Yes — it could become a major financial risk, but the risk is more likely to emerge through opacity, leverage, maturity mismatch, and over-optimistic collateral assumptions than through a simple “AI bubble” collapse alone. The highest-risk area is private credit and structured financing tied to data centers, GPUs, power infrastructure, and off-balance-sheet AI infrastructure vehicles ^[2][3][5].

• AI infrastructure spending is moving from being funded mainly by hyperscaler cash flows toward debt financing, and the BIS says the scale of expected investment needs will require a shift from operating cash flow to debt, with private credit likely playing a larger role ^[3].

• Private credit risk is elevated because much of the lending can sit outside public bond markets and bank balance sheets, making total exposure harder to measure; Apollo notes that public hyperscaler debt issuance understates AI-related credit formation because it excludes large private financings for hyperscaler infrastructure ^[5].

• The riskiest structures are likely to be the financing mechanics already appearing in AI data-center commentary and legal-risk analysis, including direct loans, SPV structures, securitizations, GPU-collateralized financing, and asset-backed securities linked to AI infrastructure ^[1][2].

  • project-finance loans to data centers;
  • GPU-backed or equipment-backed lending;
  • off-balance-sheet SPVs;
  • asset-backed securitizations linked to AI infrastructure cash flows;
  • loans backed by long-term capacity contracts whose economics depend on sustained AI demand.

• The main trigger would be a mismatch between debt-funded capex and realized AI revenue. One legal-risk analysis says AI revenues were roughly $60 billion in 2025 versus roughly $400 billion of capital expenditures, while also noting that more than $120 billion of data-center spending had been moved off balance sheet in under two years ^[2][7].

• Collateral could prove less protective than lenders expect. Because legal-risk analysis identifies GPU-collateralized financing and securitizations as part of the AI data-center financing mix, recoveries could depend heavily on collateral values, contract durability, and whether the underlying assets remain economically useful if AI demand or chip economics disappoint ^[2].

• Power, permitting, and project-execution issues are second-order credit risks. If data-center projects face delays, cost overruns, or weaker-than-expected utilization, borrowers may miss cash-flow assumptions even if long-term AI demand remains intact ^[2][7].

• The systemic-risk channel is plausible but not yet proven. The available sources point to rapid growth in AI-related debt, more private credit participation, and financing outside traditional public bond markets, but they do not by themselves prove broad systemic fragility ^[3][5].

• A milder outcome is also possible: the strongest hyperscalers may still have meaningful cash flows, and some long-term contracted revenue could absorb stress. That makes this less obviously fragile than subprime mortgages in 2007, but the combination of rapid growth, weak transparency, and complex structures is similar enough to warrant concern ^[2][3][5].

Watch these indicators:

• Rising loan-to-value ratios on data-center and GPU-backed loans.
• More covenant-lite private credit deals.
• Shorter-duration funding used for long-duration infrastructure.
• Heavy reliance on projected AI revenue rather than contracted cash flow.
• Growing exposure among insurers, BDCs, private credit funds, and bank warehouse lines.
• Falling GPU resale values or weakening data-center lease rates.
• Delays in power interconnection or rising electricity costs.

In short: AI infrastructure debt is a credible candidate for the next major private-credit stress point, especially if lenders underwrite today’s capex boom as if AI demand, chip economics, and power availability will all scale smoothly. The risk is not certain, but the ingredients for a serious credit cycle are present ^[2][3][5][8].