The Professor of Outputmaxxing — Anjney Midha, AMP

**swyx** (0:04)
We're in Periodic Labs with Anjney Midha, CEO, founder of Anthropo. Welcome.

**Anjney Midha** (0:08)
Thanks for having me. At Google, if you utilize it, 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 MFU.
So node utilization is usually what percentage of cards in the data center are just like used. And that if it's not at like 95 percent.

**swyx** (0:29)
There's no excuse.

**Anjney Midha** (0:29)
There's no excuse, right? Like I think 95 percent at Google, which is where my co-founder Seb came from. He built the Borg XBorg GQM scheduler at Google. And there, I think 95 percent was considered an outage. So 96 percent node utilization should be standard. And most single-tank clusters are not running at that. So that's one. And then MFU utilization should be, I would say the best in class today, somewhere between 60 and 70 percent. I think this is a leadership question, right? Is there an, 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 whoever's measuring what the output is, are just so many degrees of separation away that, like, the, you know, you have heard that sort of, you know, radiant metaphor, which is at the beginning of an arc, if you have two arcs that are, two lines that are just off by a few degrees, that 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 initialize the plan, which is kind of like North Star, with a team that wants to do good, but then they're required to scale so fast, instead of iteratively, that the wasted just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups.
If you spend time with people who've been in the semiconductor industry or the DSN 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.
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 margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. Obviously, I'm an investor, or I'm an investor by background over the last few years. Now we're running an AI infrastructure business called AMP. And I think that it's okay to say this time is different on the capabilities front. Like we are genuinely getting capabilities of a kind we haven't had before. That doesn't give you an excuse to say, this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now that's great for the startup mindset. But you remember this moment where Zuck went from saying, move fast, break things, to move fast with stable infrastructure. I think now we need to move fast with responsible infrastructure. They're going to say, where is the impact?
In our class yesterday, Scott Nolan, who was the founder of General Matter, came by at Stanford to speak about energy bottlenecks. He had a phenomenal idea. He said, if you look at the marginal unit economics of compute per hour, let's call it like $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge $4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash. I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.
Because if that means the public benefit is so clear to the communities that these data centers are coming up in, I'm going to feel like that computer is much more reliable. Up to 20% of all data centers this year in the US, my understandings are at risk.

**swyx** (4:13)
Of community backlash?

**Anjney Midha** (4:14)
Correct. Of not getting the community support they need to get brought up.