Monologue: The AI Data Center Overbuild

Ed Zitron (0:01)

CAUSOR MEDIA Hello and welcome to this week's Better Offline monologue. I'm your host Ed Zitron.

As ever, subscribe to the newsletter, Premium, you know all that good stuff. I'll have the links in there. And this week, I want to talk about actually a free newsletter I put out called Where Are All The AI Chips? And it turns out that the answer is either in warehouses or unpowered data centers. In an investigation with The Guardian, Aisha Downover there worked on it with me. She's awesome. I reported that Microsoft only had 2.2 million GPUs in service, far less than people believed. And while I couldn't report it at the time because I still had to run down some leads, the total power of the chips was estimated at about 1.993 gigawatts of capacity, with the overall cost of those GPUs installed somewhere in the region of $50 to $60 billion.

A few weeks ago, Bloomberg reported that despite reporting that Microsoft had added a gigawatt in each of the last three quarters, and by reporting, I mean the things that Microsoft literally said on their earnings call. That only 2 gigawatts out of their total 12 gigawatts of data center capacity was specifically for AI. In other words, despite having spent over $265 billion on capital expenditure since the beginning of 2022, Microsoft has only put about $50 billion worth of GPUs into service, and I estimate that as much as $106 billion worth of GPUs and associated hardware are now sitting either unpowered in data centers, incomplete data centers, or in warehouses, and that very little AI compute is coming online throughout the entire industry. Microsoft CEO Sachin Adela had sort of admitted this in November of last year, when he told an interviewer that he had a bunch of chips sitting in inventory that he couldn't plug in, and that's a quote, though he didn't mention the sheer scale of the warehousing or indeed how little he'd actually turned on.

Well, I'm a curious little critter, so I went looking for more evidence of the problem outside of Microsoft and found it buried in the balance sheets of multiple hyperscalers and neoclouds under the construction in progress line on the balance sheet, which is specifically where companies bury their uninstalled GPUs and incomplete data centers, and this number has grown across a ton of them, well, by a remarkable amount. Google, Meta, Oracle, Amazon, SpaceX, and Tesla, neoclouds like Core, Weave, and Iron, and co-location companies like Core Scientific and Applied Digital, all have about $374 billion of construction in progress, a figure that's likely lower than the true number because Amazon's contribution, which is about $71 billion, is only current as of the end of 2025, and there have been two, now very soon three more quarters of that, and the company only reports annual, like I said. A large chunk of that larger CIP number is Google's, which sits around $122 billion, which is truly shocking. And in every case, by the way, the number has grown every single time it's been reported for the last 12 quarters, with some fluctuation in the case of Amazon because of their logistics operations, but really right now, it's only growing. Now, not everybody reports construction in progress, so that number doesn't include Microsoft, Firmus, Sharon, Equinix, Nebius, Poolside, private operators like Vantage, any of the sovereign AI buildouts in the Middle East or Europe, the private projects built for OpenAI and Anthropic, or any of Oracle or Meta's off balance sheet construction projects, which were likely, I think, numbering in tens of billions of dollars. And because these are special purpose vehicles kept off of their balance sheet, that wouldn't be in construction in progress. Now, if I had to guess, the total construction in progress number is somewhere between $400 and $500 billion. And based on everything I've analyzed, I estimate that approximately 50% of all AI hardware and chips that have been sold is being warehoused, and will take more than two years to fully ingest. Which means that basically everything you've seen in NVIDIA sell has been a pre-order campaign that arrives sometime in 2028, 2029, or maybe beyond that. In other words, I think NVIDIA has made somewhere between $200-$300 billion in revenue on stuff that's been sold probably 24-36 months in advance, and that any scarcity around AI compute is a result of most of the capacity being sold immediately to Anthropic and OpenAI, leaving very little for the rest of the world, with more capacity taking agonizingly long to build, and not being prioritized unless you're one of the AI labs.

So what does all of this mean?

First of all, basically any announcement of how much capacity a hyperscaler has built is suspicious. The fact that Microsoft is intentionally obfuscating how much capacity, AI capacity, I mean, it has built, is a sign that the entire AI industry is doing so, and that the available AI capacity is much, much, much smaller than we think. All of those estimates of 12 or 15 gigawatts coming online, bollocks, wank, tosh. I don't know, I can't remember other terms for it. Second, if Microsoft, one of the most well capitalized and experienced datacenter developers in the world, is having trouble bringing the majority of its AI capacity online, everyone is. Really, I think Amazon is the only company, maybe Google, that has comparable experience, let alone more. And I think Microsoft was also the first AI supercomputer powered by GPUs back in 2020 for OpenAI. There's probably someone who did it before, just to mess with me. Nevertheless, if they're failing, I think everyone is. And lastly, any beliefs that anyone has about the insatiable demand for AI compute are completely distorted because very little actual capacity is coming online, likely gigawatts less than people believe, and almost all of it is flowing directly to Anthropic and OpenAI, creating an illusion of scarcity when it's actually just two unsustainable venture-backed AI labs sucking up whatever exists. And this also means we're in a terrible overbuilt situation. Right now, everyone building more AI datacenter capacity is doing so because of the massive revenue backlogs across Microsoft, Google, Amazon, Oracle, and CoreWave, ignoring the fact that anywhere from half to 75% of those backlogs are directly from Anthropic and OpenAI, neither of whom can actually pay for them, and any hyperscalers signing contracts to give that capacity to them can probably cancel the agreements. To the outside world, everything looks like a thriving industry with millions of demand both for AI GPUs, which they think are turning into money immediately, and for AI compute. With Microsoft's clearly crooked statements around adding a gigawatt of capacity every single quarter, creating the illusion that its revenue growth is from adding all of that capacity and from spending all of those capital expenditures and from diverse demand on top of it. Rather than mostly from OpenAI's compute spend, which made up 70% of Microsoft's AI revenues in fiscal year 2026, and 7% of its overall revenues in that same fiscal year.