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

Global AI infrastructure construction is burning through capital at an unprecedented pace, yet the debt engine underpinning this "super cycle" has quietly swelled to a scale that is raising market concerns. As more and more tech giants fall into negative free cash flow, the demand for external financing will inevitably continue to climb.

According to the latest estimates from Goldman Sachs, the global direct debt issuance by hyperscale cloud service providers is projected to reach $420 billion in 2027, up more than 60% from the $250 billion forecast for the whole of 2026.

Meanwhile, Bank of America’s latest report estimates that by 2030, the total external financing demand for key AI infrastructure players represented by Microsoft, Amazon, Alphabet, Meta, Oracle, SpaceX, Coreweave, and Nebius will reach $1.2 to $1.5 trillion. If debt remains the primary financing tool, it is expected to bring over $300 billion of incremental supply to the credit market each year, and could even spike to $500 billion in the short term.

This debt expansion wave is impacting not only the investment-grade bond market, but is also continuously penetrating via multiple channels such as off-balance-sheet special purpose vehicles (SPVs), infrastructure financing, and private credit.

The most direct financial consequence of the AI investment frenzy is the sharp deterioration in major hyperscalers’ cash flow positions.

Except for Microsoft, all other major hyperscalers have already slipped into negative free cash flow. This means that the expansion of future capital expenditures will almost entirely rely on external financing—with debt as the preferred channel.

Bank of America estimates that from 2026 to 2028, the combined operating cash flow of the aforementioned eight key companies will reach about $3.3 trillion, while total capital expenditures will hit $3.7 trillion (of which AI capex is $2.9 trillion), resulting in a net financing gap of about $400 billion over three years. Extending the projection to 2030, operating cash flow rises to $6.6 trillion while total capex reaches $6.9 trillion, with the net gap still at about $300 billion. If we add dividends, buybacks, and M&A, the total funding gap will expand to $800 billion.

Bank of America emphasizes that the cost advantage of debt over equity is significant—post-tax debt costs for hyperscalers are around 5%, whereas the cost of equity exceeds 11%, more than double. This makes debt financing the mainstream choice for the foreseeable future, although for some companies where debt costs have recently soared (such as Oracle), this logic becomes less applicable.

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

So far this year, total dollar-denominated debt issuance related to AI across credit markets has reached $568 billion, including $259 billion in investment-grade bonds, $256 billion in private credit, direct loans, and other bilateral/non-public financings, $40 billion in high-yield bonds, and $11 billion in institutional loans.

In addition to disclosed on-balance-sheet debt, off-balance-sheet commitments and obligations have surged to as much as $3.1 trillion—mainly consisting of $1.1 trillion in undiscounted lease payments and $1.7 trillion in purchase commitments—with $1.3 trillion added in just three months. These off-balance exposures are operated through SPVs, infrastructure financing vehicles, and are reflected directly in the private credit market.

Among external financing channels, the "supplier financing" mechanism dominated by chip suppliers is playing an increasingly pivotal role—its core function is to turn assets that were previously hard to finance into standardized bonds that can enter the bank credit market.

Take Nvidia as an example; through "take-or-pay" structures, it provides certain Neocloud customers with six years of minimum revenue guarantees, setting contractual minimum income for lenders, thereby replacing hyperscalers’ purchase commitments with supplier assurance and gaining market recognition for related financing.

Broadcom goes even further through its AI XPV Platform—in a $35 billion inaugural debt package jointly issued with Apollo and Blackstone, Broadcom directly guarantees $31 billion of senior notes and chip residual values (87% of total debt). The value of the guarantee is evident in the pricing: secured A2-rated notes yield 5.75%, while unsecured second lien notes soar to as much as 8.5%. Bank of America analysts estimate that if the platform expands to 20GW capacity, Broadcom’s peak residual value guarantee (RVG) exposure could reach about $370 billion by mid-2029.

Bank of America points out that the essence of such supplier credit mechanisms is to shift the clearing rate decision from the capital user to the supplier, thereby transforming previously unfinanceable exposures into financeable assets. At the same time, however, it also means a large volume of contingent liabilities is quietly accumulating on the chip suppliers’ balance sheets.

Despite the ongoing expansion of debt, Bank of America also warns that there is an endogenous risk correction mechanism in the market. July 2026’s sharp fluctuations in AI credit spreads already provided a preview—widening spreads forced some issuers to pause bond issuance plans, demonstrating that when debt costs exceed the economic rationale for capital allocation, issuers will shift to equity, convertible bonds, or even proactively cut capital expenditures.

The report also cautions that the main constraints on AI infrastructure construction may not be capital itself, but more physical and institutional bottlenecks such as electricity supply, regulatory approvals, and construction cycles.

The rapid rise of open-source models adds another layer of potential pressure. As cost-effective open-source solutions that can achieve near-frontier model performance permeate the market, the Token price of mainstream leading models has fallen by more than 50% since the June peak, hitting a record low. This not only undermines the business logic of frontier models but also puts the trillion-dollar IPO valuations under pressure. If the inflection point for Token economy profitability is delayed, the revenue assumptions underpinning the entire AI debt ecosystem will face a fundamental repricing.