Andrej Karpathy

Some of the earlier demos, actually, we were talking about potentially doing them outside in the parking lot because that's where all of the computer vision was working out of the box instead of inside.

But all the operating system, everything just copy-pastes.

Computer vision, mostly copy-pastes.

I mean, you have to retrain the neural nets, but the approach and everything and data engine and offline trackers and the way we go about the occupancy tracker and so on, everything copy-pastes.

You just need to retrain the neural nets.

And then the planning control, of course, has to change quite a bit.

But there's a ton of copy-paste from what's happening at Tesla.

And so if you were to go with the goal of like, okay, let's build a million human robots and you're not Tesla, that's a lot to ask.

If you're Tesla, it's actually like...

It's not that crazy.

I think it's going to take a long time, but the way you want to structure the development is you need to say, okay, it's going to take a long time.

How can I set up the product development roadmap so that I'm making revenue along the way?

I'm not setting myself up for a zero-one loss function where it doesn't work until it works.

You don't want to be in that position.

You want to make it useful almost immediately, and then you want to slowly deploy it and generalize it at scale.

At scale, hopefully.

And you want to set up your data engine, your improvement loops, the telemetry, the evaluation, the harness and everything.

And you want to improve the product over time incrementally and you're making revenue along the way.

That's extremely important because otherwise you cannot build these large undertakings just like don't make sense economically.

And also from the point of view of the team working on it, they need the dopamine along the way.