Illia Polosukhin

So users consume the models through compute, right?

So they pay at the end for everything.

Some portion goes to compute, to GPU providers.

And, you know, it can be, you know, your usual monthly subscription and gets divided by all of the compute providers, you know, portion of that.

Then portion goes to the models that they used.

So let's say we use, you know, DeepSeq.

DeepSeq, for example, wants to charge additional, on top of compute, can charge, you know, additional 10 cents per million tokens or something.

So they get received, again, from the subscription or from API that cost.

And then if the data is used at inference time, then it's accounted there as well.

So think of it as like Spotify, right?

This is a stream of that.

But if it's used at training, then it's a little bit different mechanism because at training time, we cannot actually attribute exact usage of exact data, right?

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.