2026-05-23 00:21:32
Last week I ran the first part of my What If…We’re In An AI Bubble? Series, where I asked questions and posed scenarios as to the consequences of the many, many questions I’ve asked over the last few years. It quickly became one of my most-read articles I’ve ever written, and for those of you who joined me for the first time last week, here’s a quick list of what we’ve covered already:
As I mentioned last week, I believe one of the many problems with the analysis of the AI bubble is that people are willing to consider individual facts — like that AI is too expensive for everybody involved and data centers are not being built at the speed that we believed — but never the gestalt of their consequences.
For example, if data center construction slows to a crawl (as I’ve discussed is already the case) there’s a cascade of events that will occur:
It’s really easy to say “wow, this stuff needs a lot of debt!” and “wow, this stuff takes a while!” but actually sitting and thinking about what that means logically leads you to some gruesome outcomes.
And to be clear, there’s not really an alternative to that scenario if data center construction slows. Even in an optimistic scenario, if data centers that started being built in 2024 don’t get finished until 2027 or 2028, that means that NVIDIA’s “latest” GPUs are perennially two or three years in the future.
While some capacity exists, I believe there are at least one million Blackwell GPUs sitting in warehouses waiting to be installed years into the future, which means that projects are going to launch in a year or two with potentially three-year-old GPUs, or said projects are going to have to either replace their orders with Vera Rubin or dump aged capacity onto a market saturated with Blackwell GPUs.
The argument against what I’m saying is that there’s “insatiable” demand for AI compute — that “any viable compute on the market will be used,” which is true in measurements of days or months, but breaks down in the space of a year. As I mentioned a few weeks ago, AI’s demand story is a lie, because capacity is mostly taken up by Anthropic and OpenAI, creating the illusion of demand by absorbing most available inventory, while simultaneously obfuscating the fact that other sources of demand are simply non-existent in any meaningful numbers..
Many are conflating “there’s not much available” with “there’s so many people that want GPUs” without quantifying what “so many” means or how much they want, when the remaining performance obligations from Google, Amazon, and Microsoft have, outside of OpenAI and Anthropic, effectively plateaued, as is also the case when you remove these companies from CoreWeave order book.
If there were incredible, insatiable, indisputable demand, RPOs would be exploding across the board. Instead, nobody seems interested in buying capacity at scale outside of Anthropic, OpenAI, and the hyperscalers supporting them — or, in some cases, the likes of NVIDIA providing backstops to compute providers, agreeing to buy surplus compute in the case that they’re unable to sell it themselves. This is, to be clear, something that shouldn’t happen if there was genuine, distributed demand.
The sheer scale of the supposed AI data center buildout is in the tens of gigawatts of capacity, which translates to $10 billion to $15 billion per gigawatt in annual revenue. I can find no examples of anybody but Anthropic and OpenAI spending billions on compute.
Both companies need to make or raise a combined $1.25 trillion in the next four years to afford their compute commitments across Oracle, Microsoft, Google, Amazon and CoreWeave.
The counter-argument to everything I’m saying is effectively two points:
The latter is far from compelling, but I can see how somebody would believe it.
So much money appears to be flooding into companies like AMD, Samsung, and Sandisk — tens of billions of dollars to the point that it’s creating shortages across basically every component imaginable — which naturally might make you think that demand would exist at the other end.
For the consumer, that perception becomes even more believable when you notice how consumer electronics are getting more expensive. Certain games consoles, nearly six years after their initial release, are more expensive than they were at launch. Typically, the inverse is true.
Meanwhile, smartphones and PCs are expected to ship with weaker specs or high prices, in part because of shortages of key components, caused by demand for AI data center hardware.
The thing is, demand for AI compute doesn’t have to exist for AI data centers to get built. While some have clients signed up in advance, said deals were signed so many years before construction will complete that it’s hard to guarantee that they’ll be willing — or solvent enough — to pay.
I also imagine most clients have signed contracts that have milestone dates for delivery of compute capacity. If data centers are delayed, clients likely have a contractual out, much like Microsoft does with its $17 billion compute deal with Nebius.
In any case, in a frothy debt market full of desperate speculation, these projects are being funded by the very same private credit firms that piled into SaaS companies between 2018 and 2022 under the assumption that every software company will grow in perpetuity. When due diligence is so weak in private equity and private credit that Apollo’s John Zito says that their valuations are “all wrong,” it’s hard to believe that the same financiers are diligently making sure that enough revenue exists to justify these massive data center debt deals.
The same questionable attention to detail applies to venture capital, which has seen (much like private equity) its investment model slow to a crawl since 2018, with an average TVPI (total value paid in) slow to a horrifying 0.8 to 1.2x since 2018, meaning that for every dollar invested, you’re at best likely to get even money in return.
These are the very same investors telling you that every AI company is worth perpetually-growing amounts of money, that everything will work out perfectly, that somebody will work out how to make AI profitable, and that AI is both here to stay and doing incredible things, even if they can’t really explain what those things might be.
In reality, none of these people have any idea how to turn around these rotten economics. Data centers are massive money-losing operations that in the best case scenario take five years to make a single dollar of margin, and their customers are eternally-unprofitable AI startups that rely on a constant flow of venture capital dollars.
The AI bubble is entirely built by people who hope somebody else will solve their problems. AI labs depend on venture capitalists to fund them, hardware providers to invent silicon that makes their businesses profitable, and their AI startup clients to find ways to make profitable businesses using their APIs. In turn, AI startups rely on AI labs to work out a way to make their models cheaper so that AI startups can make their business models profitable.
Put another way, everybody’s response to “how does this become profitable” is “don’t worry, somebody will work it out, but don’t worry, they’re going to at some point.”
Today, I want to explore what happens if they don’t.
Time. Space. Reality.
It's more than a linear path — it’s a prism of endless possibility. I am the Watcher, and I am well aware of how AI generated that sentence sounds.
I am your guide through these vast new realities.
Follow me and dare to face the unknown.
And ponder the question…
What if…We’re In An AI Bubble?
2026-05-22 22:21:18
New revelations about OpenAI’s finances paint a dim picture for the company, as The Information reported it generated just $5.7bn in the first quarter of 2026, with an adjusted operating margin of -122%.
This means that for every dollar of revenue the company generated, it lost $1.22.
As The Information’s Sri Muppidi noted, these operating margins were adjusted — and, presumably, didn’t conform to GAAP (or generally accepted accounting principles) standards — and excluded certain “large line items”, like stock-based compensation.
By that maths, that means that OpenAI lost $6.95 billion in the quarter, and because this is non-GAAP, it’s quite possible that losses are much higher, revenues are lower, and its margins are worse. The piece does not specify if operating margin includes or excludes training costs, nor does it break down what other exclusions there may be other than stock-based compensation.
The report also claims that OpenAI is “on track” to hit its goal of generating $30bn in revenue for 2026, although if it maintains these disastrous margins, it would end up losing $36.6bn.
Meanwhile, ChatGPT’s user growth has stalled. While weekly active users hit 920m in February, the average for the quarter sat at 905m, suggesting lower numbers in either (or both) January or March. OpenAI had expected to hit 1 billion weekly active users in 2025.
This suggests that ChatGPT’s growth has stalled.
As I’ve noted in the past, weekly active users are a fairly novel metric, with most companies using monthly active users to represent adoption. I’ve also speculated that the reason why OpenAI has favored this metric is because it’s easy to manipulate.
OpenAI reportedly had 55m paying ChatGPT customers at the end of Q1 — up from 47m people at the end of the year.
Assuming a userbase of 905m users, this means that OpenAI has a conversion rate of roughly 6%. It's likely worse, as monthly active users should, at least in theory, be a higher number, as it captures every weekly user in addition to less-active users over the course of a month.
Nevertheless, while this represents an improvement over the 2.583% rate in February of last year, it’s likely improved as a result of cheaper ad-supported ChatGPT “Go” subscribers at $5 or $8 a month, depending on geography. OpenAI also gave away a free annual ChatGPT Go subscription to literally every Indian subscriber in late October 2025, though I cannot confirm if they’re counted in the total.
As I wrote up yesterday, Anthropic leaked (or had leaked) that it believed it would have a non-GAAP EBIT operating profit in Q2 2026 entirely as a result of Elon Musk discounting two months of compute costs for that specific quarter, and it makes me wonder why we’re suddenly, in the space of 24 hours, talking about operating margins or operating profits for two companies that have hidden behind annualized revenues and obfuscated financials for several years.
If I had to guess, it’s likely that investors have begun to demand firmer, more “real company”-adjacent numbers, and while Anthropic was able to find a clever way to manipulate them as a means of raising funding, OpenAI was forced to share numbers a little closer to reality.
What’s clear is that we’re in an information war between two companies that burn billions of dollars, with one of them (OpenAI) allegedly planning to file for an IPO as soon as today.
Anthropic clearly wants to position itself as the stable, reliable, economically viable alternative to OpenAI, but can only do so with a kind of financial engineering only made possible in a media climate bereft of scrutiny.
Nothing has changed about the core economics of generative AI to suddenly make things profitable, other than the ingenuity of CFO Krishna Rao and his willingness to move numbers around a spreadsheet.
Nevertheless, it’s interesting that Anthropic appears to be leapfrogging OpenAI in revenue. In early May, Anthropic claimed to have $45bn in ARR. By contrast, in March, OpenAI claimed to have topped $25bn in ARR. While OpenAI brought in a billion dollars more than Anthropic in Q1 2026, The Information couldn’t get ahold of OpenAI’s numbers for Q2 2026, but at $45 billion in ARR - $3.75 billion in a month - Anthropic may have taken the lead.
That is, of course, if its numbers actually line up with reality, something I’ve disputed multiple times.
Nevertheless, if investors become convinced that OpenAI is falling behind, it’ll be much harder to raise another round at or above its current $852 billion valuation.
Perhaps that’s why OpenAI is rushing to go public - it realizes it might have tapped out private investors.
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2026-05-22 01:08:19
Yesterday, the Wall Street Journal ran a story about how Anthropic is “about to have its first profitable quarter,” specifically an operating profit, or EBITDA profitability:
Anthropic’s revenue is set to more than double to $10.9 billion in the second quarter, an explosive rate of growth that will help it turn an operating profit for the first time.
…
Anthropic generated $4.8 billion in sales in the first quarter. Its quarterly revenue is now growing faster than Zoom did during the pandemic, and Google and Facebook in the run-up to their initial public offerings. It is set to turn an operating profit of $559 million in the June quarter.
Interesting! That’s a lot of certainty considering we’re barely through the first half of the second quarter, and quite a specific number given the fact that June hasn’t started! And all of these numbers are mysteriously leaking exactly while it raises its funding round!
Oh there’s also one important note: The Journal adds at the bottom of the article that “...it is unclear what accounting methods Anthropic has used to book revenue and costs, as the company isn’t yet required to follow the financial-reporting requirements of a public company.” That’s right —-- Anthropic is possibly going to be EBITDA profitable for a single quarter, on a non-GAAP basis.
Anyway, I wonder how Anthropic did it? Because based on this unhelpfully-labeled diagram from the Journal, it appears (as I said last year) that its costs scale linearly with its revenues, except they…magically didn’t in the second quarter?
I wonder if it'll stay profitable?
The company might not remain profitable for the full year as it plans spending increases due to its vast computing needs.
That’s also interesting. So Anthropic may be profitable very specifically in Q2 2026, but might not be afterward. It’s almost as if it found a way to specifically cut its costs in May and June somehow…
…because it did! Remember that deal Anthropic signed with SpaceX to take over Colossus-1? Well it’s also taking over some or all of Colossus-2, paying SpaceX $1.25 billion a month starting in May and June… when it’ll have a reduced fee as it ramps up!
That’s $15 billion a year in compute costs, but reduced to an indeterminately-discounted level for the precise months that Anthropic is using to tell investors and the media that it has an operating profit. That operating profit is a result of accountancy rather than any improvements to its business model.
While I wouldn’t say this is cooking the books, it’s definitely a shiatsu-grade massaging of the numbers. Anthropic has deliberately leaked a quarterly “profit” where it knows it can suppress its costs, specifically made sure that the journalist gave it the out of “costs might increase,” and released it on the day of NVIDIA’s earnings as a means of keeping the AI bubble inflated.
Nothing has changed.
If Anthropic paid full-rate for its compute in those two months, its economics would shift back to what they’ve always been per my reporting from last year on its AWS costs — a business that has costs that linearly increase with its revenue growth.
I also severely doubt that Anthropic managed to make the cost of running its services profitable in the space of six months.
Per The Information in January, Anthropic missed on its gross margin projections, saying that its inference costs were 23% higher than the company had anticipated.
How did Anthropic, which faced a massive influx of new business to the point that Anthropic was forced to buy more compute from Elon Musk, magically become profitable? Other than that discount, of course.
I have a few guesses:
Nevertheless, the revenue side is where the real problems lie.
So, Anthropic has said it brought in $4.8 billion in revenue in Q1 2026, and projects to hit $10.9 billion in Q2 2026.
This is tough to reconcile with previous reporting.
On February 12, 2026, Anthropic claimed it had reached $14bn in annual recurring revenue (ARR). As a reminder, ARR is an accounting tool largely used primarily by startups, where a snapshot of a single month’s income is taken and multiplied by twelve. This gives you an implied monthly revenue of roughly $1.17bn.
On March 3, 2026, Dario Amodei would claim Anthropic had reached $19bn in ARR — which works out to $1.58bn per month. Two days later, on March 9, Krishna Rao — Chief Financial Officer at Anthropic — would declare under oath in a court filing that Anthropic had brought in revenues “exceeding $5 billion to date.”
Keep in mind that The Information had previously reported that Anthropic had $4.5 billion in revenue in 2025, which I already found difficult to match with Rao's statements.
While boosters may claim that “exceeding” could mean literally any number they want above $5 billion, I find it doubtful that the CFO of Anthropic would, under oath, lead the court to believe its business was 30% to 40% smaller than it was, especially when trying to convince it that the damage of being labeled a supply chain risk would ruin its business.
At this point it’s impossible to reconcile the 2025 reporting with that $5 billion number. If we assume that the ARR claims made by Anthropic are correct, we can presume that it made revenues of roughly $2.5bn in March (given that it claimed it had $30 billion in ARR on April 6), $1.58bn in February, and $1.17bn in January, for a total of $5.25 billion.
I realize that figure is in excess of what the Wall Street Journal had and, in some world, those numbers could be cherry-picked using particular periods to the point that the real revenues would be in the region of $4.8 billion. That's possible.
But they don’t make a lick of sense when you bring up what Krishna Rao said. If we believe Anthropic’s leaks —-- putting aside all of the ARR figures for a second —-- this means that Anthropic:
While I acknowledge that Anthropic has grown significantly, that level of stratospheric growth does stretch the limits of credibility. Moreover, the fact that previous ARR figures are inconsistent with the leaked charts from Anthropic further raises questions about the credibility of any numbers from the company.
The only real defense that anybody has here is that Krishna Rao, under oath, lowballed the US government and a judge to such a dramatic extent that he hid in excess of $4 billion in revenue.
And as I’ve discussed before — and FlyingPenguin helpfully collated — adding up Anthropic’s previously-reported ARR from January 2025 to March 3, 3rd 2026 already gets us to around $6.66 billion.
I can imagine this has felt like a big victory for boosters — proof that AI can be profitable, that inference is profitable, that some sort of business model is emerging…and I’m sorry, that’s not what’s happening.
Dario Amodei and Elon Musk worked out a sweetheart deal, which they - framed as a “ramp-up,” - that allowed Anthropic to artificially depress its costs. I also question how much of a ramp-up there really was, or what Anthropic’s actual compute constraints were, because it immediately loosened rate limits for Claude subscribers on announcing the deal, meaning that it immediately started having higher inference costs, which…somehow led to it making a higher profit? Or did Musk — as literally described in its S-1 — have SpaceX charge Anthropic less for two specific months to make the numbers look better?
In July, Anthropic will start paying SpaceX $1.25 billion a month, - or $15 billion a year, - on top of all of its other compute deals with Google, Amazon and Microsoft.
If we assume that its spend is comparable on AWS and Google Cloud — and it’s most-assuredly more! — that means Anthropic is spending around $3.75 billion in compute costs, or $11.25 billion a quarter, or $45 billion a year.
There’s also a very compelling argument that Anthropic’s costs will increase and will eat up that profitability, to once again quote the Wall Street Journal:
The company might not remain profitable for the full year as it plans spending increases due to its vast computing needs.
I also have to wonder: if you’re so profitable, why not IPO? Why not take this to the public markets?
Unless, of course, you’re only non-GAAP EBITDA profitable based on a two-month-long discount specifically covering the period in which you’re profitable. And, of course, when you’re not a publicly-traded company, and so you don’t actually have to publish any numbers (and no, leaking them doesn’t count), and you’re not subject to SEC oversight.
I will give Dario Amodei credit: nobody does financial engineering and a press-led information war better than Anthropic. The willingness of the press to eat up incongruent numbers and the eagerness of many to jump up and find obtuse ways to explain away the obvious problems is only made possible when a company has perfected the art of manipulation and ingratiation of those who want to feel like they’re “first.”
If you take this as incontrovertible proof that Anthropic is profitable, you are deliberately ignoring the blatantly obvious ways these numbers are being massaged. We’ve got its CFO saying numbers that don’t match up with these leaks or Anthropic’s own marketing materials, and the aggressive and deluded way in which many people ignore them is equal parts frustrating and depressing.
Let me speak directly and with more empathy than usual: if you want Anthropic to win, you should be just as skeptical of these numbers as I am. You should want to smash my face in the tarmac with the most crystal-clear, impossible-to-argue with numbers, bereft of asterisks or discounts from suppliers or obfuscated accounting metrics.
You should want better from your heroes. If you truly think this company is amazing, unstoppable, and leading the tech industry to a glorious era of innovation, there shouldn’t be this many questions, and the metrics shouldn’t be this murky.
Every other time when a company has played this level of silly, weird bullshit has led to disaster — for example, WeWork claimed to be profitable since the second month of its operations, and repeated claims of profitability throughout its existence, and it turned out that it was only “profitable” if you removed things like “some of the costs of doing business.”
I get why you’re so defensive, and I get why you want this to work. A lot of you are very excited about generative AI, and being excited about it has given you a tremendous community of equally-excited people. I get that you like these tools.
And I need you to know these companies are laughing at you.
Anthropic timed this leak to focus on a specific quarter where it artificially suppressed costs, and gave you the flimsiest proof imaginable, specifically-crafted for you to share it as a triumph and spread the idea that “AI labs are actually profitable,” when their core economics haven’t changed. Costs increase linearly with revenue, and will continue to do so in perpetuity.
I genuinely can’t wait for both OpenAI and Anthropic to file their S-1s.
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2026-05-19 23:48:42
If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. My Hater's Guides To Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle.
This week, I’ll publish the second part to my ongoing series (“What If…We’re In An AI Bubble?”) about the factors and events that will cause the AI bubble to finally pop.
Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.
AI is, as it stands, not economically viable for anybody involved other than the construction firms, NVIDIA, and the surrounding hardware companies benefitting from the irrational exuberance of a data center buildout that doesn’t appear to be happening at the speed we believed.
Every AI startup loses millions or billions of dollars a year, and nobody appears to have worked out a way to stop hemorrhaging cash. Hyperscalers have invested over $800 billion in the last three years, with plans to add another $700 billion or so in 2026 and another $1 trillion in 2027, meaning that they need to make at least three trillion dollars in AI specific revenue just to break even, and $6 trillion or more for AI to be anything other than a wash. I went into detail about this (albeit at a lower, pre-2026/2027 capex number) in a premium piece last year.
To give you some context, Microsoft made $281 billion, Meta $200 billion, Amazon $716 billion, and Google $402.8 billion in revenue in their most-recent fiscal years for every single product combined, for a total of $1.599 trillion. None of them will talk about their actual AI revenues. Yes, yes, I know Microsoft said that it had $37 billion in AI revenue run rate ($3.08 billion a month or so) and Amazon had $15 billion, or around $1.25 billion a month, but both of these are snapshots of single months that are meant to make it sound like they’re going to make that much in a year but in the end, you don’t actually know anything about how much money they’ve made from AI.
We do, however, now know that Microsoft has spent an approximate $100 billion on its OpenAI partnership after testimony from an executive during the otherwise-dull Musk-OpenAI trial, per Bloomberg:
That figure includes Microsoft’s original investments in OpenAI, as well as the costs of building infrastructure and hosting OpenAI’s computing, Microsoft deals executive Michael Wetter testified on Monday. It is cumulative through the current fiscal year which ends in June, he said.
This is a fascinating insight for a few reasons:
At the end of 2025, OpenAI claimed that it had 1.9GW of capacity (likely referring to total power draw rather than the actual critical IT of the infrastructure at its disposal), which, per analyst estimates, ($42 to $44 million per megawatt) works out to around $79.8 billion. This claim was made around six months before the release of Microsoft’s most recent quarterly results.
In other words, Microsoft has spent 4 years sinking (either through spending or allocating the capex in advance) nearly $300 billion into…building OpenAI?
Okay, fine. Microsoft also has 20 million Microsoft 365 Copilot subscribers for an absolute maximum revenue of $7.2 billion…if every single one were paying $30 a month, which they are most assuredly not as Microsoft has been offering discounts on it for years.
Based on my reporting from last year, Microsoft made around $7.5 billion from OpenAI’s inference spend and $761 million from its revenue share in Fiscal Year 2025, a year when it invested (either spent or allocated) around $88.2 billion in capital expenditures.
I didn’t report it at the time, but I also had the numbers for all of Microsoft’s revenues for the first three quarters of Fiscal Year 2025 — a total of $8.9 billion of total AI revenue, with around $4.35 billion in revenues when you removed OpenAI’s inference. If we assume that Microsoft’s other AI services grew 10% quarter-over-quarter, I estimate that Microsoft likely made around $17.9 billion in AI revenue in FY2025, or a little under a fifth of its capex.
And let’s be clear: none of these numbers include the actual operating expenses.
Data centers, after all, need electricity to run, and AI data centers in particular need a lot of electricity. And some — though, admittedly, not many — people to handle the things like maintenance, repairs, and operations. And then there are things like taxes, insurance, and the other day-to-day costs that, when you add them all together, make a big, scary number.
You can argue that “actually GPUs are profitable to run” (I disagree!), but for any of this to make sense, four things have to happen:
All four must be true. If AI revenues don’t explode, capex can stop, margins can be positive, and your best-case scenario is…you maybe broke even. If capex never stops being invested, you need revenues to explode dramatically — to the tune of effectively doubling Microsoft, Meta and Google’s entire businesses, and tripling Amazon Web Services’ annual revenue ($128 billion) — and for said revenues to be margin-positive, because if they’re not, eventually other healthy businesses will slow, leaving AI to tear a hole in overall margins. In all cases, AI revenue must stay consistent because, well, you need to get paid.
Sidenote: In all honesty, I have no idea how Meta makes this make sense, as it plans to invest at least $125 billion in capex in 2026 and has, to this point, not shown any actual, real growth in its revenue from AI, and no, those increases in conversion don’t mean actual revenue.
I also cannot find an economic scenario where this pays itself off.
Let’s assume that Anthropic is actually at $45 billion in annualized revenue (I believe it’s doing some very worrisome maths to get there), or around $3.75 billion a month. On an annualized basis, this would not be enough — assuming it had zero operating expenses (rather than losing billions) — to recover a single year of capital expenditures from Microsoft, Google, Meta, or Amazon from 2024 or 2023.
Even if OpenAI’s entire cloud spend ($50 billion) for 2026 went to Microsoft and it doubled its Microsoft 365 Copilot revenue (at full cost) to $14.4 billion, it estimates it will invest $190 billion in capital expenditures this year. Amazon’s $15 billion AI run rate, even if it doubled, wouldn’t put much of a dent in its $200 billion in investment plans. While we don’t know Google’s AI revenues, it plans to invest $185 billion in capex this year.
These AI revenues have to be completely fucking insane and they need to be that way extremely fucking soon, because otherwise the best they’ll be able to say is “our first few years of capex weren’t particularly useful but the stuff we built after it was,” which still works out to a few hundred billion dollars of waste.
Things get even worse when you realize that at least 70% of Microsoft, Google, and Amazon’s compute is dedicated to Anthropic and OpenAI, two companies that burn so many billions of dollars that Microsoft, Google and Amazon have already fed them a combined $54 billion in the last three years, with $28 billion of that coming in the last month and Anthropic due another $50 billion from Google and Amazon if certain performance obligations are met.
And there’s no real sign, outside of Anthropic and OpenAI’s compute spend (which is reliant on hyperscaler and venture capital money), of any real explosion in AI revenue. Per The Information (in a chart I love to share!), more than 50% of hyperscalers’ revenue backlogs comes from these companies:
If massive, incredible demand for AI existed, wouldn’t these remaining performance obligations be near the trillion mark? Wouldn’t there be other Anthropic or OpenAI sized chunks of revenue? There’s allegedly incredible, unstoppable, insatiable demand for compute. Why isn’t it lining up?
Let’s take a look at those RPOs!
That was a lot of numbers, so let me make it simpler: outside of OpenAI and Anthropic, these three companies do not appear to be significantly increasing their revenues, and the only way to get that revenue is to feed money to one or both of these companies.
Put aside all the theoreticals and hypotheticals and metaphors and imaginary future scenarios and tell me: what, in the next year, are Microsoft, Google and Amazon going to do about this problem? How do they solve it?
If we assume the absolute best-case scenario, these companies are making a combined $70 billion in annual revenue on investments that now — including the money invested in the companies themselves — total over $900 billion. Doubling that won’t be enough. Tripling it won’t be enough. In fact, to pay this off, these companies will need to be making over $100 billion each in AI revenue in the next year, because otherwise there is no covering these losses.
And it all comes back to a very simple point: AI is too expensive. If the margins were good, they’d be sharing the margins. If the revenues were good, they’d be sharing the revenues (and no, run rates aren’t revenues). If the business was strong, it would be a separate category in their earnings.
But LLMs are too expensive! They cost too much to run, and said costs appear to increase linearly with revenues. The more a user uses a product, the more it costs the company to run it, and the more capacity they can take up. The only way to capture any growth is to buy and install GPUs, which in turn requires you to build somewhere to put them, which takes time and money.
I’m really struggling to see the argument in favor of continued capex investment. You’re more than $800 billion in the hole with, I estimate, less than half of that resulting in operational GPUs and capacity. Said capacity is mostly taken up by OpenAI and Anthropic, two companies that burn billions of dollars and do not appear to have an answer for how they might stop.
The more you build, the more your infrastructure becomes dependent on the continued existence of two perennially-unprofitable ultra-oafs, as your existent AI product lines are, at best, add-ons to products like Google Workspace or Microsoft 365, or further expansion of cloud compute capacity with lower margins and higher up-front costs than anything you’ve ever built.
Every quarter is an opportunity to put yourself another $30 billion or so in the hole, all in the hopes that, I assume, OpenAI or Anthropic will pay you $100 billion or $200 billion over the course of a few years, because nobody else in the entire universe is spending that much on compute. You are not recovering these investments without either a massive new product line that doesn’t exist today or three or four Anthropic or OpenAI-sized compute contracts.
Put another way, Amazon needs another AWS ($128 billion a year), Microsoft another Azure ($75 billion a year, including OpenAI’s 2025 compute spend) and Google a business line at least half the size of search (around $200 billion a year). These businesses have grown to this size by providing extranormally large amounts of value from the very moment they were created and impenetrable monopolies — and while there are quite literally other cloud providers that can physically provide the infrastructure to OpenAI and Anthropic (Oracle is trying to compete and may die as a result), the actual “monopoly” here is “being able to deploy hundreds of billions of dollars.” Anthropic proved this when it took 300MW of compute from Elon Musk.
Sidenote: I have absolutely no idea what Meta does, and my chaos bet is that it starts renting out its compute to Anthropic or OpenAI when things get rough. Perhaps it does some sort of incestuous deal where Meta gets equity. I really have no idea here! It’s a crazy and stupid company run by a moron.
In Oracle’s case, as I’ve explained at length, it has to successfully build 7.1GW of capacity, have that capacity actually be margin-positive (doubtful!), and then actually get paid for it by the time it’s built in, oh, I dunno, 2032?
Sadly, I have bad news about Oracle, Microsoft, Amazon, and Google’s largest customers.
Here’s a fun game: ask an AI booster how OpenAI or Anthropic becomes profitable!
Here’s what they’ll say:
I must be abundantly clear that nobody has any proof that anyone is profitable on inference, but we have plenty of proof they’re not. They’ll likely cite known liar Sam Altman saying OpenAI is profitable on inference from a party from August 2025, or Dario Amodei saying (in a sentence around “stylized facts” that are “not exact” and are specifically “a toy model” and specifically not about Anthropic) “the inference has some gross margin that’s more than 50%.”
Here’s a really simple way to dispute this: Coatue said that Anthropic’s revenues were 85% API calls in 2025. If it’s profitable on inference, how is it still losing money? You’re gonna say “training,” but that doesn’t actually answer the question: if Anthropic’s process of providing tokens to its models is profitable, how is it losing so much money? Why offer a subscription platform at all?
As I’ll get to, Anthropic has companies paying massive amounts for tokens — hundreds of millions a year in some cases — that’s all inference. Why are you bothering with these stinky, nasty monthly subscriptions?
The “inference is profitable” argument is a bedtime story told to people that can’t reconcile the logic of a company that allows people to burn between $8 and $13.50 of every dollar of their subscription revenue.
Otherwise, you have to reconcile with the fact that both Anthropic and OpenAI are both incinerating money and have no real path to any kind of sustainability other than, well, not doing that.
One very, very specific counter-argument people make is that open source models are cheap, and can somehow be compared to OpenAI and Anthropic’s, despite the fact that we have no idea what the actual parameters of Sonnet, GPT, Opus, or any other of their models actually are.
What we do know is that both of these companies lose billions of dollars.
What we do know is that OpenAI, per The Information, plans to burn $852 billion through the end of 2030, and that as of March 6, 2026 (per CFO Krishna Rao’s sworn affidavit), Anthropic made “exceeding” (sigh) $5 billion in revenue and spent $10 billion on inference and training.
Anthropic has done a great deal of work to obfuscate how much it actually makes or spends, but I think it’s likely it burns even more than OpenAI, given the fact that it’s had to raise $75 billion in the last 6 months (assuming its new $30 billion round closes), and that’s not including an additional $30 billion from Google and Amazon if certain unknown milestones are hit.
Then there’s the issue of those RPOs. Anthropic is now on the hook for $200 billion to Google, $100 billion to Amazon and $30 billion to Microsoft, I assume over the course of the next three or four years.
So let’s lay this out.
Anthropic — based on its own affidavit from March — appears to have spent $3 to make $1 of revenue on a compute basis, and that’s before you include any and all other costs like staff or electricity or the vocal coach that Dario Amodei uses to add that bass to his voice.
Additionally, it needs $330 billion to pay its cloud obligations to Amazon, Google, and Microsoft over the next four years. I’d estimate it needs $5 billion a year for its compute deal xAI (so $20 billion over the total period) and an estimated $30 billion to cover its deal with CoreWeave. That brings us to a total of $380 billion.
It’s hard to estimate the actual costs associated with running Anthropic because so much of the reporting no longer makes sense as a result of that affidavit. Nevertheless, I think it’s fair to assume it will need at least $20 billion of operating expenses across that four year period.
We don’t even need to play in the realm of “what might Anthropic or OpenAI’s revenues be?” to understand the problem here. Both companies aggressively burn money, and neither of them have any answer as to how they might stop. Numerous reports about how Anthropic will turn “cash flow positive” in either 2027 or 2028 are fantastical, illogical, entirely driven by ridiculous projections, and should never have been reported as anything other than an attempt by companies to mislead their investors. In both cases, reporters should’ve had more asterisks on those numbers than Q*Bert reading Frank’s lines from Blue Velvet.
And we have plenty of evidence that they’re losing more money over time. In January 2026, The Information reported that Anthropic’s gross margins were 40% in 2025 — 10% lower than its “optimistic” projections, specifically attributed to “...the costs of running Anthropic models from paying customers, in a process known as inference, on servers from Google and Amazon,” adding that those costs were “23% higher than the company anticipated.”
In February, The Information ran another story saying that OpenAI’s gross margins fell from 40% in 2024 to 33% in 2025, a full 13% lower than its projected margins of 46%, all because (and I quote) “...the company having to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models.”
You know, exactly what Anthropic has had to do.
This is what I’ve referred to as the knife-catching problem for compute demand — you either don’t order enough compute and have to rush to buy some last-minute as demand intensifies, or you order too much, and, well, to quote Dario Amodei:
Basically I’m saying, “In 2027, how much compute do I get?” I could assume that the revenue will continue growing 10x a year, so it’ll be $100 billion at the end of 2026 and $1 trillion at the end of 2027. Actually it would be $5 trillion dollars of compute because it would be $1 trillion a year for five years. I could buy $1 trillion of compute that starts at the end of 2027. If my revenue is not $1 trillion dollars, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute.
And right now, as I’ve covered, there’s not enough compute being built to keep up with Anthropic or OpenAI’s voracious demands, meaning that they will both be bartering to buy whatever’s available at whatever price it’s available at. This naturally will savage their already-negative margins…
…and then what?
No, really, and then what? One of you fucking AI boosters, answer me, how does this actual reverse course? Because even if Anthropic were making $100 billion in annual revenue, it would probably be losing $300 billion or more to get there. The fact it had to raise $30 billion in February, $15 billion in April, and now $30 billion more in May all while allegedly pulling in more than $3 billion a month in revenue suggests that its COGS are fucking horrendous, and its growth is coming at a terrible financial cost.
Let’s say that Anthropic keeps growing and (as The Information suggests) hits $100 billion in annualized revenue (around $8.3 billion a month). How, exactly, does it afford to make that much money? Because right now it’s (allegedly) about to hit $45 billion in annualized revenue, and needs so much money that it’s absorbing (along with OpenAI) the majority of venture capital raised this year, and very clearly does not have any path to bring its costs down.
The answer is simple: it can’t! There is no mechanism to do so. More compute does not make OpenAI or Anthropic’s services cheaper to offer. There is no magical silicon coming that will make any of this more affordable, and no, Anthropic is not “profitable on inference,” because if it were, that massive revenue growth would have leveled out its margins rather than require it to raise a little less than the combined value of every Major League Baseball team, or more if you add the other $50 billion that Amazon and Google have promised based on secretly-held performance obligations.
The same goes for OpenAI, which “raised” $122 billion (around $45 to $50 billion in real cash, with the rest either paid in installments or on it IPOing or reaching (sigh) AGI) in February and is now already considering raising more.
Somebody might counter-argue that this is companies raising as a means of boosting their valuations, I think that’s a very convenient way of looking at two extremely problematic companies.
I should also ask why neither of them appear to be seriously considering going public. While both were rumoured earlier in the year to be planning to do so in 2026, both appear poised to raise more private capital.
I think the answer is simple: their CFOs know that doing so would reveal their actual margins, which are hot dogshit with sprinkles on top.
Nobody has a sensible or logical response here.
Which leads us right to our next point!
One important detail to keep in mind here is that as of a month or two ago, Anthropic moved all enterprise customers to token-based-billing, which will begin, I believe, a true stress-test of the true “value” of AI as costs skyrocket.
Just last week I ran the first of a two (or three, potentially) part premium series called “What If We’re In An AI Bubble?” and touched on the gruesome subject of whether organizations could afford to pay for AI long-term:
Per Laura Bratton of The Information, Uber, ServiceNow and multiple other organizations are blowing through their yearly API token budgets in a matter of months, and are currently in the “cope” stage, with Kellie Romack, CIO of ServiceNow, saying the following about a conversation with CFO Gina Mastantuono:
Romack said she recently met with ServiceNow Chief Financial Officer Gina Mastantuono to figure out how to contain costs so employees can keep using their Claude Enterprise accounts for the rest of the year.
“It’s a really hard problem,” Romack said. “She’s worried, I’m worried, and we’re working together to go figure this out.”
Let’s focus on that phrase “...can keep using their Claude Enterprise accounts for the rest of the year,” because it’s important. A public company with a CEO that previously boosted the metaverse and now has profound AI psychosis is saying that it isn’t sure whether it can continue to justify paying for Anthropic’s models through the rest of the year without containing its costs.
Earlier in the week, carnival barker and Salesforce CEO Marc Benioff said his company would spend $300 million on Anthropic tokens in 2026, and as I discussed in my premium from Friday, unrestrained AI spending is inflating the revenues of Anthropic and OpenAI in a way that isn’t sustainable for anybody involved:
For example, sources with direct familiarity with Stripe’s internal cost have told me that its technical staff (a little over 5000 people) are burning an average of $94,000 a day (around $2.8 million a month) in tokens, primarily on Anthropic’s coding models. Stripe’s EBITDA revenue was around $1.6 billion in 2024,so $33.6 million a year isn’t necessarily life-threatening, but if we assume an average salary of $150,000 per member of technical staff, that puts its raw headcount costs at around at least $765 million, making AI costs sit at roughly 4.392% of headcount.
As I said, this is one of the more-normal examples. Goldman Sachs reported a few weeks ago that AI costs are approaching 10% of total headcount costs, and “...could be on track to be on par with headcount costs in the next several quarters based on current trajectories.”
The problem is simple: nobody actually knows how much AI is going to cost them in any given quarter. This means that the current token spend you’re seeing is entirely experimental, which is why organizations keep burning through their tokens so fast.
This massive growth in spend is what underpins the “massive” (I have serious questions about its accounting) growth in Anthropic’s revenue. Executives have, across the board, given their engineers free reign to burn as many tokens as they’d like, and while I severely doubt that Anthropic actually hit $50 billion in annualized revenue outside of not-quite-fraudulent non-GAAP measurements, I believe its revenue growth has come from an artificial boost from a tech industry searching for a reason to pay somebody money.
To be very clear about what I mean, I think there is currently an AI token binge across both Anthropic and OpenAI. Enterprises do not know the actual value of AI, and do not know how much they should actually be budgeting, which is why Uber and others are running through their token budgets but not, it seems, spending less. We’re currently in an abundance phase — one where nobody is truly thinking about the costs outside of their fear of missing out — but there’s this nasty undercurrent of “wait, how much does that cost?” followed by “oh, fuck, well…you know I love AI but…”
Put another way, the current spend on AI tokens is not something that’s indicative of lasting, reliable revenue.
In some cases, the pressure to use AI for everything is turning companies’ software stacks into slop.
Things are worse elsewhere. Something is wrong at Zillow. Something about LLMs has done something to its technical leadership, something that makes them talk strange and send weird slide decks with confusing, slop-ridden sentences.
The real estate tech firm spent over $1 million on AI services in the first quarter of 2026, and in April it spent $749,000 in tokens across Cursor and Anthropic’s services, as well as through AWS Bedrock. As of the end of the month, it was nearly 75% of the way through its annual Cursor token budget of $1.1 million.
As of the middle of May, its total AI spend had already crested over $300,000, and its Cursor budget sat dangerously close to the edge at 85%.
This is particularly-concerning when you consider that Zillow’s net income for Q1 2026 was $46 million, and ranged from $2 million to $10 million each quarter of 2025.
Zillow is currently on course to spend at least $7 million on AI in 2026, and at its current pace might hit as much as $10 million, which would amount to a little less than 50% of its 2025 net income ($23 million).
You’re probably wondering how Zillow manages to spend so much on AI, and the answer — as I’ll get into in next week’s free newsletter — is that its technical executives appear to have AI psychosis, saying that the short-term goal is for “software engineers to never open a code editor again.”
The reality is chaos. In a slide deck that I’ll discuss later, Zillow revealed that while engineering resources have largely stayed the same, outputs requiring human review have increased by nearly 50%. Meanwhile, code deployments and pull requests increased by 39%, and software reviewer load increased by 29,000 hours each month, creating a massive burden on the 1,500 or so engineers working at the company.
In simpler terms, that’s about 19 hours of extra work per engineer that’s literally just looking at extra code written by LLMs.
On Blind, the anonymous social network for tech workers, Zillow workers complain about Zillow’s code “slowly becoming AI slop,” with “much more code getting approved without guardrails or input due to people not being able to keep up the other’s velocity or just not caring anymore.”
One worker claimed that “the slop is job security,” adding that they “don’t want the output to be good or documentation to be clean [as] management will replace [them] with offshore/nearshore/AI agents at the slightest whiff of evidence that the slop cannon is self sustaining.”
Another said that they felt “lost in the agentic world,” and that they “didn’t have full grasp of where we are going or what [their] role is,” with a “lot of overlap in what people are doing.” Another said that “people are burning tokens just to hit internal AI adoption targets,” adding that “this is what happens when leadership ties metrics to usage instead of outcomes,” saying that it “literally subsidized busywork.”
This is all part of what an internal slide deck viewed by this publication called “AI-Native Engineering,” promising a “path to an agentic Zillow” and “faster outcomes for customers,” though customers are never mentioned in any other slide.
The deck — pumped full of AI-generated text — talks about “generic AI being a commodity,” saying that “Zillow-aware AI is a competitive advantage,” and at no point explains what that means. It encourages engineers to go from “AI-Assisted” to “AI-Native,” with “systems enabling org-wide leverage,” with engineers moving from being “soloists” — individual developers with AI tools — to “conductors” that orchestrate AI agents, to “composers” that “define systems AI can safely play,” adding that “2026 is the transition from conductor to composer.”
Yet the strangest part is named “2027: A Tuesday,” discussing a theoretical day in the office for whoever is left at the company.
This theoretical example is, apparently, a process that would take weeks, but now takes under two hours.”
Zillow intends, based on this deck, to sacrifice everything to AI — code review, vulnerability fixes, policy checks, deployments, testing, and basically having agents take over everything, no matter how small, like having an agent do dependency updates and security hotfixes that could be handled with a simple shell script.
To quote Zillow:
Agent capabilities exist across the entire software development lifecycle-from ticket to production-with humans steering and approving rather than executing each step.
In practice, sources at Zillow tell me that there has been no actual movement toward this vision. Software engineers still open IDEs and review code manually, with one describing Zillow’s “vision” as “nonsense,” adding that “you can’t just throw buzzwords on a slide deck and change how all the engineers do their jobs.”
As for why token burn is so high, sources tell me that engineers are actively encouraged to use AI for everything, as much as possible, writing PRD (product requirement documents) in AI, then using the AI to make stuff based on the PRD, then doing a deck with AI, then writing emails with AI, using AI to brainstorm, or create weird, esoteric automations, with some managers pushing workers to have one personal AI “goal” to aspire to.
Zillow’s agentic “vision” is apparently a remit from the C-suite.
It’s hard to tell if this is AI psychosis or just classic Business Idiot bullshit.
Perhaps it’s a little of both.
Every organization I’ve talked to has exceeded or is nearing the edge of their annual token budget barely five months into the year, which means that everybody has suddenly given themselves an extra few million dollars’ worth of operating expenses for reasons that escape effectively everybody I’ve talked to.
Every engineer tells me the same thing: “I’m being made to do this, I don’t want to do this, my managers do not seem to understand, my bosses seem to understand even less than my managers, and if I don’t use AI somebody is going to fire me.”
Put another way, CEOs and CTOs are screeching at their underlings to “use AI as much as possible” to “find its incredible benefits” without anybody really knowing what those are and how much it’ll cost to get there.
This might be because Anthropic obfuscates the data that might tell customers the real costs.
Per Laura Bratton at The Information,
One reason Anthropic costs are tough to predict, ServiceNow chief digital information officer Kellie Romack told me, is that Anthropic doesn’t automatically show customers the kind of granular data that allows them to see which of its users consume which tools; how much they use the tools, and how they’re using them. Software firms such as ServiceNow, SAP, Microsoft and Workday offer such “telemetry” data to their customers, she said.
Bratton’s article has numerous quotes from executives saying that Anthropic lacks transparency and granularity into the ways that tokens are being burned across an organization, in a way that I think sounds very, very suspicious, particularly when you add the following:
Anthropic also doesn’t offer so-called service-level agreements with customers that define how well the product will perform and customer-service response times that the customers should expect, Romack and Mehta said. Such agreements are standard in the software industry.
While I’m not accusing Anthropic of anything untoward, massive, multi-million dollar contracts that involve individuals burning thousands or tens of thousands of dollars’ worth of tokens with no service level agreement, transparency or true granularity into the burn is a perfect setup for a company — not saying it’s Anthropic! — to do something dastardly with those numbers.
While an individual might be able to monitor their own personal usage, in an organization of hundreds or thousands of engineers, who’s to know if, say, the particular token burn is consistent across every member of the company, or that those costs are actually matching up with what the user is doing?
This is a company ostensibly worth $900 billion dollars acting with disregard for the basic measurement of “how much did this cost, and how did it cost so much?”
And in the end, how do you even measure it at scale? Say you’ve got 1,500 engineers, and they’re spending a combined $1 million tokens a month. How the fuck do you actually measure the return on investment for that spend?
How many tokens does it take to do one thing? Is it consistent across every model? Is it consistent across every employee? Are you even measuring how many tokens a task costs? Because if you’re not, that token budget is basically throwing a dart blindfolded.
Okay, now you’ve measured a task, did you make sure to measure it multiple times? Because LLMs can randomly do things differently even with the same prompt and same Claude.MD file and same strictures and same data sources. You’re gonna need at least 10 samples of each task, and you’re gonna need to make sure somebody who actually knows what they’re doing can measure them, because if you get a dimwit, they’re going to say it can do something it can’t.
Unless, of course, you can’t actually measure how many tokens a particular task can take with much accuracy, in which case every single AI token budget is bullshit. And each model does things differently depending on many different variables, some of them a result of the user, some of them a result of the AI labs themselves.
Alright, well, maybe you just need KPIs — measurements you can aspire toward, and by pursuing them you can start working out how much it costs to do stuff.
Wait, which metric works there exactly?
In fact, it’s pretty hard to measure anything like “efficiency” or “productivity” in any business, because every metric connected to them can be gamed, leaving managers and executives with the problematic situation where they have to start learning how things work so they can see if they’re good.
Before AI, this wasn’t as much of a problem, in the sense that inefficiencies and wasted hours weren’t directly connected to a chatbot that is specifically designed to burn money. Managers and executives could come up with whatever deranged, self-gratifying office bullshit they pleased, wasting hours of people’s time in the process, but doing so didn’t immediately connect to a massive, ever-increasing cost.
AI is a perfect storm of failed concepts and organizations, and the apex of the Era of the Business Idiot, an epoch where we’re ruled by people so thoroughly disconnected from the actual workforce that it was inevitable that a technology would be created specifically to grift them.
LLMs are dangerous for many, many reasons, but the under-discussed one is how well they play to a certain kind of executive imbecile. Generative AI is — to quote Mo Bitar — really good at doing an impression of work, much like most managers and c-suite executives, and even if it’s completely incapable of doing something, it’ll absolutely say it can and tell you you’re amazing for suggesting it.
And that’s why Business Idiots love it.
Where regular human beings would say annoying things like “that’s not possible within that timeline” or “we don’t have the resources to do it,” AI will say “of course, right away!” and burn as many tokens as possible.
When it makes mistakes, it’ll apologize — as it should because it failed you — but then promise to do better next time, all while costing so much less, at least in theory, than a regular, stinky human being.
It’ll create a PRD of a theoretical software project with the confident and vigor that you need to take it immediately to a software engineer and say “build this immediately,” and when the software engineer tells you a bunch of bullshit about it not being possible, it’ll spit out several convincing-sounding responses. Fuck, why even bother talking to that engineer at all? Claude Code can mock up a prototype that you can then shove in their fucking face before you fire them for not using AI to do it themselves.
Any executive-level fuckwit you’ve met in your life now has a seemingly-powerful tool that can burp up mimicry of open source software and, if you constantly prompt it, eventually get something half-functional onto some sort of web server. When you face bugs, it’ll try and fix them, sometimes also “fixing” (adding or deleting code) from elsewhere to be helpful, like when Cursor using Anthropic’s Claude Opus 4.6 model deleted an entire production database and all its backups. It will never, ever say no, even if it’s incapable, even if it has no thoughts, even if what you are asking is equal parts impossible and unreasonable in both its timescale and scope.
A Business Idiot, given his druthers, can sit there and fuck around and make an LLM spit out something that makes him feel like he’s coding, which in turn makes him feel that you, a lazy and stupid engineer, could do even more with the power of AI. It doesn’t matter that it costs an absolute shit-ton of money, or that there’s no way to measure its efficacy. The Lion does not concern himself with things like “efficacy” or “productivity,” and the Lion is increasingly tired of your whining! The Lion doesn’t even understand what it is you do every day other than not doing what The Lion is asking for!
You laugh, but this is genuinely how the majority of managers and executives think and act, and now they have a special chatbot that can fart out functional-enough prototypes to convince a Business Idiot they can do anything, because executives and managers do not regularly do much work and thus have no idea what it looks like other than when they look over your shoulder, which is why they wanted you back in the office!
Organizations aren’t burning millions or hundreds of millions of dollars a year on AI because it’s good, they’re doing it because they are run by people who do not know what the fuck they’re doing.
In a sane world, randomly adding a massive, ever-expanding operating expense to your business with the express intent of — to quote IT firm Workato’s CIO, “eating the costs while employees experiment” — would have the board blow up your house. In our world, one dominated by disconnected, self-involved and massively-overpaid dullards, many businesses pushing their workers to use AI are doing so because the other guy is doing it, with about as much strategy and forethought as one would expect from somebody who spends 90% of their life reading emails, going to meetings, or going to lunch.
The majority of those I see trumpeting the so-called benefits of AI do not appear to do anything of note. I have yet to see one so-called multi-agent orchestrator engineer psychopath ship something remarkable or impressive or even functional. I have yet to see any AI-obsessed boss write or create or author or do anything I can remember. I don’t see any of these fuckwits running a company on their own outside of those who have learned to sell stuff to other AI psychosis victims or executive midwits of varying size.
And why oh why is it always the language of inevitability and possessiveness? Nobody who’s this insistent, aggressive and violative with their language of “it’s here and if you don’t adopt it you’re stupid and dead” has ever been right about anything. Nobody this desperate, insistent and forceful has ever had good intentions, good vibes or brought good omens — they are always bearers of some kind of con.
Most technology is sold on elevating and ascending human beings. AI cheapens every interaction by creating a work-shaped product from a person that doesn’t respect you enough to give you work that’s barely fit for a human because it wasn’t made for one.
You must accept becoming a dogshit dealer that loves accepting and receiving low quality goods. You must celebrate intentionless and decaying slop, and defend it and the machine that made it with your entire being. You must sully yourself — treat its unexceptional, sloppy and unreliable outputs as signs of sentience, or at least the proof that digital sentience is possible. You must defend horrible, abrasive, ugly, loud monoliths of steel full of $50,000 graphics cards. You must say they are necessary, and you must aggressively antagonize those who do not.
Every time you defend generative AI you defend a machine of capital that has burned $1 trillion and created one of the most-wasteful products in history. If people disagree with you, you must attempt to harm them somehow — ostracize them, mock them, attack them, denigrate them. You will justify this as moral, because you have been manipulated by a technology built and sold by two of the greatest grifters of all time — Dario Amodei and Sam Altman.
Anything less is opposition to an industry with all the trappings of authoritarianism down to the media toadies, the propaganda and the seizure of land in the name of a nebulous “greater good.”
But man, these men got people good.
Sam Altman helped propagate a technology perfect for conning people with potential, a larger extrapolation of Altman’s own life of taking dogshit — Loopt, for example! — and parlaying it into larger opportunities. It can make a really half-hearted demo of a lot of things, and that’s good enough to sell to Business Idiot.
Dario Amodei took this grift and perfected it. Anthropic is a company purpose-built to con people into giving it by money by making people feel smart. LLMs can do work-shaped stuff, sometimes, as long as you debase yourself to accept mediocre and often-broken stuff that you have to keep a vigilant eye on, and either use a subsided product that loses Anthropic money or pay a shit ton of money as an enterprise to Anthropic and they still lose money.
These companies were only capable of growing in an economy dominated by the gullible and work-shy. Only a capitalist culture dominated by people who don’t actually do or know stuff have let this get so far. Nobody wants this, nobody wanted it since the beginning, it was forced upon everyone, and to pretend otherwise is laughable and offensive. The amount of people who use this shit a bit and become convinced that we’re mere years from it costing over a trillion dollars to somehow making trillions of dollars and being an entirely different and good product should be aware that they are being manipulated. The more you feel compelled to defend AI the more scrutiny you must show it.
I am not your enemy! If you think that I am, you are on the side of a corporation or a product. You can try it, like it, and I don’t really care, but the second I see you trying to be condescending or judgmental or aggressive toward another person for not agreeing with your product choices I immediately feel suspicious. Can’t you see how these people act? Can’t you see how strange it is to defend a thing you pay money for that has terrible economics? If it wasn’t the “in” thing, being an AI person would be considered really weird. I look forward to the day it is. I hope you guys like having the stuff you said since 2022 repeated back to you! I’ve been saving it all. Time is running out for a graceful bow, and you better act quick!
If you feel self conscious while other people dunk on AI, that’s weird! I see people say they don’t like Macs all the time. Who gives a fuck! I’m not going to go to the mat for Tim Cook. People can make their own decisions.
Those comparing AI to AOL mailing CDs to people should feel ashamed of themselves. This is like if every single time you opened a magazine an AOL CD flew at your head, your boss told you he would replace you with a modem if you didn’t go online, and the news constantly ran segments called “I didn’t receive an email: father forgets son forever because he wasn’t online” or panels with “Internet experts” who said “I am on the Internet superhighway right now, and I’m certain that within 10 years AOL Time Warner will be able to email myself to my dad.”
Imagine if Shingy was a billionaire and went on TV every day in 1999 and told you “the world must get ready, because you’re about to get a ICQ message from The Lord.”
Generative AI was purpose-built to grift an economy run by executives and managers who don’t actually do any work. Its success has been driven by a remarkable, society-wide ignorance in the management sect, and its continued proliferation is only possible through the media’s continued trust and faith in the idea that CEOs are busy because they’re actually doing work.
Yet even a Business Idiot eventually realizes that too much money is being spent, and the first one of these dimwits to cut their token budget will send the rest of them running for the doors.
We should lock them. We should make everybody who obsessed over theoretical ideas about what AI can or will do ashamed for their intellectual deceit or constant ignorance.
At the end of the AI era, the only thing that will change the rot at the heart of our economy is the acceptance that the majority of companies are run by lazy, self-involved and ignorant fuckwits, and accountability for those who refused to scrutinize them.
2026-05-16 00:44:27
Every day I read some sort of wrongheaded extrapolation about the future of AI — that today’s models are somehow indicative of AGI creating a “permanent underclass” of people that stops people from building software companies, or really doing any kind of job on the computer:
Hyperbolic? Perhaps. But even those who view the idea of a permanent underclass as overblown tell me that the meme contains a kernel of truth. Yash Kadadi, a 23-year-old start-up founder and Stanford dropout, summarized the sentiment of his peers: “There’s only a matter of time before GPT-7 comes out and eats all software and you can no longer build a software company. Or the best version of Tesla Optimus comes out,” and can perform all physical labor as well. In that world, this year is a human’s “last chance to be a part of the innovation.”
Yash, your peers are fucking idiots. You may as well be talking about breeding Grinches or Ninja Turtles, or kvetching about the upcoming threat from Godzilla. “The best version of Tesla’s Optimus [robot]” suggests that Tesla has released an Optimus robot, or that any prototypes are capable of anything approaching useful work, something that Tesla itself has said isn’t the case.
Every discussion of AI has become a discussion of anywhere between one and a million different theoreticals.
The Information’s headline that OpenAI will “save $97 billion through 2030 in latest Microsoft deal” — one that capped its revenue share (as in the actual money it sends to Microsoft) at $38 billion — hinges on the idea that OpenAI would somehow make $190 billion in revenue, because that’s what it would take to actually max out its revenue share.
The majority of articles about METR’s “time horizon” study of how long models take to complete tasks gush with mindless praise, but regularly leave out two valuable details: that these comparisons are made based on estimates of both human task times, and that the most-commonly shared task is based on how likely it is to complete a task 50% of the time:
The task-completion time horizon is the task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability. For example, the 50%-time horizon is the duration at which an agent is predicted to succeed half the time.
It’s the Sex Panther joke from Anchorman, except it’s a chart that gets written up in major newspapers and bandied about as proof of models becoming conscious.
Nevertheless, everybody appears to be having a lot of fun making stuff up or making ridiculous assertions based on OpenAI or Anthropic’s predictions. Likely gas leak victim Joseph Jacks posted last week that at its current rate of growth, Anthropic would pass Google’s revenue by 2028. Multiple different people I’d rather not link to are posting benchmarks of Anthropic’s still-to-be-released Mythos model as proof that we’re in the early-to-middle stages of the entirely-fictional AI 2027 “simulation,” despite the entirety of this ridiculous, oafish extrapolation relying on the idea that at some point LLMs become conscious and start doing their own research.
None of these people seem to want to engage with reality, even in their extrapolations.
Whether or not you believe the bubble will burst, it’s hard to argue (not that anybody nobody bothers to try) with my recent reporting about the lack of data centers coming online or the fact that the majority of AI revenue comes from two companies that are, in the end, hyperscalers feeding themselves money. Nobody has presented any real argument as to how Oracle completes its data centers or avoids running out of money given the fact that it needs OpenAI to be able to pay it $70 billion or more a year in the next four years to survive. The lack of any real, thoughtful response to my assertions outside of ultra-centrists and people that can’t count is a sign that I’m onto something, and I take it as a badge of pride.
But what I haven’t done recently — not since AI Bubble 2027, at least — is try my own hand at extrapolating the future based on the things I have read, seen and reported on.
Today, I’m taking a different approach, inspired by one of my favourite comic series. In Marvel’s “What If…?” writers asked questions that would entirely change the course of the Marvel Universe, such as What If The Fantastic Four Didn’t Get Their Powers, or Loki Was Worthy of Mjolnir.
I’ll be honest that there are a lot of unanswered questions I have about the AI bubble that make precise, time-based predictions almost impossible. We’re in the midst of one of the most insane market rallies in history driven around the exploding valuation of NVIDIA and data center related stocks despite there being a great deal of compelling evidence that millions of Blackwell GPUs are sitting in warehouses, meaning that the market is rallying around the idea of data centers getting built without ever confirming whether that’s actually true.
In the past, I’ve approached things from an investigative perspective, proving what I believe to be one of the greatest misallocations of capital in history. Today, I’m going to have a little more fun, exploring both the worrying signs I see and their potential consequences in the form of questions, mixing my own reporting with a little bit of fiction.
My reasoning is simple: I think people are very good at ingesting and remembering specific facts and events, but much worse at understanding their consequences. For example, Dave Lee of Bloomberg — who I adore and admire! — said that An OpenAI Bubble Is Not An AI Bubble and makes numerous correct assertions about OpenAI, but fails to consider that OpenAI accounts for $718 billion of Oracle, Microsoft, and Amazon’s backlogs, meaning that OpenAI’s collapse would leave Oracle destitute, Microsoft and Amazon short-changed, Cerebras without 80%+ of its revenue, and CoreWeave without a major client and in breach of loan covenants guaranteed by OpenAI’s revenue.
Even if Anthropic were able to mop up some of that fallow capacity, it too relies on endless venture capital and hyperscaler welfare to pay, well, increasingly-large shares of hyperscaler revenue.
I feel as if many people are willing to ask if we’re in an AI bubble, but few seem to want to talk about what might happen. It’s really easy to say “stocks are overvalued” or “OpenAI is deeply unprofitable,” but thinking much harder than that starts to make you feel a little crazy. Data center construction now makes up a larger chunk of all construction spending than commercial real estate. OpenAI has made promises that total over a trillion dollars, and Anthropic $330 billion. NVIDIA represents 8% of the value of the S&P 500, and that valuation is based on the idea that it will never, ever stop growing, which is only possible if data center construction never stops. CoreWeave, IREN, Nebius, and Nscale all rely on hyperscaler contracts that are related to OpenAI, and if those contracts go away because OpenAI does, they’re screwed.
Most people can say that these things are true, but very few of them are willing to think about their consequences, because when you do so, things begin feeling completely and utterly fucking insane.
Put another way, for me to be wrong, all of these data centers will have to get built, OpenAI will have to make and raise $852 billion in the next four years, the underlying economics of generative AI will have to improve in a dramatic and unfathomable way, and do so in such a way that it creates hundreds of AI startups that can substantiate $400 billion of annual compute revenue. For NVIDIA to continue growing its revenues at an historic rate, it will also have to, by 2028, be selling over $1 trillion in GPUs, which will require there to be funding to buy these GPUs, at a time when hyperscaler cashflows are dwindling and banks are worried they’re “choking” on AI data center debt.
The AI bubble is supported almost entirely by magical thinking and people ignoring obvious warning signs again and again and again in the hopes that at some point something changes. You can quote whatever story you like about Anthropic’s skyrocketing revenues (which are absolutely inflated) — there’s no getting away from the fact that it loses billions of dollars year, and if your answer is that it will turn profitable in 2028, please tell me how because there is no proof that it’s possible.
I also kind of get why nobody wants to think about this stuff. Even though it’s become blatantly obvious that the economics don’t make sense, the stock market continues to rip based on equities connected to the AI bubble in a way that defies logic but rewards positive speculation. Major media outlets continue publishing positive stories about the power of AI that seem entirely-disconnected from what AI can do, and millions of dollars are being spent by companies based on a theoretical return on investment.
No, really, per The Information’s Laura Bratton quoting PagerDuty CIO Eric Johnson:
“I am preparing myself to be surprised” by the bills, he said. “We believe that there’s a lot of value here. Unfortunately, it’s fairly new technology, so there’s some open questions that we’re gonna be working through” around its costs and getting a return on the investment.
We are fucking years into this man, how is the question of return on investment still an open question?
Okay, we know the answer: we’re in a bubble. Everybody is pressuring everyone else to “integrate AI,” to “get every engineer AI,” to “become more efficient using AI,” with token spend becoming some sort of vulgar status symbol despite the whole point of the AI push being that workers can be replaced, or enhanced, or, I dunno, something measurable. In the end, all that’s being measured is how many tokens employees are burning, leading to Amazon staff deliberately setting up “agents” to burn more tokens to seem more “engaged with AI” than they really are, all because dimwit managers and executives don’t understand what people do at their jobs and can only comprehend Number Go Up.
As a result, it’s far easier to fall in with the groupthink, even if it’s hysterical, nonsensical and based on flimsy ideas like “it’s just like Uber” (it isn’t) or “Amazon Web Services burned a lot of money” (it burned less than half of OpenAI’s $122 billion funding round on capex for the entirety of Amazon in the space of 15 years, adjusted for inflation), because thinking that everybody’s wrong requires you to disagree with the markets, most of social media, your boss, and your most annoying coworkers.
People also don’t really like thinking about bad things happening. They’re happy to make vague leaps in a direction that makes them feel prepared for the worst (such as the specious statements about all of these data centers being for the military or a theoretical bailout), especially if it makes them feel smart, but in doing so they get to avoid the actual bad stuff — the economic ramifications for ordinary people, the years of depression ahead for the tech industry, and the calamitous results for the market.
So, today, I’m going to have a little fun thinking about the actual consequences of everything I’ve been writing. I’m going to thread in both my own and others’ reporting, and take these ideas to their logical endpoints as far as I can.
This is going to be the first of a two-part exploration of what the actual consequences of the AI bubble bursting might be.
I’ll also caveat this by saying that these are, ultimately, explorations of potential future events rather than cast-iron guarantees. People seem to be resistant to being told the truth, so perhaps it’s time to explore these ideas as theoretical — fictional, even — so that people are more willing to take them in.
This series is all about simple scenarios, and one very simple question.
Time. Space. Reality.
It's more than a linear path — it’s a prism of endless possibility. I am the Watcher, and I am well aware of how AI generated that sentence sounds.
I am your guide through these vast new realities.
Follow me and dare to face the unknown.
And ponder the question…
What if…We’re In An AI Bubble?
2026-05-13 00:17:30
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During every bubble there’s one very obvious thing that keeps happening: things are said, these things are repeated, and are then considered fact. Sam Bankman-Fried was the smiling, friendly, “self-made billionaire” face of the crypto industry. NFTs were the future of art, and would change the way people think about the ownership of digital media.
The actual evidence, of course, never lined up. NFT trading was dominated by wash trading — market manipulation through two parties deliberately buying and selling an asset to raise the price. Cryptocurrency never took off as anything other than a speculative asset, and altcoins are effectively dead. Sam Bankman-Fried was only a billionaire if you counted his billions of illiquid FTX tokens, but that didn’t stop people from saying he wanted to save the world weeks after the collapse of Terra Luna, a stablecoin that he himself had bet against and may have helped collapse.
Three months before his arrest, a CNBC reporter would fly to the Bahamas to hear SBF tell the story of how he “survived the market wreckage and still expanded his empire,” with the answer being that he had “stashed away ample cash, kept overhead low, and avoided lending,” as opposed to the truth, which was “crime.”
The point is that before every scandal is somebody emphatically telling you that everything’s fine. Everything seems real because there’s enough proof, with “enough proof” being a convincing-enough person saying that “most of FTX’s volume comes from customers trading at least $100,000 per day,” when the actual volume was manipulated by FTX itself, and the “$100,000 a day in customer funds” were being used by FTX to prop up its flailing token.
In the end, the “proof” that SBF was rich and that FTX was solvent was that nobody had run out of money and that nothing bad had happened to anybody. SBF was a billionaire sixteen times over because enough people had said that it was true.
Anyway, one of the most commonly-held parts of the AI bubble is that massive amounts — gigawatts’ worth — of data centers have both already been and continue to be built…
…but then you look a little closer, and things start getting a little more vague. While Wood Mackenzie’s report said that there was “25GW of data center capacity added to the funnel” in Q4 2025 does not say how much came online. CBRE said back in February that “net absorption of 2497MW” happened in primary markets in 2025, with other reports saying that somewhere between 700MW and 2GW of capacity was absorbed every quarter of 2025. At the time, I reached out for any clarity about the methodology in question and received no response.
Okay, so, I know data centers are getting built and that they exist. I believe some capacity is coming online.
But gigawatts? Or even hundreds of megawatts? How much data center capacity is actually coming online?
Why did Anthropic get so desperate it took on a years old data center, xAI’s Colossus-1, full of even older chips from a competitor — one whose CEO described the company as “evil,” and that’s currently facing a lawsuit from the NAACP over allegations the facility’s gas turbines are polluting black neighborhoods?
Remember, Colossus-1 is an odd data center, with around 200,000 H100 and H200 GPUs and an indeterminate amount of Blackwell GB200s, weighing in at around 300MW of total capacity…which isn’t really that much if we’re talking about gigawatts being built every quarter, is it?
So, I have two very simple questions to ask: how long does it take to build a data center, and how much data center capacity is actually coming online?
These simple questions are surprisingly difficult to answer. There exists very little reliable information about in-progress data centers, and what information exists is continually muddied by terrible reporting — claiming that incomplete projects are “operational” because some parts of them have turned on, for example — and a lack of any investor demand for the truth. Hyperscalers do not disclose how many data centers they’ve built, nor do they disclose how much capacity they have available.
I find this utterly inexcusable, given the fact that Amazon, Google, Meta and Microsoft have sunk over $800 billion in capex (and more if you count investments into Anthropic and OpenAI) in the last three years.
So I went and looked, and what I found was confusing.
So, you’re going to hear people say “well Ed, data centers are being built,” and what I’m talking about is data centers that have been fully constructed and then turned on. It’s really, really easy to find data centers that are under construction, but as I’ve discussed in the past, that can mean everything from a pile of scaffolding to a near-complete data center.
Yet finding the latter is very, very difficult. I’ve spent the last week searching for data centers that broke ground in 2023 or 2024 that have actually been finished, and come up surprisingly empty-handed. Some projects are stuck in construction hell, eternally dueling with planning departments over permitting, some are chugging along with no real substantive updates, some, as is the case with Nscale’s Loughton, England data center, have done effectively nothing for the best part of a year, some are perennially adding more capacity to the order as a means of continuing raking in construction bills, and some are claiming their data centers are “operational” as only a single phase has turned on.
You should also know that even once construction has finished, the buildings themselves must be fully filled with the necessary cooling, power and compute hardware, at which point it can be configured to meet a client’s specifications (which can take months), at which point the unfortunate soul building the facility can actually start making money.
I think it’s also worth revisiting how difficult data center construction is, and how large these new projects are.
This starts with a very simple statement: nobody has actually built a 1GW data center (to be clear, it’s usually a campus of multiple buildings networked together) yet. There are campuses — such as Stargate Abilene — which promise to reach 1.2GW, but nearly two years in sit at two buildings at around 103MW of critical IT load each with, based on discussions with sources with direct knowledge of Abilene’s infrastructure, a third building sitting fully-constructed but with barely any gear inside it.
It’s fundamentally insane how many different companies are trying to build these things considering how difficult even the simplest data center is to build.
Take, for example, American Tower Corporation’s edge data center in Raleigh, North Carolina, which I’ll mention a little later. This is a 1MW facility — or one-thousandth the size of a gigawatt facility — occupying 4000 sq ft of real estate at first and expanding to 16,000 if ATC actually gets it up to 4MW. That’s about two-and-a-bit times larger than the typical American home. And, from ground-breaking to ribbon-cutting, it took eleven months to complete. And that’s not including all the other necessary time-consuming bits, like finding land, securing permits, and so on.
That’s a simple one. People want to build data center campuses a thousand times larger than that. Look at how difficult it is.
In fact, it’s so difficult that the companies can’t build all of it at once. Larger data center campuses are almost always divided into “phases,” in part because that’s the smartest way to build them, and in part with the express intention of convincing you that they’re “fully operational.”
For example, CNBC’s MacKenzie Sigalos reported in October 2025 that Amazon’s Indiana-based (allegedly) 2.2GW Project Rainier data center was “operational,” but only seven out of a planned 30 buildings were actually operational, and her comment of “with two more campuses [of indeterminate capacity] underway.” This comment was buried two videos and 600 words into a piece that declared the data center was “now operational,” with the express intent of making you think the whole thing was operational.
To give her credit, at least she didn’t copy-paste the outright lie from Amazon, which claimed that Rainier was “fully operational” in a press release the same day. You’ll also note that Amazon never provides any clarity about the actual capacity of Rainier.
Sigalos did exactly the same thing when the first (of eight) buildings of Stargate Abilene opened, declaring that “OpenAI’s first data center in $500 billion Stargate project is open in Texas,” burying the comment that only one was operational with another nearly complete several hundred words earlier.
These are intentionally attempts to obfuscate the actual progress of the data center buildout, and if I’m honest, I’ve spent months trying to work out why big companies that were supposedly building large swaths of data centers would be trying to do so.
Unless, of course, things weren’t going to plan.
In its last (Q3 FY26) quarterly earnings call, Microsoft CEO Satya Nadella claimed that “[Microsoft] added another gigawatt of capacity this quarter, and [remained] on track to double [its] overall footprint in two years.” A quarter earlier, he claimed to have added “nearly one gigawatt of total capacity,” with Karl Keirstead of UBS saying that he “...thought the one gigawatt added in the December quarter was extraordinary and hints that the capacity adds are accelerating.”
As I’ll discuss below, I can find no evidence of anything more than a few hundred megawatts of Microsoft’s data center capacity coming online. While I’ll humour the idea that it doesn’t announce every new data center, and that there may be colocation and neocloud counterparties (67% of CoreWeave’s revenue comes from Microsoft, for example) that make up the capacity, as I’ll also discuss, I don’t know where the hell that might be.
So, to be aggressively fair, I asked Microsoft to answer the following questions on May 4, 2026:
A Microsoft representative from WE Communications promised to "circle back" by 5PM ET on Monday May 4th, but did not return further requests for comment via text and email, which is incredibly strange considering the simple and straightforward nature of my questions.
That’s probably because the vast majority of its publicly-announced or documented data center capacity doesn’t appear to be getting finished.
In September 2025, CEO Satya Nadella claimed that Microsoft had added 2GW of capacity “in the last year,” and acted as if Fairwater, a project with two actively-constructed data centers with one in Wisconsin that broke ground in September 2023 and another in Atlanta that broke ground in July 2024, was something to be “announced” rather than “a very expensive project that has taken forever.” Nadella also claimed that there are “multiple identical Fairwater datacenters under construction,“ though he neglected to name them.
To be clear, “Fairwater” refers to a project where multiple data centers are linked with high-speed networking to make one larger cluster, a project that sounds ambitious because it is, and also unlikely because it’s yet to have been built.
Fairwater Atlanta — the latter of the Fairwaters — was “launched” in November 2025 and it’s unclear how much capacity it has. Cleanview claims it’s at 350MW of capacity, and Microsoft’s own community outreach page claims construction would be completed by the beginning of October 2025, but, as I’ll get to, it’s unclear whether this is just one phase, given that reporting shows multiple other buildings still under construction.
I have serious doubts that Microsoft stood up a 350MW data center in less than a year, given everything else I’m about to explain.
Fairwater Wisconsin is also a data center of indeterminate size, but Cleanview claims Phase 1 is 400MW, quoting a story from FOX6 News Milwaukee from September 2025 that said that Microsoft was “investing an additional $4 billion to expand the campus,” featuring a video of a very much in construction data center saying the following:
Microsoft is in the final phases of building Fairwater, the world’s most powerful AI data center, in Mount Pleasant. Microsoft is on track to complete construction and bring this AI data center online in early 2026, fulfilling their initial $3.3 billion investment pledge.
So, $3.3 billion — at a rate of around $14 million per megawatt per analyst Jerome Darling of TD Cowen — is about 235MW of capacity, which is a lot lower than 400MW.
Seven months later, Satya Nadella said that the Fairwater datacenter in Wisconsin was “going live, ahead of schedule,” a sentence written in the present tense, but also said that it “will bring together hundreds of thousands of GB200s in a single seamless cluster,” which is in the future tense.
It’s a great time to remind you that Microsoft claims that it brought online roughly eight times that capacity (around 2GW) in the past six months.
To make matters worse, it doesn’t appear that Fairwater Wisconsin is actually operational. Ricardo Torres of the Milwaukee Journal-Sentinel reports that Microsoft has said it isn’t actually online, and that while there “...is equipment inside the data center conducting start-up opportunities…the company anticipates [they] will continue to happen for the next several weeks.”
Epoch AI’s satellite footage of Fairwater Wisconsin — which mentions a completely wrong capacity because it’s uniquely terrible at calculating it (it claimed Colossus-1 has 425MW capacity, for example) — notes that as of April 2026, one building appeared to be operational, with a second under construction.
So, that’s one building in Wisconsin that might be complete, and based on the permitting application from August 2023 dug up by Epoch, the project is designed to have 117MW of capacity, which is a lot lower than 235MW. While Epoch didn’t have permitting for building two, it did for three and four, which are designed to have around 719MW of capacity, and as of April 2026 still appear to be slabs of concrete.
In simpler terms, there’s at most around 117MW of capacity running at Fairwater Wisconsin.
Sidenote: To be clear, I think some revenue is being generated from a Fairwater data center, as my reporting from last year on OpenAI’s inference spend involved a few million dollars’ worth of billing for “Fairwater,” but it’s unclear whether that referred to Fairwater Atlanta or Wisconsin.
The Fairwater data centers are Microsoft’s most-publicized data centers, yet they’re shrouded in secrecy, with the Atlanta Journal-Constitution having to file an open records request to find the site being developed by QTS, a data center developer owned by Blackstone. Videos of Fairwater Atlanta from last November show a giant campus with two large buildings and a patch of yet-to-be-developed dirt. DataCenterMap refers to it as “under construction.”
Epoch AI’s satellite footage notes that as of February 2026, building four’s roof was complete and “all mechanical equipment appears to be installed,” but “there is still a lot of construction activity around the building.” Based on air permits filed as part of the project (that Epoch found), it appears that each building is powered by a number of Caterpillar 3516C Generator Sets at around 2.5MW each, with building one having 47 (117.5MW), building two having 13 (32.5MW), building three having 30 (75MW), and building four having 35 (87.5MW).
If we’re very generous and assume that three buildings are complete, that means that Fairwater Atlanta is at around 225MW of capacity (not IT load!).
So, that’s about 342MW of data center capacity being built by one of the largest companies in the world, in its most-publicized and written-about data centers.
Put another way, for Microsoft to come remotely close to its so-called 2GW of capacity in the last six months, it will have had to bring online a little under six times that capacity.
I’m calling bullshit.
I really did want Microsoft to give me some answers, but I’m very confused as to how it can remotely claim it brought even a gigawatt of capacity online in the last year.
I also question whether Microsoft is actually building multiple other “identical” Fairwater data centers, as I can’t find any announcements or pronouncements or mentions or hints as to where they might be.
In fact, I’m having a little trouble finding where else Microsoft has been building data centers, and those I can find are extremely suspicious.
In Microsoft’s announcement of its Wisconsin data center, it mentioned two other projects — one in Narvik Norway that had already been announced months beforehand by OpenAI, and another with Nscale in Loughton, England that was also announced by OpenAI that very same day as part of the entirely fictional Stargate project.
If you’re wondering how those are going, Microsoft had to take over the entire Narvik project (which does not appear to have started construction) from OpenAI, and the Loughton data center (which OpenAI also backed out of) is currently a pile of scaffolding.
For two straight quarters, Microsoft has said it’s brought on an entire gigwatt of capacity,and I have to ask: where?
Because when you actually look at the projects it’s announced, very little appears to have been built, and that which has is nowhere near its theoretical capacity.
To be specific about what Microsoft is claiming, it’s saying it’s brought around 4GW of capacity online in the space of two years, and at a 1.35 PUE, that’s about 2.96GW of critical IT load, which works out to the power equivalent of around 284,600 H100 GPUs, which may be possible — after all, Microsoft apparently bought 450,000 H100 GPUs in 2024 — but I can’t find much evidence of data centers that could house that many GPUs, nor that might be in construction.
Let’s dig in.
Microsoft broke ground on three data centers in Catawba County North Carolina in 2024 — one in Hickory, another in Lyle Creek, and another in Boyd Farms:
Alright, maybe I’m being unfair! Maybe it’s just a North Carolina problem. There must be another that broke ground and got built…right?
Microsoft also broke ground on a data center in Quebec City, Canada in September 2024, and as of April 2026, “generator testing has been completed,” and “civil works will continue until Autumn 2026.”
Okay, well, maybe it’s a Canada problem. What about Microsoft’s New Albany, Ohio data center that broke ground in October 2024? Well, as of March 2026, “spring activity would resume,” and “beginning soon, soil will be delivered to the site via a designated truck route. I’ll note that Microsoft specifically says that Ames Construction is currently leading it, and that it will “resume the lead role in project communications” once the final phase of construction is done at some unknown time.
Alright, well, how about the August 2025 ground breaking in Cheyenne, Wyoming that was allegedly “due to launch in 2026”?
Well, Microsoft hasn’t updated its community page since it said there’d be a community meeting planned for November 2025 and that “neighbors within the vicinity will be notified ahead of construction,” which sounds like construction is yet to commence. Not to worry though, it announced on April 14, 2026 that it planned to expand it to “accelerate innovation and economic growth”
How about that 2023-announced Southwest Hortolândia Brazil data center? That’s right, the last update was in September 2025, and the update was “construction activities continue to progress in alignment with local regulations.” A piece from Folha De S.Paulo from March 2026 mentioned that Microsoft “had begun operating its first artificial intelligence data centers in Brazil,” but satellite footage shows that it’s barely finished.
What about the Newport, Wales data center it announced in 2022? Well, as of November 2025, a politician was standing on a concrete slab saying how many jobs it’ll theoretically bring in, which it won’t.
What about Microsoft’s four data centers in Irving, Texas, announced December 2024? The best I’ve got for you is a news report about a data center in Irving Texas breaking ground in January 2025. Its San Antonio data center, announced in July 2024? Well, construction was underway as of December 2025, and it appears that construction will begin in the summer of 2026 on another one in the area.
How about the two data centers outside of Cologne, Germany, announced in November 2024? Well, as of September 2025, Microsoft has…plans to build one of them?
…what about the 900 acres of land it bought in June 2024 in Granger, Indiana? Great news! According to 16NewsNow, Microsoft officials “could break ground on a proposed data center…in late April or early May [2026].”
How about Project Ginger West, a data center planned in Des Moines. Iowa since March 2021? Hope you like waiting, because Microsoft itself says that it’s estimated to finish construction in Summer 2028. Ginger East, announced a few months later? Mid-2028. Project Ruthenium (announced 2023)? I don’t have shit for you I’m afraid.
This company claims it’s built four fucking gigawatts of capacity, but when I go and look to see what it’s actually built I’ve failed to find a single announced data center from the last three years that got turned on outside of its Fairwater Atlanta and Wisconsin sites.
To be clear, all of these sites are somewhere in the 200MW to 300MW range. For Microsoft to have brought online 4000MW of data center capacity in the last two years would require it to have completed thirteen or more of these projects, all while choosing not to promote them, with every project operating in such a veil of secrecy that no local or national news outlet reported a single one of them.
I truly cannot work out how Microsoft has brought on any more than 500MW of capacity in the last year based on my research, and think Microsoft is deliberately obfuscating whether said capacity was contracted rather than actively in-use, much like CoreWeave refers to itself having 3.1GW of “total contracted power” but only added 260MW of active power capacity in a single quarter at the end of 2025.
Sidenote: If you’re wondering why CoreWeave didn’t include how much active power it added in its Q1 2026 earnings press release, it’s because (per its own earnings presentation) it only added 150MW, in a quarter it contracted 400MW. It also said it added six new data centers, which I doubt.
However, the exact verbiage used in Microsoft’s earnings transcripts is that it “added another gigawatt of capacity,” which sounds far more like it’s saying it brought them online…
…but it didn’t, right? It obviously hasn’t.
Where are all the data centers, Satya? Where are they? Why are your PR people too scared to tell me?
No, really, where are they?
So, to be fair, analyst Ben Bajarin, one of the more friendly pro-AI posters, argues that actually all of that capacity is secretly behind-the-scenes, something I’d humour if there was any kind of paper trail to a bunch of Microsoft data centers that were secretly being built.
I’d also be more willing to humour it if any of the data centers that have been publicized as “breaking ground” had actually been finished, or if both Fairwater Atlanta and Wisconsin weren’t so deceptively-marketed.
My only devil’s advocate is that Microsoft could, in theory, be working with colocation partners to stand up several gigawatts of capacity through shell corporations and SPVs, but even then, not a single one has any sort of trail to Microsoft? All of that capacity?
It’s really, really weird, and the only answers I get are smug statements about how “Fairwater is ahead of schedule.”
But if I’m honest, I’m having trouble even making these numbers add up.
Considering how loud, offensive and conspicuous the AI bubble has become, it feels like we should have a far, far better understanding of how much actual capacity has been built.
I also think it’s time to start being realistic about how long these things are taking to build.
For example, I was only able to find a few data centers that for sure, categorically, definitively opened, and for the most part, it appears that a data center takes around 18 months to go from groundbreaking to opening.
And these, I add, are all facilities that are relatively modest — at least, when compared to the kinds of gigawatt-scale campuses that are reportedly in active development.
Digging deeper, I found a lot of projects stuck in development Hell:
While there are absolutely data centers under construction, and some, somewhere, are actually being completed, the vast majority of projects I’ve found are either in a mysterious limbo state or, in most cases, under construction years after breaking ground.
Across the board, the message seems to be fairly simple: it takes about 18 to 24 months to build any kind of data center, and the bigger they are, the less likely they are to get completed on schedule.
Those that actually “come online” aren’t actually fully constructed, but have brought on a single phase — something I wouldn’t begrudge them if they were anything close to honest about it. In reality, data center companies actively deceive the media and customers about the actual status of projects, most likely because it’s really, really difficult to build a data center.
In any case, what I’ve found amounts to a total mismatch between the so-called “rapid buildout” of AI data centers and reality.
It also doesn’t make much sense when you factor in how many GPUs NVIDIA sold.
In October last year, NVIDIA CEO Jensen Huang told reporters that it had shipped six million Blackwell GPUs in the last four quarters, though it eventually came out that he was counting two cores for every GPU, making the real number three million. I disagree with the framing, I think it’s incoherent and dishonest, but I’ve confirmed this is what NVIDIA meant.
In any case, if we assume two cores per GPU, a B200 GPU has a power draw of around 1200W, for around 3.6GW of IT load for 3 million of them. I realize that NVIDIA also sells B100 and B300 GPUs (similar power draw) and NVL72 racks of 72 GB200 GPUs and 36 CPUs, but bear with me.
Blackwell GPUs only started shipping with any real seriousness in the first quarter of 2025, which means that a good chunk of these data centers were built with H100 and H200 GPUs in mind. Nevertheless, I can find no compelling evidence that significant amounts — anything over 500,000 GPUs — of Blackwell-based data centers have been successfully brought online.
When I say I struggled to find data centers that had been both announced and brought online, I mean that I spent hours looking, hours and hours and hours, and came up short-handed.
I want to be clear that I know that there is Blackwell capacity actually being built, and believe that the majority of that capacity is retrofits of previous data centers, such as Microsoft’s extension to its Goodyear Arizona campus which it began building in 2018 that likely houses Blackwell GPUs.
But I no longer believe that the majority of Blackwell GPUs are doing anything other than collecting dust in a warehouse. Blackwell GPUs require distinct cooling, a great deal more power than an H100, and cost an absolute shit-ton of money, making it unlikely that a 2023 or early-2024 era data center could handle them without significant modifications.
I fundamentally do not believe more than a million — if that! — Blackwell GPUs are actually in service.
If that’s the case, NVIDIA is likely pre-selling GPUs years in advance — experimenting with the dark arts of “bill-and-hold” — and helping certain partners like Microsoft install the latest generation to create the illusion of utility, availability and viability that does not actually exist.
If I’m honest, I also have serious questions about the current status of many H100 and H200 GPUs. Based on what I’ve found, I’d be surprised if more than 3GW of actual capacity was turned on in the last two years, which means that NVIDIA has sold anywhere from double to triple the amount of GPUs that the world can hold.
While the Anthropic-Musk compute deal is an obvious sign about xAI’s lack of demand for compute, it’s also, as I mentioned earlier, a clear sign that AI data centers are mostly not getting finished, and those that do get finished are taking two or three years even for smaller builds.
While it sounds a little wild, I think in reality only a few hundred megawatts — if that — of actual, usable AI compute capacity is being spun up every quarter. If I was wrong, there’d be significantly more progress on, well, anything I could find.
Why can’t Microsoft offer up a data center that isn’t called Fairwater, and why are its Fairwater data centers taking so long? How much actual capacity has Microsoft brought online? Because it certainly isn’t fucking 2GW in six months.
I’m willing to believe that Microsoft has a number of collocation agreements with parties that don’t disclose their involvement. I’m also willing to believe that Microsoft doesn’t publicize every single data center it’s building or has built.
2GW of capacity is a lot. It’s nearly ten times the (likely) existing capacity of Fairwater Atlanta. If Microsoft is bringing so much capacity online, why can’t we find it, and why won’t they tell us? And no, this isn’t some super secret squirrel “they’re building secret data centers for the government” thing, it’s very clearly a case where “capacity” refers to “something other than data centers that actually got brought online.
Despite their ubiquity in the media, AI data centers are relatively new concepts that are barely five years old. They are significantly more power-intensive than a regular data center, requiring massive amounts of cooling and access to water to the point that the surrounding infrastructure of said data center is often a massive construction project unto itself.
For example, OpenAI and Oracle’s Stargate Abilene data center is (in theory) made up of two massive electrical substations, a giant gas power plant and eight distinct data center buildings, each with around 50,000 GB200 GPUs, at least in theory. Every data center requires that power exists — as in it’s being generated in both the manner and capacity necessary to turn it on, either through external or grid-based power — and is accessible at the data center site.
This means that every single data center, no matter how big, is its own construction nightmare. You’ve got the power, the labor, the permits, the planning, the construction firm, the power company, the specialist gear, the temporary power (because on-site power is slow), the backup power (because you can’t just rely on the grid for something you’re charging millions for!), the cooling, the uninterruptible power supplies — endless lists of shit that needs to go very well or else the bloody thing won’t work.
These are very difficult and large projects to complete. Edged Computing’s (theoretically) 96MW data center in Illinois is 200,000 square feet in effectively two large squares. For comparison, every single inch of gambling space in Caesar’s Casino Vegas is around 130,000 square feet. These things are fucking huge, fucking difficult, and fucking expensive, and all signs point to capacity not coming online.
Let’s go back to Anthropic mopping up Musk’s fallow data center capacity, which stinks of desperation for both companies. If there were modern data centers full of GB200s being turned on and available anywhere in the next month or two, wouldn’t it be more financially prudent to wait for it, even if it’s just on an efficiency level? A franken-center made up of H100s and H200s with some GB200s stapled onto the side feels like a stopgap solution.
I have similar questions about the results of adding this capacity — that “...Anthropic plans to use [it] to directly improve capacity for Claude Pro and Claude Max subscribers,” “doubling” (whatever that means) the 5-hour rate limit and removing the recently-added peak rate limits.
What’s the plan here, exactly? Less than a month ago Anthropic’s Head of Growth, Amol Avasare, said that Anthropic was “looking at different options to keep delivering a great experience for users” because Max accounts were created before the era of Claude Code and Cowork. How does adding 300MW of capacity magically resolve that problem? Was that always the plan?
Or was this a knee-jerk reaction to the surging popularity of OpenAI’s Codex? Because the original justification for peak hours was that Anthropic needed to manage “growing demand for Claude,” demand that I bet Anthropic claims hasn’t gone anywhere.
It’s also important to remember that last year, OpenAI’s margins (which are already non-GAAP), per The Information, were worse than expected because (and I quote) it had to “..to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models.”
In other words, Anthropic has deliberately tanked its already-negative 2026 gross margins by desperately buying the fallow compute from a company whose CEO threw up the nazi salute, called the company “misanthropic and evil,” and has the “right to reclaim the compute” if Anthropic “engages in actions that harm humanity.”
Surely you’d wait a few months for some new, less tainted source of compute, right? And surely it wouldn’t be such a big deal, because new data centers get switched on every day, right?
Right?
So, let’s get to brass tacks.
Anthropic and OpenAI have now committed to spending $748 billion across Amazon Web Services, Google Cloud, and Microsoft Azure, accounting for more than 50% of their remaining performance obligations. The very future of hyperscaler revenue depends both on Anthropic and OpenAI’s continued ability to pay and both of them having something to actually pay for.
I also think it’s fair to ask why Microsoft’s theoretical gigawatts of new compute aren’t producing tens of billions of dollars of new revenue.
Microsoft’s $37 billion in annualized AI run rate (sigh) is mostly taken up by OpenAI’s voracious demands for its :compute, and only ever seems to expand based on OpenAI’s compute demands and the now 20 million lost souls paying for Microsoft 365 Copilot. There’s supposedly incredible, unstoppable demand for AI compute, and Microsoft is apparently sitting on gigawatts’ worth, but somehow those gigawatts don’t seem to be translating into gigabillions, likely because they don’t fucking exist.
All of this makes me wonder what Google infrastructure head Amin Vahdat meant last November when he said that Google needed to double its capacity every six months to meet demand. Many took this to mean “Google is doubling its capacity every six months,” but I think it’s far more likely that Google is taking on capacity requests from Anthropic that are making said capacity demands necessary. Similarly, I think CEO Sundar Pichai’s comment that it would have made more money had it had more capacity to sell was a manifestation of a distinct lack of new capacity rather than a result of bringing on swaths of new data centers that immediately got filled.
I also need to be blunt on two things:
Look, I know it sounds crazy, but I’m telling you: I don’t think very many data centers are coming online! While I keep wanting to hedge my bets and say “I bet a few gigawatts came online,” I cannot actually find any compelling literature that backs up that statement. I’ve spent hours and hours looking, and I’ve come up with a few hundred megawatts delivered in the past two years. Every major project is stuck in the mud, a phase or two in, or facing mounting opposition from locals that don’t want a Godzilla-sized cube making a constant screaming sound 24/7 so that somebody can generate increasingly-bustier Garfields.
I’m not even being a hater! It’s just genuinely difficult to find actual data centers that have been announced that have also been fully turned on.
So, humour me for a second: if hyperscalers are bringing on hundreds of megawatts of capacity a year, then that means that the ever-growing quarterly chunks of depreciation ripped out of their net income are just a taste of what’s to come.
Last quarter, Google’s depreciation jumped $400 million to $6.482 billion, with Microsoft’s jumping nearly a billion dollars from $9.198 billion to $10.167 billion, and Meta’s from $5.41 billion to $5.99 billion. While Amazon’s technically dropped quarter-over-quarter, it still sat at an astonishing $18.94 billion.
Remember: depreciation only increases when an item is actually put into service. If Microsoft, Google, Amazon and Meta are sitting on tens of billions of yet-to-be-installed GPUs, and said GPUs are only being installed at a snail’s pace every quarter, that means that these depreciation figures are set to grow dramatically. In fact, year-over-year, Google’s depreciation has jumped 30.7%, Amazon’s 24.7%, Microsoft’s 23.9%, and Meta’s an astonishing 34.9%.
And that’s with an extremely slow pace of deployment.
Sidenote: This also really makes me doubt that Microsoft has been bringing a gigawatt of GPU capacity for two quarters straight. A gigawatt of GPU capacity would be about $2 billion a quarter or more in depreciation. A $400 million bump in depreciation is about $9.6bn ($400 million times 24 quarters (6 years)), at about $50,000 per B200 GPU, or around 192,000 GPUs at 1200W each, for around 230.4MW.
Hell, someone could probably sit down and work out their potential capacity based on depreciation alone.
I do kind of see why the hyperscalers are sinking capex into these big AI infrastructure gigaprojects now, though. Shareholders are currently tolerating the capex because they think stuff is coming online, and that’s where the “incredible value” is. When a $20 billion or $30 billion a quarter depreciation bill first rears its head — as I said, Amazon is close, reporting $18.945bn in depreciation and amortization expenses in the most recent quarter — it’ll become obvious that the only people seeing value from AI are Jensen Huang and one of the massive construction firms slowly building these projects.
Actually, it’s probably important to state that I don’t think the majority of these projects are doing anything untoward I just don’t think any of them realized how difficult it is to build a data center, and unlike basically any other problem the tech industry has ever faced, simply throwing as much money as possible at it doesn’t really change the limits of physical construction.
I think every one of these data center projects is its own individual construction nightmare, and thanks to the general market psychosis around the AI bubble, nobody has thought to question the core assumption that these things are actually getting built.
With all that being said, I’m not sure that anyone building these things is moving with much urgency either. Perhaps they don’t need to — perhaps hyperscalers are happy, because they can continually string out both the AI narrative and put off those massive blobs of depreciation.
But we really do need to reckon with the fact that nearly two years in, Stargate Abilene has only two buildings’ worth of actual, operational, revenue generating capacity, and nobody has given me an answer as to how it doesn’t have even a quarter of the 1.7GW of power it’ll need to turn everything on, if it ever gets fully built.
Maybe they can really pick up the pace, but as of early April, barely any actual gear was in the third building.
And then we get to the other problem: Oracle.
As I’ve discussed before, Oracle is building 7.1GW of total capacity for OpenAI, and keeps — laughably! — saying 2027 or 2028, when at this rate, Stargate Abilene won’t be done until mid-2027, and the rest either never get finished or are done in 2030 or later.
This is setting up a horrifying situation where Oracle desperately needs OpenAI to pay it for capacity that doesn’t exist, and if it ever gets built, it’s likely to be years after OpenAI has run out of money, which is the same problem that Microsoft, Google, and Amazon have with their $748 billion of deals with Anthropic and OpenAI, though thanks to the $340 billion or more necessary to build the Stargate data centers, Oracle’s problems are far more existential.
I’ve repeatedly — and correctly! — said that the problem is that these companies didn’t have the money to pay for their capacity, but Oracle lacks Microsoft or Google’s existing profitable businesses to fall back on if these data centers are delayed, with its existing business lines plateauing and its only real growth coming from theoretical deals with OpenAI and GPU compute with negative 100% margins.
Anthropic’s desperation for new sources of compute also suggests that it’s bonking its head against the limits of its capacity, and will continue to do so as long as it continues to subsidize its users. I also think that the slow pace of construction will eventually lead to OpenAI facing similar problems.
These companies need to continue growing to continue to raise the hundreds of billions of dollars in funding necessary to pay Oracle, Google, Microsoft, and Amazon their respective pounds of flesh.
It’s now very clear that the whole “inference is profitable” and “most compute is being used for training” myths are dead, because if they weren’t, Anthropic would either need way more compute or way higher-quality compute. Colossus-1 was specifically built as a training cluster, yet its current use is “reduce rate limits for our subsidized AI subscriptions,” which is most decidedly inference provided by three-year-old hardware.
Despite writing over 9000 words and driving myself slightly insane trying to find out, I still haven’t got an answer as to how much actual data center capacity has come online. Hyperscalers have clearly been retrofitting old data centers to fit their new chips, and based on my research, I can find no compelling evidence that they’ve added more than a few hundred megawatts a piece since 2023.
What I do know is that, across the board, a data center of anything above 50MW (or lower, in some cases) takes anywhere from 18 to 36 months to complete, and nobody has actually built a gigawatt data center despite how many people discuss them.
For example, Kevin O’Leary — known as “Mr. Dogshit” to his friends — is allegedly building a 9GW data center in Utah, but he may as well say that he’s building a unicorn that shits Toyota Tacomas, as doing so is far more realistic than a project that will likely cost $396 billion, assuming that locals and bankers don’t drag him to The Other Side like Dr. Facilier.
Nobody has built a 1GW data center, so I severely doubt Mr. Dogshit will be able to do anything other than create another scandal and lose a bunch of people’s money.
In other words, any time you hear about a “new data center project,” add a year or two to whatever projection they give. If it’s 2027, assume 2029, or that it never gets built. Anything being discussed as “finished in 2030” may as well not exist.
Sidenote: In general, the only projects that take anything less than a year are tiny — a megawatt max — other than Elon Musk’s Colossus-1, a Frankenstein’s monster of GPUs that vary between 1 and 3 years old.
While Musk claims it took 122 days to build, that was A) only the first 100,000 H100 GPUs and B) only possible because they used an old powered shell from an Electrolux factory and thirty horrible, inefficient methane gas turbines. It cost way, way too much, and was obviously such a liability that Musk flogged it the second he could.
In any case, what I’m suggesting is that very, very few data centers are actually getting finished, and if that’s true, NVIDIA has sold years worth of chips that are yet to be digested.
And if that’s true, somebody is sitting on piles of them.
I’m trying to be fair, so I’ll assume that an unknown amount of data centers got retrofitted to fit Blackwell GPUs. But I also refuse to believe that even half of the three million Blackwell GPUs that got shipped have actually been installed. Where would they go? You can’t use the same racks for them that you would with an H100 or H200, because Blackwell requires so much god damn cooling.
Another sign that these things aren’t actually getting installed is Supermicro’s $1.4 billion or so of B200 GPUs left in inventory from a canceled order from Oracle.
Why not? Isn’t this meant to be a chip that’s extremely valuable? Isn’t there infinite demand? Is there not a place to put them? Apparently Oracle wanted to use faster GB200 GPUs from Dell, but why aren’t there other customers lining up to buy these things?
Also…how was Oracle able to cancel an order of over a billion dollars’ worth of GPUs?
Can anybody do that? Because if they can, one has to wonder if this doesn’t start happening as people realize these data centers aren’t getting built.
Pick a data center. It’s probably barely under construction, or if it’s “finished” it’s actually “partly done” with no real guide as to when the rest will finish.
Remember that $17 billion deal with Microsoft and Nebius signed? The one that’s a key reason why Nebius’ stock is on a tear? Well, its existence is based on the continued construction of a data center out in Vineland, New Jersey facing massive local opposition, and multiple sources now confirm that construction has been halted due to local planning issues. The data center is horribly behind schedule already, and Microsoft has the option to cancel its entire contract if Nebius fails to meet milestones.
That data center is a major reason that people value Nebius’ stock! It cannot make a dollar of revenue without its existence! It has the funds and blessing of Redmond’s finest — the Mandate of Heaven! — and it can’t get things done! This is bad, and indicative of a larger problem in the industry — that it’s really difficult to build data centers, and for the most part, they’re not being fully built!
You’ve heard plenty about data centers getting opposed and canceled — how about ones that fully opened? No, really, if you’ve heard about them please get in touch, because it’s really difficult to find them.
Why don’t we know? This is apparently the single most important technology movement since whatever the last justification somebody made up was, shouldn’t we have a tangible grasp? Because the way I see it, if these things aren’t coming online at the rate that people think, we have to start asking for fundamental clarity from NVIDIA about where the GPUs are, and when they’re coming online.
NVIDIA’s continually-growing valuation is based on the conceit that there is always more demand for GPUs, and perhaps that’s true, but if this demand is based on functionally selling chips two years in advance. That makes NVIDIA’s yearly upgrade cadence utterly deranged. Buy today’s GPUs! They’re the best, for now, at least. By the time you plug them in they’re gonna be old and nasty. But don’t worry, it’ll take two years for you to install the next one too!
To be clear, Blackwell GPUs are absolutely being installed! But three million of them?
People love to use “enough to power two cities” to illustrate these points, but I actually think it’s better to illustrate in real data center terms.
Stargate Abilene has taken two years to build two buildings of around 103MW of critical IT load. 3 million B200 GPUs works out to about 3.6GW of IT load. Do you really think that nearly thirty five Stargate Abilene-scale buildings were built in 2025? If so, where are they, exactly?
You may argue that other data centers are smaller, and thus it would be easier to build. So why can’t I find any examples of where they’ve done so?
By all means prove me wrong! It’s so easy! Just show me a data center announced or that broke ground in 2023 and find obvious proof it turned on. I’ll even give you credit if it’s partially open!
The problem is that I keep finding examples of “partially complete” and those are the only examples of “finished” data centers.
Isn’t this a little insane? This is all we’ve heard about for years, everybody is ACTING like these things exist at a scale that I’m not sure is actually true!
I expect a fair amount of huffing and “well of course they’re coming online” from the peanut gallery, but come on guys, isn’t this all kind of weird? Even if you want to marry Sandisk and name your children “Western” and “Digital,” why can’t you say with your whole chest several data centers that got finished? We have macro level “proof” but when you try and look at even a shred of the micro you find a bunch of guys with their hands on their hips saying “sorry mate that’ll be another $4 million.”
Something doesn’t line up, and it’s exactly the kind of misalignment that happens in a bubble — when infrastructural reality disconnects from the financials. NVIDIA is making hundreds of billions of dollars and it’s unclear how much of it is from GPUs installed in operational data centers. It feels like Jensen Huang might have run the largest preorder campaign of all time.
This has massive downstream consequences. Sandisk, Samsung, SK Hynix, Broadcom, AMD, Microsoft, Google, Oracle, and Amazon’s remaining performance obligations total [find] and are dependent on being *able* to sell gigawatts worth of computing gear or compute access. If data centers are not getting built in anything approaching a reasonable timeline, that makes the future of these companies only as viable as the construction projects themselves. Even if you truly believe Anthropic will be a $2 trillion company and a $200 billion customer of Google, the compute capacity has to exist to be bought, and it does not appear to be built or, in many cases, anywhere further than the earliest stages of construction.
If they don’t get built in the next few years, there’s no space for that solid state storage or those instinct GPUs. There’s no reason for NVIDIA to have reserved most of TSMC’s capacity, either.
There’s also no reason to get excited about Bloom Energy, as it’s not making real revenue on those until Oracle finishes its data centers sometime between the next two years and never.
And if they don’t get built, hundreds of billions of dollars have been wasted, with large swaths of those billions funded by private credit, which in turn is funded by pensions, retirements and insurance funds.
I’ve got a bad feeling about this.
