“Overinvestment Concerns Turn Into Credit Anxiety”: Debt-Fueled AI Data Centers Face Tests From Financing to Survival

Doubts spread over recovery of AI infrastructure investments
Caution spreads from stock and bond markets to banks
Higher financing barriers put pressure on vulnerable companies

Anxiety over recovering investments is growing in the financial markets that funded the race to expand artificial intelligence (AI) data centers. Despite large orders, rising capital spending and interest burdens have prompted investors to question companies’ ability to repay their debts. As initial public offerings (IPOs) are delayed and related bond prices fall, banks unable to sell construction loans to other investors are also finding it harder to provide additional funding. Across an industry that still requires massive investment, concerns are spreading that some companies may be unable to withstand rising financing costs.

CoreWeave Debt Insurance Costs Hit Record High

According to Bloomberg on the 21st (all dates hereafter are local time), specialized cloud companies that acquire and rent out graphics processing units (GPUs) and data centers needed for AI computing have recently come under financing pressure. At U.S.-based CoreWeave, a leader in the so-called neocloud industry, the cost of insurance against default has surged to a record high. On the 15th, the premium on CoreWeave’s five-year credit default swaps (CDS) reached 1,194.5 basis points intraday (1 basis point = 0.01 percentage point). That level implies a market-priced probability of default over the next five years exceeding 60%. The bond market has begun to question the repayment capacity of companies that have expanded AI infrastructure through heavy borrowing.

The financial strain stems from massive upfront investment and the growing borrowing that has supported it. Neocloud companies must purchase expensive chips and secure data centers before providing computing services to customers, so they finance a substantial portion of their initial investments with debt. CoreWeave’s capital expenditure plan for this year, announced in August, was USD 35 billion–39 billion (approximately USD 36.1 billion–USD 40.2 billion), roughly three times the market’s annual revenue forecast of USD 13 billion (approximately USD 13.4 billion). Already-swollen financing costs are also weighing on earnings. The company’s second-quarter net interest expense was USD 640 million (approximately USD 660 million), accounting for a quarter of its quarterly revenue of USD 2.575 billion (approximately USD 2.66 billion), while its net loss for the same period reached USD 626 million (approximately USD 645 million).

Turning large order contracts into actual revenue also requires additional funding. CoreWeave reported a backlog of USD 104 billion (approximately USD 107 billion) as of the end of June, citing it as evidence that it can service its debt. The figure represents expected future revenue from contracts signed with customers and can be recognized only once equipment delivery and service provision requirements are met. With facilities needed to fulfill those contracts yet to be built, rising yields demanded by bond investors are adding to the burden of securing follow-on financing. New chip releases could also reduce the value of existing equipment, creating additional investment needs for replacements.

Investors Raise the Bar for AI Company Financing

As funding requirements for capacity expansion continue to swell, investors are becoming more selective about AI companies. SB Energy, a U.S. data center developer backed by SoftBank, recently saw its IPO timetable disrupted by investor disagreements over its valuation. Its proposed valuation of USD 50 billion (approximately USD 51.6 billion) raised concerns among investors because it does not yet have a single operating data center. Marketing of the offering to investors was also postponed while the company responded to additional questions from the U.S. Securities and Exchange Commission (SEC). Alongside the high valuation, investors cited its dependence on major customer OpenAI as a concern.

Another factor heightening investor caution is the substantial financing burden that will persist after the listing. Future revenue from data center contracts secured by SB Energy totals USD 430 billion (approximately USD 443 billion), most of it tied to OpenAI. The funding needed just to build the facilities required to fulfill those contracts is understood to reach USD 174 billion (approximately USD 179 billion). This means the company will have to keep raising funds in bond markets and elsewhere even after boosting its capital through a listing. Investors have already shown a lukewarm response to USD 5 billion (approximately USD 5.16 billion) in debt financing considered alongside the listing, fueling doubts about whether follow-on investment funds can be raised as planned.

AI Overinvestment Concerns Begin to Register in Financial Markets

As markets apply stricter standards to the profitability of AI businesses, concerns about overinvestment raised since the beginning of the year are increasingly being reflected in financial markets. In January, the International Monetary Fund (IMF) warned that if AI-driven productivity gains fall short of expectations, technology companies could scale back investment and stock price corrections could persist. Recently, concerns about recovering investments have also emerged in data center businesses with major technology companies as customers. Over recent weeks, bonds linked to “Hyperion” and “Project Excalibur,” whose tenants include Meta, Nvidia and Oracle, have traded significantly below face value. This reflects growing investor anxiety about whether future rental income can cover debts accumulated during construction and operation.

As concerns about investment recovery spread, trading in CDS used to protect against default has also surged. According to Morgan Stanley, the weekly average trading volume of CDS linked to bonds issued by Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom and Nvidia rose from less than USD 2 billion (approximately USD 2.06 billion) in July to more than USD 9 billion (approximately USD 9.28 billion) in September. Beyond bond investors, investors managing equities and multiple asset classes are also turning to CDS to protect against losses on AI-related investments. Caution is further heightened by the difficulty of assessing total debt burdens amid a complex web of intercompany investments and loan guarantees. On the 19th, Mark Habib, head of digital infrastructure at international credit rating agency S&P Global, pointed to a structure in which major technology companies effectively guarantee data center construction and financing for smaller AI companies. His warning was that, because individual transactions have not been sufficiently disclosed, more debt exposure may be hidden than is currently known.

Table 1. Delays in AI Data Center Loan Sales and Burdens on Banks

Banks Even Consider Selling AI Loans at a Discount

The burdens of the complex web of AI financing are also mounting for banks that funded data center construction. Banks recover funds by first providing loans and then selling portions to other investors, or by having project operators issue asset-backed securities (ABS) and other instruments to repay existing loans. They channel the recovered money into new projects, but the time needed to find investors willing to take over the loans has slowed that funding cycle. In May, JPMorgan, Mitsubishi UFJ Financial Group (MUFG) and others were reported to have spent more than 6 months trying to sell portions of USD 38 billion (approximately USD 39.2 billion) in construction loans for Oracle-leased data centers to investors. Some banks also sought to sell loans at a discount to nonbank financial institutions.

Banks have sought to sell loans even at a loss because funds tied up in existing loans also constrain financing for subsequent projects. When loans underwritten with the intention of selling them remain on balance sheets, banks must continue holding capital against potential losses, while headroom under borrower- and industry-specific lending limits shrinks. Individual banks’ limits reportedly also proved an obstacle when loans for Oracle- and OpenAI-related data centers were being sold in portions to other financial institutions. The purchasing institutions likewise faced constraints on increasing lending to the same borrowers, delaying the sales. Meanwhile, funds remained tied up at the banks that originally underwrote the loans, reducing their capacity to provide additional loans needed for new data center construction.

Growing Reliance on Private Credit

Data center operators unable to obtain the money they need from banks face having to secure more expensive funding in the private credit market. Private credit managers demand high interest rates and fees in exchange for assuming construction risks or providing funds in line with project schedules. Refinancing options when loans mature are also limited. This means companies may have to offer investors higher returns to secure the funds needed to repay existing loans and invest in facilities. Deals offering double-digit returns to finance AI computing equipment have already emerged. According to the Financial Times (FT), Anthropic recently offered Blackstone participation in USD 9 billion of subordinated loans as part of a chip financing transaction it is pursuing, with a coupon rate of 10%. Including the issuance discount and fees, investors’ returns would exceed 10%. The interest rate on subordinated funding Anthropic raised through a similar transaction in June was 8.5%.

Higher financing costs are also increasing the burden of large-scale capital spending planned for the future. According to a forecast released by market research firm Gartner on the 14th, AI companies’ infrastructure investment will reach USD 1 trillion (approximately USD 1.03e+3 billion) this year, with an additional USD 6 trillion (approximately USD 6.19e+3 billion) expected to be invested in 2027–2030. Gartner assessed that spending by businesses and consumers on AI services during this period would be insufficient to generate adequate returns across the planned capital investments. Neocloud providers in particular are vulnerable to cost pressures because they must recoup their investments through computing services to sustain their businesses. Gartner projected that more than 80% of neocloud providers would cease operating independently by 2030 because of cost burdens and a failure to differentiate their businesses.