Chris Lattner

Specialization happens.

If you're building, if you're training GPT-5, you want some crazy supercomputer data center thingy.

If you're making a smart camera that runs on batteries, you want something that looks very different.

If you're building a phone, you want something that looks very different.

If you have something like a laptop, you want something that looks maybe similar, but a different scale.

And so AI ends up touching all of our lives, robotics, and lots of different things.

And so as you look into this,

These have different power envelopes.

There's different trade-offs in terms of the algorithms.

There's new innovations in sparsity and other data formats and things like that.

And so hardware innovation, I think, is a really good thing, right?

And what I'm interested in is unlocking that innovation.

There's also, like, analog and quantum and, like, all the...

the really weird stuff right and so if somebody can come up with a chip that uses analog computing and it's 100x more power efficient think what that would mean in terms of the daily impact on the products we use that'd be huge now if you're building an analog computer you may not be a compiler specialist right these are different skill sets right and so you can hire some compiler people if you're running a big company maybe but it turns out

these are really like exotic new generation of compilers.

Like this is a different thing, right?

And so if you take a step back out and come back to what is the status quo, status quo is that

if you're Intel or you're Nvidia, you keep up with the industry and you chase and okay, there's 1900 now, there's 2000 now, there's 2100 and you have a huge team of people that are like trying to keep up and tune and optimize.

And even when one of the big guys comes out with a new generation of their chip, they have to go back and rewrite all these things, right?

So really it's only powered by having hundreds of people that are all like frantically trying to keep up.