Illia Polosukhin

Good point.

Good point.

So how do we do this?

Well, we have closed source where we don't know what's running there.

We don't know what model they give us.

They may change.

They may update something under us, et cetera.

And they see all of our data.

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,