Dwarkesh Patel

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?

Right.

Yeah.

Okay.

And how does the pie model work?

And like what is actually happening is that it's like predicting I should do X thing, then it's like there's an image token, then some action tokens, like what it actually ends up doing, and then more image, more text description, more action tokens.

Basically, I'm like looking at what stream is going on.

Right.

I find it super interesting that, so I think you're using the open source Gemma model, which is like Google's LLM, the release open source, and then adding the section expert on top.