Illia Polosukhin

Because like model will compress everything at the end.

And so instead, what we want to offer, if you kind of use the data from this kind of system, you can get portion of so-called model token.

So model token then is a way to receive revenue from this model being used.

And so you're kind of getting the upside of this model being used in production.

Yeah, so we kind of offer this environment where if you already have built a model, you can just upload it and charge for it.

If you're a content creator that has a paywalled content, you can upload it and receive it only at inference time, so it's not going to be used at training time.

And if it's an open content...

and people training a model in our system using this content, then they'll receive it.

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.