AI debt financing and private credit: leverage inside the AI bubble

What is AI debt financing and where does private credit fit?

AI debt financing covers every borrowed dollar that pays for AI capacity: bank loans, bonds, leases, and loans from private credit funds. Private credit means lending by non-bank funds that raise money from investors and lend directly to companies. These funds can write large loans quickly, which suits builders who need capacity fast.

Debt matters beyond its size because it carries fixed obligations. Equity holders absorb a slow quarter, but lenders expect interest and principal on schedule. The more of an AI build-out rests on debt, the less room the builders have when revenue arrives late, and the more the outcome depends on the dates in the loan agreements.

How is debt structured around an AI data centre?

A common structure places a data centre in a separate company. The company owns the building and equipment, signs a contract with a tenant who pays for capacity, and borrows against that contract and the assets. Lenders look at the tenant’s promise to pay and at the value of the asset. A strong tenant lowers the interest rate, and a weak one raises it.

Loan terms vary: some loans carry floating rates, some require repayment in instalments, and some end with a large final payment. Read 3 terms first: the interest rate basis, the repayment schedule, and the final maturity date. Compare the maturity with the length of the tenant contract, because a loan that outlives its contract carries refinancing risk.

How much does leverage magnify a fall in AI asset value?

Leverage magnifies changes in value. In an illustration, a data centre costs 100 units, with 70 units of debt and 30 units of equity. If the asset falls in value by 10%, the equity falls by 10 units, or 33%. A 20% fall removes 20 units, or 67%. A 30% fall removes the equity altogether, because the asset then equals the debt.

Chips lose value faster than buildings, so the mix of assets matters. A facility heavy in short-life equipment reaches the point where debt exceeds asset value sooner. Lenders respond with tighter terms or demands for repayment. Ask what share of the asset base is short-life equipment and how the loan balance falls over time in comparison.

Which coverage ratios show whether AI debt is safe?

Four ratios give a fast reading. Loan to value divides debt by asset value. Interest cover divides operating income by interest. Debt service coverage divides cash flow by interest plus principal due. Tenant concentration shows the share of revenue from the largest tenant. Lenders set minimum levels for the first three and watch the fourth, so each is a place where stress becomes visible.

Set your own thresholds before you read a filing. For example, treat interest cover under 2 as a warning, and a single tenant above half of revenue as a concentration flag. Thresholds are choices, and writing yours down keeps the reading honest. Re-run the ratios each quarter and note which direction each one moves.

Why does private credit matter for AI debt financing?

Private credit funds report less often and in less detail than banks and bond markets. Loans are usually held to maturity, valued by models, and traded rarely. That structure reduces day-to-day price swings, and it also hides stress until a payment is missed or a loan is restructured. Lower visibility makes the sector harder to measure from outside.

Visibility is the practical issue for a decision maker. Look for the facts that do appear: fund disclosures, rating agency comments, bank exposure to the funds, and reported amendments or deferred payments on loans. A rise in amendments is an early signal, since lenders often change terms before they take losses.

When does AI debt financing turn into an AI bubble risk?

Debt becomes a bubble risk when it pays for capacity ahead of signed demand, when lenders rely on the same few tenants, and when refinancing dates cluster in one period. In that case a single disappointment can reach many loans at once. The EQOS Asia AI study scores the enterprise AI market at φ(S) = 0.337 and notes that a shock can push several dimensions below threshold (April 2026).

The same study cites 95% of generative AI projects failing in production (MIT 2025). Tenants whose projects fail buy less capacity, so contract revenue can fall short of the plan behind the loan. The EQOS Asia method page explains the score, and the Λ simulator lets you score your own case.

What should a decision maker do about AI debt financing?

Build a debt map for your exposure: who borrowed, from whom, on what terms, and against which tenant contract. Mark loans whose maturity falls before the tenant contract ends, and loans whose lender gives little disclosure. Review the map each quarter, and set a rule for the amount of leverage you accept in a counterparty.

If your business sells to, builds for, or lends to AI firms in Singapore, ASEAN, or Hong Kong, a 30-minute pre-diagnostic with EQOS Asia scores the structure of your exposure. You bring the contracts and the counterparties, and the session returns a score and a ranked set of options.

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About the author

Stéphane Jambu

President, EQOS Forecast Asia

Stéphane Jambu leads EQOS Forecast Asia and works as a consultant in e-commerce, search visibility, and AI. LinkedIn profile