AI版次贷危机？1.8万亿美元表外敞口，正成为这轮狂欢的定时炸弹
Original Author: Bu Shuqing
Original Source: Wall Street Sights
Amidst the boom in AI infrastructure construction, an unprecedented scale of debt expansion is quietly taking shape – and the most dangerous part of it has never appeared on any balance sheet.
A recent Goldman Sachs report predicts that capital expenditures by hyperscale cloud companies will reach $1.1 trillion to $1.4 trillion by 2027, far exceeding market consensus. However, according to in-depth research by Morgan Stanley, this already staggering figure is just the tip of the iceberg.
Nearly $1 trillion in purchase commitments, over $800 billion in non-cancelable lease contracts, and tens of billions in supplier financing arrangements together constitute approximately $1.8 trillion in off-balance-sheet exposure – liabilities that exist off the balance sheet but genuinely lock in future cash outflows.
The market has not yet fully priced 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, with capital expenditure growth consistently outpacing revenue and free cash flow growth, while the real impact of depreciation pressure has yet to hit.
Meanwhile, private credit institutions like Apollo and Blackstone are using SPVs (Special Purpose Vehicles) to transfer leverage to the supply chain level, creating a highly cyclical and opaque financing structure. If the commercialization of AI falls short of expectations, or if enterprise customers massively shift to cheaper alternatives, the fragility of the entire financing chain will be exposed.
Debt Issuance Frenzy: AI Has Become the Biggest Variable in Public Markets
According to Morgan Stanley's latest "AI Debt Financing Tracking Report," as of the end of May 2026, global AI-related bond issuance reached $236 billion, a surge of 357% compared to the same period in 2025.
Morgan Stanley expects total AI debt issuance to exceed $570 billion for the full year, with the pace accelerating in the second half as financing needs for capital expenditure intensify.
In April alone, AI-related bond issuance exceeded $74 billion, hitting a new high for the year, with project finance structures (used for data center construction) accounting for 85% of high-yield bond supply and 40% of investment-grade bond supply. Meanwhile, five hyperscale cloud companies – Amazon, Meta, Google, Microsoft, and Oracle – now constitute 4% of the entire investment-grade bond index.
In terms of leverage, the overall gross leverage ratio of hyperscale cloud companies has risen from 0.9x in Q3 2025 to the current 1.8x, increasing by about 0.3x per quarter, already exceeding the leverage level of the entire energy sector.
Morgan Stanley points out that due to supply pressure, related credit spreads have drifted from the AA range to the A range and could widen further. Meta's credit spread is now wider than the CDX IG benchmark.
In terms of free cash flow, Morgan Stanley predicts that the free cash flow of Amazon and Meta will approach zero or turn negative in 2026, meaning incremental financing will depend almost entirely on new debt.
$1.8 Trillion Off-Balance-Sheet Exposure: Invisible Liabilities, Locked-In Cash Outflows
Todd Castagno of Morgan Stanley's Global Valuation, Accounting & Tax team notes in the report that focusing solely on capital expenditure figures severely underestimates the true financial commitments of the AI construction cycle. Beyond disclosed capital expenditures, there are three key categories of off-balance-sheet exposure:
Purchase commitments of approximately $982 billion. The total value of long-term procurement contracts for hyperscale cloud companies and Nvidia is close to $1 trillion. Under accounting standards, these obligations are not recorded as liabilities until goods are delivered, unless the company expects a contract loss. Thus, nearly $1 trillion in future cash outflows currently does not appear as any liability on the balance sheet.
Notably, Nvidia's own inventory and purchase obligations have risen to about 32% of consensus FY2027 revenue forecasts, far above the historical range of 15% to 20%, indicating that supply chain commitment risk is extending to the chip supplier side.
Non-cancelable lease commitments of approximately $822 billion. Over $800 billion in lease contracts have been signed but not yet commenced, and are not included in current lease liabilities. Furthermore, variable lease payments, renewal options, and residual value guarantees also exist 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/FY2027) to 44%/64%, while Oracle's might rise from 76%/115% to 101%/189%.
Unpaid capital expenditures in accounts payable of approximately $110 billion. Days Payable Outstanding (DPO) for hyperscale cloud companies has lengthened significantly – Oracle up 370% year-over-year, Meta up 73%, Microsoft up 69% – meaning the entire supply chain is essentially fronting the cost of AI construction, 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-backed" private credit transaction for Anthropic, exemplifying the logic of this model: Broadcom provides a backstop for the SPV, Anthropic uses the raised funds to purchase Google chips manufactured by Broadcom, and Google holds a 14% equity stake in Anthropic; Morgan Stanley, which arranged the transaction, also provides loans to the participating investors.
Morgan Stanley's AI ecosystem financing correlation map shows a multi-layered circular relationship involving clients, investors, supplier financing, and buybacks among OpenAI, Oracle, Nvidia, Microsoft, CoreWeave, AMD, and Amazon. The same funds repeatedly circulate among a few core entities, with SPVs being the primary tool to achieve this circularity.
It is understood that Apollo's insurance subsidiary, Athene, is particularly active in this structure – raising funds by selling annuities to retirees and then injecting the 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 Monetization Gap: A Delayed Shock
Current financial data contains a systematic optimism bias. A significant portion of capital expenditure is currently recorded as "Construction in Progress" (CIP) and has not yet begun to depreciate, artificially inflating reported profit margins and understating future expense pressure.
Oracle, Meta, and Google saw their CIP balances increase by approximately 200%, 90%, and 55% year-over-year, respectively.
Once these assets are gradually transferred to depreciation, the shock will be released concentratedly.
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 FY2028; for Meta, it could rise from 9% to 19%.
Against this backdrop, the only path to maintaining profit margins is a simultaneous significant increase in revenue – yet current upward revisions to revenue forecasts lag far behind the upward revisions to capital expenditure forecasts.
Data shows that the consensus forecast for Google's 2026 capital expenditure has been raised by 139% compared to a year ago, Meta and Amazon by 85% and 81% respectively, with Oracle seeing the largest upward revision at 175%.
Meanwhile, the magnitude of revenue forecast revisions is clearly lagging, revealing a structural mismatch where capital expenditure outpaces commercialization.
Additionally, over $2 trillion in Remaining Performance Obligations (RPO) is highly concentrated in a few large, long-term contracts, making counterparty concentration risk a non-trivial concern – if any major participant in the circular system runs into trouble, it could trigger a chain reaction.
Timing Mismatch, Not an Immediate Solvency Crisis
Morgan Stanley concludes that the aforementioned risks currently do not constitute an imminent solvency crisis but rather a superposition of timing mismatches and information disclosure gaps: depreciation pressure is deferred, capital expenditure outpaces monetization progress, leverage is shifted to suppliers and the private credit layer, and the comparability of capital intensity across companies is severely compromised by differences in accounting classification.
Hyperscale cloud companies are clearly aware of the limited window of current market sentiment and are seizing the opportunity to maximize financing scale.
Goldman Sachs analyst Ryan Hammond points out that if AI infrastructure investment reaches 2% to 3% of GDP, analogous to historical construction cycles for railways and the automotive industry, capital expenditure could reach $1.1 trillion by 2027. In an extreme scenario, considering the cash flow of hyperscale cloud companies and the capacity of the investment-grade credit market, the upper limit might reach $1.4 trillion.
However, all of this presupposes that Large Language Models (LLMs) can 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.
If a structural shift occurs on the demand side, the currently carefully constructed financing system will face a fundamental stress test.
