AI Companies Have Raised $466 Billion in Debt This Year. Who Pays if the Boom Slows?

Somewhere in the United States, a new data center is being built with thousands of expensive AI chips. Its future customers may include the world's biggest technology companies, but those customers are not necessarily the ones paying to construct it.

Instead, banks, bondholders and private investment funds provide much of the money upfront, expecting years of payments from companies purchasing computing capacity.

That financial structure has expanded dramatically. According to Morgan Stanley data reported by the Financial Times, companies raised approximately $466 billion in AI-related debt during the first nine months of 2026, compared with $101 billion over the same period last year.

The number raises an uncomfortable question: if the AI boom slows before the infrastructure becomes profitable, who actually absorbs the losses?

The $466 Billion AI Debt Boom Has a Hidden Structure

Building an AI data center requires land, electricity infrastructure, cooling systems and powerful processors before the first customer receives a bill.

Large companies such as Microsoft, Amazon and Alphabet can finance substantial investments with operating cash flow. Smaller operators often depend on loans secured by equipment, customer contracts or the facilities themselves.

As Coinpaper previously explained in its investigation into AI infrastructure financing, the company developing an AI model may be different from the company owning the servers and the institution financing them.

One example is Anthropic's chip infrastructure. According to UBS research, an approximately $35 billion private-credit transaction financed specialized equipment through a separate vehicle, with customer payments supporting the debt.

Such arrangements can distribute financing across multiple institutions rather than concentrating it on one technology company's balance sheet.

This is also why the identity of the ultimate borrower matters. Coinpaper's earlier examination of OpenAI and Anthropic's data centers found that developers, cloud providers and financiers can occupy different positions in the same project.

What Happens if AI Revenue Falls Short?

Consider a hypothetical data center that borrows $1 billion against equipment and long-term customer contracts.

If demand develops as expected, customer payments help cover operating expenses and interest, while lenders receive their contracted returns.

But if customers reduce spending, projects suffer delays or financing becomes more expensive, the company may struggle to meet its obligations.

Losses generally reach shareholders first through declining equity values. If the borrower defaults, unsecured creditors may face losses, while secured lenders can attempt to recover money by taking control of pledged equipment and other assets.

The actual outcome depends on contractual guarantees, collateral values and creditor seniority, as Coinpaper explained in its examination of GPU-backed loans.

CoreWeave illustrates why those distinctions matter. Its bond yields exceeded 16% in October, even as the company reported a massive contracted revenue backlog.

The gap between expected future sales and financing pressure has made CoreWeave's borrowing costs a closely watched signal of AI credit risk.

Could Ordinary Investors End Up Paying?

Potentially, although not every AI financing problem would become a wider financial crisis.

Corporate bond funds, pension funds and insurers may own AI-related debt directly or through investment vehicles. Shareholders also face losses if companies issue new equity, cut spending or fail to earn sufficient returns on their investments.

Recent problems surrounding Oracle's data-center financing demonstrate how loan prices can decline even before a borrower defaults.

Yet the sudden slowdown in AI borrowing suggests lenders are becoming more selective about which projects deserve financing.