Illia Polosukhin

But again, this is like only in that condition because we cannot track if somebody else doing it somewhere else.

But the idea is like, you know, as kind of the network effects here, you know, kind of come from every direction, right?

The more usage come in, the more, you know, people want to serve their models because their users are

the more computers there because more users are using it, more models are there, the more content people want to upload because more things are getting paid versus not getting paid anywhere else.

So this is kind of like a system to really combine all those different components into one

kind of vision of this user on the eye where, you know, we know where data comes in.

We have the providence.

We know kind of how models are trained.

We have the compute that's available around the world.

I mean, compute is also like a really big challenge because right now, if I'm a model developer, I'm a closed source model developer.

I actually don't trust other model, like other compute providers.

Like that's why people trying to like have access to their own GPUs because if they give weights to somebody else, they don't know if those people will leak them.

So actually solving a trust problem between model builders and compute providers as well while we're doing it.

And this allows to actually distribute the compute because of this kind of control, the companies you guys know, all building massive clusters.

I mean, they're useful for training, but then for inference, they still use them as well because again, of this afraid.

And so here you can actually have cluster in Japan and in Norway, in maybe Nigeria,

in Brazil that actually serve the local markets, lower latency, you know, better energy efficiency, et cetera.

It's a smaller cluster.

It serves only this.

And then you don't have any trust issues between all those parties, right?