The AI Boom Is Running on Borrowed Money. One Critic Says the Debt Is Priced for Perfection.

Every dollar spent training or running an AI model traces back to a data center somewhere, and a growing share of those data centers were built with borrowed money. One of the AI industry’s most persistent critics says that debt is being priced as if nothing can go wrong.

What he said

A data center, in this context, is a large facility packed with the specialized computer chips that train and run AI models. Building one costs billions of dollars: land, power connections, cooling systems, and the chips themselves. Ed Zitron argues, in a newsletter post on Where’s Your Ed At, that the companies borrowing to build these facilities are taking on risk that lenders haven’t priced in: “the companies building them don’t even have any experience building AI data centers, securing power.”

His central number: “The finance industry has fed somewhere between $100 billion and $150 billion of debt” into AI data centers outside the major cloud companies (Amazon, Microsoft, and Google, which mostly self-fund their own data centers), “all under the assumption that ‘everything will be alright.’” He describes a self-reinforcing cycle: “the more debt it raises, the more expensive the debt becomes, and the more money spent on AI capex, the more expensive that capex becomes.” Capex, short for capital expenditure, means money spent building physical infrastructure like data centers, as opposed to day-to-day operating costs.

Who he is

Zitron is the CEO of the media relations firm EZPR and host of the Better Offline podcast. He has spent the past several years writing about the technology industry, with a specific and sustained focus on what he considers unsustainable spending and hype in the generative AI boom. He is not a financial analyst by trade, and his newsletter is opinion journalism, not a bank’s credit analysis. His track record is built on being early and loud about AI industry financials that later became mainstream concerns, which is why his argument is worth examining even without a finance credential behind it.

What he gets right, and where it’s incomplete

Zitron’s point that data center construction is being financed like routine, predictable infrastructure, when it isn’t, is a real and checkable claim. He writes that “every data center project is its own unique monster,” pointing to geography, power availability, and cooling as variables that differ site by site. Lenders who treat these projects as interchangeable are underpricing the risk that any single one runs over budget or behind schedule, which is a standard warning sign in any capital-intensive industry, not a uniquely AI one.

Where the argument is thinner: the post doesn’t name which specific lenders or borrowers hold this debt, so a reader can’t independently check exposure at any single company. It also doesn’t compare this AI-era buildout to prior infrastructure booms, like telecom’s fiber-optic overbuild in the late 1990s, where debt-funded capacity also outran near-term demand and some lenders took losses while the infrastructure itself later proved useful. Debt-funded infrastructure booms have gone both ways before, and Zitron’s post doesn’t address that comparison directly.

Why it’s notable

Most AI coverage focuses on model releases and product launches, not on how the physical infrastructure underneath them gets paid for. Zitron’s post is a reminder that the AI industry’s growth has a financing layer, and that layer runs on debt held by lenders and investors who aren’t AI companies themselves. If the assumption that “everything will be alright” turns out wrong at scale, the fallout wouldn’t be limited to AI labs. It would hit whoever holds that debt.

What it means for builders

This isn’t a reason to panic about your AI subscription disappearing tomorrow; nothing here suggests an imminent cutoff. But it’s a reason to build with some awareness that the compute you rely on sits on top of financing that not everyone agrees is stable. If you depend on a specific AI infrastructure provider, especially a newer one you haven’t vetted, check whether they’re a major cloud provider funding their own buildout or a smaller operator financed through the kind of debt Zitron describes. The practical move isn’t to switch providers reflexively. It’s to avoid architecture that would be painful to migrate away from on short notice, and to know which of your dependencies would be hardest to replace if one supplier hit financial trouble.

End of article