AI Version of a Subprime Crisis? $1.8 Trillion in Off-Balance-Sheet Exposure is Becoming a Time Bomb for This Rally

Original Author: Bu Shuqing

Original Source: Wall Street Sights

Beneath the fervor of AI infrastructure buildout, a debt expansion of unprecedented scale is quietly taking shape—and the most dangerous part of it has never appeared on any balance sheet.

According to a recent Goldman Sachs report, capital expenditures by hyperscale cloud companies could reach $1.1 trillion to $1.4 trillion by 2027, far exceeding market consensus. However, a deep-dive study by Morgan Stanley suggests that even this staggering figure is merely the tip of the iceberg.

Approximately $1 trillion in procurement commitments, over $800 billion in unexecuted lease contracts, and tens of billions in supplier financing arrangements collectively form an off-balance-sheet exposure of roughly $1.8 trillion—liabilities that exist outside the balance sheet yet firmly lock in future cash outflows.

The market has yet to fully price in these risks.

Morgan Stanley warns that the leverage ratio of hyperscale cloud companies has surged from 0.9x to 1.8x in just two quarters. Capital expenditure growth continues to outpace revenue and free cash flow growth, while the real impact of depreciation pressures has yet to hit.

Meanwhile, private credit institutions like Apollo and Blackstone are using SPVs (Special Purpose Vehicles) to shift leverage down the supply chain, creating a highly circular and opaque financing structure. If AI commercialization falls short of expectations, or if enterprise clients shift en masse to cheaper alternatives, the fragility of the entire financing chain will be laid bare.

The Debt Issuance Frenzy: AI Is Now the Biggest Variable in Public Markets

According to Morgan Stanley's latest "AI Debt Financing Tracker," global AI-related bond issuance reached $236 billion by the end of May 2026, a 357% surge year-over-year.

Morgan Stanley expects full-year AI debt issuance to exceed $570 billion, with the pace accelerating in the second half of the year as financing needs for capital expenditure concentrate.

In April alone, AI-related bond issuance exceeded $74 billion, hitting a new high for the year. Project finance structures (used for data center construction) accounted for 85% of high-yield bond supply and 40% of investment-grade bond supply. Meanwhile, the five hyperscale cloud companies—Amazon, Meta, Google, Microsoft, and Oracle—now constitute 4% of the entire investment-grade bond index.

On the leverage front, the overall gross leverage ratio for hyperscale cloud companies has risen from 0.9x in Q3 2025 to the current 1.8x, increasing by approximately 0.3x per quarter. This has already surpassed the leverage level of the entire energy sector.

Morgan Stanley notes that due to supply pressure, related credit spreads have drifted from the AA range to the A range and may widen further. Meta's credit spread is now wider than the CDX IG benchmark.

In terms of free cash flow, Morgan Stanley predicts that Amazon and Meta's free cash flow in 2026 will approach zero or turn negative, meaning incremental financing will rely almost entirely on new debt.

$1.8 Trillion in Off-Balance-Sheet Exposure: Invisible Liabilities, Locked-In Cash Outflows

Todd Castagno of Morgan Stanley's Global Valuation, Accounting & Tax team points out in the report that focusing solely on capital expenditure figures severely underestimates the true financial commitments of the AI construction cycle. Beyond disclosed capital expenditure, three key categories of off-balance-sheet exposure exist:

Procurement Commitments: Approximately $982 Billion. Long-term procurement contracts for hyperscale cloud companies and Nvidia total nearly $1 trillion. Under accounting rules, unless a company expects a loss on the contract, these obligations are not recorded as liabilities until the goods are delivered. Therefore, nearly a trillion dollars in future cash outflows currently do not appear as any liability on balance sheets.

Notably, Nvidia's own inventory and procurement obligations have risen to approximately 32% of consensus revenue estimates for fiscal year 2027, far exceeding the historical range of 15% to 20%. Supply chain commitment risk has now extended to the chip supplier side.

Unexecuted Lease Commitments: Approximately $822 Billion. Over $800 billion in lease contracts have been signed but are not yet in effect and are therefore not included in current lease liabilities. Additionally, arrangements such as variable lease payments, renewal options, and residual value guarantees also remain off the balance sheet.

Morgan Stanley estimates that if finance leases were included, Microsoft's capital expenditure as a percentage of sales would jump from 33%/50% (FY2026/2027) to 44%/64%, while Oracle's would rise from 76%/115% to 101%/189%.

Unpaid Capital Expenditure in Accounts Payable: Approximately $110 Billion. Days Payable Outstanding (DPO) for hyperscale cloud companies has lengthened significantly—up 370% year-over-year for Oracle, 73% for Meta, and 69% for Microsoft. This means the entire supply chain is effectively fronting the capital for AI buildout, with suppliers bearing the liquidity pressure that should belong to buyers.

SPVs and Circular Financing: Leverage Shifted into the Shadows

Another core dimension of off-balance-sheet risk is the circular financing structure built through SPVs.

This week, Apollo and Blackstone jointly completed a $35 billion "chip-collateralized" private credit deal for Anthropic, perfectly illustrating the logic of this model:

Broadcom provided a backstop for the SPV. Anthropic used the funds raised to purchase Google chips manufactured by Broadcom, while Google holds a 14% equity stake in Anthropic. Morgan Stanley, which arranged the transaction, also provided loans to the investors participating in the deal.

Morgan Stanley's AI Ecosystem Financing Linkage Map reveals a complex web of circular relationships between OpenAI, Oracle, Nvidia, Microsoft, CoreWeave, AMD, and Amazon, involving customer, investor, supplier financing, and buyback arrangements. The same capital circulates repeatedly among a small number of entities, with the SPV serving as the core tool for achieving this circularity.

Reportedly, Apollo's insurance subsidiary, Athene, is particularly active in these structures—raising funds by selling annuities to retirees and then injecting that capital into SPVs to participate in AI infrastructure financing.

This model shifts leverage from the visible balance sheets of hyperscale cloud companies to the supplier and private credit ecosystem, making the true systemic risk exposure difficult for external observers to identify and aggregate.

The Depreciation Cliff and the Monetization Gap: A Deferred Shock

Current financial data contains a systematic optimistic bias. A large portion of capital expenditure is currently recorded as "Construction in Progress" (CIP) and has not yet begun to depreciate. This artificially inflates reported profit margins and underestimates future expense pressure.

Oracle, Meta, and Google have seen their CIP balances grow approximately 200%, 90%, and 55% year-over-year, respectively.

Once these assets are gradually transferred into depreciation, the impact will be concentrated and severe.

Morgan Stanley predicts that the cumulative depreciation for Microsoft, Oracle, Meta, and Google over the next three years will exceed $520 billion. For Oracle, depreciation as a percentage of revenue could rise from the current 7% to 28% by fiscal year 2028. For Meta, it could rise from 9% to 19%.

In this context, the only path to maintaining profit margins is a simultaneous and substantial increase in revenue. However, the upward revision in revenue forecasts is currently lagging far behind the upward revision in capital expenditure forecasts.

Data shows that consensus capital expenditure forecasts for 2026 have been revised up by 139% for Google compared to a year ago, 85% for Meta, 81% for Amazon, and a whopping 175% for Oracle.

Meanwhile, the magnitude of revenue forecast revisions is significantly lagging. A structural mismatch, where capital expenditure outpaces commercialization realization, is clearly visible.

Furthermore, over $2 trillion in Remaining Performance Obligations (RPO) is highly concentrated among a few large, long-term contracts. The counterparty concentration risk is non-negligible—if any major participant in the circular system faces trouble, it could trigger a chain reaction.

A Timing Mismatch, Not an Imminent Solvency Crisis

Morgan Stanley's conclusion is that these risks do not currently constitute an imminent solvency crisis, but rather a combination of timing mismatches and information disclosure gaps: depreciation pressure is deferred, capital expenditure outpaces monetization, leverage is shifted to suppliers and the private credit layer, and the comparability of capital intensity across different companies is severely compromised by differences in accounting classifications.

Hyperscale cloud companies are clearly aware of the limited window of current market sentiment and are racing to maximize their financing scale.

Goldman Sachs analyst Ryan Hammond points out that if AI infrastructure investment reaches 2% to 3% of GDP, drawing parallels to the historical construction cycles of railroads and the automotive industry, capital expenditure could reach $1.1 trillion by 2027. In an extreme scenario, considering the cash flows of hyperscale cloud companies and the capacity of the investment-grade credit market, the upper limit could reach $1.4 trillion.

However, all of this hinges on Large Language Models (LLMs) being able to continuously improve token pricing and maintain sufficient enterprise customer stickiness. A growing number of companies are turning their attention to AI products with comparable performance but significantly lower prices.

Should a structural shift occur on the demand side, this carefully constructed financing system will face a fundamental stress test.