Private credit crisis erupts: From redemption wave to CDS shorting tools, the Federal Reserve and Treasury Department have begun to intervene

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The private credit market is facing a systemic risk test caused by multiple overlapping pressures. Waves of redemptions are spreading, Wall Street is accelerating the launch of private credit credit default swaps (CDS) as short-selling tools, and regulatory authorities are tightening their stance. The Federal Reserve and the Treasury Department have both intervened to express concern, and the vulnerabilities of this asset class are being exposed to the market at an unprecedented speed.

Behind the full-blown outbreak of the private credit crisis lies a deeper structural fracture: the underlying logic fueling the rapid expansion of private credit—namely, corporate financing demand driven by the AI data center construction boom—is facing harsh pushback from reality.

According to the latest data from Sightline Climate, of the roughly 16 GW of data center capacity scheduled to come online in the United States by 2026, an estimated 30% to 50% will face delays or cancellations, and the actual capacity under construction at the moment is only about 5 GW. This large-scale stagnation on the supply side directly impacts the private credit asset pool, which relies on the growth of AI infrastructure financing demand.

Meanwhile, Wall Street is preparing in advance for the next crisis—the launch of private credit CDS tools marks that institutional investors have begun pricing in the systemic pressure of this market. The intervention by the Federal Reserve and the Treasury further confirms regulators' high alert to potential risk spillovers.

Analysts point out that the underperformance of AI infrastructure projects is putting pressure on the quality of underlying assets, liquidity mismatches are triggering waves of redemptions, the introduction of short-selling tools such as CDS is providing a new negative feedback mechanism for the market, and regulatory intervention is further reinforcing market pricing of systemic risks. The stacking of multiple pressures is creating a "perfect storm" in the private credit market.

AI Infrastructure Bubble: The Underlying Logic of Private Credit Expansion Is Shaking

The high-speed growth of the private credit market in recent years has been closely bound to the financing boom for AI infrastructure construction. However, this underlying narrative is now facing severe real-world challenges.

According to Sightline Climate’s "2026 Data Center Outlook" report, the U.S. plans to add at least 16 GW of data center capacity across 140 projects this year, but only 5 GW is currently under construction.

The report clearly points out that an estimated 30% to 50% of 2026 projects will face delays, mainly driven by: power supply bottlenecks (25% of projects have yet to disclose power supply plans), increasingly effective community opposition, and risks of shortages in grid equipment.

This gap will further widen in the coming years. According to reports, of the announced 21.5 GW planned capacity for 2027, only about 6.3 GW is actually under construction. From 2028 to 2032, the vast majority of planned projects have not even broken ground—of the 37 GW in planned infrastructure, only 4.5 GW has started construction, and a large number of projects do not yet have a definite completion timeline.

Canaccord Genuity analyst George Gianarikas characterizes this situation as "the U.S. data center boom hitting a massive wall of logistics friction." He warns that if domestic manufacturing and grid integration cannot be fundamentally accelerated, "the digital expansion boom at the end of the 2020s will face a series of unfulfilled promises."

One of the core bottlenecks restricting data center construction is the severe shortage of key electrical equipment. Tight supply of transformers, switchgear, and energy storage devices is not only caused by surging demand for AI data centers but is also compounded by increased grid expansion needs driven by the popularization of electric vehicles and heat pumps.

According to Bloomberg, U.S. domestic manufacturing capacity cannot keep up with the pace of demand growth, forcing builders to rely heavily on imports. Philippe Piron, CEO of GE Vernova’s Electrification business, points out:

Before 2020, the typical delivery period for high-power transformers was 24 to 30 months, but AI companies often require delivery within 18 months. The surge in demand has now pushed lead times to as long as five years, and some companies have had to refurbish transformers from decommissioned power plants as emergency substitutes.

This predicament reflects the deep structural problem of hollowing out in U.S. manufacturing. Over the past decade, the array of re-shoring policies promoted by the U.S. government have yet to deliver substantial increases in capacity, and companies continue to rely on Chinese supply chains—this directly contradicts America's strategic goals for competition with China in the AI domain.

Funding Gaps: $5 Trillion in Demand, Government May Have to Back Over $1 Trillion

The financial pressures at the capital level cannot be ignored either. Despite the massive capital expenditures by hyperscale cloud companies, overall financing needs are still not sufficiently met.

According to J.P. Morgan analysis, the total funds required to fully support this AI cycle is no less than $5 trillion. Even considering the large-scale capital spending and debt financing by tech giants, the U.S. government still needs to fill a funding gap of over $1 trillion.

This financing pressure forms the core background for private credit's deep involvement in AI infrastructure construction. However, as project delay and cancellation risks rise, the asset quality of private credit that relies on these project cash flows for debt repayment is coming under increasing scrutiny.

Shreeti Kapa, Executive Director at Goldman Sachs, cited a consensus from an investor dinner in a recent report:

"There simply isn’t enough compute power. Every participant faces severe compute constraints—from chip manufacturing to data center site permitting, to electricity, memory, and labor—these bottlenecks are real and will remain for a considerable time."

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