Grant Harvey

And that makes sense because when you're doing a more specific run, you have a more constrained problem space.

So you're like, okay, we know it needs to focus in this area because we're looking for this chemical property.

That makes sense.

Yeah, I guess that was the thing that was throwing me off is like, I know LLMs have a context limit of a million.

So you can't necessarily put all the chemical data in the world in LLM and expect to get that.

Yeah, yeah, yeah, yeah, yeah.

That's cool.

What about Avatar?

Because what I thought was really interesting about Avatar, which is the other AI tool that you use, is it's actually tracking the battery life cycle.

And you mentioned safety.

So I'm curious if you could talk a little bit about that and why that's a big deal.

Because...

Batteries are living chemistry, and being able to track them is really important in how they're performing.

Yeah, yeah.

Well, especially with, you mentioned data centers, I imagine they need to be very, very efficient with their power, right?

So obviously you're developing your own robots.

I'm curious what your thoughts are in terms of whether you are potentially working on something like this or whether you just have general thoughts on the direction, how to actually give robots enough power so that they can be as efficient as possible.

It seems like it's a battery problem to me, but I'm curious if that's something you're actively working on or thinking about.

Smart.

Yeah.