AI's Trillion-Dollar Black Hole: Money is being burned, but aside from two loss-making companies, who is actually paying for it?

Original Author: Ed Zitron

Original Translation: TechFlow

Foreword: The Bank for International Settlements (BIS) annual report reveals a truth deliberately ignored by tech giants: the trillion-dollar AI capital expenditure has already exceeded the cash flow and profitability of these companies. The ultimate destination of this money is merely to sustain OpenAI and Anthropic, two model labs operating at massive losses. If Microsoft, Google, and Amazon decide to stop their quarterly $30 billion GPU purchases, the entire supply chain will collapse.

This month, I published a two-part in-depth series analyzing the "bubble-within-a-bubble" structure of the AI bubble — from the unsustainable and reckless growth of semiconductor companies to the personality cult surrounding Sam Altman and Dario Amodei. On Friday, I'll release the long-awaited "A Skeptic's Guide to SoftBank," which you won't want to miss.

On Sunday, the BIS released its annual report, essentially reiterating what I've been saying all along:

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

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

Disappointing returns could trigger a sudden withdrawal of funding and transform the capex boom into a prolonged investment slump, 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 find it difficult to replace lost revenue and repay their debts.

Nonsense. 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 send seismic shocks through its supply chain, delivering successive blows to NVIDIA, Oracle, Microsoft, and various new cloud providers (most notably CoreWeave) that provide it with computing power.

Since then, OpenAI's sticky tentacles have penetrated more aspects of the tech industry. It has signed agreements with companies like Google, Amazon, Cerebras, and Broadcom, while also accepting more investment, including a massive commitment from SoftBank. SoftBank can only fulfill these promises by selling precious shares of companies like ARM and NVIDIA, or by taking on debt.

The concept of systemic risk has never truly left my work. I've spent a lot of time over the past year thinking about it — hence, my writing has examined the potential consequences of an AI spending pullback on the financing side of the industry, particularly private credit and the semiconductor sector.

The BIS isn't concerned about revenue plummeting — which it would if hyperscalers "slow down or halt aggressive capital expenditure deployment" as it fears — but rather about revenue plummeting *and* borrowers in the AI supply chain being unable to service their growing debt burden.

Again, this is something I've repeatedly sounded the alarm about. CoreWeave has been a favorite 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 reliance on OpenAI as a customer.

On a larger scale, we have Oracle — a company I detailed extensively in the "A Skeptic's Guide to Oracle" newsletter.

Unlike new cloud providers like CoreWeave, Oracle is an older company that, for most of its existence, sold database and ERP software to some of the world's largest companies and public sector institutions. It pivoted to offering AI compute power when its core business lines began to stagnate. Due to its sheer size, it was able to raise insane amounts of debt.

As I've pointed out before, Oracle was a heavily indebted company even before the AI bubble. By chance, because of its dalliance with OpenAI, Larry Ellison felt compelled to turn the debt knob 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 2026. As of the end of May, it had $129.5 billion in outstanding debt. This doesn't include its various lease commitments, which total nearly $38 billion, nor the 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. If that company can't pay its bills, Oracle is finished. Oracle's existence — and Larry Ellison's personal wealth — depends on OpenAI honoring its promise to spend $300 billion on computing power.

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

If Microsoft, Google, Amazon, and Meta decided to stop spending $30 billion or more each quarter on GPUs, RAM, storage, and data center construction, it would tear a hole in the side of what people consider a permanent super-cycle.

I need to make clear how foolish it is to think the so-called semiconductor boom isn't a fleeting opportunity to fill your boots before a global stock market disaster. A disaster so severe it would drive Futurum Group to suicide.

Hyperscalers — whose capex will exceed their cash flow by Q3 2026 — are getting such poor returns on AI investment that none of them will actually disclose revenue beyond vague "annualized revenue" figures, implying all these investments are 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 capex 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 this level of capex, none that doesn't implicitly accept that most current spending is wasteful, serving only to inflate stock prices and incubate two different, massively 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 for adding $600 billion or more in entirely new revenue to current services. The only revenue they generate comes from compute spending by Anthropic and OpenAI, which I estimate constitutes 20% or more of the cloud revenue for Google, Amazon, and Microsoft.

I must also make clear that the costs for these companies far exceed 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 spend similar amounts to handle Anthropic's equally voracious compute needs, especially considering Amazon's $11 billion+ cost for the Anthropic-dedicated Rainier project data center.

This is the severely under-discussed part of the AI bubble. Anthropic and OpenAI have raised less than $300 billion combined since 2019, but I estimate their true costs are at least $500 billion, considering the hyperscaler capex investments necessary for their existence. This doesn't even factor in the $340 billion or more Oracle is spending on the 7.1GW "Stargate" data center for OpenAI. These aren't startups; they are subsidiaries of big tech companies, existing as separate divisions solely to inflate equity positions and hide the truth: AI capex is a complete waste of money, even when you include two obese, loss-making spendthrifts burning hundreds 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 would be $4.2 billion less than Microsoft's Q1 2025 capex.

Beyond OpenAI, Microsoft arguably has no AI business. While it boasted in April of a $37 billion annualized AI revenue run rate (meaning a non-specific month multiplied by 12), that equates to roughly $3.08 billion per month, or less than one-tenth of its $31.9 billion quarterly capex. Worse, Microsoft revealed this number was "up 12% year-over-year," implying its annualized AI revenue in Q3 FY2025 was $16.59 billion, or about $1.38 billion per month.

However, my reporting last November 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 alone, OpenAI likely accounted for about 70% of Microsoft's AI revenue. I'd be surprised if this changed dramatically over the year, as OpenAI's inference spending in Q1 FY2026 was $3.648 billion.

All of this is to say that the only real outcome of all this capex appears to be propping up the two deeply unprofitable companies, Anthropic and OpenAI, and then recouping a small fraction as revenue, made possible only by hundreds of billions in venture capital subsidies.

Now, OpenAI and Anthropic account for 50% or more of the hyperscalers' remaining performance obligations (RPO), totaling approximately $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 capex in AI. Hyperscalers have no meaningful AI revenue in any form other than their own pseudo-startup investments. The fact that A) they continue to invest, 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 capex, besides anything it might gain from reselling capacity to others — but don't worry, he thinks (this is a quote!) that Meta has a use case for compute! No, sorry, those GPUs aren't driving meaningful growth in ad revenue. I've covered this before.

The record sales of NVIDIA, Micron, SanDisk, SK Hynix, and Samsung are a direct result of a purely speculative asset bubble, driven by the reckless and directionless cap ex of some of the world's largest and richest companies.

Anyone investing in data centers is building speculative capacity for a demand that doesn't exist outside of Anthropic and OpenAI. If that demand existed, CoreWeave, the new AI cloud company, would have healthy and diversified revenue streams, instead of 65% of its revenue coming from Microsoft (for OpenAI) and NVIDIA, with the rest from Google (for OpenAI), Anthropic, Meta, and of course, OpenAI. There are simply no other large-scale AI compute consumers, and the only reason we haven't hit that harsh 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 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 about 40GW of data center capacity, with about 30GW of IT load. If we generously assume data centers generate about $12 per megawatt in revenue, that would create roughly $435 billion in annual compute demand by 2030.

Let's be perfectly clear about one thing: the only companies that can afford to spend money on compute right now are either hyperscalers or companies subsidized by hyperscalers. Even then, besides OpenAI's ~$50 billion compute spend in 2026 and what I estimate to be 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 backed by hyperscalers or deep within Zuckerberg-style AI psychosis.

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 fraction of the compute costs are unprofitable AI companies propped up by hyperscalers.

While this might read like a radical stance, 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 is no rational reason to do so, other than the fantasy thinking driven by a crazy market desperate to avoid the idea that tech has no high-growth ideas left.

Outside of creating OpenAI and Anthropic, the current capex is almost entirely wasted. Microsoft 365 Copilot sucks. GitHub Copilot sucks. Google AI Overviews suck. Google Gemini is a follower LLM, and therefore also sucks. Meta's LLM is 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 capex.

These products are almost universally hated, generate almost no revenue, and even in the modestly successful case of GitHub Copilot (approximately $1.08 billion annualized revenue by end of last year), it's only because users' compute is heavily subsidized, leading Microsoft to move users to token-based billing, angering customers used to burning thousands of dollars in tokens for a $39 monthly fee.

Yes, All This Money Could Be Wrong

Sundar Pichai, Andy Jassy, Satya Nadella, and Mark Zuckerberg are losers. They may have billions of dollars, they may run giant tech companies, but they are losers, selling a doomed technology based on unreliable, inefficient, and overpriced tech ill-suited for the reliable, deterministic, "set it and forget it" characteristics people actually associate with AI.

The Big Four Losers are the only reason anyone takes these big, loser models seriously, which signals that the tech industry and our economy are also being driven by losers. Every bit of "progress" we see in LLMs comes from forcefully jamming a square peg into a round hole — billions in training costs, hundreds of billions in capex, 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 have poured every dollar and ounce of brainpower into trying to make LLMs into something they are not. 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 yet. I refuse to accept the premise that LLMs generating code or replicating open-source software is evidence they will someday become powerful autonomous tools. I consider those who extrapolate to that point either intellectually bankrupt, deeply cynical, or gullible enough to click on every email claiming their PayPal account has been compromised.

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

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

Why is everyone investing in data centers? Because the hyperscalers are! Why are Micron and memory companies selling so much memory? Because A) GPUs use tons of HBM, B) that HBM consumes three times the wafer space of regular DRAM, leaving less room for other cheaper, lower-margin memory types, and C) because the servers for these AI GPUs are also filled with standard 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 thought that because Anthropic and OpenAI need tons of compute, *everyone* will need tons of compute. And because banks and private credit are desperate for ways to invest, everyone is so excited, making it super easy to get them hyped about building big, sexy, expensive things!