“Our seven and eight-year-old TPUs are still at 100% utilization.”

Not necessarily cheaper, actually, because for serving, there is more need to co-locate compute networking and storage. And so then we get to the inability to specialize. So for training, you actually have this big density, uniform deployment, et cetera. Whereas for serving, you're going to have to have the mix of storage, compute, and accelerators. Furthermore, it's a very interesting aspect of this that we could go into more detail on as well. But for serving, you actually don't want to have too much in one place. You want to be serving your workloads from all over the planet. But now we wind up having individual model endpoints, and there actually can be variations of models. So now we have to spread these models out across the world, also accounting for locality. So they will be smaller. They'll actually be less vertically integrated, probably, on the inference side.

Super interesting. So if you made a data center five years ago, you know, state-of-the-art accelerator five years ago was very different, vastly less efficient than the ones that are being made today. Yes. Do you actually go back and like swap out the chips in those old data centers? And I know this kind of relates to there's been an ongoing debate, I think, of like what is the actual useful life of a chip.

Wow.

I think I saw

this. I didn't realize this from the case. Yeah,