Illia Polosukhin

And we have open source where we can run it ourselves, but normalization.

And so what we kind of introduced is this new concept we call decentralized confidential machine learning.

And so this is kind of in between these two steps, where what we do is we offer a decentralized cloud.

And this is GPUs and CPUs, so all the compute you need.

It runs in a very specific mode where everything is confidential.

So all the data is end-to-end encrypted.

It runs in a confidential vault so that neither the owner of hardware nor the network is able to see what's happening inside.

you then able to, first of all, guarantee privacy for the user, right?

Because now every interaction is private, but you also can run models, right?

Somebody can upload a model, it's encrypted and it only gets decrypted inside this confidential environment.

So now you can also monetize running models in this environment as well, right?

So we kind of effectively created this middle ground between private and like closed source and open,

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?