Lenny Rachitsky (00:00:00): You lead work on Codex. Alexander Embiricos (00:00:01): Codex is OpenAI's coding agent. We think of Codex as just the beginning of a software engineering teammate. It's a bit like this really smart intern that refuses to read Slack, doesn't check Datadog unless you ask it to. Lenny Rachitsky (00:00:12): I remember Karpathy tweeted the gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else has solved, he gives it to Codex, lets it run for an hour and it solves it. Alexander Embiricos (00:00:21): Starting to see glimpses of the future where we're actually starting to have Codex be on call for its own training. Codex writes a lot of the code that helps manage its training run, the key infrastructure. So we have a Codex code review that's catching a lot of mistakes. It's actually caught some pretty interesting configuration mistakes. One of the most mind-blowing examples of acceleration, the Sora Android app, like a fully new app, we built it in 18 days and then 10 days later, so 28 days total, we went to the public. Lenny Rachitsky (00:00:45): How do you think you win in this space? Alexander Embiricos (00:00:47): One of our major goals with Codex is to get to proactivity. If we're going to build a super system, has to be able to do things. One of the learnings over the past year is that for models to do stuff, they're much more effective when they can use a computer. It turns out the best way for models to use computers is simply to write code. And so we're kind of getting to this idea where if you want to build any agent, maybe you should be building a coding agent. Lenny Rachitsky (00:01:04): When you think about progress on Codex, I imagine you have a bunch of evals and there's all these public benchmarks. Alexander Embiricos (00:01:10): A few of us are constantly on Reddit. There's praise up there and there's a lot of complaints. What we can do is as a product team just try to always think ab...