Dwarkesh Patel

And then I remember when I was a teenager like watching demos where we would go buy a Taco Bell –

and drive back.

And only now do we have them actually deployed.

And even then, you know, they may make mistakes, et cetera.

And so maybe it'll be many more years before most of the cars are self-driving.

So why wouldn't robotics, you know, you're saying five years to this, like, quite robust thing, but actually it'll just feel like 20 years of just, like...

Once we get the cool demo in five years, then it'll be another 10 years before we have the Waymo and the Tesla FSD working.

So for years using, I mean, not since 2009, but we've had lots of video data, language data, and transformers for five, seven, eight years.

And lots of companies have tried to build transformer-based robots with lots of training data, including Google, Meta, et cetera.

And what is the reason that they've been hitting roadblocks?

What has changed now?

What is preventing you now from scaling that data even more?

If data is a big bottleneck, why can't you just increase the size of your office 100x, have 100x more operators?

We're operating these robots and collecting more data.

Why not ramp it up immediately 100x more?

Just to give an order of magnitude, how does the amount of data you have collected compare to internet-scale pre-training data?

And I know it's hard to do, like, a token-by-token count because, yeah, how does video information compare to internet information, et cetera?

But, like...

Using your reasonable estimates, what fraction of... That's right.

When you say self-sustaining, is it just like learning on the job or do you have something else in mind?