Pavan Davuluri

And now they have the ability to add natural language interaction, and we can direct that to a copilot interaction in addition to all the jobs that you do on Windows today.

Bingo, correct.

And so now you can, exactly, you start with files, you start with settings, you start with apps, and then now we have the ability to add new capabilities like an interaction, a natural language query that gets redirected to the M365 copilot, for instance.

It's a great question.

I think it is a world that is evolving rapidly and somewhat concurrently.

In fact, part of me feels like that's sort of the magic for us is when we think about this in the long term and a Microsoft stack in this space, I do think computing is going to become hybrid.

And so there's going to be a ton of compute that happens in the cloud.

Frontier models are there.

Our large co-pilot experiences are served through that.

And there's robust reasons for why that is a great value proposition in itself for customers.

And then there's computing on the edge, and computing on the edge will evolve as well in my mind.

In fact, one of the big step function changes we made in Windows was to be able to add neural processors into the Windows architecture.

That was important.

They're essentially highly accelerated compute for AI, and we want to be able to do that in an energy efficient manner so it doesn't consume your battery life, for instance.

And so I think those investments of building a more robust platform on Windows on the edge will complement those use cases of how AI is built in the cloud.

Certainly, we're seeing use cases where customers would like to be able to use AI on the edge.

They have a lot to do with data locality.

And so when you have infrastructure and data on the edge, it makes sense for us to run inference on the edge in those instances.

There's privacy and performance and latency requirements that drive it.

Our solution for that is a thing that we call the Microsoft Foundry on Windows.