Illia Polosukhin

giving actually better properties, right?

Because everything private from the user perspective, the models don't get leaked, right?

Because they always exist encrypted and only decrypt inside this customization environment.

And it also offers you a way to even monetize for creators their data.

So let's say you record this podcast, it gets scraped, it gets used for

training you know we got nothing or you can upload it into this network and if it's used for inference or even for training we can receive something as creators of this podcast or you guys in this case right so that's interesting so when does the payment happen then does it happen like when like the person who trains the model then takes it out and charges users for it like when when would you get paid in that model as a creator

Yeah, so because it's kind of a three-sided marketplace, or actually like even four-sided because you have compute.

So a few things happen, right?

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?