And if you think, if you have a, your own view on does explain about explainability matter more than accuracy or, um, maybe an easier way to start is how do we responsibly deploy the systems that are probabilistic by nature in an environment that we know is demanding precision.

So in general, how do you take a probabilistic decision engine and bring it in? Right.

And, um, I see this a lot in customers I talk to.

I've probably talked to like 300 in my time here at Cloud.

And what I see it comes down to, Lindsay, is like we work with things we don't understand every single day and make life or death decisions, health and wealth decisions, and liberty decisions every day.

We have no idea how it works.

It's the human brain.

How can you see the practice of cancer medicine looking different just three years from now?

Like, what is that for you?

You know, a couple things.

I think what I'm seeing for the first time is that, um, we're actually programming the human body, right? And to me, and, and what, what I find so interesting, Lindsay, is that, you know, I, I, I right now in my, uh, my role at Google, I work a lot with video models, and what's astonishing is that the technique behind the video models, it's learning physics.

P- and Navier-Stokes equations are, like, complex equations of how do you get a space- SpaceX ship to Mars? Like, all the equations for the gases and the rockets, that's all called Navier-Stokes.

It's a high-dimensional physics, hard to learn.

We're learning it through observation.

We're learning gravitational pull.

It's completely different when it's detection of cancer in a 38-year-old woman with four kids, right?