Andrej Karpathy

in the data.

It really just comes down to extremely good execution from an engineering team who knows what they're doing.

They understand intuitively the philosophical insights underlying the data engine and the process by which the system improves.

And how to, again, like delegate the strategy of the data collection, how that works, and then just making sure it's all extremely well executed.

And that's where most of the work is, is not even the philosophizing or the research or the ideas of it.

It's just extremely good execution is so hard when you're dealing with data at that scale.

That comes to a very large extent to what we are trying to achieve in the product for MAP, the release we're trying to get out, the feedback from the QA team where the system is struggling or not, the things we're trying to improve.

And then, of course, all of us drive it, and we can also see it.

It's really nice to work with a system that you can also experience yourself.

It drives you home.

It's...

Yeah, I think there's a ton of, it's a source of truth.

It's your interaction with the system and you can see it, you can play with it, you can perturb it, you can get a sense of it, you have an intuition for it.

I think numbers just like have a way of, numbers and plots and graphs are much harder.

It hides a lot of.

Yeah, I think like Elon also, he always wanted to drive the system himself.

He drives a lot, and I want to say almost daily.

So he also sees this as a source of truth, you driving the system and it performing.

I would almost reframe the question in some way.

Sorry.