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Original Author: Ed Zitron

Original Translation: TechFlow

Introduction: The Bank for International Settlements (BIS) annual report reveals a truth deliberately ignored by tech giants: Trillion-dollar AI capital expenditures have already exceeded these companies' cash flow and profitability. Ultimately, this money flows to support just two massively loss-making model labs, OpenAI and Anthropic. If Microsoft, Google, and Amazon decide to halt their quarterly $30 billion GPU purchases, the entire supply chain would collapse.

This month, I published a two-part series deep-diving into the "bubble within a bubble" structure of the AI bubble – from the unsustainable, reckless growth of semiconductor companies to the personality cult surrounding Sam Altman and Dario Amodei. On Friday, I'll release the long-awaited "SoftBank Skeptic's Guide." You won't want to miss it.

On Sunday, the Bank for International Settlements (BIS) released its annual report, echoing much of what I've been saying:

In the short term, the ongoing AI investment boom raises questions about the sustainability of the current economic expansion. The five major hyperscalers plan to spend over $1 trillion on AI-related capital expenditures between 2025 and 2026. These commitments exceed these companies' profits and free cash flow, leading some to issue debt to raise additional funds.

It's gratifying to see the central banks' central bank articulate what I've been saying for years. But this part makes me feel both validated and terrible for the entire world:

Disappointing returns could trigger a sudden withdrawal of financing and transform the capital expenditure boom into a prolonged investment bust, with potential knock-on effects on financial conditions... If hyperscalers slow down or halt aggressive capital expenditure deployment, many borrowers in the supply chain may struggle to replace lost revenue and repay debt.

No kidding. Last April, I wrote an article titled "AI Is a Systemic Risk to the Tech Industry," outlining how the failure of one model lab, OpenAI, would have seismic effects on its supply chain, delivering successive blows to Nvidia, Oracle, Microsoft, and the various new cloud providers supplying them compute power (most notably CoreWeave).

Since then, OpenAI's sticky tentacles have reached further into the tech industry, signing agreements with companies like Google, Amazon, Cerebras, and Broadcom, while also accepting more investments, including massive commitments from SoftBank. SoftBank can only fulfill these commitments by selling precious shares of companies like ARM and Nvidia, and by taking on debt.

The concept of systemic risk has never really left my writing. I've spent much of the past year thinking about it – therefore, my work has examined the potential consequences of an AI spending pullback on the financing side of the industry, particularly private credit, and the semiconductor industry.

The BIS isn't concerned about a revenue crash – which would indeed happen if hyperscalers "slow down or halt aggressive capital expenditure deployment" as they fear – but rather about a scenario where revenue crashes and borrowers in the AI supply chain cannot repay their growing debt burdens.

Again, this is something I've repeatedly sounded the alarm about. CoreWeave has been a darling of this newsletter. In March 2025, I published "CoreWeave Is a Ticking Time Bomb," focusing on the company's overwhelming toxic debt pile and its dependence on OpenAI as a customer.

On a larger scale, there's Oracle – a company I detailed extensively in the "Oracle Skeptic's Guide" newsletter.

Unlike new cloud providers like CoreWeave, Oracle is an older company that spent most of its existence selling database and ERP software to some of the world's largest companies and public sector institutions. Oracle pivoted to offering AI compute power when its core business lines started stagnating, and due to its massive size, it was able to raise staggering amounts of debt.

As I've pointed out before, Oracle was a heavily indebted company even before the AI bubble. As it happens, due to its dalliance with OpenAI, Larry Ellison felt the need to turn the debt dial up to eleven.

Oracle's spending has pushed its free cash flow into negative territory – negative $23.7 billion as of the end of fiscal year 2026 – and it had $129.5 billion in outstanding debt as of the end of May. This doesn't include its various lease commitments, which total nearly $38 billion, nor an additional $260 billion in lease commitments signed but not yet commenced.

All of this is to say that Oracle has taken on massive debt for the benefit of one company, OpenAI, and if that company can't pay its bills, Oracle is done for. Oracle's existence – and Larry Ellison's personal wealth – depends on OpenAI honoring its commitment to spend $300 billion on compute power.

This is both the most obvious and the least discussed part of the AI bubble – the over $1 trillion in capital expenditures from hyperscalers is fueling a massive semiconductor boom, which is based, at best, on the highly improbable assumption that large language models will transform into something completely different.

If Microsoft, Google, Amazon, and Meta decide to stop spending $30 billion or more per quarter on GPUs, RAM, storage, and data center construction, it would tear a hole in the side of what's perceived as a permanent super-cycle.

I need to emphasize how foolish it is to believe the so-called semiconductor boom isn't just a fleeting opportunity to fill your boots before a global stock market disaster. A disaster so severe it would make Futurum Group want to off itself.

Hyperscalers – whose capital expenditures will exceed their cash flow by Q3 2026 – are seeing such poor returns on AI investment that none of them will actually disclose revenue beyond vague "annualized revenue" figures. This means all these investments are essentially based on the idea that something completely different will happen in the future.

This future must generate at least $2 trillion in entirely new revenue for them by 2030, because if it doesn't, virtually all the capital expenditures will have been used to prop up Anthropic, OpenAI, and whatever Meta is doing with its chatbot.

There is no convincing or rational argument for continuing the capital expenditure spree, at least not one that doesn't implicitly accept that most of the current spending is wasteful, serving only to inflate stock prices and incubate two different large, loss-making AI labs. Those millions of H100, B200, and B300 GPUs won't usher in a digital god; they won't create recursive self-improvement; they won't be the fulcrum generating $600 billion or more in entirely new revenue for existing services. The only revenue they generate comes from compute spending by Anthropic and OpenAI, which I estimate accounts for 20% or more of Google, Amazon, and Microsoft's cloud revenue.

I must also be clear that these companies' costs extend far beyond equity investments. While Microsoft invested $13 billion in OpenAI, Microsoft executive Michael Wetter revealed in the Musk v. Altman case that the partnership has cost over $100 billion, implying that OpenAI's infrastructure costs alone are at least $87 billion. I imagine Amazon and Google must be spending similar amounts to handle Anthropic's equally voracious compute needs, especially considering Amazon's $11 billion-plus cost for the Anthropic-dedicated Rainier project data center.

This is the severely under-discussed part of the AI bubble. Anthropic and OpenAI have collectively raised less than $300 billion since 2019, but I estimate their true cost is at least $500 billion, considering the hyperscaler capital expenditure investments necessary for their existence. This doesn't even account for the $340 billion or more Oracle is spending to build the 7.1GW "Stargate" data center for OpenAI. These aren't startups; they're subsidiaries of big tech companies, existing as separate divisions solely to prop up equity positions and hide the truth: AI capital expenditure is a complete waste of money, even if you include two obese, spendthrift subsidiaries losing tens of billions annually.

As I reported two weeks ago, OpenAI spent $17.2 billion on Microsoft Azure in 2025, a year in which it lost $20.9 billion on $13.04 billion in revenue. Even if that were profit (it isn't), it's still $4.2 billion less than Microsoft's Q1 2025 capital expenditure.

Beyond OpenAI, Microsoft arguably has no AI business. While it boasted in April of having $37 billion in AI annualized revenue (meaning a non-specific month multiplied by 12), that's only about $3.08 billion per month, or less than a tenth of its $31.9 billion in quarterly capital expenditures. Worse, Microsoft revealed this number grew "12% year-over-year," implying its AI annualized revenue in Q3 FY2025 was $16.59 billion, or about $1.38 billion per month.

However, my November report on OpenAI's inference spending showed it spent $2.947 billion in Q3 FY2025, annualizing to about $11.7 billion. This means that in that quarter at least, OpenAI likely accounted for about 70% of Microsoft's AI revenue, and I'd be surprised if that changed dramatically over the year, given OpenAI's inference spending in Q1 FY2026 was $3.648 billion.

All of this is to say, the only tangible result of all this capital expenditure appears to be propping up two deeply unprofitable companies, Anthropic and OpenAI, and then recouping a fraction of that through revenue that is only possible thanks to tens of billions in venture capital subsidies.

Now, OpenAI and Anthropic account for 50% or more of hyperscalers' remaining performance obligations, roughly $748 billion.

Aside from the mistaken belief that OpenAI or Anthropic can actually afford to pay without money from Google, Amazon, or Microsoft, there is simply no logical or rational reason to invest further capital in AI. Hyperscalers have no meaningful AI revenue in any form other than their own pseudo-startup investments. The fact that A) they continue investing, and B) the market, analysts, and journalists act like everything is fine, is both absurd and irrational.

Side note: I'm not discussing Meta because Meta has no AI story. Mark Zuckerberg has wasted every ounce of its capital expenditure, aside from anything it might get by reselling capacity to others – but don't worry, he thinks (and these are his words!) Meta has uses for compute! No, sorry, those GPUs aren't driving meaningful ad revenue growth, as I've discussed before.

The record sales of Nvidia, Micron, SanDisk, SK Hynix, and Samsung are the direct result of a fully speculative asset bubble, driven by the reckless and directionless capital expenditure of some of the world's largest and richest companies.

Anyone investing in data centers is building speculative capacity for demand that doesn't exist beyond Anthropic and OpenAI. If that demand existed, the AI data center new cloud company CoreWeave would have a healthy and diversified revenue stream, rather than getting 65% of its revenue from Microsoft (for OpenAI) and Nvidia, with the rest from Google (for OpenAI), Anthropic, Meta, and of course, OpenAI itself. There are simply no other large-scale consumers of AI compute power, and the only reason we haven't hit that grim reality yet is that data centers take 18-34 months to complete.

Even if there were, I can find almost no evidence of anyone besides OpenAI, Anthropic, and the hyperscalers having the demand or funding necessary to justify data center construction.

I really need to emphasize this.

If we assume Nvidia CEO Jensen Huang's prediction of $1 trillion in Blackwell and Vera Rubin sales comes true, that would represent roughly 40GW of data center capacity, with an IT load of about 30GW. If we generously assume data centers generate about $12 per megawatt in revenue, this would create about $435 billion in annual compute demand by 2030.

Let's be very clear about one thing: The only companies that can currently afford to spend money on compute are either hyperscalers or companies subsidized by hyperscalers. Even so, besides OpenAI's ~$50 billion in compute spending in 2026 and my estimate of a similar amount for Anthropic, there doesn't seem to be more than a few tens of billions in demand. If there were, CoreWeave, IREN, Nebius, Cipher Mining, and other new cloud providers would have hundreds of billions in remaining performance obligations, not ones that only expand under the support of hyperscalers or the depths of Zuckerberg-style AI psychosis at Meta.

Let me put it more simply: Those hundreds of billions of dollars in data centers are being built for no one. The only companies that can "afford" to pay even a tiny fraction of the compute costs are unprofitable AI companies propped up by hyperscalers.

While this might read as a radical position, I think looking at the current state of affairs and saying "screw it, I think hyperscalers should spend $1 trillion next year" is far more radical.

There's no rational reason to do so, other than a fantasy driven by a market desperate to avoid thinking that tech has no high-growth ideas.

Aside from creating OpenAI and Anthropic, the current capital expenditure is almost entirely wasteful. Microsoft 365 Copilot sucks. GitHub Copilot sucks. Google AI Overviews sucks. Google Gemini is a follower LLM, so it also sucks. Meta's LLMs are very dangerous. Amazon Rufus sucks, and Amazon should be investigated by the SEC for implying it drove $10 billion in "annualized revenue" in Q3 2025, because it absolutely did not. Alexa+ sucks. Everything sucks, and it would be just as bad if big tech had spent only a quarter of the capital expenditure.

These products are almost universally reviled, generate almost no revenue, and even in the case of the moderately successful GitHub Copilot (~$1.08 billion annualized revenue by end of last year), it's only because users' compute was heavily subsidized, leading Microsoft to move users to token-based billing, angering customers used to spending $39 a month to burn through thousands of dollars in tokens.

Yes, All This Money Could Be Wrong

Sundar Pichai, Andy Jassy, Satya Nadella, and Mark Zuckerberg are losers. They might be worth billions, they might run giant tech companies, but they are losers, selling a doomed technology based on unreliable, inefficient, and overly expensive tech that is ill-suited for the reliable, deterministic, "set it and forget it" nature people actually associate with AI.

The Big Four Losers are the only reason anyone takes these large losing models seriously, marking that the tech industry and our economy are also driven by losers. Every bit of "progress" we see in LLMs comes from forcefully jamming square pegs into round holes – billions in training costs, hundreds of billions in capital expenditure, endless tools, scripts, wrappers, and layers trying to squeeze out anything resembling the promised autonomy.

All the king's horses and all the king's men are pouring every dollar and ounce of brainpower into trying to make LLMs into something they are not, and as a society, we are expected to coddle these things, act like they're great, and give them credit for things that haven't happened. I reject the premise that LLMs' ability to generate code or copy open-source software is evidence they will become powerful autonomous tools, and I consider those who extrapolate to that point either intellectually bankrupt, deeply cynical, or gullible enough to click every email claiming their PayPal account has been compromised.

I assure you, all this money could be wrong! Hyperscalers absolutely can spend a trillion dollars on something that doesn't do what they say, because these companies are perfectly happy to mislead you. To quote Nik Suresh:

A large part of the economy is driven by people who, simply put, are very suggestible. That is to say, getting them excited and willing to spend money is very, very easy.

Why is everyone investing in data centers? Because hyperscalers are doing it! Why are Micron and memory companies selling so much memory? Because A) GPUs use a lot of high-bandwidth memory, B) that high-bandwidth memory consumes three times the wafer space of regular DRAM, leaving less room for other, cheaper, lower-margin memory types, and C) because servers for these AI GPUs are also packed with memory!

These data centers aren't being built because creditors have any "insight" into the massive compute needs generative AI tools have and will have. They saw the "success" of ChatGPT and Claude (two heavily subsidized products), and figured that because Anthropic and OpenAI need a lot of compute, everyone will need a lot of compute. And because banks and private credit are desperate for ways to invest, everyone is so excited that it's super easy to get them excited about the prospect of building big, sexy, and expensive things!