AI Financing Risks Beyond The Bubble: Hidden Debt And Complex Deals Could Threaten Financial Stability

A sharp sell-off in US technology stocks has intensified concerns over the scale of Big Tech’s artificial intelligence (AI) investments. The NASDAQ fell 2.86%, marking its steepest one-day decline since 5 June. Disappointing results from Intel and Amazon added to investor concerns about whether heavy AI spending will deliver returns quickly enough.

Beyond the risk of an AI bubble, a more complex financial vulnerability may be emerging. Financing arrangements linking technology giants, chipmakers, data-centre operators and AI start-ups could make it harder to identify where debt and other financial exposures ultimately lie.

AI Infrastructure Spending Raises Questions Over Hidden Liabilities

The central concern remains whether investments in data centres, chips and computing infrastructure will generate sufficient profits. S&P Global estimates that six major US hyperscalers could spend more than $7 trillion on AI-related infrastructure between 2025 and 2030. The group includes Amazon, Microsoft, Alphabet, Meta and Oracle, alongside SpaceX.

However, S&P Global’s head of digital infrastructure, Mark Habib, has also warned that companies may have AI-related debt and liabilities that are not fully visible. He pointed to financing structures through which large technology companies support data-centre projects or smaller AI businesses.

S&P Global cannot quantify these additional exposures because some transactions may be unreported or fall outside its ratings coverage. The agency has not identified a mismatch between market perceptions and its credit ratings that would require immediate rating changes.

The financing of AI infrastructure is becoming more intricate than conventional corporate borrowing. Debt, leases, joint ventures, special-purpose vehicles and residual-value guarantees are increasingly being used to fund expansion.

Interconnected Financing Could Spread Risk Across The Sector

Some arrangements connect companies in several roles such as investors, suppliers, customers and financial backers. For example, Google has invested in Anthropic and also provides the AI company with cloud-computing capacity. Such relationships can support growth, but they may also complicate the assessment of how much demand is supported by independent customers.

Financing structures involving data-centre projects and AI chips can also link lenders, infrastructure providers and technology companies. These arrangements are not inherently problematic. However, they may make it more difficult to determine where financial exposure sits if AI revenues fall short or infrastructure demand weakens.

The risk could extend beyond technology companies. Private-credit funds, infrastructure investors and banks are increasingly financing AI infrastructure. Loans held by private-credit funds are generally valued periodically using models rather than continuously through market prices. This could make emerging losses less visible during periods of financial stress.

There may also be a mismatch between the life of the infrastructure and the equipment it houses. Data-centre buildings can last for decades, while chips and servers may become outdated much sooner. If the equipment loses value faster than expected, lenders could be left with collateral worth less than anticipated.

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