出力最大化の教授——アンジニー・ミドハ氏（AMP）

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The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.

The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.

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For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.

PaLM: Scaling Language Modeling with Pathways

It’s not necessarily that xAI is uniquely incompetent (it’s clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.

While GPU access is a bottleneck, simply increasing CapEx won’t automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.

From building Discord’s developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP’s independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.

We go deep on AMP’s vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind’s unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.

We also discuss Anthropic’s culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.

We discuss:

Why 95% utilization was considered an outage at Google

Why AI infrastructure waste compounds at frontier-lab scale

Why “move fast and break things” does not work for AI data centers

How data center backlash, power grids, and community incentives shape AI scaling

AMP’s vision for making FLOPs flow like megawatts

Why compute needs an independent system operator

How interruptible demand and dynamic prioritization worked inside Google

Why DeepMind research hoarding creates negative externalities

AMP’s 1.2GW base-load ambition and the need for 6GW of spike capacity

Why end-of-life prediction could become one of AI’s most important healthcare applications

Frontier Systems, output maxing, and full-stack alignment

Why APIs and abstraction layers become lossy as organizations scale

Superconductors, standards, and the dream of lossless systems

SF Compute, open protocols, and the future of compute marketplaces

Why non-NVIDIA chips can still benefit from NVIDIA’s reference architecture

Trust boundaries and why chip startups need visibility into future model architectures

Why VCs often underestimate researchers as CEOs

Scientists as star athletes of the mind

Why great CEOs need to be confrontational up and down the stack

Why leading the frontier matters more than “winning”

How Anthropic cracked coding

Why culture is fragile, not a permanent moat

Why hardship was a feature, not a bug, for Anthropic

Why Anthropic’s P0 was coding from day one

Periodic Labs, physics as the constraint, and technical reality

Silicon Valley mercenaries, missionary teams, and what happens after a breakthrough

Anjney Midha

LinkedIn: https://www.linkedin.com/in/anjney

X: https://x.com/AnjneyMidha

AMP PBC

Website: https://amppublic.com/

X: https://x.com/amppublic

Timestamps

00:00:00 Introduction

00:00:09 Why AI Compute Is Being Wasted

00:03:17 Responsible Infrastructure and Data Center Backlash

00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts

00:12:41 Foundry, Frontier Labs, and Research Hoarding

00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction

00:24:08 Frontier Systems, Output Maxing, and Alignment

00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips

00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs

00:38:17 AI Coachella and First-Principles Thinking

00:42:43 Leading vs Winning in Frontier AI

00:45:54 How Anthropic Cracked Coding

00:48:25 Culture, Hardship, and Anthropic’s P0

00:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries

00:56:26 Rishi Valley, Singapore, and Money as a Measure

00:58:47 Closing Thoughts

Transcript

Introduction: Anjney Midha, AMP, and Compute Waste

Swyx [00:00:00]: We’re in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.

Compute Utilization: Node Allocation, MFU, and Alignment

Anjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you’re measuring in these clusters. One is node allocation, and then the other’s MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it’s not at, 95%-

Swyx [00:00:29]: There is no excuse

Anjney [00:00:29]: There’s no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that’s one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? 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, 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, degrees of separation away that, the, The Have you ever heard the radian 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-

Swyx [00:01:33]: It spreads out

Anjney [00:01:34]: It spreads out, right? Or 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 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 wastage 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. 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. I’m, obviously I’m, 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. We are genuinely getting capabilities at, of the, 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-

Responsible Infrastructure and Data Center Backlash

Swyx [00:03:10]: Fast and stable infrastructure

Anjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who’s the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let’s call it, $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.

Swyx [00:03:57]: Wow. Yeah.

Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I’m going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.

Swyx [00:04:13]: Of community backlash?

Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.

Swyx [00:04:19]: Wow. That’s a huge number.

Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it’s a little bit of overstated. These things can get over-reported, but it-

Swyx [00:04:27]: They don’t just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-

Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there’s a new AI deal. If we’re bringing up a data center in your community, we’re actually going to reduce the cost of your electricity bill. Okay, now we’re talking. Right? The community’s going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that’s not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don’t know when it’s going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That’s certainly not how we’re procuring compute. Or we’re, we’re trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who’ve been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they’re adults. I trust them.

Swyx [00:05:44]: They can run LAN. They can run power.

Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They’ve, they’ve had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there’s a lot of short-term thinking going on in the compute layer, and it’s going to catch up to us. It’s not going to be good.

AMP Grid: Making FLOPs Flow Like Megawatts

Swyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?

Anjney [00:06:28]: In systems design, right, there’s, there’s two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we’re building here, which is basically a compute grid. We’re trying to do for compute what the electric grid-

Swyx [00:07:02]: Power

Anjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we’re actually the opposite of a full stack integration like approach.

Swyx [00:07:12]: Super horizontal.

Anjney [00:07:13]: Where it’s much more horizontal and it’s, it’s multi-cloud, it’s multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There’s stranded pools of compute all over the place and there’s no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we’re working on, it’s extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I’m actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we’d like all of these folks to participate on the grid. There’s, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they’ll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what’s called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren’t competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what’s called an ISO, an independent system operator of the grid. So that’s how we see ourselves. Economically, that’s what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-

Swyx [00:09:28]: Did your scheduling

Anjney [00:09:28]: They did that at Google. And, -

Swyx [00:09:32]: And you have infra shops from Discord as well.

Anjney [00:09:35]: I have some.

Swyx [00:09:35]: I don’t know, I don’t know if Discord is like the primary identity, but what-whatever, I’m just kind of-

Anjney [00:09:39]: No, D-Discord was-

Swyx [00:09:40]: Choosing a well-known name.

Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-

Swyx [00:09:56]: It’s the same thing, yeah

Anjney [00:09:57]: It’s the, it’s the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-

Swyx [00:10:08]: For the calls, yeah.

Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world’s 200 million plus monthly active gamers, right? And so that’s, that’s how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-

Swyx [00:10:31]: Bundling and unbundling

Anjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-

Swyx [00:10:36]: Horizontal

Anjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world’s best, research labs and so on. We’re sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.

Swyx [00:11:53]: Like priorities.

Anjney [00:11:54]: It’s a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who’s saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That’s a, that’s a made up example.

Swyx [00