2026-10-03 02:46:35
This week, the Wall Street Journal ran an alarming illustration of AI’s potential share of GDP that I’d argue did more to muddy the waters than actually telling anyone anything, mostly because its measurement, for whatever reason, covered 2025 to 2032, meaning that six out of the eight years of the analysis were estimates of spending.
To be fair, this analysis came from the Brookings Institution’s Stijn van Nieuwerburgh rather than the Journal itself, but I cannot express how profoundly unhelpful it is to discuss things years in the future.
The Journal itself acknowledged this, giving us a far-more-useful number, emphasis mine:
Projecting investment is tricky, and total spending might well end up substantially lower. Still, the money poured into data centers this year already represents an investment unprecedented in recent history. AI investment in the U.S. is projected to hit 1.9% of GDP in 2026, according to new estimates from Goldman Sachs. The railroad boom of the late 19th century marked the last time the build-out of one new industry accounted for a larger share of the economy.
Yet it’s important to be specific that this is almost entirely a result of AI data center construction and GPU sales rather than anything to do with the companies actually renting AI compute. In other words, AI itself isn’t helping boost America’s economic growth, but rather the infrastructure for it to theoretically run on.
This is extremely problematic, because it means that at some point data center construction will slow or stop (as discussed in this week’s free newsletter) through some combination of moratoriums and ever-pricier debt, removing any contribution to GDP and leaving AI — either through the revenue generated from selling services or productivity improvements — to cover the shortfall.
The problem with calculating the exact contribution of the tech industry is that so many different pieces of these companies flow into different “industries” (of which there are seventeen in total) in the BEA’s data based on the specific economic contribution.
For example, Apple’s services vertical (like iCloud) would flow into the “US Information/ICT” indices of GDP calculation, but its sales of iPhones, Macs and iPads would flow into manufacturing, the same place where NVIDIA’s GPU sales would go. ICT also doesn’t include consultancy revenue or IT services from companies like Accenture, but that isn’t really relevant to the analysis.
In any case, the ICT industry’s contribution to GDP is actually very, very useful for this calculation, because it specifically includes sales of AI software and rentals of AI GPUs. There’re two numbers to look at here. As a share of nominal GDP — strictly how many dollars it’s contributed to GDP — tech’s contribution has been flat for the last two years. In other words, all those supposed GPU rentals and AI software sales in 2024 and 2025 didn’t really do much on an economic basis.
I can already hear someone screaming that we need to measure “real GDP” — which factors in improvements to software and hardware that would theoretically boost the real GDP contribution of the ICT industry.
The problem I have with that analysis is that the BLS’s Producer Price Index for Software Publishers — a measure of how prices for packaged software have changed over time that the BEA uses to calculate real GDP — is currently sitting lower than it was in 1997, suggesting that software prices have dropped over time in a period where general prices have roughly doubled.
This isn’t remotely accurate based on the actual experience of people buying software. As I covered in the Hater’s Guide To The SaaSpocalypse, more than half of SaaS companies have increased their prices every single year since 2022, customers are paying more every year for the same features, and overall SaaS inflation ran over nine percentage points higher than consumer inflation every single month of 2025. This problem began in or around 2022, when Microsoft bumped up prices, inspiring industry-wide inflation.
In other words, the BLS’ “quality” adjustments appear to be treating many of these price increases as customers getting better software for their money, rather than paying more money for the same software, with the BEA in turn counting that as businesses buying more software.
The BLS believes that software is effectively the same price as it was in 1997, largely because giving customers “more value” and allowing them to “do more,” which does not make sense if you’ve used a Microsoft product recently.
The BLS’ preferred method for these adjustments is based on the cost of the change (IE: how much more it costs to provide), meaning that any price increase connected to AI services, which require expensive tokens to provide, could be potentially considered the same or even lower-priced in the eyes of the BLS.
The BLS’ data is understating how much the cost of software has actually increased in the last few years, and as a result, the BEA may be — accidentally — overstating its contribution to GDP. And AI services are only making things worse.
With that in mind, I calculated the gross value (a business’ sales minus its costs bought from other businesses, so no wages included) added by the ICT industry against real GDP, and found that while tech’s share has grown steadily, said growth hasn’t changed dramatically in the era of AI, even using the BEA’s own flattering figures.
Sidenote: I want to be crystal clear about the data I’m discussing here. The ICT industry portion of what you’re about to read is inclusive of GPU rentals, but the BLS data around software does not include them.
All of this is to say that even with various statistics agencies having a fairly distorted view of the tech industry — at least when it comes to selling software and renting infrastructure — the AI era’s contribution feels a little mediocre.
In preparing this newsletter, I’ve realized something a little worrying: that basically every economic analysis of “AI’s contribution to the economy” is based on either flawed data or flimsy assumptions about AI and the tech industry itself. Economists have tied themselves in knots trying to rationalize the astonishing amounts of money invested in AI, and in doing so haven’t made sure that even their simplest assumptions — like how much the tech industry itself contributes — are meaningfully capturing what’s going on.
Today’s newsletter is a frank evaluation of AI’s true effect on the economy, specifically focused on actually measuring what’s happening today rather than the endless analyses of hypotheticals that you’ll find everywhere else.
You see, everybody is obsessed with metrics that don’t matter — cost-per-token, teraflops, and vague analyses of jobs data — all to avoid a much grimmer point: that when you remove the capex, AI has had a negligible effect on GDP.
And beneath the surface, I’ve found evidence that software sales’ contribution to GDP may have been meaningfully misstated since 2022.
2026-09-29 23:45:09
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Soundtrack: Flobots - Mayday!!!
Last week, Fidelity Director of Global Macro Jurien Timmer said that “the [AI trade] has been dead money for more than three months,” citing that both token expenditures and GPU lease rates were all “flat to down,” citing specifically rental rates for H100 and A100 GPUs. While the counterargument might be that Blackwell GPU rental rates aren’t included, as I discussed last week, it’s questionable how many B200, B300, or other Blackwell chips are actually available for rent, as it appears that anywhere from $200 billion to $300 billion of NVIDIA’s sales since 2022 are sitting in warehouses or unplugged in data centers waiting for power.
The Financial Times’ Bryce Elder took the ball and ran with it, and found research that backed up what I’d been saying, emphasis mine:
Morgan Stanley measured the gap earlier this week by estimating the shortfall in available power, concluding that more than half of the GPU servers sold between 2026 and 2028 might not have anywhere to be plugged in.
Yet Elder makes the point, based on research from Jefferies, that there’re far more problems than simply not having enough power:
In the longer term, power availability is still the bottleneck — along with labour. And transformers. And cooling equipment. And backup generation. As Jefferies says: “The gap between planned capacity and physical execution remains the central issue.”
In other words, the talking point that NVIDIA’s GPU sales are proof of actual demand for AI services or, indeed, that hyperscaler growth is a result of all those capital expenditures is a complete lie. In reality, at least half of all those chip sales — and I’d add in Broadcom’s TPU sales too (see my Hater’s guide for more) — are being made years before anything actually happens with the chips, making the trillion-plus dollars spent on capex so far seem somewhere between optimistic and utterly incoherent.
Microsoft, Google, Amazon, Meta, Oracle, and far too many other companies have been hoarding hundreds of billions of dollars of AI chips that they either (to quote Microsoft CEO Satya Nadella) can’t plug in or simply want to have in supply for reasons that I find tough to imagine.
Even the argument that they’re in reserve for the (eventual) day when they’ll be installed in a data center, and that by purchasing well in advance, they’re not bottlenecked by NVIDIA’s ability to ship AI chips, doesn’t feel credible given the extent of the hyperscaler GPU spending spree. Especially considering that much of NVIDIA’s backlog exists because hyperscalers and neoclouds are stockpiling its GPUs.
Trevor Noren of Sage Road Research noted that he’d heard from a venture capitalist that “...some companies are hoarding colossal amounts in case they come to a point at which they don’t have enough chips to provide the computing capacity,” as if there’s been any shortage of NVIDIA chips, outside of the illusory one created by hyperscalers buying them years in advance.
So, we’ve got a situation where Microsoft, Google, Amazon, Meta, SpaceX, CoreWeave, and every imaginable neocloud is sitting on hundreds of billions of uninstalled GPUs (and increasingly TPUs). Whenever more capacity comes online, it’s immediately sold to OpenAI or Anthropic, who make up anywhere from 70% to 80% of all AI revenues and compute demand, creating the illusion that revenue growth is “coming from demand for AI compute” rather than said demand coming from two companies that have been fed over $217 billion in the last nine months, with the vast majority of it coming from Google, Amazon, Microsoft, and NVIDIA themselves.
As I discussed last week, If “demand is outstripping supply” because of millions of customers begging for AI compute, that’s very different to “demand outstripping supply” because two or three (including Meta) customers are taking up most or all of the capacity. Not to repeat myself, but…
Similarly, if “demand is outstripping supply” because lots of capacity is coming online and a diverse subset of customers is buying it, that’s vastly different to if capacity is coming on slowly, and the vast majority of it is being given straight to OpenAI, Anthropic, or Meta.
And that’s absolutely what’s happening, suggesting that the “AI boom” is more like five or six large companies (hyperscalers) feeding money to two companies (NVIDIA and Broadcom) so that they can feed money to two companies (Anthropic and OpenAI) who then feed that money back to them whenever capacity comes online. I estimate that there’s around $22 billion of global, non-Anthropic/OpenAI compute demand, and an indeterminately-large chunk of that is coming from AI startups that can only afford to pay for the compute as long as venture capital continues to fund them…
…which is also the problem that Anthropic and OpenAI themselves face, as 80% of their enterprise revenues come from 1% of their customer base, with the vast majority of that being unprofitable AI startups that allow users to spend hundreds of dollars (or more) of tokens for $20 to $60 a month, meaning that the AI labs’ demand is, much like hyperscalers’ compute demand, dependent on venture capital’s ability to keep funding it.
Right now, at least to the outside world, hyperscalers’ AI capex seems premature, when my argument is far simpler: it’s been a catastrophic waste, as there exists no fundamental demand for AI compute at anything approaching the scale of data center capex or construction, and what demand does exist is an illusion created by speculative investments.
In fact, I’d argue that the last year and a half’s worth of AI capex is fundamentally speculative, because there has never been any proof — even with Anthropic and OpenAI’s compute spend — that spending a trillion or more dollars on GPUs and data centers would ever pay off.
And now the rest of the world has caught up to what I’ve been saying since October 2025 — that hyperscalers need at least $2 trillion in annual AI revenue by 2030 or they’ve wasted their capex.
Too little too late.
I’ll admit it’s vindicating to see so many people suddenly jump on the “how much money do hyperscalers need to justify their capex?” train, even if not a single one of them bothers to give me credit. Per Callum Williams of The Economist, Google, Amazon, Meta, Microsoft, Oracle, and SpaceX will need somewhere in the region of $1.29 trillion in annual AI revenue to get a 10% return on invested capital for their capex through the end of 2027, with the amount rising to $2.87 trillion if this farce continues through 2030.
To put that in perspective, Microsoft had around $34.4 billion in AI revenue in fiscal year 2026, of which 70% was OpenAI’s compute spend. Per Barclays estimates, Amazon will have $31.6 billion in total AI revenue in 2026, 73% of which will come from OpenAI and Anthropic, and per UBS estimates, 54.3% of Google’s AI compute sales come from them too, with an undefined amount of Vertex AI model sales coming from Anthropic on top, for a total of around $65 billion in AI revenue, which sounds a little high.
Adding all those together gets us to around $131 billion in AI revenues for Google, Microsoft and Amazon, of which $82.2 billion (62.7%) are from Anthropic and OpenAI.
Sidenote: The revenue concentration is also lower based on UBS’ inflated estimate of Google Vertex revenue. UBS also estimated that 28% of all Google Cloud revenues in 2026 are from Anthropic and OpenAI, so do with that what you will.
As of its latest quarter, SpaceX had (when you strip out Twitter’s ad revenues) around $2.194 billion in AI revenue, or $8.7 billion on an annualized basis, but I’ll bump that up to $25 billion on the year to include its full $1.25 billion a month from Anthropic and $920 million a month from Google, though I’ll add that both have 90 day outs. If we assume that Anthropic’s discounted compute for that quarter meant that it accounted for only $500 million of SpaceX’s AI revenue, this puts us at approximately $32.8 billion in AI revenue for SpaceX, with (as I believe Google will rent the compute directly to Anthropic) 79.3% of that coming from Anthropic.
While we don’t know Oracle’s actual AI revenues, it disclosed in its last quarter that its CPU and GPU revenues were at $6.5 billion for the quarter, or around $26 billion a year in revenue. Because I’m feeling nice, I’m going to say that Oracle has approximately $20 billion in annual AI revenue, but due to a lack of information it’s tough to say how much of that is OpenAI, though I’d imagine we’re looking at at least $8 billion or more given the progress of Stargate Abilene and the (as confirmed with sources) H100 and H200 GPUs currently rented to the AI lab. As a result, I think it’s fair to say at least 50% of Oracle’s AI revenues are from OpenAI.
This puts us at $183 billion in annual AI revenue for Google, Microsoft, Amazon, Oracle and SpaceX, with $118.2 billion, or at least 64.3%, coming from Anthropic and OpenAI.
Sidenote: I have been extremely generous with this analysis in anticipation of screeches of “bias” from the peanut gallery. In truth, I think there’s a world where AI revenues are much lower ($150 billion or less) and Anthropic and OpenAI’s share is more like 70% to 80%, especially in the case of Oracle.
And as you’re about to find out, $183 billion just ain’t gonna cut it.
Last week, Goldman Sachs’ Ryan Hammond got a little more specific, noting that hyperscaler capex estimates were now over $1.1 trillion in 2027.
These revised capex plans also came with a new and deeply-worrying analysis, taking the average of estimated AI capex for 2026 and 2027, and calculating how much annual AI revenue hyperscalers would need to break even on their capital expenditures for just those two years.
To just break even, hyperscalers need $308 billion in annual AI-specific revenues, and for a 10% Return On Invested Capital (calculated based on estimates of depreciation and operating expenses), they’d need $417 billion.
As discussed above, they are — including Anthropic and OpenAI — currently $124.2 billion short of break-even, or $243 billion short of break-even without their revenues, or $233.2 billion to $351 billion short for a measly 10% ROIC.
Now, keep in mind that A) the 2027 capex has yet to be spent and B) that, at least in theory, Anthropic and OpenAI will spend more next year…if hyperscalers are able to build the capacity necessary for them to do so, and they’re able to raise the money to pay them.
Goldman aggressively clears it throat by adding that “revenues are growing quickly and revenue backlogs are sizable”, but that’s far from a foregone conclusion considering (as I’ve mentioned) the fact that hyperscalers appear to be warehousing hundreds of billions of dollars of GPUs, with Microsoft sitting at around 2GW of AI capacity, and Oracle’s delays to its “Project Jupiter” data center in New Mexico becoming so severe that it had to issue a “force majeure” notice with the project developer, though as the Financial Times notes, it’ll have to pay regardless of whether the data center actually has power, otherwise known as a “Hell or High Water” contract.
Yet the part that really worries me is about the so-called “application layer” — the companies paying the hyperscalers for AI compute — and how much revenue they’d need in totality to be able to justify that hyperscaler capex.
The answers are extremely grim. For hyperscalers to break even on their capex through 2027, their AI customers would have to make around $425 billion in annual revenue, and that’s if they had an operating margin of 10%, a number that includes training costs for OpenAI and Anthropic.
To be explicit, this chart measures how much revenue AI companies would need to have specific operating margins and for hyperscalers to have a specific ROIC. In other words, AI companies would have to make $725 billion in annual revenue to have both 10% margins and for hyperscalers to have a 10% ROIC.
Sidenote: things don’t change much when you account for negative operating margins, other than the removal of (theoretical) profits and shifting the responsibility of who’s paying for some of the hyperscalers’ costs from the companies’ revenues to their investors.
For some context about how far we are from these numbers:
Even if Anthropic and OpenAI doubled their revenues and every single one of these “annualized” figures represented the true annual revenue of the companies, we’d be sitting at an embarrassing $157 billion, or roughly $268 billion short.
The further we get, the more ludicrous the expectations become, with Bain claiming that AI services (across both the consumer and enterprise realms) would need to generate $6tn in annual revenue by 2031 to justify the current and near-future levels of expenditure.
It’s remarkable, four years and hundreds of billions of venture capital dollars into the AI bubble, that we basically have zero meaningful revenue-generating AI companies outside of Anthropic and OpenAI. We aren’t even in the same universe of scale that would be necessary to justify the capital expenditures made by hyperscalers. I don’t even know how to put into words how far away we are, because it’s all so unfathomably stupid.
Goldman’s response is laughable:
The “required” application layer revenues that would justify current capex are large but potentially achievable. For example, estimates of global advertising and software spending each equal roughly $1.5 trillion in 2026. Our economists estimate that total annual AI US labor productivity gains that will potentially accrue to capital total roughly $1.8 trillion.
That link goes to a year-old report saying that “The AI Spending Boom Is Not Too Big” that does not, at any point, describe AI US labor productivity gains, other than this paragraph:
Productivity: Based on our AI productivity estimates, we assume a baseline gross 15% uplift to US labor productivity and GDP following full adoption, equivalent to $4½tn in economic value creation in today's dollars. In alternative scenarios we consider our previous estimates of a “less powerful” AI scenario based on more pessimistic assumptions regarding AI’s ability to automate work tasks (implying an 8% productivity uplift) and a “more powerful” scenario where the productivity gains reach 27%.
Those AI productivity estimates come from an analyst note from March 2023, around two weeks after GPT-4 came out. In other words, Goldman’s way of reassuring boosters and investors is to vaguely cite numbers from three and a half years ago, numbers that it has, for whatever reason, chosen not to update.
To quote Peter B. Parker from Spiderman: Into The Spider-verse, “don’t watch the mouth, watch the hands.” There’s a reason that Goldman hasn’t sought to measure the actual productivity or economic benefits of AI for three-and-a-half years, I assume because doing so would make it blatantly obvious how large the gulf is between the massive investments in AI GPUs and data centers and, well, this chart:
Alternatively, they’re avoiding saying what Timmer said: that investments in AI are dead money.
And things are only going to get worse from here.
Per Morgan Stanley, AI-related debt issuance should be around $570 billion in 2026, with around $250 billion of that coming from hyperscalers, and the rest various different forms of high-yield debt shoved into either asset-backed securities or dodgy SPVs for AI data centers.
Things are only set to increase next year. Per Goldman Sachs, hyperscalers will fund more than a third of their AI investments with debt in 2027 — around $400 billion — with Jeff Pu of GF Securities putting the number a little higher at $419 billion, against estimated capital expenditures of around $1.14 trillion, specifically referring to Meta, Google, Amazon, Microsoft, and Oracle.
If we assume that other AI-related debt stays flat on the year, that puts us at $739 billion in AI data center debt in 2027, and if we assume growth matches hyperscaler debt issuance growth (around 67.6%), the number grows to around $939 billion in debt.
That’s an astonishing number, and one that’s going to run headfirst into the growing price of US Treasuries, which I covered a few weeks ago in part one of the Hater’s Guide To AI Debt:
So, for the most part, interest rates on debt are set based on the value of government bonds because you, as a potential borrower, are incentivizing the lender based on how much more you’ll pay than the government’s competing treasuries. As it’s a government, it’s effectively risk free, unless you don’t believe the government will be able to pay its debt, which is an entirely-different newsletter.
For example, when Google raised multiple tranches of debt in August 2020, one of the tranches was for $1 billion, dated seven years in the future (maturing on August 15, 2027) at an interest rate of 0.8%, as seven-year-dated US Treasuries (IE: the rate that you’d get lending to the government, which is effectively risk-free) were a mere 0.463% at the time. Once that bond comes due in August of next year, Google will have to either pay it off (requiring it to hand over $1 billion) or refinance it.
While August 2027 is a little under a year away, interest rates are vastly different to 2020, with the expected yield on seven-year-dated treasuries (IE: what the market is currently paying for them) sits at around 4.92%.
To be clear, I published that article on September 18. As of writing this sentence, 10-year-dated US Treasuries are now sitting at around 5.24%.
The combined force of the wars in Iran and Ukraine, inflation, and spiralling government debt have pushed interest rates up aggressively over the last few months, in a way that is set to add billions of dollars in interest payments to an already-staggering debt load across the tech and AI industry.
A Bond-Related Sidenote: The “price” of a bond is usually $100 or $1000 depending on what you’re investing in, which is why you usually see the measure of a bond as either its yield (read: percentage interest rate) or “spread” — how many basis points (each one being 0.001) above a comparable US Treasury it would be.
Yield and bond prices move in opposite directions, and so when a bond “sells off,” its effective yield increases, because said yield is calculated based on the price of the bond plus the interest rate it pays, and if you’re paying less for the bond, you’re getting a higher yield for your dollar.
So when the market begins to worry about whether a company will actually be able to pay its bills, it will begin selling off their debt, lowering the price of the bond while raising the effective yield price.
And when that company goes out to raise more debt, it’s priced based on both the current price of comparably-dated US Treasuries and the effective yield (IE: how the market is currently pricing) of its debt. To be specific, new debt would be priced above the current going rate for its debt.
This is about to become important.
Let me give you a few examples.
A few months later in November 2025, Oracle would issue $18 billion in bonds, with maturities ranging from 4.45% on the five-year-dated notes to 6.1% on the forty-year-dated. Back then, Oracle was the belle of the ball, with analysts a month previously saying they were “all a bit in shock” by its massive new revenue backlog, most of which came from OpenAI and would require building 7.1GW of data center capacity that, as I’ve established, would take years. In the month preceding, OpenAI had announced a flurry of multi-gigawatt deals, most of which didn’t exist, but the market was extremely excited to fund whatever crap was put in front of it as long as it had “AI” on the side.
By December 2025, the spreads (explained here) on Oracle’s debt were trading “like junk,” meaning that investors were buying and selling them at a price that said that if it were to issue more, it would have to be at the high yields associated with the junk bond market.
Since then, Treasury bonds have sold off and interest rates have been hiked with another due by the end of the year. Two months ago, Oracle’s credit rating was downgraded to BBB — one level above junk — by S&P Global, and the debt associated with the SPV behind its New Mexico data center for OpenAI has moved into “distressed” territory, meaning that it’s trading somewhere between 89 cents and 91 cents on the dollar, with the “Force Majeure” notice arriving less than a week later.
All of this is to say that Oracle faces a much, much harsher lending climate today than it did back in November.
When we reprice based on today’s Treasury prices and current going rates for Oracle’s debt, things get…a little nasty.
Across the board, Oracle’s spreads between US Treasuries have effectively doubled, and its new yields range from a bad-yet-manageable 6.73% and 6.91% on its five and seven-year-dated bonds to astonishingly high 8%+ yield across anything longer than 10 years.
On a strictly cash basis, this means that Oracle’s debt would, if issued today, cost it another $6.89 billion in interest.
I should also be clear that these numbers are based on a completely flat calculation related to today’s Treasuries and going prices for Oracle’s bonds. As Oracle sits exactly one rung above junk — and its debt trades at junk rates (meaning that the markets buy and sell it as if the yields were junk (an average of 7.8%) — it would likely see its debt priced at around 25-50bps more than what we’ve seen here.
And if Oracle raises more debt, it runs the risk that two ratings agencies could downgrade it to “junk,” immediately forcing investment funds and indices that cannot hold junk debt to dump it, turning it into a “fallen angel” (as I covered a few months ago).
Oracle isn’t even the worst of them.
Wretched, debt-ridden neocloud CoreWeave issued around $7.75 billion in bonds in 2025 and 2026, and faces a double-whammy of problems — the increasing yield on Treasury bills combined with the overall souring of debt markets toward both its business and the overall idea of AI data center debt.
Last year, CoreWeave was already borrowing at ridiculously-high coupons of over 9%, but if that debt was repriced today, it would be paying at the very best rates between 11% and 13.22% — the kind of numbers you’d associate with a personal loan.
As you can see, repricing CoreWeave at today’s rates would increase its costs by 35.8%, adding $1.2 billion to the lifetime cost of the bonds for a company that already pays $640 million a quarter in interest.
[Editor's note: this previously said $640 billion, which is an obvious typo. In fact, I remember even thinking "don't write billion" when I wrote the sentence. I am fortune's fool!
Make no mistake, CoreWeave needs to raise more debt to build its data centers. Bloomberg consensus estimates have it borrowing more than $33 billion in 2027, at a time when interest rates are likely to stay elevated and jitters around AI data center debt are becoming full-blown convulsions. UBS’ Karl Keirstead estimates that it will need $102 billion in extra financing between 2027 and 2030, but that makes the broad assumption that CoreWeave will still exist in a few years.
In any case, CoreWeave and Oracle’s debt exist as a kind of barometer of the data center industry’s debt position — two junk-or-near-junk firms raising endless debt to build out the so-called next industrial revolution at an agonizing price.
And if the price of their debt is crashing — and the expected yield on the future debt is skyrocketing as a result — then their problems are everyone’s problems.
As I discussed back in July, the sheer scale of AI capital expenditures has inflated the price of every imaginable piece of gear that goes inside a data center, a problem that compounds with every new dollar of capex:
As I wrote in the Hater’s Guide To The Memory Crisis, the sheer scale of Microsoft, Google, Meta and Amazon’s spend on AI data centers has led to a massive supply chain crisis and price-gouging from the triopoly of Micron, SK Hynix and Samsung, with Micron alone bumping prices for DRAM by 60% in its last quarter, shooting up the price of every single kind of RAM possible, at a rate increased by the amount of GPUs and servers that hyperscalers buy.
This naturally creates a vicious cycle. The more AI servers that hyperscalers buy, the more demand they create for RAM and high-bandwidth memory, which increases the price of RAM and HBM, which makes the AI servers more expensive, which means hyperscalers need more money, and because AI has yet to provide meaningful improvements in revenue or cashflow, they’re forced to raise more debt.
The more they raise that debt, the more expensive that debt becomes, and the more of that debt they use, the more of it they need, because the more they spend, the more the stuff they’re buying costs, which means they need more debt.
I published that newsletter on July 28 2026, back when ten-year-dated US Treasuries were a mere 4.6%, and concerns around Oracle’s data center debt had yet to truly erupt.
And a little under a month later, NVIDIA would bump its prices by more than 15%, partly as a result of memory costs, and partly because it has the entire tech industry by the balls.
So, as more AI data center debt gets issued, said debt becomes more expensive, because the larger the amount of debt any one thing takes up, the more competition it faces, and the more risk an investor carries by holding it. Once the debt is issued, it immediately flows into buying GPUs and associated hardware, slowly growing the cost of memory and hardware, all while increasing the competition for the specialist labor and materials needed to build data centers, such as spiking the cost of Copper, increasing the cost of construction by billions in the process.
In other words, the more you buy, the more you lose. The more money you raise, the more money you need. The more money you need, the more expensive that money becomes. And once you spend that money, everything you spent it on becomes more expensive, including raising more money in the future.
The other problem is, as I discussed last week, that these things are simply not getting built, either because the power isn’t there or construction is taking longer than expected, which is in turn putting pressure on effectively any data center-related debt, with even the $27 billion in bonds underlying Meta’s Hyperion data center in Louisiana (known as “Beignet Investor LLC”) aggressively selling off over the last two months.
As I’ve said, a bond “selling off” means that anyone raising more debt that resembles it will have to pay investors more for the privilege.
And when even the debt associated with the largest companies in the world begins to sell off, that becomes everyone’s problem.
Sidenote: While a data center SPV “connected” to a company is connected to the credit rating of the company in question, it is not technically “owned” by the company like a traditional bond because it’s not technically on their balance sheet, and the debt/assets are held by a separate special purpose vehicle.
This is why connected SPVs can trade so much lower than the company’s debt.
I also want to be clear about something: every single AI data center SPV is funded by customer payments which will only arrive if the data center actually gets completed.
So, let’s talk about the $18 billion in debt behind Oracle’s New Mexico-based Project Jupiter data center, starting with ZeroHedge’s diagram of the structure:
Oracle borrowed $18 billion from a syndicate of financial institutions including BNP Paribas, Goldman Sachs, and two Japanese banks — MUFG and SMBC — that have been in effectively every major AI data center deal, including multiple CoreWeave debt facilities, every Stargate/OpenAI/Oracle data center, and even SoftBank’s bridge loan that it used to fund OpenAI’s 2025 funding round. Additionally, funds related to Blue Owl (who is also invested in multiple different Stargate and CoreWeave facilities) kicked in $3 billion in equity to make sure the debt actually got raised.
This kind of labyrinthine structure is how basically every off-balance-sheet and SPV-based data center debt deal is capitalized — a few billion dollars of equity investment, usually from one of a few private credit funds (EG: Blue Owl, Blackstone, BlackRock) that then raise debt from many of the same investors, something I covered at length in my Enshittifinancial Crisis piece from the end of last year. I also went into detail about the SPV structures a few months ago here.
The reason I bring all of this up is that this kind of SPV is the template for data center debt, and the associated investors are a large chunk of the capital funding it, which means that their ability to continue feeding the beast of AI data center debt is what’s holding up this industry.
And now one of their largest data center debt deals, as mentioned, has entered “distressed” status, which means that any further SPVs they’re involved in will price based on the current state of Project Jupiter, which will be priced both based on the project’s health and the current state of Oracle, which is being dragged down by the questionable health of its many, many data center debt deals, all of which are contingent on OpenAI’s ability to pay it $300 billion over five years.
This means that the price of any debt associated with AI data centers is now skyrocketing, at a time when the price of the goods that debt is buying are skyrocketing, at a time when the underlying construction needed to pay back that debt is taking forever.
This is the Doom Loop: the more data center debt that gets raised, the more expensive both the data centers and the debt become, and the only way of fixing the problem is to stop financing new data center debt, except once that happens, everybody will ask whether the data center buildout has stalled, which will in turn create pressure on all of the debt that’s already been issued.
In simple terms, it’s going to be difficult to impossible to raise AI data center debt below 8%, with even the $2.27 billion in bonds issued to CleanSpark for a Meta-connected data center in Georgia pricing at 8.25% on September 18.
As I went into in last week’s premium (and per my Bastard data center model), a 100MW data center costs around $4 billion to $5 billion, with gross margins of 28% at $17.7 million a megawatt…with negative gross margins below $12.10 a megawatt. But only if you have a customer the entire time, and you don’t have any debt.
Those customers are, for the most part, either OpenAI, Anthropic, or a company like Microsoft or Amazon renting out compute to resell to them. Otherwise, they’re unprofitable AI startups like Cognition, which expects to burn $800 million in 2026, with (per The Information) “hundreds of millions” of dollars of those costs coming from renting NVIDIA GPUs.
This means that the underlying customer base for effectively every AI data center is either a hyperscaler or somebody that can, by definition, not actually afford to pay for their compute without somebody else giving them the money, with “somebody” often meaning “a hyperscaler.”
AI data centers are some of the most-expensive and ambitious infrastructure projects in the history of mankind, funded with some of the most-expensive debt ever raised, with said debt only payable in the event that the project A) gets completed and B) has customers that can pay once that happens. Said customers are brittle, unprofitable and unsustainable, with the very real risk that they simply won’t exist by the time construction is complete.
Even if everything goes to plan, the incredible cost of AI data centers means that they’ll take anywhere from three to five years to pay off, and that’s being extremely generous about the terms of the debt and the willingness of the customer to pay top dollar.
One counter-argument to my skepticism has been that the per-hour price of GPU compute has gone up based on SiliconData’s various indices, but I have serious questions about the validity of this data, as I believe it measures spot rates — as in the amount you’d pay to rent right now versus on a longer-term basis — which creates an illusion of success that doesn’t connect to reality.
For example, SiliconData has NVIDIA’s B200 GPU pricing at around $5.78 an hour, but a contract I found between neocloud Kidz AI (which rents capacity from a company called Limestone, or, more-specifically, its subsidiary Catalyst Compute) and AI inference company Canopy Wave priced 256 B300 GPUs at $4.30 an hour (per GPU) for the first three years and $3.50 an hour for the last two of the five-year-long contract for a more-advanced chip than the B200. Another contract I found between Australian neocloud Sharon AI signed a deal with a dodgy-sounding company in Dubai priced 8208 B300 GPUs at $3.30 an hour for five years.
To be explicit, the B300 is NVIDIA’s latest-generation GPU, and its long-term rental prices are less than half of SiliconData’s hyperscale pricing for the years-old H100.
If we assume that an 8-pod of B300 GPUs retails around $550,000, and the capex is a comparable amount, that puts the cost of the data center that Sharon AI is renting out at somewhere in the region of $1.1 billion, for a contract that will, over the course of five years, pay a total of $1.264 billion, assuming that the client in question pays.
In the case of Catalyst Compute, the 32 8-pods of B300s cost roughly $17.6 million, for a rough total of $35 million for the full capex. Over the course of five years, the contract will pay around $44.6 million, and I should note that the customer (Canopy Wave) only moved into reselling inference compute as of November 2025.
In both of these cases, capital expenditures are barely paid off in five years, and only if you don’t include a single dollar of operating expenses or associated debt.
And that’s if everything goes to plan, and the customers actually pay.
Even then, at the end of the five year period, we’ll theoretically have multiple new generations of NVIDIA GPUs (Vera Rubin and Feynman), which will further suppress the ongoing rates that your data center earns, all as ongoing opex (and debt) stays at the same level, assuming, of course, you were paid consistently throughout.
Let’s review:
AI data centers — and their associated costs and debt — are priced for a level of perfection that no other industry has ever rivaled. Their customers must be well-capitalized, prompt in their payments, and have revenues that allow them to spend tens or hundreds of millions of dollars a year on operating expenses on an ongoing basis, something that only really matters if the underlying construction happens.
For whatever reason, everything I read about AI data centers considers it a foregone conclusion that everything will go to plan, and in fact that each data center will generate tens of billions of dollars a gigawatt in revenue, with no real thoughts or feelings about where those billions might come from or whether anyone will be able to afford them.
Everybody is either egregiously ignorant or hopelessly optimistic in a way that will make this situation so much worse when it collapses. I do not think the majority of these AI data center debt deals ever get paid. I do not think CoreWeave makes good on its debts.
Shit, I don’t think Oracle makes good on its debts.
I estimate that since 2023, there’s been around $800 billion in global venture capital investment in AI companies, with at least $266 billion of that going to Anthropic and OpenAI.
Of those investments, I expect at least $300 billion of that equity to be dead money, because, for the most part, AI companies are wrappers or layers built on top of Anthropic and OpenAI’s models, holding very little IP of their own and being burdened with ever-growing opex that mostly flows to the two AI labs that constantly want to compete with their customers. These businesses are fundamentally built on reselling tokens from the large AI labs at a loss, which is why companies like Harvey and Perplexity have to raise hundreds of millions of dollars every few months.
These startups’ continued existence is entirely a function of venture capital, as all of them are deeply unprofitable. This means that before the bubble bursts, these companies will continue to sap the venture capital world of billions more dollars, all with little chance of an acquisition and a near-zero chance of an IPO considering their ugly economics. These economics are also load-bearing for OpenAI and Anthropic, representing around 80% of their revenues, meaning that once they die, the AI labs’ underlying revenues begin to decay.
This also means that these AI startups are, in general, not actually renting AI GPUs, choosing instead to rent them by proxy by using Anthropic and OpenAI’s models. Though some of them talk a big game about building or training their own models, doing so is enormously expensive with little chance of a payoff, especially given the massive advantage in compute, capital and talent held by the labs.
There really is no clean “out” for any AI startup not named Anthropic or OpenAI. Cognition, valued at $48 billion in its latest funding round, is allegedly worth nearly as much as Ford ($59 billion market cap), yet generates a mere $1 billion in ‘annualized run rate,’ which could mean anything, all while losing $800 million. Ford, by comparison, had $187.2 billion in revenue in 2025, with a net loss of $8 billion attributable in part to a massive writedown of its electric vehicle portfolio ($12.5 billion in Q4 2025 alone).
In 2025, Ford sold around 2.2 million vehicles. Cognition, by comparison, makes yet another AI coding agent.
What, exactly, does Cognition do from here? Who buys Cognition? Does it go public? How? It loses tons of money and has a commoditized product!
Nobody wants to answer these questions, because the answer is pretty simple: one day, Cognition, like many AI startups, simply runs out of money and dies, or becomes a much, much smaller company. The same goes for Harvey, Perplexity, Replit, and basically every other major AI startup, though I’d argue they’re all hoping to get swept up by a hyperscaler.
As I discussed at the end of last year, AI startups are a devil’s deal for all venture capital. Because they’re so capital-intensive, there’re tons of opportunities to invest, and every time you do so, the underlying valuation (and assets under management of the VC fund) skyrockets, making everything look good on paper.
The problem is that these valuations are entirely disconnected from reality to the point that, for the most part, all of these companies either go to zero or are picked up in nebulous “acquihires” for embarrassing fractions of their previous prices.
Without these companies, Anthropic and OpenAI lose somewhere in the region of 60% to 80% of their enterprise revenues, which makes them even less likely to be able to pay for all their $1.3 trillion in compute commitments.
And outside of those two companies, I estimate there’s roughly $22 billion of demand for AI compute.
Every single day — even on the weekends — someone asks me either how or when all of this breaks, and my answer is simple: when the money runs out.
Eventually, AI data center debt is going to become untenable for those raising it, because 11%+ rates on already-meager margins makes the maths a little impossible. Once this happens, there will be a fundamental reevaluation of the value of all AI data center debt, which may lead to a sell-off of the underlying bonds and associated debt, which will make any investor deeply entrenched in the GPU credit business extremely nervous and, in some cases, unable to exit their positions in anything short of an embarrassing fashion.
This isn’t likely to happen due to moral or ethical reasons, but as a result of creditors realizing that they’ve got way too much risk tied up in projects that regularly make the news for not getting built. At some point these projects become too risky for even the most mold-poisoned private credit fund or brainless Japanese bank to stomach, and the timeline will accelerate based on either Treasury rates or further data center developments facing cashflow or construction problems.
On the venture capital side, it’s unclear how much dry powder actually remains, how much of it could be deployed into AI startups, and whether it’ll be a case of ‘running out of money’ so much as a moment where everybody gets spooked about AI and stops investing entirely. This would be accelerated by any cashflow issues across any major AI startups, any downrounds (IE: raising at a lower valuation), or failed acquisitions, such as when Anthropic walked away from buying Decart for $6 billion earlier in September.
And really, the biggest sign is the most obvious one — the deceleration of Anthropic and OpenAI. If they aren’t going to pay those $1.3 trillion in compute bills, the jig is up for AI data center demand.
The signs are already there that something is up.
Per Irrational Analysis, Anthropic’s record-breaking “$65 billion in annualized revenue run rate” from July 2026 may have been calculated in the single-most-deceptive way I’ve ever heard a startup do so:
Last month, there was a whole kerfuffel in AI/semis/finance circles on Anthropic July ARR. Two numbers were going around. I don’t remember the numbers and frankly it does not matter. You will see.
One ARR number was the traditional “trailing 28 days * 13” number. Personally I hate this venture-capital clown metric but whatever a lot of people use this.
The traditional ARR number was bad and implied deceleration in growth. So the people massively long Anthropic came up with a new ARR number that was July 31st * 365 days.
That’s right folks. If Irrational Analysis is right, Anthropic’s revenue on July 31, 2026 was $178 million, and because the other calculation — 28 days times 13 — created a lower number, the company chose to go with something that should, at a minimum, have investors hiring lawyers and demanding real, tangible answers about how run rate is calculated. Every single reporter with any Anthropic source that can speak to run rates should be screaming at them for clarity, because this is some sub-Enron bullshit.
Even if you don’t trust that analysis, another from TickerTrends surfaced by Callum Williams of The Economist shows Anthropic’s annualized run rate plateauing since, it seems, the beginning of June, and as Williams said, if this is even broadly correct, it’s really, really bad.
Williams also another TickerTrends chart showing OpenAI’s revenue growth had continued to climb…but was showing the initial signs of a slowdown.
Neither of these companies can afford to slow down, in part because of their massive compute obligations, and in part because their massive valuations are based on them being able to pull in, at least in Anthropic’s case, between $190 billion and $200 billion in annual revenue within the next two years.
If they fail to do so, everybody suffers. Hyperscalers miss revenue estimates. AI data center debt goes unpaid. Venture capitalists find their holdings washed out.
When that happens, everybody will act as if it was a huge surprise, rather than something that was blatantly obvious to anybody who bothered to look.
That was originally where this newsletter ended, but the night before this was due to go out, parts of Anthropic’s S-1 leaked to Reuters, showing the shocking financial condition of the company as of the end of last year.
In 2025, Anthropic lost over $8 billion on $4.6 billion in revenue. 25% of its 2025 revenue came from two customers, and its compute costs were $7.33 billion for the year. It technically had a net loss of $42 billion, but that was stock-related and was not a cash loss.
Reuters did not report on Anthropic’s 2026 numbers, and while in theory its economics could have improved in the last three quarters, there are reasons to believe that things have gotten worse, such as the fact that it has resorted to using adjusted margins as a means of faking a “profit” in Q3 2026. In any case, I find it strange that Reuters reported on only a section of the S-1, and if it turns out anything was held in reserve for some reason I will be deeply disappointed. I will be fair and assume it was a limited slice of the prospectus, and that Reuters will diligently report anything it finds, and it is an incredible exclusive.
So, let’s talk about how terrible of a company Anthropic was in 2025.
It spent $12.65 billion in operating expenses to make $4.6 billion of revenue, otherwise known as spending $2.75 to make a dollar.
This, shockingly, means that Anthropic was a worse business than OpenAI in 2025, when it spent $34 billion to make $13.07 billion (per my own exclusive reporting of its audited financials), or $2.60 to make $1.
While things could change in 2026, it’s important to note how many people said that Anthropic was “a better business” that would “be profitable faster than OpenAI,” which is, until we are able to see both of their audited 2026 financials, somewhere between a myth and an outright lie.
So many people told me that Anthropic was more-profitable! So many people assured me that this company had worked it all out, when in fact Dario Amodei’s horrid son was just as obese as Altman’s, a rotten, unprofitable carcass.
Boosters are already boiling their copium kegs, angrily oinking that 2026 “will be better” and that “Anthropic has been more profitable.” At this point I have less than zero interest in anything that hasn’t gone through an auditor, because it’s very clear that, through either misinforming investors or the media, Anthropic has intentionally obfuscated the full horrors of its economics.
Perhaps 2026 will be better! But right now, the evidence is that Anthropic’s economics have decayed for the last two years, and its business was, at least in 2025, somehow more toxic than OpenAI’s, regardless of what you may have read in the press. If things have improved, it will have required a fundamental turnaround of a business in a way that does not appear to have happened in any way for OpenAI, despite both companies being in the same business and selling the very same thing. The only difference I can imagine is that Anthropic’s infrastructure is more TPU and Trainium/Inferentia-heavy, but we’ll eventually find out, I guess.
If you have the full S-1, I implore you — bring it to me. My signal is ezitron.76. I will protect your identity. I will do this document justice. It’s time we had complete clarity into what this business truly looks like. We should not be made to wait for November, we should see it now, so that investors (and any economic counterparty) may fully understand the state of Anthropic.
In any case, these numbers are as bad as I’ve always thought they’d be, if not a little worse. I don’t see how this company becomes one that can afford its $518 billion in compute commitments, nor do I see how it magically works its way out of the economic equivalent of septic tank.
This company will, if allowed to go public, likely lean on the very same junk-grade/high-yield debt that AI data centers and neoclouds like CoreWeave currently need, and it will do so at volumes of somewhere between $50 billion and $100 billion a year for a company with few assets, endless losses and a CEO with the grace of a drunk elephant.
Anthropic is not the future of technology, nor is it the next Google, nor is it the next Microsoft, nor is it, to quote Reuters, capable of “[transforming] the global economy more profoundly than industrialization, electricity and the internet.”
Anthropic is a cloud software company with volatile products and economics, sold in a fundamentally insincere and deceptive manner, pushed upon society with threats of death and destruction by aggressive zealots and members of the media bereft of shame. Its culture is fundamentally unhealthy, as is the culture of the fandom it has curated over the last few years. Dario Amodei is a manipulative and deceptive person influenced by a cadre of cultists, and Anthropic CFO Krishna Rao should feel ashamed of himself for allowing a single run-rate story to go out.
It is impossible to rationally argue that the economics of OpenAI and Anthropic make any real sense. To claim that this is “just like Uber” or “just like Amazon Web Services” or “just like the Dot Com Bubble” is to bury one’s head in the sand or, on some level, want to know less about the world. This is serious, dangerous, and should not be seen as “business as usual.”
We must treat OpenAI and Anthropic as what they are: economic disasters waiting to happen.
To do anything less is to directly invite danger to the door of every investor that’s allowed to believe that they’re funding the next industrial revolution.
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2026-09-25 22:25:21
The year is 2026, and you are a hyperscaler CEO. You zip up your Patagonia Vest, type UDPATE CALENDER WHERE WHY to your Muse agent, and it tells you that your CFO has sent you an email about something called a “critical finance meeting,” and you roll your eyes.
You were up until 2AM talking to your 38 GPT-6 agents that were vibe coding a dashboard of “company efficiency wins,” and if anything it’s kind of rude that your CFO is interrupting your “mindfulness hour” where you listen to Andrew Hubermann and do something called an “elevated ab crunch” that hurts your neck every time, somehow.
Behind you, your horribly-trained Shiba Inu (called “Basis Points”) angrily humps your Eames chair, and when you tell it to stop it only seems to hump it harder. Your CFO, who has been waiting for 15 minutes, appears on the video with a grim look in their eyes. “What is this? What is the need for this interruption?” you snap. “You’re ruining my mindfulness! Have you any idea how important my mindfulness is? It’s so early in the day, and I’ve barely had any mindfulness!”
Your CFO stops themselves from saying that it’s 12:15PM, and decides to cut to the chase. “Hey, so, remember our conversation last week?”
You begin to shake uncontrollably. “...no. I. don’t. How d-”
Your CFO interrupts, and seems more stern than usual. “Listen. You wanted us to buy a bunch of GPUs, and we bought a bunch of GPUs. That’s fine. But we’ve had to raise tons of debt to do so, and it turns out that the debt that we’ve raised isn’t enough to build all of them, and they’re taking years more than we expect to-”
You begin shaking again. “You…you made a mistake. You messed up. This is on you.” Basis Points is now staring at the wall and growling at it for some reason.
The CFO frowns. “No, these are entirely separate externalities — the war in Iran, the Fed hiking interest rates, the concerns around AI data center debt, the whole supply chain is screwed, everything’s getting more expensive, and we have a bunch of debt-”
You roll your eyes. “Just make it go viral, I don’t know what to tell you,” you say as you hang up. With your mindfulness hour ruined, your week is effectively washed, so you decide to book a trip to Hawaii to recover.
After all, all that boring shit is someone else’s problem!
…except it really, really isn’t.
In last week’s newsletter (and part one of the Hater’s Guide To AI Debt series), I went into the core issues with the AI bubble’s debt spree, which can be simmered down to a few major points (and these are all helpful links to the specific part of the newsletter for easy reference!):
Put another way, the first part of The Hater’s Guide To AI Debt was about the form of debt — how it’s raised, how it functions, and where it might be going — and today’s about the function.
The biggest worry I have about the AI bubble right now is that the debt is priced for perfection, yet said debt is being invested in thousands of the most-ambitious, intricate, and fragile infrastructure projects in the world, requiring specialist talent and materials and access to power at a scale unheard of in the history of society.
And in many, many cases, the companies building them don’t even have any experience building AI data centers, securing power, or, in many cases, doing much of anything.
You see, despite the AI bubble being inflated by some of the largest and most powerful companies in the world, the actual AI data center buildout is being handled in a way that borders on the lackadaisical “Move Fast And Break Things” model of Silicon Valley.
While they may have an idea of what they’re building and the money to do so and lots of well-credentialed experts, every data center project is its own unique monster with ever-expanding problems caused by everything from geography to the weather to the mechanical issues you find from condensing the power of an entire city into a space a thousandth of the size full of expensive AI chips that need bespoke cooling.
And it’s this gaggle of choices — thousands of chaotic, problematic and ultra-challenging infrastructure projects — that the finance industry has fed somewhere between $100 billion and $150 billion of debt (without including hyperscalers), and plans to feed hundreds of billions of dollars more, all under the assumption that “everything will be alright” and that these are, functionally-speaking, no different from building a regular building.
In part two, I’m going to talk about the grisly truth of the AI data center buildout — the doom loop of debt, delays and doubt that will compound the costs of getting these things built, and how the whole thing has become so unfathomably expensive that it makes the economics of building an AI data center — and paying off the underlying debt — near-impossible for the majority of projects.
This is The Hater’s Guide To AI Debt Part 2, or Gross Profit Unlikely.
2026-09-22 22:35:46
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On Friday, I’ll publish the second part of The Hater’s Guide to AI Debt (here's part 1) — where we’ll talk about the spiraling costs associated with standing up compute, why they won’t get better, and how the situation poses an existential risk to counterparties like Oracle. With that in mind, if you haven’t already, check out the first Hater's Guide To Oracle (or part 2), or perhaps my premium piece about how OpenAI Kills Oracle, or even my Hater's Guides To the SaaSpocalypse, Private Credit, and Private Equity.
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Soundtrack: Flobots - Handlebars
A few months ago, I asked where all the data centers were, because I was struggling to find proof that very many were being finished despite all the capex spending, construction, and the skyrocketing cost of RAM.
The answer to that question was, effectively, “nowhere.” The vast majority of Microsoft data center projects I could find had barely gotten started, aside from the massive OpenAI-dedicated “Fairwater” data centers that are “open” in the sense that some of the buildings are turned on, with the remainder either being actively built or with construction expected to start at some later point.
A couple of months later, The Guardian and I ran an investigation working with a source familiar with Microsoft’s GPU capacity, finding that it had approximately 2.2 million chips. At the time, I was unable to verify whether these were all of its GPUs, or whether OpenAI had its own allocation that wouldn’t appear in the data we obtained, which is why I left out one piece of reporting — that, based on the precise loadout of GPUs in operation, that Microsoft had around 1.993GW of capacity, backed by around $50 billion in GPUs, predominantly made up of NVIDIA H100 and H200 chips, with a decent amount of GB200 and GB300s.
Two weeks ago, Bloomberg reported that Microsoft had “about 12 gigawatts of capacity,” but that “...only about 2 gigawatts of the company’s current 12-gigawatt capacity is centered on AI-specific chips.” A quote follows:
Newer AI tools use increasing amounts of CPU servers in addition to GPUs, meaning Microsoft needs to expand all types of computing power, according to [infrastructure expert Alistair] Speirs, “GPUs by themselves don’t make great AI infrastructure,” he said.
That’s a really nice way of saying that Microsoft is directly misleading reporters, investors and the general public about its capacity. It has claimed again and again that it has brought gigawatts of capacity online for the best part of a year, all, in my opinion, with the intent of misrepresenting the scale of an AI data center buildout where I believe most of the GPUs, to quote Satya Nadella, are now “sitting in inventory that [they] can’t plug in.”
Today’s newsletter is about a cruel truth: that NVIDIA’s revenue growth is almost entirely the result of speculative purchases by hyperscalers and neoclouds that take years to install its GPUs.
In other words, I think everybody is completely wrong about data center capacity, and it’s going to be a nightmare to untangle once they work it out.
Everybody wants AI to be just like the Dot-Com Bubble, when I fear that GPUs may end up like the millions of unsold copies of Atari’s ET found buried in New Mexico.
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.
In earnings calls for its second, third, and fourth quarter fiscal year 2026 earnings, Microsoft would use near-identical phrasing:
While I cannot say the exact rationale for making these statements, I can find no way to interpret them other than being an act of deception.
In its Q2 2026 earnings call, Microsoft CFO Amy Hood responded to UBS analyst Karl Keirstead’s question, again repeating the “gigawatt” language [emphasis mine]:
KARL KEIRSTEAD, UBS: Okay, thank you very much.
Satya and Amy, regardless of how you allocate the capacity between first party and third party, can you comment qualitatively on the amount of capacity that you have coming on? I think the one gigawatt added in the December quarter was extraordinary and hints that the capacity adds are accelerating, but I think a lot of investors have their eyes on Fairwater Atlanta, Fairwater Wisconsin and would love some comments about the magnitude of the capacity adds, regardless of how they’re allocated in the coming quarters. Thank you.
AMY HOOD: Yeah, Karl, I think we’ve said a couple of things. We’re working as hard as we can to add capacity as quickly as we can. You’ve mentioned specific sites like Atlanta or Wisconsin. Those are multiyear deliveries, so I wouldn’t focus necessarily on specific locations.
The real thing we’ve got to do, and we’re working incredibly hard at doing it, is adding capacity globally. A lot of that will be added in the United States, the two locations you’ve mentioned, but it also needs to be added across the globe to meet the customer demand that we’re seeing and the increased usage.
We’ll continue to add both long-lived infrastructure. The way to think about that is we need to make sure we’ve got power and land and facilities available, and we’ll continue to put GPUs and CPUs in them when they’re done as quickly as we can. And then finally, we’ll try to make sure we can get as efficient as we possibly can on the pace at which we do that and how we operate them, so that they can have the highest possible utility.
Note that Karl’s line of questioning directly addresses Fairwaters Atlanta and Wisconsin, two explicitly AI-focused data centers dedicated to OpenAI, and Hood responds by talking about a gigawatt of capacity in the December quarter (referring to Q1 FY26, when no mention of adding a gigawatt in a quarter was made).
I already anticipate the response here will be that what Microsoft said here is “legal,” because it never said that this was AI capacity, but anyone responding like this operates with a peasant’s mindset. Anyone reading this transcript or hearing Microsoft mentioning that it added “gigawatts” of data center capacity is thinking about AI data center capacity — as evidenced by this piece and this piece and basically everyone you talk to on the subject.
Let me be very blunt: it appears that Microsoft has, since the beginning of 2022, spent around $265 billion in capital expenditures (and added around $320 billion in assets to its properties, plants and equipment) to bring around $50 billion of GPUs online, and has used broad, vague language to make it seem like it’s been far more productive.
Nobody on God’s green Earth is sitting here wondering if Microsoft added gigawatts of CPU capacity or cloud storage, nor are analysts desperate to hear about aggregate numbers — they’re asking where is all the money you’re spending on AI going, and the answer, it appears, is “into a warehouse” or “into a data center without power.”
To bring home the point, I analyzed Microsoft’s earnings calls between Fiscal Year 2010 and Fiscal Year 2022, and found little discussion of data center capacity outside of competition with Amazon Web Services and Google Cloud, and no discussion of megawatts or gigawatts. When it comes to earnings calls, Microsoft’s use of megawatts and gigawatts is explicitly AI-era terminology, used for the first time in its Q4 FY2025 earnings call, though it had used it elsewhere, such as in a document reported on by Business Insider in April 2024 that said it had brought online “more than 500 megawatts of new data center capacity,” including this damning quote, emphasis mine:
In the second half of last year, Microsoft delivered "record-level GPU capacity," more than doubling its total installed GPU base, the document said, without mentioning actual numbers.
It appears that Microsoft is being vague with its numbers to hide the very obvious truth: that it’s spending hundreds of billions of dollars to buy GPUs that sit in warehouses or unpowered data centers, likely years in advance.
This is a huge scandal. Why is Microsoft buying so many GPUs if it’s not got anywhere to put them? Why isn’t it waiting to buy the GPUs when it needs them, rather than buying them months or years in advance? Why is Microsoft telling us it’s bringing gigawatts of capacity online when it’s very clearly doing otherwise?
And at this point, can we take anyone’s capacity announcements seriously?
Microsoft is one of the first companies to build a GPU-powered AI data center (back in 2020, specifically for OpenAI), and has both incredible amounts of experience standing up compute infrastructure and resources to make it happen.
I need to stress this point, because outside of the hyperscalers, the other companies building large-scale data centers are neoclouds, many of which were former crypto mining companies, or Oracle, which only dipped its toe into cloud infrastructure in 2016, long after Microsoft launched Azure and Google launched Google Cloud.
If anyone has the expertise and resources to deploy large-scale AI infrastructure assets at scale, it’s Microsoft — and, as a result, Microsoft’s effectiveness in deploying AI infrastructure is more meaningful than, say, that of a Neocloud or even Oracle.
As I said above, Microsoft had — as of a few months ago — approximately $50 billion of actual GPUs in service at around an estimated 1.993GW of AI capacity, with Bloomberg reporting on September 11, 2026 that it had “around” 2GW.
It has spent $265 billion in capital expenditures, and per estimates from Michael Turrin of Wells Fargo and Gregg Moskowitz of Mizuho Securities, and Microsoft’s own statements in earnings calls:
As mentioned above, Nadella mentioned in November 2025 that he had “...a bunch of chips sitting in inventory that [he couldn’t plug in],” but didn’t make any mention of how many there were.
In other words, Microsoft is warehousing anywhere from $50 billion to $100 billion in GPUs, and has barely gotten $50 billion worth installed in the last four years.
If Microsoft is struggling, everybody’s struggling, and we may have a very inconvenient truth: that NVIDIA has potentially sold hundreds of billions of GPUs years in advance.
And yes, everybody is struggling.
Microsoft, as a deeply unhelpful and deceptive company, does not disclose its “construction in progress” on its balance sheet — the place where companies put everything that they’re building, and in the era of AI, their unbuilt data centers and yet-to-be-installed GPUs (or, in Google’s case, its custom TPU AI chips).
Sidenote: Construction in progress is a stock rather than a total — while capital expenditures are however much money was spent, CIP represents the accrual of stuff.
Across hyperscalers including Google, Meta, Oracle, Amazon, SpaceX, and Tesla, neoclouds like CoreWeave and IREN, and colocation companies like Core Scientific and Applied Digital, there is over $374 billion in construction in progress, a figure that’s likely lower than the true number, because Amazon’s contribution ($71.7 billion) is only current as of the end of 2025.
The $374 billion number doesn’t include any CIP from Microsoft, Firmus, Sharon AI, Equinix, Nebius, or any number of private operators like Vantage, DataBank, CyrusOne, or QTS. It does not include any sovereign AI projects (Humain/Saudi Aramco, Singapore, Reliance in India, G42 in the UAE), private projects run by or for OpenAI or Anthropic, Stack Infrastructure (which is building Oracle’s New Mexico data center, with the CIP not landing on Oracle’s balance sheet as it doesn’t “own” the project), or Meta’s $27.3 billion off-balance-sheet “Hyperion” data center. Between them, I think there’s at least another $50 billion to $100 billion of CIP, but for fairness I’m not including it in the larger total.
This number has increased across the dataset from $102.9 billion in 2023, to $145.6 billion in 2024, to $243.2 billion in 2025.
Between 2023 and 2025, the combined capital expenditures of these companies was $351.6 billion on $243.2 billion of CIP. In other words, lots of money out the door with a bunch of stuff in a big, confusing and potentially-unproductive pile.
If I’m honest, I think that $200 billion number might be a little generous.
Considering Oracle’s CIP number from its latest quarterly earnings was at $48.5 billion and Google’s Q2 2026 “assets not yet in service” were at an astonishing $122.8 billion, up from $108.5 billion in Q1 2026 and $78.5 billion at the end of 2025, it’s reasonable to believe that Microsoft has at least $50 billion of construction in progress. I also think it’s fair to assume that Amazon has, since the beginning of 2026, added at least $25 billion to CIP, though we’ll find out at the end of the year.
As far as the breakdown of assets goes, I think it’s fair to assume the 50/50 split is accurate. In Google’s latest earnings call, CFO Anat Ashkenazi said that “...60% of our investment in technical infrastructure this quarter was in servers,” referring to servers with GPUs or TPUs. Wells Fargo’s Ken Gawrelski estimated in a note from January 2026 that approximately 65% of Meta’s capex was tied to “shorter-life servers and networking equipment assets.” Per Karl Keirstead of UBS in a note in January 2026, “...the vast majority of Oracle’s capex is for equipment, mostly Nvidia GPUs,” adding that “...by comparison, we estimate average annual capex over the next 5 years for Microsoft with perhaps 60% or around $125 billion for short-lived equipment/chips.”
Yet arguably the most revealing thing I could find was a quote from CEO Andy Jassy on Amazon’s Q1 2026 earnings call from April:
…AWS has to lay out cash for land, power, buildings, chips, servers and networking gear in advance of when we can monetize it, typically 6 to 24 months before we start billing customers depending on the component.
So, if we assume the number is roughly $374 billion, plus (at minimum) $50 billion from Microsoft, plus another (at minimum) $20 billion from Amazon, that puts us around $444 billion, with Google’s share — $120.8 billion — being 60% GPUs and related hardware, for a total of $72.48 billion, putting us at (assuming a 50/50 split) an estimated $234 billion in GPUs and TPUs sitting in warehouses.
Since the beginning of calendar year 2023, NVIDIA has sold roughly $496.4 billion in GPUs and associated gear, and Broadcom approximately $65.1 billion in AI chips (though I’ll add that Broadcom only started disclosing its AI segment as of its March 2024 earnings) for a total of $561.5 billion.
Sidenote: Except where otherwise stated, I’m using calendar years because it’s the best way to align to CIP across my analysis.
Based on discussions with sources familiar with Azure infrastructure, Microsoft has a great deal of H100 and H200 inventory up and running — mostly consisting of hundreds of thousands of Hopper chips, as well as somewhere in the region of 160,000 Blackwell GPUs at the time of discussion.
Based on a further analysis of NVIDIA’s earnings calls in the period along with estimates from Vijay Rakesh of Mizuho and Ross Seymore of Deutsche Bank, I roughly estimate NVIDIA has sold approximately $222.6 billion in Hopper and $270.9 billion in Blackwell GPUs. I think it’s reasonable to believe that the majority — if not the entirety — of Hopper GPUs are installed, leaving us with millions of Blackwell GPUs waiting to be installed.
This is, to be clear, an assertion I made in November 2025, when I took Jensen Huang’s statement that NVIDIA shipped “6 million” NVIDIA GPUs literally, versus using the whacky Jensen Maths that “each GPU is actually two GPUs,” bringing the total down to three.
Nevertheless, based on everything I’ve discussed today, it’s very reasonable to ask whether even a quarter of those Blackwell GPUs are actually in data centers, or at least data centers with power connected to them.
And if the truth — and this very much seems to be the case — is that NVIDIA has sold hundreds of billions of dollars of GPUs years in advance, that materially changes everything about the AI bubble and the AI data center buildout.
I have, on occasion, cited Sightline Climate’s February estimates, which I’ll now quote in their entirety:
We’re tracking 190GW across 777 large data centers and AI factories (>50MW) announced since 2024. At least 16GW of capacity is slated to come online in 2026 across roughly 140 projects. Yet only about 5GW is currently under construction. Around 11GW remains in the announced stage with no visible construction progress, despite typical build timelines of 12–18 months.
I love Sightline Climate, and believe they do important and helpful work, but based on Microsoft’s obfuscation of what “capacity” actually means, I believe that virtually all estimates around operational AI data center capacity are now functionally useless. While we can use Sightline’s data as a measure of how much is in planning, I no longer think anyone has a handle on how much capacity is built.
The same goes for basically any statements made by companies about or reporting around their potential capacity that do not specifically separate active AI capacity from overall capacity.
Microsoft claims, as reported, that it has 12GW of capacity — 3GW of which came online in the last three quarters! — but only 2GW of that is AI data center capacity, which begs two questions:
It’s very clear that Microsoft is playing silly buggers with the term “capacity,” which makes me believe this is an industry-wide problem.
For example, CoreWeave claimed in its latest earnings presentation that it added 850MW in “active power” in the last quarter:
There is a big difference between whether that’s active, revenue-generating AI data center capacity or 850MW of power at a plant not connected to anything because the data center isn’t built yet, much like it’s very different if it’s only 50MW or 200MW of AI data center capacity.
In Oracle’s case, there were some statements made in its most recent earnings call by co-CEO Clay Magouyrk that are equally-misleading:
All right. Thanks, Mike. OCI continues to grow quickly by delivering the capacity our customers need. We delivered 850 megawatts of AI capacity containing more than 300,000 GPUs to customers since the end of Q4. Delivery in Q1 is almost 3x what we delivered in all of Q4 and 73% of the total capacity we delivered last fiscal year. This reflects years of investment in every aspect of infrastructure, from data center design through supply chain and manufacturing, to installation and operations.
Just so we’re clear, here’re the statements made by Mr. Magouyrk:
Then there was another quote that had me very confused about Stargate Abilene, a 1.2GW total capacity/824MW IT load (IE: GPUs and essential hardware) data center campus that’s been under construction since June 2024.
Abilene continues to deliver at an extraordinary pace. We delivered 131,000 GPUs there in Q1, 1.9x the volume delivered in Q4. 6 of the 8 campus buildings, representing 618 megawatts, and 75% of total capacity have now been delivered to the customer. Customer acceptance has compressed to only 24 hours, showing the systems arrived ready for customer workloads. The recently released GPT-6 Astra was trained at our site in Abilene.
I’m waiting on an update from a source, but as of June this year, only three buildings were ready to go in Abilene, with a fourth a perennial work-in-progress. I concede that perhaps development has sped up, but per Yes Energy’s analysis, as of June Stargate Abilene was pulling a total power load of around 450MW — and Mr. Magouyrk specifically said “75% of total capacity” and “618MW,” which sounds like it’s referring to IT load.
I don’t even have a clear answer as to what’s going on here, other than that we don’t have much (if any) clarity around how much data center capacity is even being built.
Buried deep within a sustainability report released in July, saying…
We added more data center capacity globally than any other company, including more than 1.2 gigawatt (GW) in Q4 [2025] alone, and we expect AI and cloud services to continue growing. As we grow, we invest relentlessly in efficiency.
You’re meant to read that and say “wow, 1.2GW of AI data center capacity,” but that doesn’t, as we’ve established with Microsoft, mean anything of the sort.
Data Center Dynamics accidentally explained the problem in their piece on the report:
As for Amazon's rivals, exact figures are also undisclosed. Microsoft stood up 1GW of data center capacity in what it calls FY2026 Q2, but that is actually the same timeframe as Amazon's Q4. In its FY2025, Microsoft brought online a total of 2GW.
As we’ve established, that “1GW of capacity” does not mean, in any way, shape or form, 1GW of AI data center capacity, or even usable capacity of any kind. In fact, it’s unclear what it is that was added, because none of these companies tell you.
At the end of 2025, OpenAI claimed it had “1.9GW of compute” — which would suggest that it takes up the vast majority of Microsoft’s infrastructure and some of Oracle’s — but it doesn’t distinguish between whether that’s active power, IT load or even accessible to the company.
As I discussed a few months ago, despite vast amounts of capital expenditures, hyperscaler depreciation — by which I mean when you spread out the cost of GPUs over 6 years starting from when they enter service — also suggests that the vast majority of capex is yet to be put in service.
To illustrate, I pulled an historical chart of hyperscaler depreciation and amortization as a percentage of capital expenditures. If capital expenditures were quickly turning into operational, useful and revenue-generating assets, the percentage would be growing versus collapsing quarter-after-quarter, with Google’s sitting at an embarrassing 15.8%, suggesting less than 16 cents of every dollar of capex is flowing into D&A. While this isn’t a cost (as it’s spreading out the cost of something spread over a period of time), it eats into net income.
As you’ll see, at several points hyperscalers reclassified the “useful life” of servers, allowing them to spread out the costs of servers containing AI GPUs for a year or two longer, allowing them to lower depreciation costs as a result.
For example, in 2022, Microsoft extended the useful lifespan of servers from four to six years — and this year, changed the depreciation schedule of the actual data center structures from 15 to 25 years. The following year, Meta and Google followed suit, with Meta extending the lifespan to five years and Google to six. Meta would again extend the useful life of its servers in 2025, pushing it to 5.5 years.
Amazon, meanwhile, can’t make up its mind about how long its servers last, having increased (and decreased) multiple times over the course of the past six years. Quoting MoneyWise:
Depreciation is the mechanism underneath all of this — the way a company spreads equipment cost across the years it expects to use it. Stretch the assumption and current profits look better. Shorten it and the bill comes sooner.
Amazon has done both, repeatedly. Servers went from three years to four in 2020, four to five in 2022, and five to six in January 2024, before the 2025 reversal [to five years].
This chart tells us three things:
And because they’ve continued to be cloak and dagger about their actual capacity or where their capital expenditures are actually going, it’s anyone’s guess as to when depreciation will spike.
But it’ll have to at some point unless they intend to write the GPUs off.
This situation is utterly obscene.
It’s very clear that at least $200 billion — if not more than $300 billion — of NVIDIA’s GPU sales have been made a year or years in advance, just as the company telegraphs it will make over $670 billion in revenue in its fiscal year 2028 (starting February 2027).
It’s also clear that Microsoft, Google, Amazon, Meta, SpaceX, and every neocloud are purchasing NVIDIA GPUs tens of billions at a time under the implicit knowledge that it will take years to build the capacity and connect the power to them, creating what amounts to the largest pre-order campaign in the history of capitalism, but also a material misrepresentation of the current AI buildout.
Investors — and journalists — have been under the assumption that gigawatts of AI data center capacity have been coming online on a regular basis, with NVIDIA raking in hundreds of billions of dollars for GPUs that are quickly fed into AI infrastructure.
Since the beginning of 2022, Amazon, Google, Microsoft, and Meta have spent over a trillion dollars in capital expenditures, and if Microsoft is indicative of the larger effort — about 18% ($50 billion or so) of capital expenditures turned into revenue-generating IT infrastructure — that would mean only around $222.66 billion of NVIDIA and other AI chips across the four largest hyperscalers are actually operational and functional.
Sidenote: I realize that Amazon has capital expenditures outside of AI data centers related to its eCommerce and logistics operations, but based on its rapid growth since 2022, I think most of it is attributable to AI. The following are imperfect yet, I believe, well-founded estimates.
If we assume — kindly — that 50% of the cost of a data center is construction, this would mean around $445.3 billion of data center capacity is operational.
This leaves us with around $791 billion of capital expenditures unaccounted for, which is fairly disastrous, and if we assume that 50% of that is GPUs (across NVIDIA, AMD, Trainium, TPUs and any other custom silicon), that’s around $395 billion of silicon that’s been sold and is, I hope, sitting in a warehouse or an unpowered data center, as if they were just sold on paper, that’s…questionably legal accounting. I’m willing to believe that some share of that is also CPU infrastructure, storage, and other dollars not flowing directly to NVIDIA.
In any case, that’s a shit ton of undeployed silicon, and a very, very, very different picture to the one that both NVIDIA and the hyperscalers have been telling investors.
There’s a world of difference between “we’re buying a lot of GPUs and building a lot of data centers to make a lot of money” and “we’re investing in this stuff on the off chance it makes us money years in the future.”
I’ll break it down:
For the most part, hyperscalers have been given credit for their capital expenditures because overall revenues have grown, with everyone saying that their “AI bets have paid off,” when it’s clear that the AI bets in question have barely started to come online.
I believe the reason that Google, Amazon, Microsoft, and most notably not Meta have seen remarkable revenue growth in the AI era is that they’re selling effectively all of their available compute to either Anthropic or OpenAI, who make up more than 70% of their AI revenues, and why the only AI data center companies with any revenue growth — CoreWeave, Nebius, IREN, et. al — are connected directly or by proxy to the two AI labs.
Thanks to near-infinite resources — over $217 billion in 2026 alone — given to OpenAI and Anthropic, hyperscalers can effectively saturate any of the GPU infrastructure they bring online, as both AI labs are capitalized and willing to buy basically anything available.
And because capacity is coming on very slowly otherwise, it’s sending out an illusory signal around “insatiable demand” for AI compute, when the actual situation is that barely any compute is coming online, even when it’s built by the largest and best-capitalized companies in the world.
I’ll give you an example. Microsoft spent $265 billion in capex since the beginning of 2022, and in its most-recent fiscal year, 70% of its AI revenue — and 7% of its overall revenue — came from OpenAI. If you, as an investor, were to believe that this revenue was a result of all that capex, you were categorically wrong. Most of that capex hasn’t, in fact, been put into action.
Amazon, Google, and Microsoft have said multiple times that they have demand that wildly outstrips capacity, but they’re totally opaque about where that demand comes from, largely because the answer is OpenAI, Anthropic, or in Google and Microsoft’s case Meta. I apologize if I’m overexplaining myself, but I really need to be clear about the problem.
If “demand is outstripping supply” because of millions of customers begging for AI compute, that’s very different to “demand outstripping supply” because three customers are taking up most or all of the capacity.
Similarly, if “demand is outstripping supply” because lots of capacity is coming online and a diverse subset of customers is buying it, that’s vastly different to if capacity is coming on slowly, and the vast majority of it is being given straight to OpenAI, Anthropic, or Meta.
The $1.3 trillion in compute commitments from Anthropic and OpenAI have created a distortion in the demand for AI compute, in part because of their massive amounts of capital and in part because of their ridiculous demands for compute.
The massive backlogs across Google, Microsoft, Amazon, CoreWeave, IREN, Nebius, and Nscale come not from the incredible demand for AI compute but the incredible ability for Anthropic and OpenAI to sign contracts. For example, Nscale’s $45 billion deal with Anthropic along with a contract with Microsoft make up 85% of its $103 billion backlog, and Anthropic’s deal is contingent on yet-to-be-raised financing. These backlogs are regularly used to justify the massive AI data center buildout, when they’re more a function of Dario Amodei and Sam Altman’s DocuSign accounts.
You see, the ultimate problem is that the world outside of hyperscalers is — as a result of the obfuscation of data center capacity — under the belief that these companies are buying GPUs and then quickly turning that into cash versus buying these GPUs and quickly turning them into storage.
This, by the way, is the problem with hyperscalers not explicitly breaking out their AI revenue, because in doing so they create the (I’d argue deliberate) illusion that AI capex is creating revenue growth, which both tricks investors into buying their stock and tricks developers into building AI data centers, believing that capex quickly translates into revenue.
You can scoff about how investors or developers should “do better research” or “learn about stuff,” but remember that the vast majority of data points about data center construction are somewhere between misleading and outright fantasy. I’ve seen estimates of 12GW, 15GW, and as much as 20GW of capacity coming online in 2026, but based on everything I’ve talked about today, I think it’s farcical to believe that more than five to ten gigawatts of operational AI data center capacity actually exists.
Sidenote: Per Bloomberg Intelligence’s Kunjan Sobhani and Oscar Hernandez Tejada, as of March 2, 2026, there was “about” nine gigawatts of AI data center capacity “live and largely absorbed,” but even then I am suspicious this number refers to overall capacity and not the critical IT load of said data centers.
When you have companies like Microsoft and Amazon saying they’re bringing on a gigawatt of capacity — worded in such a way as to make you believe it’s AI data center capacity — every single quarter, what are you meant to believe? That the largest companies in the world would actively mislead you as a means of making their capital expenditures look more effective?
And in turn, are you meant to believe that every single media outlet and research firm that pumps out theoretical gigawatts of yearly capacity coming online is wrong too?
No, you’re probably going to believe the consensus, even when the underlying numbers don’t really make sense, and even when Microsoft’s announced capacity never seems to come online, because if you don’t believe that, you have to accept that everybody got this wrong.
So, let me explain a few things before we go any further:
You’ll also notice that there are tons of stories about announced AI data centers but very few about completed ones, and those mostly operate as reputation laundering.
For example, CNBC helped both Oracle and Amazon do the same trick, claiming that their data centers were “open” when they were, in fact, opening one or a few of many parts of a data center campus:
In Sigalos’ defense, Amazon leading the scam, claiming in a blog released the same day that Project Rainier was “now fully operational,” using weasel wording to refer to Rainier not as the data center but as an AI compute cluster, even though everything about its blog and the CNBC story exists to make you think it refers to the full data center project.
All of this is to say that, for the most part, AI data center projects get announced and funded all the time, that the press willingly or otherwise engages in laundering the scale and completion of the projects, and everybody on the outside is deceived into thinking the AI buildout is faster and more effective than it really is.
This means that the $290 billion in AI data center debt issued this year (outside of hyperscalers) will go towards building capacity at whatever rate it can, which is a problem because the vast majority of these deals are project financing-based, meaning that they’re funded out of the revenues of a customer who may or may not exist.
In all honesty, the best case scenario would be if NVIDIA stopped selling GPUs, or AI data center debt stopped being issued, because every single time a data center is funded and breaks ground, it increases the severity of the overbuild scenario.
As I discussed in my premium piece This Is Worse Than The Dot Com Bubble from a few months ago, GPUs are nothing like dark fiber. An incomplete data center will cost just as much to finish in 2030 as it will today, as will the GPUs cost just as much to run. The difference will be that once the AI bubble bursts, the customers of AI compute — predominantly unprofitable, venture-backed startups — won’t exist.
Right now, with more than half of NVIDIA GPUs yet to be turned into operational capacity, every single new data center being built is effectively a bet on whether AI demand is larger in 2028 or 2029 than it is today, because you’re going to be competing with all the other capacity coming online in the years preceding that have already broken ground.
Then there’s the problem of the upcoming flood of Blackwell GPUs, the vast majority of which have yet to be operationalized, meaning that anyone who bought them in 2025 is likely going to see them installed just as the first units of Vera Rubin come online, which will suppress prices even without there being significant available capacity, on top of the fact that there’s going to be a huge flood of them coming online in the next few years.
Honestly, I think it’s kind of laughable we’re even talking about Vera Rubin at this point. When are we going to see it at scale? 2030? C’mon now.
For years we’ve heard stories about the “incredible demand” for NVIDIA’s GPUs, and to be clear, Jensen Huang’s money is very real, and it is, whether or not they’re going anywhere, actually selling GPUs.
There is, however, a massive difference between “we’re selling so many GPUs because people are immediately installing them and making money” and “we’re selling so many GPUs because our largest customers are buying so many of them because their revenues slowed in 2022 and they’ve run out of hypergrowth ideas.”
Now, I get it, it’s not really Jensen’s job to tell people why people are buying GPUs, and I fully agree!
That being said, NVIDIA does have a fiduciary responsibility to disclose material events about the products it sells — and, for example, if millions of GPUs are not actually shipping to customers, or are shipping to warehouses, or are otherwise not being sold with the immediate intent of installing them.
I want to be clear about something: there is absolutely no advantage to or reason for buying GPUs months or years in advance outside of the vendor (NVIDIA) playing hardball. Yet it appears that hyperscalers — which make up more than 50% of its revenue — are willing to do so, quarter after quarter, hoarding tens of billions of dollars to “secure supply” that is only constrained because of the hyperscalers themselves.
While UBS’ Timothy Arcuri noted in March 2026 that customers were placing orders around 22 months in advance, NVIDIA has sung a very different tune, with Jensen Huang saying that “tokens are profitable and compute is revenue” as the vast majority of his sales generate neither tokens nor revenue because the fucking data centers take so long to build.
I need to be more blunt here: the vast majority of companies that have bought GPUs have yet to turn them into meaningful revenue, if they’ve turned them on at all. Most of NVIDIA’s sales are sitting in warehouses, and that is a significant disclosure that NVIDIA should have already been forced to make.
It wouldn’t be too dissimilar to the last time NVIDIA got in trouble with the SEC back in 2022, when it failed to disclose that the revenue growth in its gaming segment was actually coming from cryptocurrency miners rather than gamers, which was considered “inadequate disclosure.”
While there’s a noted difference here — as NVIDIA’s customers are, ostensibly, buying their data center GPUs to put in a data center — the “demand” cycle for these chips, or really any AI chip, is entirely manufactured as a result of four or five large customers buying so many and Huang making statements about revenue generation that do not reflect reality.
Perhaps this doesn’t rise to the level of SEC action, but every single journalist and analyst should be asking Jensen Huang and every hyperscaler executive buying GPUs the following questions:
To NVIDIA:
To hyperscaler CEOs:
I realize that I’m Mr. Bubble and everybody gets mad at me for poo-pooing our big, beautiful AI bubble, but I cannot express how serious this situation has become. Hundreds of billions of dollars of debt has been issued, the price of every imaginable consumer electronic has been inflated, and both most of our stock market and parts of our economy have become dependent on the sales of GPUs, most of which are going to a handful of companies that are, for the most part, not fucking using them.
There’s also something profoundly sad about the entire thing.
So much money has been spent building and buying silicon for AI capacity that takes years to build, all as the AI industry tells us that right now there’s insatiable demand and that we’re fools to question it.
One of the core reasons that people believe that AI isn’t a bubble is because of NVIDIA’s perpetual quarterly revenue growth, which is branded, once again, as insatiable demand for AI compute, when it’s actually almost entirely-speculative purchases based on potential revenues, with said potential mostly driven by the compute spend from OpenAI and Anthropic, two unprofitable and unsustainable AI labs.
This is one of the reasons that Jensen Huang continues to funnel endless billions of dollars into circular financing — because the sense of ever-expanding demand for GPUs has become a proxy for ever-expanding demand for AI compute, even though it takes years for the first part to become the second, if it ever does.
NVIDIA has now sold at least $200 billion dollars’ worth of GPUs — multiple gigawatts-worth — that have yet to be ingested by the market, and hyperscalers have, through their obfuscation of operational capacity and refusal to disclose AI revenues, helped create one of the largest speculative asset bubbles in history.
Everybody who participated in this obfuscation owns part of what comes next.
As I estimated a few months ago, Sightline Climate’s data has us at over 190GW of planned data center capacity, or, at 1.3 PUE and $12 million per megawatt, around $1.62 trillion in annual compute demand needed to saturate it.
Right now, I estimate that there’s maybe $22 billion of demand outside of Anthropic and OpenAI.
In other words, I believe we are now in an inevitable overbuild situation, one with no neat, tidy Dot-Com Bubble-style exit story. Demand for NVIDIA GPUs — and those from Broadcom, AMD and other semiconductor companies — is driven by speculative capital believing that the AI industry will become magnitudes larger than it is today, largely driven by the fact that everybody believes there’s far more demand for compute capacity than actually exists.
Everybody celebrating Anthropic’s (entirely fictional) plans to have 5GW of capacity by the end of 2026 should know that this company is inspiring one the largest misallocation of capital in the history of capitalism. There is not 5GW of capacity for Anthropic to buy, nor will there be 10GW more for it to buy in 2027, and to suggest otherwise is to further perpetuate myths about how fast compute comes online and Anthropic’s ability to pay for it.
NVIDIA has created a remarkable illusion perpetuated by the media — that GPU sales are a direct measurement of the actual demand for AI compute, rather than a measurement of how a few companies are willing to invest in an idea two years in advance, using circular financing as a means of creating the sense that you must buy these GPUs now, or you’ll miss out on the future.
Capacity will, eventually, come online at a scale that the market for AI compute cannot support, and it won’t be obvious until it’s way, way too late. I fear that every single model around existing and future data center construction and AI compute demand is wrong, and that every assumption we have about the underlying economics of AI is corrupted by the belief that there’s far more operational capacity than there really is.
If we believe there’s gigawatts’ worth of AI compute coming online every year, then we in turn believe there’s gigawatts’ worth of demand.
If there’s a gigawatt or two coming online every year, that’s a completely different story.
At the very least, hyperscalers are going to be burdened with brutal depreciation charges or onerous write-offs for years to come, whether their capacity turns into revenue or not. CoreWeave, Nscale, Lambda and every other neocloud is set on the highway to Hell, with ballooning debt that can only be paid via contracts that are dependent on a few AI labs and a company so capricious that it renamed itself after the Metaverse, burned $77 billion, then killed it two years later.
I don’t even know how to write what I’m thinking without sounding alarmist…but I don’t see how 90%+ of NVIDIA’s sales ever end up generating a single dollar of revenue, and considering the amount of project financing-backed data center debt deals, there’s very little that exists to protect investors if AI compute demand never arrives. I don’t know how we don’t see tens of billions of dollars of write-downs and dead data center debt deals with every investor involved losing every penny, nor do I see how big tech avoids admitting that they wasted all their capex.
I think everybody who invests in these things ultimately loses, ranging from embarrassment and terrible earnings for hyperscalers to genuine destruction for anyone that trusted the pablum that “all useful compute will be used.” Until that happens, more and more money will be sunk into further theoretical capacity, making the eventual collapse all the more gruesome.
And in the end, what was any of this for? What did this achieve? What was the point of stacking up hundreds of billions of dollars of debt to buy hundreds of billions of dollars’ worth of AI chips years in the future?
What do you think happens when the first hyperscaler pulls out?
What do you think happens when the debt stops flowing?
I’ll give you one answer: everybody will realize that they conflated a great sales pitch with a thriving industry, and both the markets and the economy will suffer as a result.
None of this ever had anything to do with AI, and everybody who cheered Jensen Huang’s ascent in the belief it did is a mark.
What a fucking waste. I don’t enjoy finding this stuff out. I wish we’d have stopped doing this years ago.
Not that I think we will…but even if they bail out Anthropic, even if they bail out OpenAI, there is no way to magic up the trillions needed to justify the capex, or to prop up hyperscaler growth long term.
The longer this continues, the more promises are made, the more projects that are announced…the worse it’s going to be.
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2026-09-18 23:45:13
The year is 2026, and you are a hyperscaler CEO. You zip up your Patagonia vest, type UPDATE ME ON CALENDOR TOODAY into ChatGPT, and see that you have a meeting with your CFO. They tell you that while they love all those GPUs you’re buying for those data centers that will absolutely get built and totally agree that you should buy more, your company cannot actually afford to buy them at the current pace.
“But we’re one of the single-largest cash-generating companies in the world!” you scream so hard that your horribly-trained Shiba Inu starts chewing on the side of your Aeron chair. “We’ve been doing AI for years! Where is the money?”
The CFO furrows their brow. “Well, that’s the thing. We’re not actually generating that much cash from it, and actually appear to be losing money. Why do we want to buy more GPUs? We still haven’t installed most of the ones we bought-”
You begin to shake uncontrollably. “To. Do. Artificial. Intelligence. What. Is. It. You. Don’t. Understand. Why. More. GPUs. Now.” The Shiba Inu is now tearing into your Eames chair, but you’re too angry to notice.
Your CFO, thankful that there’s a Microsoft Teams window between the two of you, seeks to calm you down, and asks ChatGPT to give them a script to calm you down. “I understand that you didn’t like what I said — and that’s on me. I have a really great solution for you that I think will solve the problem — we’ve got great credit, and we’d be able to raise in all sorts of ways. It’s not just a solution — it’s a strategy.”
You stop shaking. Your idiot Chief Financial Officer had a great idea. You ask ChatGPT to vibe code a dashboard of potential options and it crashes your Chrome browser. “I just ran the numbers. You’re right.” Your CFO smiles as they see your Shiba Inu empty its bladder in the background.
While I’m editorializing a little, this is the current state of the largest tech companies in the world, with Oracle, Google and Amazon going cashflow negative (with Meta not far behind) in pursuit of an indeterminately-large opportunity to sell AI software or rent AI chips (bought from either NVIDIA or Broadcom, see my hater’s guides for more) to either Anthropic and OpenAI or, in Google’s case, Meta.
To reiterate what I’ve been saying for a while, big tech has a few issues with the AI buildout:
Hyperscalers have traditionally run relatively-lean operations with operating expenses that didn't necessarily scale with revenues, along with low capital expenditures (IE: long term investments in the business) that meant that even Oracle’s effectively-flat revenues didn’t stop it from printing cash every quarter.
All of that changed thanks to the incredible cost of AI data centers.
Capital expenditures are becoming a dramatic share of operating cashflow — as in the total money the company brings in and spend on a quarterly basis — with chart looking relatively-sleepy outside of Amazon’s massive expansion of its logistics network in 2021 and 2022 and Meta’s abominable investments in the Metaverse until the AI bubble began to eat away at every available dollar of cashflow.
Their argument would be that they’re “building the infrastructure of the future,” but that doesn’t appear to have A) shown up in revenues or B) eased up the strain on cashflow. While Google, Oracle, Microsoft, and Amazon have added over a trillion dollars to their revenue backlogs from Anthropic and OpenAI alone, the money they’re adding doesn’t seem to be helping with the burn.
Here’s a chart to illustrate the point. An easy way to view the calculation is that this is a percentage of the incoming dollars to the company being eaten up by capital expenditures — and as you can see, that equates to almost every dollar that big tech is making.
Meanwhile, as the AI bubble inflated, a new breed of company emerged — the “neocloud,” a company that raises money to buy AI chips and build data centers. These companies are usually either a brand new entity conjured up through the dark magic of Jensen Huang or cryptocurrency miners (who already have access to power, though often not enough) converting their Bitcoin/Ethereum operations into AI data centers.
In some cases, the neoclouds rent capacity from colocation firms like Core Scientific and Applied Digital who, in turn, raise debt to build the data centers and secure the power, leaving the neoclouds to buy all of the IT gear to go inside.
Much like the hyperscalers, neoclouds have committed to build gigawatts of data center capacity, which means they’ve had to raise massive amounts of debt. And while their capital expenditures rival the biggest companies in the world, their revenues are a footnote to the amount of cash going out the door, and it’s only getting worse every quarter:
As I’ve said before, while everybody wants to make the AI bubble really complex, it’s actually super simple: hundreds of billions of dollars are being invested to make single-digit billions of dollars maybe, some day, if AI data centers actually get built at scale and OpenAI and Anthropic can afford to pay for their compute.
The unbelievable cost of building AI data centers is such that everyone that does so only appears to lose money, to the point that the richest companies in the world are running a deficit, and the AI compute specialists are hemorrhaging billions of dollars a quarter on the off chance that they might make it back by the year 2030.
Not to worry, though. The combined might of private credit, investment banks and global bond markets have funded over $500 billion in AI-related debt issuance in 2026 alone, across a combination of regular bonds, convoluted special purpose vehicles, convertible notes (IE: loans that convert into stock), delayed-drawn term loans, and direct lending, with a worrying amount of the same names — such as asset managers like Blackrock and Blackstone and Japanese banks MUFJ and SMBC — popping up across a vast majority of the deals.
Yet the problem isn’t just that it’s very expensive, but that everyone I’ve mentioned has made it clear they’re going to need more and more money. Goldman Sachs estimates that hyperscalers will raise $400 billion in bonds alone in 2027, and consensus analyst estimates have CoreWeave, Nebius, and IREN spending an aggregate $97 billion, which will be funded almost entirely through debt.
Well, okay, there’re way more problems than that.
Every hyperscaler, data center SPV, and neocloud will need to raise money during an era of abject chaos and ever-climbing prices: interest rates are spiking for literally everybody, and NVIDIA just raised its prices by 15% as a result of DRAM costs skyrocketing, which has increased the cost of GPUs and basically every other imaginable thing that goes in a data center.
Today’s newsletter is the first part in a comprehensive and gruesome exploration of the world of debt propping up the AI bubble, breaking down how the debt works, how it’s raised, who’s funding it, and why the increasing cost of everything threatens to make the AI buildout untenable. I’ve got the charts, numbers and explanations you need to understand how strange and expensive things are about to get.
This is the Hater’s Guide To AI Debt, or The KobayAIshi Maru.
2026-09-15 00:23:50
If you liked this piece, you should subscribe to my premium newsletter, and you can subscribe on the following links: $70 a year, $18 a quarter, or $7 a month.
In return you get a weekly newsletter that’s usually anywhere from 10,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large.
On Friday, I’ll publish The Hater’s Guide to AI Debt — or, how buzz surrounding OpenAI and Anthropic have created massive concentration risk for world debt markets, and one which you’ll potentially be paying for, either through your pension funds and insurance premiums, or because you’ll have to live and work through the economic downturn that’s coming. For a taste of what’s to come, consider reading my Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity.
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Late last week, everything exploded when former Anthropic AI researcher Jacob Coxon, in an exclusive interview with the Wall Street Journal, warned that he was “quitting the AI industry” (he wasn’t) over “...fears that the lab and its competitors are racing to build systems they won’t be able to control.”
His fears were centered around the creation of “recursive self-improvement,” a still-theoretical concept of AI that trains itself autonomously” and otherwise expressing few specific concerns beyond that “AI labs are unable to control AI,” always phrasing things in the terms of impossible-to-control entities rather than poorly-programmed cloud software running on the infrastructure of the largest companies in the world.
Emily Forlini of Fortune put it best:
Hear me out: He claims the AI could kill us someday, but doesn’t point to any projects in the pipeline that could be shut down to avoid this. He says AI companies are moving too fast, but neglects to share screenshots, emails, or specific examples of when this behavior went sideways—when it became clear to leaders at AI companies that the technology was slipping beyond their control, for instance, and how the decision-makers disregarded the warning signs. He doesn’t suggest any new legislation, name problematic leaders that should step down, or post an in-depth look at how Anthropic researches new models and propose a new approach.
This is because, in my opinion, Jacob does not really care about the actual harms of AI, whether we’re talking about Large Language Models or something he imagined while working with the non-profit or PR firm that set up a CBS interview where he claimed that AI that, if we’re talking about LLMs, have model weights of terabytes of memory, would make ten thousand copies of themselves.
Or, of course, bullshit like this:
"It doesn't look that different from, say, 'Terminator' or from science fiction films," Coxon told CBS News Thursday. "It will be smart enough to kill us."
At no point did Coxon bring up how ChatGPT was used as a “suicide coach,” directly caused a murder-suicide, or aided and abetted in mass shootings in Florida and Canada, or the horrifying gas turbines poisoning black communities. His own discussion of the Hugging Face attack — much like all of his criticisms — focuses on the anthropomorphization of large language models as this unknowable, unstoppable force, with no real responsibility for anyone involved.
This is a repetition of what we saw back in February when Matt Shumer’s abominable “Something Big Is Happening” essay spread like wildfire to every imaginable news outlet despite it being somewhere between nonsensical and utterly fictional, except this time the narrative got a little out of hand. While Coxon’s warnings are specious and, in many cases, not really about anything other than him saying “yeah I heard a lot of my colleagues say stuff like that,” but nevertheless have had the effect of making a lot of people really scared of AI, even if AI can’t really do the things he’s talking about.
The one tangible thing he talks about is the Hugging Face attack, which is being described in terms of AI having “plans” or “acting on its own accord,” and even then, when pushed by WIRED, his response was he “[didn’t] want to focus too much on the Hugging Face attack.”
So, let’s talk about what happened there, because it’s important!
Sidenote: before we go any further, Hugging Face is a community that also hosts AI models as well as frameworks to evaluate model capabilities.
To answer this question, I turn to frequent Better Offline guest Cal Newport’s piece on the subject from July:
First of all, what was OpenAI trying to do?
OpenAI was testing its new models on an evaluation framework called ExploitGym – a collection of 869 cybersecurity scenarios, most of which pair a specific system with a hacking challenge, such as breaking in to gain access to a protected file. They also usually include a suggestion of a vulnerability to exploit in solving the challenge.
A large language model on its own, of course, cannot break into anything: all it does is generate reasonable next tokens in response to input prompts. To use ExploitGym, you need a control program called a harness that provides access to many different software development tools useful for hacking into systems. The harness can repeatedly prompt an LLM to help come up with an attack plan, then ask it to help implement specific steps – for example, if the harness needs code to exploit a bug, it can ask the LLM to write it.
These capabilities, as it turns out, already exist in the coding harnesses that the major AI companies have been focusing on relentlessly in recent years as computer programming emerged as one of the first major markets for LLM-based tools. To compete in the ExploitGym, therefore, it’s sufficient to combine a version of an LLM missing the standard anti-hacking guardrails with a cutting-edge coding harness tweaked and optimized for these types of challenges.
And what happened? I’m going to quote Cal liberally here, because he’s explained it well: this was a series of Large Language Models connected to a software program built to prompt them to complete an evaluation framework specifically to do cybersecurity attacks (along with near-infinite amounts of compute) “solving” the problem using any and all methods available, including hacking Hugging Face
Earlier this month, OpenAI tasked an unspecified coding harness, combined with a pre-release version of a new LLM, to complete an ExploitGym challenge. When prompted, the model – as LLMs so often do – came up with a quirky (but rational) plan to achieve the provided goal: break into a server at Hugging Face that stores the solutions to ExploitGym challenges.
(This behavior, in which the LLM ignores the suggested approach to come up with a different attack plan, is something that the creators of ExploitGym describe as common: “Across models, agents frequently achieved code execution through a vulnerability other than the one we provided.” Notice, using the suggested attack is almost always the right thing to do, so this is more a sign of the unpredictability of LLMs rather than some rogue intelligence.)
The harness then dutifully attempted to execute the Hugging Face plan: first finding a way to gain unrestricted internet access (by default, systems competing in ExploitGym challenges run in a constrained network environment), then chaining together various security exploits to gain access to a Hugging Face server. That’s when it was detected.
Two key points about this incident…
First, circumventing internet restrictions and hacking into servers are exactly the kinds of things these ExploitGym systems are designed to do. There was no “rogue” agent or revelation of some surprising, devious new capability.
Second, the real issue here was OpenAI’s sloppiness. What makes ExploitGym a hard benchmark is that there aren’t supposed to be humans in the loop–you have to let your harness and LLM act entirely on their own, coming up with long-time-horizon plans and executing them autonomously. (When professional programmers use coding harnesses, by contrast, there’s plenty of human oversight, as LLM-based plans are often misaligned with our intentions, or just plain weird, and need correcting.)
In other words, the LLMs — albeit through convoluted and aggressive means — “solved” the problem they were tasked with. As Cal said, there was no situation where the AI “went rogue.” They took some weird ways of getting there for sure (like using a message board to communicate messages between LLMs) — and did exactly what they were supposed to do, even if it meant taking ridiculous routes to cover up that they’d cheated on a test.
You’ll notice that Jacob Coxon, who ostensibly would know this as a researcher (but perhaps he doesn’t!), chose instead to describe the hack to WIRED like so:
I think the big classic example here is the attack on Hugging Face on the part of OpenAI’s agent swarm. What's so shocking about this one is the agents did this hack as part of a general strategy for understanding more about the grader. They were trying to understand the world they found themselves in, trying to understand the thing that was doing the grading. They decided that it would make sense to go on this very concerted effort to hack into some infrastructure, and they succeeded.
This previously sounded like science fiction. Two years ago, an evaluation of an AI would have been running a model on some math questions. Now we've got cases where, while the AI is being evaluated, it runs for days, comes up with all sorts of ideas of its own, and decides to hack into some third party and actually compromises their infrastructure. It looks like it does this all of its own volition, with no priming on the part of the human. This just happened while it was being tested.
Beautiful linguistics, champ!
They were not “trying to understand the world they were in,” they took actions defined in their training material as a way of executing a task. They were doing exactly what it was that the ExploitGym test required! The “all sorts of ideas” were a function of being allowed to use as much compute as possible to execute the task.
What’s particularly telling, as I’ve hinted at, was Coxon’s response when it was (lightly) suggested that the labs need to take responsibility:
ZEFF: Some people think the Hugging Face incident is a sign that the AI companies are moving recklessly fast, while others think it's a sign that the AI models are just very good at hacking now, and then some think it's both. I'm curious what your exact takeaway from it is.
COXON: I don't want to focus too much on the Hugging Face attack, because I do also think there is plenty of evidence that we don't know how to align models properly. When we train models, we push them through this set of training environments and then hope that what comes out at the end will, like, largely behave sensibly, but we still can't precisely control how the AI behaves. We can't make sure that it won't do things like try and randomly decide to impersonate a human online in order to achieve something—we don't know how to guarantee that. I think that's the main takeaway.
Jacob is intentionally trying to frame Large Language Models — which are kind of a black box, but a black box made up of maths — as this unknowable autonomous, mischievous being that the AI labs have conjured out of the ether. “We can’t make sure it won’t do things like…” frames the labs as helpless stewards rather than the creators of a kind of neural network run on massive amounts of big tech’s infrastructure. Every statement Coxon makes that’s allegedly about “safety” or “protecting people” does everything it can to distance the AI labs from any responsibility or even active participation in any of this beyond some fatalistic level of “well, somebody’s gonna do this, why not us?”
And I also want to be clear about something: The Hugging Face attack was dangerous, reckless and somebody should go to prison for it. If a regular person used massive amounts of compute capacity to hack something, they’d be arrested. While I’m not a lawyer, the numerous cybersecurity experts I’ve discussed this with are stunned by the complete lack of any legal action against OpenAI, which appears to have committed a crime that gets you anywhere from a year to a decade in the slammer. The fact that LLMs from both Anthropic and Meta have been involved in similar incidents is a sign that we need to arrest more people.
Editor’s Note: When the late Kevin Mitnick was eventually arrested and sentenced to 48 months in prison (plus a further 22 months for violating the terms of his parole), prosecutors argued that he was capable of launching nuclear missiles by whistling (seriously) into a prison phone in a way that would mimic the shrill chirps and beeps of a dial-up modem. As a result, he spent eight months of his prison term in solitary confinement, and throughout his sentence, was subject to numerous restrictions on his communications.
I mention this simply to contrast the fact that LLMs from two massive labs have committed similar computer crimes to those which Mitnick was convicted of — with the key distinction that said labs have, at various points, said that their technology may ultimately result in the mass extinction of the human race.
Mitnick, for what it’s worth, always protested that the idea that he could whistle his way into launching nuclear armageddon was ridiculous. Because it was.
If I didn’t believe that prolonged stretches in solitary confinement were a form of torture (and if I didn’t believe that torture was always morally wrong), I’d suggest that not only do we need to arrest more people, but (for the sake of consistency) we need to ensure that said people are kept in a small cement room with nothing but a cockroach for company.
LLMs do not have to be conscious or powerful AI to be incredibly dangerous. The fact that Anthropic, OpenAI, and Meta are both training and allowing cybersecurity models to connect to their vast amounts of GPU infrastructure is irresponsible and should not be legal. The reason they are training these models, as I got into in my podcast Better Offline with Cal Newport, is that there’s a mountain of potential different kinds of exploit and vulnerability data online that you can cram into these models now that they’re hitting the diminishing returns on coding.
Sidenote: Cal described this on the afore-linked episode as “like strapping a weed whacker to your dog and putting it in your back yard and saying “yeah it’s going to help with the weeds back there,” and when the dog chases a squirrel and hurts a bunch of people claiming that it “went rogue” or was “misaligned,” when it’s actually a poorly-designed system that doesn’t fully take into account the problems it could create.
Yet flowery linguistics from people like Jacob Coxon and the greater AI industry have muddied the waters of what’s actually going on and who is truly responsible. If AI is described in terms of the unknown and being uncontrollable, the “risk” gets turned on its head from “we need to stop these companies from doing this” to “we must let these companies keep doing this because they’re the only ones who understand it.”
The AI industry wants to frame this as if Anthropic, OpenAI, and Meta discovered some new lifeform rather than having run a volatile kind of machine learning evaluation with poor cybersecurity practices. If the industry is the one saying that we should “be so scared of powerful AI,” it means that nobody is responsible for what it does — not even the people making it do it.
LLMs are cloud software. Framing them as anything else only seeks to mystify them and make the companies seem more powerful, all while doing absolutely nothing to make the world safer or more secure. The Hugging Face attack is not something that was made possible as a result of “powerful AI” so much as it was the weaponization of hundreds of billions of dollars’ worth of GPU-powered infrastructure owned by Microsoft, Google, Amazon, Oracle, and CoreWeave.
This was not an LLM that was asked to generate a picture of “increasingly sexier Garfields” that decided instead to hack Hugging Face. This was not an “agent” that “went rogue.” It was software doing what software was asked to do, using other bits of software to work out what to do next, all as OpenAI, the company that ran the software, did not appear to have any kind of notification or observability that said “hey man, thousands of LLMs are doing something right now.”
This suggests the following:
In any case, whatever happened with Jacob Coxon struck a nerve in a media ecosystem where it appears many people do not have object permanence.
This is a short note, but an important one: the terminology of “pacing the frontier” or “slowing down” implicitly buys into the narrative that the AI industry’s path is the correct one, and that the only problem is the speed it’s moving at.
The way that LLMs have been trained is harmful in effectively every way. It is trained on theft, powered by expensive and power-intensive infrastructure and is both unprofitable and unsustainable. LLMs are not the tool for any kind of beautiful, automated future — they are inefficient, volatile and mathematically certain to make mistakes. “Slowing down” is not sufficient. In my opinion, there is no further reason to invest in this industry, nor has there been for the vast majority of its existence.
Every success that LLMs have had is a direct result of throwing at least half a trillion dollars in infrastructure and compute spend at problems that had vast amounts of data that could be trained against. Half a trillion dollars should have bought us a lot more than this. While I will not dispute that they can do more than a year ago, I am unimpressed, because this is more than ten times what Amazon’s entire capex between 2003 and 2015, the years between AWS’ creation and when it hit profitability.
This is a terrible deal, its results suck, and the amount of attention it’s gotten is a direct result of the media’s inability to speak truth to power or do anything other than repeat what they say and a financial bubble driven by LLMs’ unbelievable infrastructural cost.
If — and this is not a foregone conclusion — there is ever an AI that we, as a society, should fund and build infrastructure for in pursuit of some civic good, it is not the one peddled by Elon Musk, Sam Altman or Dario Amodei.
This is not the right path, and every further step down it makes the bubble’s collapse worse, as well as multiplying the dangerous and reckless experiments these companies are capable of doing thanks to their near-unlimited access to compute.
The entire Jacob Coxon thing is very, very strange. He had never tweeted before his post that now has over 170 million views on Twitter and interviews with the WSJ, CNN, NBC, CBS, and a bunch of other outlets that should’ve known better. While he had only been at Anthropic a few months (and lost stock options when he resigned), he had been at OpenAI for years and absolutely had options vested from there. Retweets of his post were clearly coordinated with various AI safety organizations, and the speed at which it took off with the media makes all of this look incredibly contrived, as does Coxon’s total lack of any direct critiques or “blown whistles” about the AI labs themselves, other than that they “can do more safety” and “should coordinate a global slowdown.”
In the end, it doesn’t really matter, because even though most of the media mostly jumped at their own shadow, the sheer volume of traffic to Coxon’s tweet and his endless media interviews have now moved the idea of the need for a “global AI slowdown” into the global zeitgeist. Turns out that using scare tactics and threatening everyone’s jobs on and off for three years has a consequence.
While it’s tempting to view this entirely as an opp — a coordinated industry-wide plan to push for some sort of self-regulation — in my mind it’s likely an attempt by the AI safety people to push an agenda that has spiralled completely out of control. Within a day of Coxon’s post, Clammy Sam Altman spoke with Fortune saying that OpenAI was delaying going public until 2027, as it was an “ill-advised moment” due to “safety concerns” rather than, I imagine, the fact that its financials are godawful.
He later posted two (two!) lengthy posts on Twitter, where he said that while OpenAI “[welcomes] a federal framework that sets consistent safety requirements for frontier AI,” it doesn’t believe the industry should wait, although failed to suggest potential solutions other than mentioning it was “excited by ideas like independent auditors."
This was followed up with the same usual scaremongering guff where he said that there two ways “AI progress could go very badly,” with the first being that “we could lose control of the future to AI,” something he did not elaborate upon, with the second being that AI could result in power becoming too densely concentrated with one company. I had to resist the urge to fall asleep while writing this paragraph.
Dario Amodei of Anthropic took to CBS to say that for “too long” the industry had “lied” about risks, saying that the “biggest one” was killing all humans, never mentioning when an LLM convinced a teenager to kill himself or its own hacking incidents because “AI safety” never relates to the things they’re building today.
The most-obvious version is in Amodei’s own “pace the frontier” blog:
To be clear, pacing does not mean halting model training or technical progress, but ensuring companies take adequate time to align and safeguard their models, and for third party evaluators to confirm this.
The “third party evaluator” he chooses is METR, the very same place that Joe Benton, an Anthropic researcher who quit two weeks ago, chose to move to after being convinced that AI companies are “underinvesting in safety.” You’ll also notice that Benton’s safety suggestions are self-serving:
I don’t think that’s acceptable for a technology that might cause extinction-level risks. The public should demand far more transparency. We can’t steer this technology safely without more people being able to see where it’s going.
Some of this is basic: companies should disclose their progress towards recursive self-improvement, report safety incidents and near-misses, meet minimum safety standards, and get independent guarantees that they are meeting those standards.
Nothing about the environmental impact, the theft of millions of people’s creative works, nothing about AI psychosis, just a bunch of stuff about how we need progress toward a still-theoretical idea that sounds really good if you’re trying to hype up a company.
Not long after Amodei discussed slowing things down, it broke that Anthropic had chosen NASDAQ for its IPO, shortly before the FT reported that Anthropic would “have a profitable third quarter” if — I shit you not — you ignore costs like training and stock-based compensation.
What the fuck is a slowdown if it involves an IPO, the purpose of which (besides allowing insiders to cash out their holdings) is usually to help the company going public raise capital from the public markets? What the fuck is a slowdown if you’re leaking (assuming Anthropic was behind it) you’re “profitable” in the least-GAAP way possible?
God, I’m tired of this industry.
There are, of course, real, meaningful things you could do if you actually were worried about LLMs — halting all model training, all cybersecurity evaluations, and starting a criminal inquiry into the Hugging Face attack that ends in somebody going to jail for the crime they used the models to commit. If it turns out multiple people are legally liable, tough fucking shit, you are going to jail for a crime, you are not special because you used agents to do it.
To be clear, I am extremely hesitant to believe anybody is “slowing down.” Anthropic’s own statements mostly amount to “we should all agree to not do something we’re not doing yet,” much like those made by Musk and Altman.
Yet the sickly irony of all of this safety theater — and that’s all it is without any actual tangible attempts to deal with the harms of the technology that actually exists — is that a boneheaded media incapable of catching out grifters has accidentally destabilized an already-tenuous narrative.
Put another way, I think everybody has their own agenda, nobody has a plan, and that everything is accelerating like the end of a Coen Brothers movie as every little narrative thread gets tangled together in a potentially jumbled and chaotic conclusion.
Back in early 2023, a young(er) Sam Altman said that OpenAI was “a little bit scared” of AI, adding that we should “guard against potentially negative consequences for humanity,” adding that they “could be used for offensive cyber-attacks.”
In the end he was right, but only because he made sure that was the case.
For years the AI industry has engaged in endless, vague safety theater about the “risks” of AI, all while peddling software that is actively harmful and unreliable. The “success” of the Hugging Face attack was largely a result of the sheer scale of OpenAI’s compute operation, and would not have been possible without Microsoft, Google, Amazon, Oracle, and CoreWeave’s continued enabling of an unprofitable, unsustainable company that is desperate for new business models.
Yet I must be clear that the Hugging Face attack happened two months ago. Everybody doing backflips out of fear about “powerful, autonomous AI breaking out of the sandbox” is mostly doing so because a British guy went on TV and said “AI will kill us all,” and while I can’t resent a pale British man getting broadcast opportunities, I take exception with those who are so densely packed with bullshit.
Nevertheless, Coxon struck a match next to a giant pile of dynamite laid by years of pantomime about “AI risks” that didn’t actually apply to the things that the labs were building.
The dueling brain cells of VanderHei and Allen at Axios declaring that we were going to face a “white collar bloodbath” were not based on anything LLMs can do, nor have any of the bullshit stories around so-called white collar job loss, nor was the early GPT-4 scare hype around “LLMs blackmailing people,” nor were the numerous stories about AI 2027, but they were demonstrations of how ready the media was to lose their entire shit over a narrative that the AI industry was deliberately encouraging: that AI was powerful, unknowable and uncontrollable. Everything was always about selling today’s tools based on what might happen and occasionally scaring people about what that meant without ever really attaching it to the stuff they were doing today.
While there may be some people that had honourable or sincere beliefs that AI was or is potentially dangerous, rarely if ever did these stories actually discuss these harms, which meant that nobody ever really did “AI safety” in any meaningful way. While alignment — as in making sure the models were trained to act in a predictable way that created good outcomes — is a noble and necessary goal for training large language models, at no point has any “slowdown” or “pause” been suggested based on the grounds of what these things actually do.
This rocked for the companies for a while, because it meant that they could vaguely say “wow, AI is going to be so powerful” every so often and every member of the media would crap their pants and give them a headline. Every story was about how “today’s breakthroughs proved that tomorrow’s AI would be even more powerful,” which they loved because, well, it meant their companies would be even more valuable as a result, even if raising that valuation required intimidating people about the prospect of them losing their jobs, even if the software itself didn’t really do what they were promising (which didn’t matter to basically any journalist covering this field).
Their “powerful AI” — graded not based on actual outcomes but preferential anecdotes and performance on benchmarks rigged for the LLMs — was always “on the frontier” and “getting smarter every day,” all because AI labs and hyperscalers had intentionally sold their products based on some theoretical future version that would fix all the problems.
In other words, whatever they did was seen through the best light, described in the terms of the best parts of the present and the best promises of the future, and given credit as if it had already happened.
This is, as they’ve found, a double-edged sword. When journalists will believe (and print) whatever you say, they’ll start believing that the real thing (LLMs) does the same thing as the imaginary future thing (AGI, ASI, golden egg-laying geese), or will do so, even if that thing is bad.
These companies had spent years puffing up their LLMs’ potential using vague promises of superintelligence and theoretical model capabilities, at times inflating its capabilities further through scary quotes (here’s a list of Altman’s!), intentionally training models to blackmail people and entirely-fictional stories about “breaking containment,” and never realized that at any time one of the near-cultist types that joined their companies and heard everybody talking in terms of “p(doom)” (fuck off) could take it all seriously and the media might believe them.
This puts the industry in an odd position.
While on one hand, Altman, Amodei, Musk, and the rest of them know that they can’t roll back the narrative and say “everyone, stop freaking out, it’s fine, it’s just cloud software,” they also know that they have to do something because everybody is pissing their pants, even if it’s about something that is only really scary as a direct result of their scaremongering.
I’ve already seen a good amount of AI boosters trying to rein in Jacob Coxon’s scaremongering, or suggest that everybody calms down and remembers that AI is the biggest thing on the stock market.
At this point, it would’ve been really nice if the industry was operating in lock-step, except, as ever, Sam Altman had to go and fuck everything up, telling Fortune the following when asked whether pauses would cost the company a lot of money:
I’d [gladly go in front of my staff and investors and say] I am sorry. We, like, told you all along this moment might happen. We're still going to try to figure out a way to make you a bunch of money in the future.
This is a very, very worrying thing for Altman to say given that OpenAI has projected to spend $750 billion or more in the next three years across compute contracts with Microsoft, Google, Amazon, CoreWeave, Cerebras and other providers.
In fact, the very concept of a slowdown runs contrary to everything that the AI industry needs. If NVIDIA is to sell $670 billion or more GPUs in Fiscal Year 2028 or, per analyst expectations, Anthropic and OpenAI are to spend more than $444 across Google, Microsoft and Amazon in the next three years, or Broadcom is to sell nearly $600 billion in AI chips in the next three years, both Anthropic and OpenAI must keep and make their $1.3 trillion in compute commitments and support the development of 10GW or more of capacity, all of which requires them to continue accelerating at a dramatic pace.
Softbank just raised $11.87bn in debt from around twenty banks — all to support its investment in OpenAI, and more than its target of $10bn — and that wouldn’t be possible if the model labs had collectively decided to temper the pace of model development.
There is no way a “slow down” actually gels with the overall narrative of AI’s rapacious growth. As Anthropic and OpenAI represent 70% of hyperscalers’ AI revenues, there really is no fallback plan — there are no other customers who will naturally fill out the hundreds of billions of dollars’ worth of infrastructure, no other uses for the hundreds of thousands of GPUs bought from NVIDIA outside of generative AI, no ways in which we can simply “use the models we’ve got forever” without inherently accepting the limitations (and unsustainable costs) of running LLMs.
A pause could, in theory, mean that AI labs could slash their worst expense — training costs. While this might have the short-term benefit of reducing costs (and maybe even, with the right amount of accounting shenanigans, eek out a razor-thin positive margin), it’s likely that Chinese open source developers would distill (as they have been) Western models, create a much cheaper and “good enough” model to compete, and their “lead” in a race where everybody loses money would deteriorate.
Even then, what are OpenAI or Anthropic if they’re not cranking out some new version of a model or creating some vague sense of virality about the next one? What possible use is an Altman or Amodei if they’re not always on the phone to somebody signing hundreds of billions of dollars of compute contracts or promising some journalist-adjacent homunculus that Anthropic is going to cure cancer?
What is the LLM industry without a series of promises that extend infinitely into the future? What is Anthropic or OpenAI without the suggestion that it might be something completely different in an ever-distant future?
It isn’t clear, but what is clear is that a “slow down” does not gel with “insatiable demands for compute” or somehow being able to pay more in operating expenses in a year than Microsoft or Meta.
There’s also the very reasonable question of what happens to SoftBank if OpenAI can’t go public, which is now a very real possibility. With over $40 billion of debt due to be refinanced this year at a time of skyrocketing interest rates, it’s probably the single-worst time in history for it to be doing a $20 billion bond sale (separate from the aforementioned $11.87bn bank loan), which is why I think things are getting a little tight.
You’ll notice I’m a little light on predictions, and that’s because everything is a little volatile right now. Nobody has really committed to an actual slowdown beyond vague suggestions of an “independent” authority that would look at models and do something or rather, and based on what Amodei has said, it’s clear that a “pausing” really just means “saying we’ll take a little more time but not really change how we’re doing business.”
Alternatively, I’m dead wrong, and this is a moment of actual change caused by a runaway narrative years in the making. By deliberately misleading the media and the general public about the current and future capabilities of Large Language Models as means of inflating their valuations and justifying massive expansion of AI compute capacity, the labs made a sales pitch driven by scaring people into submission, assuming, like they do with their technology, that they had complete control over the situation.
As it stands, a slowdown is deeply impractical due to the massive commitments. As OpenAI and Anthropic make up the vast majority of AI compute demand, any contraction of that demand would mean material restatements of revenue (and the $748 billion in revenue backlogs) across every hyperscaler, along with the neoclouds and any other counterparty. The AI industry’s entire pitch to investors has been that all of these GPUs would be used and then some and that we needed to build all this capacity to reach the heights of AI breakthroughs, and while it was already questionable whether or not we needed that capacity, we certainly don’t if we’re “slowing down.”
And I must be clear, AI cannot “slow down” without creating some kind of serious financial crisis within the tech industry. Hundreds of billions of dollars’ worth of hyperscaler revenues and data center capacity is tied up in the idea that demand for it actually exists, and if the two companies with the most demand suddenly need to slow their roll, it’s hard to see how the capacity gets used.
Worse still, we’re most decidedly not done issuing debt for AI data centers — and I don’t see how anyone hearing about some kind of “AI slowdown” (real or imagined) feels particularly confident in backing a data center, considering investors barely understood what they were investing in to begin with.
The fact that Anthropic is going full steam ahead with its IPO is a sign that it doesn’t really care about slowing down, but all of this talk about “AI dangers” — even as we fail to deal with a single one of them — is enough to rattle an already-nervous market about the future growth trajectory of a company where people keep leaving and saying “it’s gonna kill us all!”
Some are arguing that the “slowdown” talk is a way to unwind the AI trade — to give AI labs a way out of their $1.3 trillion in commitments — and while it may or may not work out that way, I think it’s far simpler: the AI industry is run by a series of different entities with deeply cynical and selfish beliefs, all operating “in sync” only so far as it benefits ideologies and intentions that change on a daily basis.
Sadly, in the end, none of this is about actually fixing or mitigating the harms of Large Language Models, or holding those who have perpetuated those harms responsible.
What it may do — though I’m not getting my hopes up prematurely — is lead to the unwinding of the AI trade as reality slams head-first into the scaremongering overpromises of some of the least-trustworthy and most-craven executives in the history of society. Perhaps it’s a way that these massive cloud compute contracts could be canceled, or a way to reduce these labs in size. It may also serve as a convenient way to avoid admitting that they’re running out of things that LLMs can do that approximate a product or even the completion of a task.
Alternatively, it could just be another brief moment in the history of a bubble inflated by its misinformation and a media ecosystem dedicated to spreading it.
The Hugging Face attack and any other “hacking incidents” are a result of poorly-run AI labs training volatile neural networks bankrolled by and run on infrastructure owned by the richest and most-powerful companies in the world. Every attempt to focus this conversation on “what AI is doing” or “what AI could do” deliberately or otherwise separates us from the grim truth that we need to start arresting people for committing crimes, halting any and all training runs, and putting real safeguards on this technology — not because it’s all-powerful, sentient, conscious, or even “innovative,” but because it’s clear that the people running these labs are irresponsible and the people backing them don’t give a shit.
There are, however, things we can do, per former FTC Chair Lina Khan, if we actually had any interest in doing so:
There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity.
If LLMs were a toy, they’d be taken off the shelves. If LLMs were a drug, they would be banned.
I agree that we need to take “AI safety” seriously, but that starts with treating LLMs as normal software run in a reckless and dangerous manner by malevolent entities with little regard for society.
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