Moody's says AI financing is expanding private credit as refinancing risks build

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AI financing expands private credit

Highlights

Moody's reports AI-driven capital demand now extends private credit into land, electricity, semiconductors, GPUs and TPUs, broadening technology financing exposure.
Capital expenditures by the five largest U.S. hyperscalers and CoreWeave are projected to approach $1 trillion in 2027, financed via cash flows, debt, JVs and SPVs.
Moody's warns that 17% of 607 tracked borrowers use payment-in-kind interest as a wave of maturities from 2027-2029 raises refinancing risks, especially with rising circular financing in the tech sector.

AI funding broadens private credit exposure

As reported by Reuters, citing Moody's Ratings, financing demand linked to AI now reaches beyond data-center construction into land, electricity, semiconductors, graphics processing units and tensor processing units. The ratings agency says this wider capital need is helping expand private credit into new parts of the technology financing chain.

Moody's expects capital spending by the five largest U.S. hyperscalers and CoreWeave to reach nearly $1 trillion in 2027. It says that spending is being financed through cash flows, debt and structures including joint ventures and special-purpose vehicles.

Refinancing pressure grows for weaker borrowers

A wave of loans maturing between 2027 and 2029 is likely to test the ability of weaker borrowers to refinance at higher interest rates, Moody's says. The agency adds that about 17% of the 607 borrowers it tracks use payment-in-kind interest, a structure that lets companies defer cash interest payments but increases their overall debt burden.

Moody's also flags circular financing risks as technology companies, chipmakers and AI developers increasingly invest in, lend to and buy from one another. It says those correlated exposures could weaken at the same time during a downturn, raising risk across the private credit market.

In our earlier coverage of the U.S. AI data-center boom, we examined how rising funding costs, construction delays and power constraints were starting to undermine project timelines and investor confidence. The piece highlighted that only a portion of planned builds looked credible, while examples such as CoreWeave volatility and Cipher’s cost overruns showed how quickly financing needs can escalate. It also noted that local opposition and grid limitations could further slow expansion even as demand for AI computing remains strong.

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