Chris Lattner

For Mojo.

Well, so I mean, I think you have to zoom out.

So I've been working on a lot of related technologies for many, many years.

So I've worked on LLVM and a lot of things and mobile and servers and things like this.

But the world's changing.

And what's happened with AI is we have new GPUs and new

machine learning accelerators and other ASICs and things like that that make AI go real fast.

At Google, I worked on TPUs.

That's one of the biggest, largest scale deployed systems that exist for AI.

And really what you see is if you look across all of the things that are happening in the industry, there's this new compute platform coming.

And it's not just about

CPUs or GPUs or TPUs or NPUs or IPUs or whatever, all the PUs, right?

It's about how do we program these things, right?

And so for software folks like us, right, it doesn't do us any good if there's this amazing hardware that we can't use.

And one of the things you find out really quick is that

Having the theoretical capability of programming something and then having the world's power and the innovation of all the smart people in the world get unleashed on something can be quite different.

And so really where Mojo came from was starting from a problem of we need to be able to take machine learning, take the infrastructure underneath it and make it way more accessible, way more usable, way more understandable by people.

normal people and researchers and other folks that are not themselves like experts in GPUs and things like this.

And then through that journey, we realized, hey, we need syntax for this.

We need to do programming language.