Lenny's Podcast: Product | Career | Growth
Why humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead)
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- You lead work on codecs. - codecs is opening-ice coding agent. We think of codecs 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 data dog unless you ask it to. - I remember Carpat, they each weeded the gnarliest bugs that he runs into that he just spends hours trying to figure out nothing else as salty gives it to codecs, lets it run for an hour and it solves it. - Starting to see glimpses of the future where we're actually starting to have codecs be on call for its own training. - codecs writes a lot of the code that helps like manage its training run, the key infrastructure and so we have a codecs code review is like catching a lot of mistakes. It's actually caught some like pretty interesting configuration mistakes. One of the most mind blowing examples of acceleration to 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. - How do you think you win in this space? - One of our major goals with codecs is to get to productivity. If we're gonna build a super system, it has to be able to do things. One of the learnings over the past year is that for models to do stuff, they are 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. - When you think about progress on codecs, I imagine you have a bunch of emails and there's all these public benchmarks. - A few of us are like constantly on Reddit. You know, there's this praise up there and there's a lot of complaints. What we can do is we as a product team just try to always think about how are we building a tool so that it feels like we're maximally accelerating. People rather than building a tool that makes it more unclear what you should do
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