AI版のサブプライム危機？1.8兆ドルのオフバランスシートエクスポージャーが、今回の狂騒の時限爆弾となりつつある

Original Author: Shuqing Bu

Original Source: Wall Street Sights

Under the fervor of AI infrastructure construction, a debt expansion of unprecedented scale 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 expenditure by hyperscale cloud companies will reach between $1.1 trillion and $1.4 trillion by 2027, far exceeding market consensus. However, according to in-depth research by Morgan Stanley, this already staggering figure may only be 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 collectively constitute approximately $1.8 trillion in off-balance-sheet exposure—liabilities that remain 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 arrive.

Meanwhile, private credit institutions represented by Apollo and Blackstone are transferring leverage to the supply chain level through SPVs (Special Purpose Vehicles), forming a highly circular 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 is the Biggest Variable in Public Markets

According to Morgan Stanley's latest "AI Debt Financing Tracking Report," global AI-related bond issuance had reached $236 billion by the end of May 2026, a surge of 357% compared to the same period in 2025.

Morgan Stanley expects total AI debt issuance for the year to exceed $570 billion, with the pace accelerating further in the second half as financing needs for capital expenditure are concentratedly released.

In April alone, AI-related bond issuance exceeded $74 billion, hitting a new high for the year, with project financing structures (for data center construction) accounting 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 account for 4% of the entire investment-grade bond index.

On the leverage front, the 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 surpassing the leverage level of the entire energy sector.

Morgan Stanley points out that due to supply pressure, related credit spreads have shifted 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 Amazon and Meta's free cash flow in 2026 will approach zero or turn negative, at which point 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 noted in his report that focusing solely on capital expenditure numbers severely underestimates the true financial commitments of the AI construction cycle. Beyond disclosed capital expenditures, there are three key types of off-balance-sheet exposure:

Purchase Commitments of approximately $982 billion. The total long-term procurement contracts of hyperscale cloud companies and Nvidia approach $1 trillion. Under accounting standards, unless companies expect contract losses, these obligations are not recorded as liabilities until goods are delivered. Therefore, nearly a trillion dollars in future cash outflows are currently not reflected as any liability on the balance sheet.

Notably, Nvidia's own inventory and purchase obligations have risen to about 32% of consensus revenue forecasts for FY2027, well above the historical range of 15% to 20%. Supply chain commitment risks are 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 execution, 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%, and Oracle's could rise from 76%/115% to 101%/189%.

Unpaid Capital Expenditures in Accounts Payable of approximately $110 billion. The Days Payable Outstanding (DPO) for hyperscale cloud companies has lengthened significantly—Oracle's DPO increased by 370% year-over-year, Meta's by 73%, and Microsoft's by 69%. This means the entire supply chain is effectively financing AI construction, with suppliers bearing the liquidity pressure that should be borne by 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 transaction for Anthropic, exemplifying the operational logic of this model:

Broadcom provided backing for the SPV, Anthropic used 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 provided loans to investors participating in the deal.

Morgan Stanley's AI ecosystem financing correlation map reveals multiple circular relationships involving customers, investors, supplier financing, and repurchases among OpenAI, Oracle, Nvidia, Microsoft, CoreWeave, AMD, and Amazon. The same funds circulate repeatedly among a few entities, with SPVs being the core tool for achieving this circularity.

It is reported that Apollo's insurance subsidiary, Athene, is particularly active in the aforementioned structure—raising funds by selling annuities to retirees and then injecting these funds into SPVs to participate in AI infrastructure financing.

This model shifts leverage from the visible balance sheets of hyperscale cloud companies to suppliers and the 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 suffers from a systematic optimism bias. A significant amount of capital expenditure is currently recorded as "Construction in Progress" (CIP) and has not yet begun depreciation. This artificially inflates reported profit margins and underestimates future expense pressure.

The CIP balances for Oracle, Meta, and Google have increased by approximately 200%, 90%, and 55% year-over-year, respectively.

Once these assets are gradually transferred to depreciation, the impact will be released in a concentrated manner.

Morgan Stanley predicts that the cumulative depreciation for the four companies—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 corresponding significant increase in revenue—yet the current upward revisions to revenue forecasts lag far behind the upward revisions to capital expenditure forecasts.

Data shows that consensus forecasts for Google's 2026 capital expenditure have been revised up by 139% compared to a year ago, with Meta and Amazon seeing increases of 85% and 81%, respectively, and Oracle recording the largest increase at 175%.

At the same time, the magnitude of revisions to revenue forecasts lags significantly behind. A structural mismatch where capital expenditure precedes commercial deployment is clearly visible.

Furthermore, over $2 trillion in Remaining Performance Obligations (RPO) is highly concentrated among a few large, long-term contracts, meaning counterparty concentration risk cannot be ignored. If any major participant in this circular system encounters problems, it could trigger a chain reaction.

A Timing Mismatch, Not an Imminent Solvency Crisis

Morgan Stanley concludes that the aforementioned risks do not currently 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 comparability of capital intensity across companies is severely limited due to differences in accounting classification.

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, similar to the historical construction cycles of the railroad and automotive industries, 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 be $1.4 trillion.

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

If a structural shift occurs on the demand side, this meticulously constructed financing system will face a fundamental stress test.