Tim Davis

But, you know, we are sort of working on that programming model evolving.

And then on top of that, and so you can think of Mojo and that programming language called Mojo, you can think of that as a comparator to say CUDA, which is a low level programming model for programming GPUs.

The primary difference is we can go across any type of hardware.

not just GPUs.

Then above that, one of the things we realized at Google, and this is sort of a long-winded story, is we realized we needed to build essentially a new AI framework.

And we call that MAX.

It is a modeling and serving framework.

And so really, this was carried through all of the lessons and the mistakes and the learnings that we took from building TensorFlow as a high-performance,

AI framework, TensorFlow now has sort of fallen off in terms of popularity and you have PyTorch and other things.

But what's interesting and what we realized was, I'll go back to one of the comments I said earlier, we realized we needed to build a framework that could realize this vision of what you train is what you serve.

And back in 2017, 2018 at Google, we saw the inference training flip.

We saw it all the way back then.

And it's a funny story because when we first went out and started pitching to get capital for Modular, a lot of the investors that we pitched to were like, yeah, no, inference is the thing.

Inference scales to the size of your user base, training scales to the size of your research team.

I assure you, inference is going to be the thing everyone wants to focus on.

And we were...

I won't name names, but we were told at the time, oh, that's wrong.

Everyone should be doing training.

You should be focused on training.

And we were like, yeah, I think training is going to become a lot smaller.