Chris Lattner

Right, and you look at GPUs.

Well, there's a couple of major vendors of GPUs and they maybe don't always get along, but their architectures are very similar.

You look at CPUs.

CPUs are still super important for the deployment side of things.

And you see new architectures coming out from all the cloud providers and things like this, and they're all super important.

to the world, but they don't have the 30 years of development that the entrenched people do.

And so what modular can do is we're saying, okay, all this complexity, it's not bad complexity, it's actually innovation.

And so it's innovation that's happening and it's for good reasons, but I have sympathy for the poor software people.

I mean, again, I'm generally a software person too.

I love hardware.

But software people want to build applications and products and solutions that scale over many years.

They don't want to build a solution for one generation of hardware with one vendor's tools.

And because of this, they need something that scales with them.

They need something that works on cloud and mobile.

Because their product manager said, hey, I want it to have lower latency and it's better for personalization or whatever they decide.

Products evolve.

And so the challenge with the machine learning technology and the infrastructure we have today in the industry is that it's all these point solutions.

And because there are all these point solutions, it means that as your product evolves, you have to switch different technology stacks or switch to a different vendor.

And what that does is that slows down progress.

Well, so it's not really about a programming language?