The Professor of Outputmaxxing — Anjney Midha, AMP

We're in Periodic Labs with Anj Middha, CEO, founder of AMP welcome. Thanks for having me. At Google, if utilize so there's two types of utilization usually, right, that you're measuring in these clusters. One is node allocation, and then the other is m f u. So node at utilization is usually, like, what percentage of cards in the data center are just, like, used. And that, if it's not at, like, 95% There's no excuse. There's no excuse. Right? Like, I think 95% of Google, which is where my cofounder, Seb, came from, he built the Borg export GQM scheduler at Google. And there, I think, 95% was considered an outage. So 96% node utilization is should be standard, and and most singleton clusters are not running at that. So that's one. And then MFU utilization should be, I would say, the best in class today is somewhere between 6070%. I think it's a leadership question. Right? Is is there an and and, fundamentally, it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes, theoretically, they are. But in practice, the number of people in the chain, the supply chain between, like, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is are just so many, you know, degrees of separation away that, like, the you know have you heard that sort of, you know, radian metaphor, which is at the beginning of of of an arc, if you have two arcs that are two lines that are just off by a few degrees, that It spreads out. It spreads out, right, at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of Frontier Labs and other teams, that's what's happening is they're they they initialize the plan, which is kinda like North North Star with a team that wants to do good, but then they're required to scale so fast instead of iteratively that the waste, it just compounds really fast at scale. And so I I think we know the answer, which is just do iterative bring ups. You know? If you spend time with people who've been in the semiconductor industry or the data center industry for a long time, this is not new. And I don't think AI should be an excuse. Like, sure, something what is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. You know, like, AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the the margin of error now is so much lower and the cost of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I mean, obviously, I'm I'm an …