producing things that are actually correct?

We're here to talk about Codex, which first, I've been using it, um, since actually, since I worked here, the first version of this, and then now you guys have the new version of this. I was playing with it all weekend long, and I've been very, very impressed by this, and it's amazing how far this technology has come in a few years. I would love to find out the early story. Like, where did the idea of even using a language model to do code come from?

Well, I mean, I remember back in the GPT-3 days-

Mm

... seeing the very first signs of life, of take a docstring and a Python definition of, of a function-

Mm-hmm

... name, and then watching the model complete the code. And as soon as you saw that, you knew this is going to work, this is going to be big. And I remember at some point we were talking about these aspirational goals of imagine if you could have a language model that would write a thousand lines of coherent code. [chuckles] Right? That was, like, a big goal for us. And the thing that's kind of wild is that that goal has come and passed, and I think that we don't think twice about it, right? I think that while you're developing this technology, you really just see the holes, the flaws, the things that don't work. Um, but every so often it's good to, like, step back and realize that, like, actually things have just, like, come so far.

It's incredible how used we get to things improving all the time, and how it has just become like a, a daily driver, and you just use it every day, and then you reflect back to like a month ago, this wasn't even possible. Um, and this just continues to happen. Uh, I think that's quite fascinating, like how quickly humans adapt to new things.

Now, now, one of the struggles that we've always had is the question of whether to go deep on a domain.

Mm-hmm.

Right? Because we're really here for the G, right, for AGI, general intelligence.

Mm-hmm.

And so to first order, our instinct is just push on, making all the capabilities better at once. Coding's always been the exception to that, right? We, we really have a very different program that we use to focus on coding data, on code metrics, on trying to really understand how do our models perform on code. Um, and that, you know, we've started to do that in other domains too, but, but for programming and coding, that that's been like a very exceptional focus for us. And, you know, for GPT-4, we really produced a single model that was just a leap on all fronts. Um, but we actually had trained, you know, the Codex model-