Lenny Rachitsky: Let's come back to the IDE questions. So there's a few routes you could have taken, and other companies are doing different routes. So there's building an IDE for engineers to work within and adding AI magic to it, there's another route of just a full AI agentic dev product, and then there's just a model that is very good at coding, and focusing on building the best possible coding model. What made you decide and see that the IDE path was the best route? Michael Truell: The folks who were from the get-go working on just a model were working on end-to-end automation programming. I think they were trying to build something very different from us, which is, we care about giving humans control over all of the decisions in the end tool that they're building. And I think those folks were very much thinking of a future where end-to-end, the whole thing is done by AI, and maybe the AI is making all the decisions too. And so, one, there was a personal interest component. Two, I think that always, we've tried to be intense realists about where the technology is today, very, very, very excited about how AI is going to mature over the course of many decades. But I think that sometimes people... There's an instinct to see AI do magical things in one area, and then kind of anthropomorphize these models, and think it's better than a smart person here, and so it must be better than a smart person there.
So there's building an IDE for engineers to work within and adding AI magic to it, there's another route of just a full AI agentic dev product, and then there's just a model that is very good at coding, and focusing on building the best possible coding model.