The rapid expansion of artificial intelligence (AI) infrastructure, driven by major technology companies, is raising concerns about financial risks that could mirror those seen during the global financial crisis (GFC). Industry analysts are warning that the convergence of large-scale investments, complex financing structures, and emerging market dynamics may pose systemic challenges if left unchecked.

By mid-2026, five leading hyperscalers—Microsoft, Meta, Alphabet, Amazon, and Oracle—held approximately $464 billion in long-term bonds on their balance sheets, equivalent to roughly 1.4 percent of gross domestic product (GDP). Additionally, these firms accounted for around $195 billion in right-of-use (ROU) assets and liabilities, bringing combined exposures to about 2 percent of GDP. However, the bulk of their financial commitments reside off-balance-sheet, through special purpose vehicles (SPVs) designed to finance the construction and operation of new data centers essential for AI processing.

These SPVs are typically backed by lease agreements with hyperscalers, allowing private sponsors to inject equity and secure bank loans or private credit. Larger projects may also raise capital by issuing exempt 144A bonds to qualified investors. The investments cover land acquisition, construction, power infrastructure, cooling systems, and GPU installations. As of mid-2026, commitments related to "uncommenced leases" among these five companies totaled about $1.1 trillion, representing roughly 3.3 percent of GDP and rising to 4 percent when including current ROU assets. These liabilities are expected to grow substantially in the coming years, potentially increasing more than fivefold.

Concerns are mounting over the sustainability of this expansion. A significant portion of demand for hyperscaler computing power is cyclical, largely driven by AI labs such as Anthropic and OpenAI, which rely on these cloud resources. This has led to a bilateral oligopoly in the market, where AI firms and hyperscalers are interdependent. However, pressure is intensifying as open-weight AI models offering cheaper alternatives gain traction, particularly from competitors like Europe’s Mistral and China’s DeepSeek. Such shifts risk reducing margins and amplifying competitive pressures on hyperscalers.

Community opposition to large data centers and their power consumption adds another layer of risk. The long-standing example of the Shoreham nuclear power plant on Long Island — completed but never producing electricity due to local resistance — serves as a cautionary tale for current infrastructure projects.

Financial engineering further complicates the picture. Once data centers become operational, SPVs typically restructure liabilities into various tiers of 144A bonds, which are sold to sophisticated investors such as hedge funds, insurers, mutual funds, and pension funds. These investors often use derivatives like total return swaps to extend their market exposure, amplifying leverage and risk. This synthetic leverage can substantially exceed the original $1.3 trillion exposure from data center financing.

Rising interest rates or a spike in yield could trigger margin calls, forcing forced asset sales and potentially spreading panic throughout the financial system. Factors that could precipitate such a scenario include ballooning liabilities from hyperscaler projects, ongoing inflationary pressures leading to tighter monetary policy, an oversupply of data center capacity eroding profits, and the breakdown of current market oligopolies. Credit default swap spreads linked to hyperscaler debt have begun to widen, signaling growing investor concern.

Experts advocate for regulatory oversight to mitigate these emerging risks before they escalate, comparing the situation to past crises where inadequate supervision resulted in significant taxpayer-funded bailouts. Meanwhile, some AI company executives and employees appear eager to capitalize on current valuations by selling shares in initial public offerings, possibly anticipating an inflection point in the market.

Although uncertainties remain about the ultimate trajectory of AI’s economic impact, the financial risks associated with the hyperscaler-led infrastructure boom warrant close monitoring to prevent potential systemic instability.