Expose: AI is more than a bubble; it’s a data center debt bomb

We’ve previously described the tremendous debt that AI companies have assumed with serious doubts that it would ever be repaid. This article expands on that by pointing out the huge losses incurred by the AI startup darlings and that AI poster child Open AI doesn’t have the cash to cover its costs, which are greater than most analysts assume. Also, a dire forecast from the Center for Public Enterprise.

In Saturday’s print edition, The Wall Street Journal notes:

OpenAI and Anthropic are the two largest suppliers of generative AI with their chatbots ChatGPT and Claude, respectively, and founders Sam Altman and Dario Amodei have become tech celebrities.

What’s only starting to become clear is that those companies are also sinkholes for AI losses that are the flip side of chunks of the public-company profits.

OpenAI hopes to turn profitable only in 2030, while Anthropic is targeting 2028. Meanwhile, the amounts of money being lost are extraordinary.

It’s impossible to quantify how much cash flowed from OpenAI to big tech companies. But OpenAI’s loss in the quarter equates to 65% of the rise in underlying earnings of Microsoft, Nvidia, Alphabet, Amazon and Meta together. That ignores Anthropic, from which Amazon recorded a profit of $9.5B from its holding in the loss-making company in the quarter.

OpenAI committed to spend $250 billion more on Microsoft’s cloud and has signed a $300 billion deal with Oracle, $22 billion with CoreWeave and $38 billion with Amazon, which is a big investor in rival Anthropic.

OpenAI doesn’t have the income to cover its costs. It expects revenue of $13 billion this year to more than double to $30 billion next year, then to double again in 2027, according to figures provided to shareholders. Costs are expected to rise even faster, and losses are predicted to roughly triple to more than $40 billion by 2027. Things don’t come back into balance even in OpenAI’s own forecasts until total computing costs finally level off in 2029, allowing it to scrape into profit in 2030.

The losses at OpenAI that has helped boost the profits of Big Tech may, in fact, understate the true nature of the problem. According to the Financial Times:

OpenAI’s running costs may be a lot more than previously thought, and that its main backer Microsoft is doing very nicely out of their revenue share agreement.

OpenAI appears to have spent more than $12.4bn at Azure on inference compute alone in the last seven calendar quarters. Its implied revenue for the period was a minimum of $6.8bn. Even allowing for some fudging between annualised run rates and period-end totals, the apparent gap between revenues and running costs is a lot more than has been reported previously.

The apparent gap between revenues and running costs is a lot more than has been reported previously. If the data is accurate, then it would call into question the business model of OpenAI and nearly every other general-purpose LLM vendor.

Also, the financing needed to build out the data centers at the heart of the AI boom is increasingly becoming an exercise in creative accounting. The Wall Street Journal reports:

The Hyperion deal is a Frankenstein financing that combines elements of private-equity, project finance and investment-grade bonds. Meta needed such financial wizardry because it already issued a $30B bond in October that roughly doubled its debt load overnight.

Enter Morgan Stanley, with a plan to have someone else borrow the money for Hyperion. Blue Owl invested about $3 billion for an 80% private-equity stake in the data center, while Meta retained 20% for the $1.3 billion it had already spent. The joint venture, named Beignet Investor after the New Orleans pastry, got another $27 billion by issuing bonds that pay off in 2049, $18 billion of which Pimco purchased. That debt is on Beignet’s balance sheet, not Meta’s.

The widening gap between the expenditures needed to build out AI data centers and the cash flows generated by the products they enable creates a colossal risk which could crash asset values of AI companies. The Center for Public Enterprise reports that it’s “Bubble or Nothing.”

Should economic conditions in the tech sector sour, the burgeoning artificial intelligence (AI) boom may evaporate—and, with it, the economic activity associated with the boom in data center development.

Circular financing, or “roundabouting,” among so-called hyperscaler tenants—the leading tech companies and AI service providers—create an interlocking liability structure across the sector. These tenants comprise an incredibly large share of the market and are financing each others’ expansion, creating concentration risks for lenders and shareholders.

Debt is playing an increasingly large role in the financing of data centers. While debt is a quotidian aspect of project finance, and while it seems like hyperscaler tech companies can self-finance their growth through equity and cash, the lack of transparency in some recent debt-financed transactions and the interlocked liability structure of the sector are cause for concern.

If there is a sudden stop in new lending to data centers, Ponzi finance units ‘with cash flow shortfalls will be forced to try to make position by selling out position’—in other words to force a fire sale—which is ‘likely to lead to a collapse of asset values.’

The fact that the data center boom is threatened by, at its core, a lack of consumer demand and
the resulting unstable investment pathways, is itself an ironic miniature of the U.S. economy as a
whole. Just as stable investment demand is the linchpin of sectoral planning, stable aggregate
demand is the keystone in national economic planning. Without it, capital investment crumbles.