The "debt black hole" behind the AI boom: $1.2 trillion in external financing may be needed over the next 5 years.

The construction of global AI infrastructure is burning through capital at an unprecedented pace, but the debt engine supporting this "supercycle" has quietly swelled to a scale that is alarming the market. As more and more tech giants fall into negative free cash flow, the need for external financing will inevitably continue to rise.

According to Goldman Sachs' latest estimates, the global direct debt issuance of hyperscale cloud service providers is expected to reach $420 billion in 2027, an increase of more than 60% from the estimated $250 billion for the whole of 2026.

Meanwhile, Bank of America estimates in its latest report that by 2030, the total external financing needs of core AI infrastructure entities, represented by Microsoft, Amazon, Alphabet, Meta, Oracle, SpaceX, Coreweave, and Nebius, will reach $1.2 trillion to $1.5 trillion. If debt is the main financing channel, it is expected to bring more than $300 billion in incremental supply to the credit market every year, and may even reach $500 billion in the short term.

This wave of debt expansion has not only affected the investment-grade bond market, but has also continued to penetrate through multiple channels such as off-balance-sheet special purpose vehicles (SPVs), infrastructure financing, and private lending.

Massive cloud service providers are facing a cash flow crisis, with debt becoming their only option.

The most direct financial consequence of the AI investment boom is the sharp deterioration of the cash flow situation of major hyperscalers.

Apart from Microsoft, all other major Hyperscalers have fallen into negative free cash flow. This means that future expansion of capital expenditures will almost entirely depend on external financing—with debt being the preferred option.

Bank of America estimates that from 2026 to 2028, the combined operating cash flow of the eight core companies mentioned above will be approximately $3.3 trillion, while total capital expenditures will reach $3.7 trillion (of which AI capital expenditures will be $2.9 trillion), resulting in a net financing gap of approximately $400 billion over the three years. Extending the forecast period to after 2030, operating cash flow rises to $6.6 trillion, and total capital expenditures rise to $6.9 trillion, with the net gap still remaining at approximately $300 billion. If the needs for capital operations such as dividends, share buybacks, and mergers and acquisitions are added, the total funding gap will widen to $800 billion.

Bank of America emphasizes that debt has a significant cost advantage over equity—Hyperscaler's after-tax debt cost is approximately 5%, while equity financing costs exceed 11%, a difference of more than double. This makes debt financing the mainstream choice for the foreseeable future, but the applicability of this logic is diminishing for some companies whose debt costs have recently risen sharply (such as Oracle).

The scale of off-balance-sheet "time bombs" continues to expand.

Besides direct bond issuance, another significant risk exposure in the AI financing system comes from off-balance-sheet arrangements.

So far this year, the total issuance of AI-related cross-credit market dollar debt has reached $568 billion, including $259 billion in investment-grade bonds, $256 billion in private credit, direct loans and other bilateral/private financing, $40 billion in high-yield bonds and $11 billion in institutional loans.

Beyond the disclosed on-balance-sheet debt, off-balance-sheet commitments and obligations totaling $3.1 trillion—primarily consisting of $1.1 trillion in undiscounted lease payments and $1.7 trillion in procurement commitments—increased by $1.3 trillion in just three months. These off-balance-sheet exposures operate through channels such as special purpose vehicles (SPVs) and infrastructure financing, directly impacting the private credit market.

Supplier financing becomes the key to breaking the ice for "unfindable" assets.

In external financing channels, the "supplier financing" mechanism, led by chip suppliers, is playing an increasingly crucial role—its core function is to transform assets that were originally difficult to finance into standardized bonds that can enter the bank credit market.

Taking Nvidia as an example, it provides a six-year minimum income guarantee for some Neocloud customers through a "take-or-pay" structure, setting a contractual income floor for lenders, thereby replacing the underwriting endorsement of hyperscale cloud vendors and making related financing recognized by the market.

Broadcom goes even further with its AI XPV Platform—in a $35 billion initial debt offering jointly issued with Apollo and Blackstone, Broadcom directly guaranteed $31 billion of the senior notes and chip residual value (representing 87% of the total debt). The value of the guarantee is clearly evident in the pricing differences: the guaranteed A2-rated notes have a rate of 5.75%, while the unguaranteed second-tier lien notes are as high as 8.5%. Bank of America analysts estimate that if the platform expands to 20GW, Broadcom's peak residual value guarantee (RVG) exposure will reach approximately $370 billion by mid-2029.

A Bank of America report points out that the essence of this type of supplier credit mechanism is to transfer the power to determine the settlement rate from the fund user to the supplier, thereby transforming exposures that would otherwise be unable to access the conventional credit market into financeable assets. However, this also means that a large amount of contingent liabilities is quietly accumulating on the balance sheets of chip suppliers.

Credit feedback mechanism: the self-correcting boundary of debt bubbles

Despite the continued expansion of debt, Bank of America also points out that the market has an inherent risk correction mechanism. The sharp fluctuations in AI credit spreads in July 2026 provided a preview—widening spreads forced some issuers to suspend bond issuance plans, confirming that when debt costs exceed the economically reasonable boundaries of capital allocation, issuers will turn to equity, convertible bonds, or even proactively reduce capital expenditures.

The report also warns that the main constraints hindering the pace of AI infrastructure construction may not be capital itself, but rather physical and institutional bottlenecks such as power supply, regulatory approvals, and construction cycles.

The rapid rise of open-source models poses another layer of potential pressure. As open-source solutions capable of achieving near-cutting-edge model performance at extremely low cost penetrate the market, the token prices of mainstream cutting-edge models have fallen by more than 50% since their June peak, hitting record lows. This not only impacts the business logic of cutting-edge models but also puts pressure on their trillion-dollar IPO valuations. If the profitability turning point of the token economy fails to materialize, the revenue assumptions upon which the entire AI debt ecosystem is based will face a fundamental reassessment.

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