Sergey Levine

very heterogeneous robotic systems, you can probably actually do a lot better than just having like, you know, mechanical people in effect.

And it can be a big productivity boost for the real people, and it can allow you to solve problems that are very difficult to solve now.

You can, you know, for example,

I'm not an expert on data centers by any means, but you could build your data centers in a very remote location because the robots don't have to worry about whether there's like a shopping center nearby.

Yeah, these are very tough questions.

And also, you know...

Economies of scale in robotics so far have not functioned the same way that they probably would in the long term.

Just to give you an example, when I started working in robotics in 2014, I used a very nice research robot called the PR2 that cost $400,000 to purchase.

When I started my research lab at UC Berkeley, I bought robot arms that were $30,000.

The kind of robots that we are using now at Physical Intelligence, each arm costs about $3,000, and we think they can be made for a small fraction of that.

So these things— What is the cause of that learning rate?

Well, there are a few things.

So one, of course, has to do with economies of scale.

So custom-built, high-end research hardware, of course, is going to be much more expensive than kind of more productionized hardware.

But the other—and then, of course, there's a technological element that as we get better at

building actuated machines, they become cheaper.

But there's also a software element, which is the smarter your AI system gets, the less you need the hardware to satisfy certain requirements.

So traditional robots and factories, they need to make motions that are highly repeatable, and therefore it requires a degree of precision and robustness that you don't need if you can use cheap visual feedback.

So AI also makes robots more affordable.

and lowers the requirements on the hardware.