Is a Critical Moment for Capital Approaching? AI Narrative Shifts from Technology to Financing as Giants Face Cash Flow Crunch and Bond Market Alerts Sound

Author: Long Yue
The narrative around AI investment is undergoing a transformation. Initially, it was a technological story, then it evolved into a capital expenditure narrative, and now it is becoming a financing story.

Recently, Greg Jensen from Bridgewater Associates summarized the current situation in one sentence: "We are entering a critical moment for capital."

Morgan Stanley predicts that total spending on AI infrastructure will reach $3.2 trillion by 2028, with approximately $1.75 trillion needing to be raised through the credit markets. Funding sources have expanded from traditional investment-grade bonds to leveraged loans, private credit, and securitized products. Money is available, but it won't be cheap.

The bond market has already begun to react. According to Apollo, AI-related bond issuance has accounted for 40% of long-duration supply. The credit spreads of hyperscalers have widened significantly this year, while the overall investment-grade market has remained almost unchanged.

"Bond King" Gundlach bluntly stated, that issuing long-term bonds backed by GPUs is akin to "using bananas to create a 30-year ABS."

Cash Flow is Heading in the Wrong Direction

Capital expenditure forecasts are being repeatedly revised upwards, while free cash flow projections are continuously declining.

Morgan Stanley has significantly lowered the free cash flow forecasts for major hyperscalers for 2027. Among them, Oracle's situation is the most striking, with its 2027 free cash flow forecast nearing -$40 billion.

The scale of spending commitments is equally alarming. The procurement commitments of hyperscalers have exploded to $982 billion. Morgan Stanley points out that real capital expenditures are now largely off-balance-sheet. This means that merely looking at the balance sheet will severely underestimate the actual funding pressure.

The Credit Market Has Started Pricing Risks

Investors are demanding higher risk premiums.

Morgan Stanley data shows that credit spreads related to hyperscalers have "widened significantly this year, far exceeding the overall investment-grade market"—high-quality hyperscalers (HQ Hyperscalers) saw spreads widen by about 25 basis points, while ordinary hyperscalers widened by about 22 basis points, and the overall investment-grade market spread changed by 0.

Oracle is one of the most closely watched cases. The credit default swaps (CDS) of other hyperscalers generally range from 30 to 80 basis points, while Oracle's CDS soared from about 40 basis points in mid-2025 to a peak of nearly 190 basis points in April 2026, currently hovering around 180 basis points. Meta's CDS has risen moderately to about 75 basis points. The credit market has clearly identified the one it is most concerned about.

Strong Balance Sheets, but Problems Lie Ahead

Morgan Stanley's Q1 2026 data shows that the total leverage ratio of hyperscalers is only 1.3 times, with a net leverage ratio of 0.5 times, a cash/debt ratio as high as 128%, and a median rating of AA-. In contrast, the overall total leverage ratio of non-financial investment-grade entities is 2.4 times, with a rating of BBB.

But the problem lies in the future. Morgan Stanley has significantly raised its forecast for cloud computing capital expenditure growth from 14% to 29% for 2027, and hyperscalers are also "reaffirming their confidence in investment returns." With expenditure forecasts doubling, the financing gap is widening accordingly.

"Bond King" Gundlach: Using GPUs as Collateral is Like Using Bananas for ABS

Regarding the financing schemes backed by AI assets that have emerged in the market, renowned bond market investor Jeff Gundlach offered sharp criticism.

He warned against a $50 billion fund consortium plan, stating that it "is unlikely to withstand the test of time." He questioned on social media: "Using assets with unknown lifecycles as collateral for long-term debt? Why not do a 30-year ABS deal backed by bananas in the warehouse?" He added that these are still "brand new engineering bananas with unknown lifecycles."

Gundlach's analogy points to the core issue: GPUs depreciate rapidly, and the technology iteration cycle is much shorter than the debt maturity period. When long-term financing is secured against such assets, the actual value of the collateral is highly uncertain.

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