AI bubble burst and credit markets: the debt channel

How is the AI build-out financed, and where does debt enter?

Companies fund AI from three sources: their own cash flow, new shares, and borrowed money. Large groups use cash flow first. Smaller builders of data centres and chip capacity borrow more, because their projects need money years before the first customer pays. Each source reacts differently when prices fall.

The EQOS Asia AI study estimates about $200B a year of investment in AI research and deployment. A flow of that size needs several channels, and debt fills the gap that cash and shares leave. The share of debt in a project tells you how much a price fall can turn into a repayment problem, so ask for it before you judge a project.

Why does debt make an AI bubble burst harder to absorb?

Equity absorbs losses without a deadline. Debt carries a schedule of interest and principal, and a missed payment forces action. If AI revenue arrives later than planned, a borrower with debt must refinance, sell assets, or renegotiate, while a borrower funded by shares can wait. Lenders also hold a claim ahead of shareholders, which means their losses begin only after equity absorbs the first part of a fall.

That difference explains why credit stress follows the price fall by months. Prices adjust at once, and repayment problems appear when the next payment date arrives. A map of payment dates across a sector shows when the pressure would peak, which gives you a calendar to follow and a reason to check the news on those dates.

What do credit spreads show about an AI bubble burst?

A credit spread is the extra interest a borrower pays over a safe benchmark. A rising spread says lenders want more reward for the same loan. Watch the spreads of companies that borrow for AI projects against the spreads of the wider market, since a gap between the two is the early sign. Compare the same maturities, because a long loan always pays more than a short one, and record the gap in percentage points each month.

A widening gap alone proves nothing, because spreads move for many reasons. Combine it with other facts: new issues postponed, borrowers offering higher rates, and lenders asking for more collateral. Three of these together form a stronger message than a single reading, and the pattern matters more than any one number.

Which AI borrowers face refinancing risk if the AI bubble bursts?

Refinancing risk falls on borrowers whose loans mature before their projects earn enough to repay. A data centre that signs customers slowly and owes a large amount in 2 years carries more risk than one with long contracts and a distant maturity. Dates and contracts matter more than names.

For any AI borrower you follow, list the maturity dates, the signed customer contracts, and the cash held. If contracts cover the interest and the cash covers the next maturity, the borrower can wait for better conditions. If neither holds, the borrower depends on lenders staying calm, and a single nervous lender can start a chain of refusals. Early talks with lenders, before the maturity date, give a borrower more room than talks held in the final month.

Which lenders hold AI debt, and why does that matter in a burst?

AI loans sit with banks, bond funds, insurers, and private credit funds. A bank may lend directly, a fund may buy the bonds, and an insurer may hold both through its investment portfolio. Each holder reports losses on its own timetable, so the news arrives in waves over several quarters, and each wave can surprise a different group of savers.

This spread of holders means that credit stress can reach savers who own no AI shares at all. A bond fund in a pension plan may hold AI-related bonds. Reading the top positions of a bond fund follows the same steps as for a stock fund, and it takes the same few minutes.

How might the debt channel of an AI bubble burst affect Asia?

Singapore and Hong Kong host banks, bond markets, and funds that finance projects across Asia, including data centres. Stress in global credit markets reaches these centres through funding costs and the appetite of international lenders. Local borrowers feel it when new loans become more expensive, when approvals take longer, or when lenders ask for more guarantees than before.

Businesses that depend on regional lenders can ask 3 questions: who funds our suppliers, when do their loans mature, and what happens to our contracts if they refinance on worse terms? Asking the questions early costs little and gives your team time to prepare. Groups that run several countries can read the enterprise page for cross-border decisions, where funding sources and currencies add further links to check.

Which 4 signals show credit stress from an AI bubble early?

The first signal is spreads on AI-related borrowers against the market. The second is refinancing dates that cluster in one year. The third is covenant terms, the promises a borrower gives to lenders, which lenders tighten when worried. The fourth is the share of a lender’s book tied to AI projects.

Record the 4 signals with a date and a color each quarter. If 3 turn red, discuss your exposure with an adviser. This page is general information, not investment advice, and the EQOS Asia pricing page lists the analysis formats if you want a structured view of your own case, and a 30-minute pre-diagnostic applies the scoring to it under NDA.

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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