AI Credit Market Faces Volatility Warning as Tech Firms Pour $8 Trillion Into Infrastructure

KKR Sounds Alarm on AI Credit Market Concentration

The AI credit market is facing mounting credit risks as tech companies embark on an unprecedented AI borrowing spree, according to a stark warning from KKR. The investment firm, which manages $796 billion across private equity and credit markets, cautioned that global credit markets could experience significant volatility if the booming AI sector faces a downturn1

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. With tech firms projected to invest nearly $8 trillion in AI infrastructure by 2030, roughly a fifth of the investment-grade index could end up exposed to AI-related debt, creating what Christopher Sheldon, co-head of credit and markets at KKR, described as "an unusually concentrated investment cycle"1

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. The true extent of exposure could be substantially higher once off-balance-sheet financing arrangements are factored in, including credit guarantees, leases, and future commitments.

Hyperscalers Drive Record AI Infrastructure Investments

Hyperscalers have issued approximately $250 billion of debt in 2026 alone, with expectations that this figure will surge even higher next year as AI financing needs escalate3

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. Estimates indicate that around $1 trillion in AI capital expenditure (capex) is expected this year, climbing to approximately $1.2 trillion next year, with Oxford Economics projecting cumulative AI infrastructure investments of roughly $3.8 trillion from 2024 to 20283

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. A significant portion of this spending is now being debt-financed as tech firms have burned through their substantial cash reserves. AI-related debt currently totals about $600 billion, representing approximately 6.3 per cent of the US investment-grade market—more than double the 2.6 per cent average highest sector exposure over the past 29 years1

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Risky Corners of US Credit Market Show Strain

The AI borrowing boom has penetrated the riskiest corners of the US credit market, where skeptical lenders are demanding higher compensation to fund borrowers whose future earnings remain largely unproven2

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. AI-related issuance by low-rated firms has totaled $88 billion this year, according to Goldman Sachs, with most borrowing coming from US issuers—a dramatic increase from just $20 billion in the first 11 months of 20252

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. Investors in leveraged finance markets, including junk bonds and loans, are taking a harder look at these less-established borrowers, questioning their revenue projections and the value of their collateral. Even near investment-grade BB+ rated issuers are paying roughly 9% to 10% yields, while lower-rated borrowers could face borrowing costs reaching 14% to 15%2

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. SoftBank Group, which raised funds this month with a BB+ rating, paid yields of 8.625% on 3.5-year notes, 9.25% on 5.5-year debt, and 9.75% on 7.5-year bonds—rates typically associated with significantly lower-rated companies2

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Off-Balance-Sheet Financing Masks True Exposure

The scale of systemic risks may be far greater than currently visible due to extensive off-balance-sheet financing arrangements. According to Morgan Stanley, some $3 trillion of financing and leasing structures involving Nvidia, Broadcom, and hyperscalers such as Amazon, Meta, and Alphabet are being kept off their balance sheets3

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. Meta's Hyperion data center facility in Louisiana exemplifies this approach. The Facebook owner's largest data center project, with 5 gigawatts of compute capacity, has absorbed more than $50 billion in investment3

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. The project was financed through a joint venture vehicle called Beignet Investor, which issued $27 billion of 6.581% senior secured notes maturing in 20493

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. Because Meta's stake is a minority 20%, the project's liabilities remain off-balance sheet. Tal Reback, managing director at KKR, pointed to "a market in which seemingly distinct exposures are increasingly driven by the same underlying economics"1

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Warning Signs Emerge in Bond Markets and Data Center Projects

Cracks are beginning to appear in the AI financing edifice. The Beignet bond, initially given an A+ credit rating by S&P Global, has seen its price slide to 91 cents on the dollar, pushing yields as high as 7.55%—approximately 230 basis points above Treasuries, wider than the 185 basis points at launch3

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. Credit default swap (CDS) rates for firms in the AI ecosystem are ringing alarm bells, with Meta's climbing above July's peak to close to 100 basis points, and most of Big Tech's CDS now at record levels3

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. Oracle recently declared a force majeure notice citing potential delays in securing power for an AI data center in New Mexico, which could now be delayed by up to a year3

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. Data center loans linked to Oracle have come under strain recently because of construction delays, permitting challenges, and power constraints, prompting scrutiny of the backstop provided by hyperscalers1

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Asymmetric Risk Profile Concerns Debt Investors

The investment proposition for debt investors in AI-related debt is fundamentally asymmetric. Unlike equity investors who could potentially benefit from massive gains if AI investments pay off, debt investors' upside is strictly limited to contractual returns driven by coupon, principal, and at most some spread compression3

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. As Lotfi Karoui, multi-asset credit strategist at PIMCO, noted, while returns are largely contractual, the risks range from high debt levels to project delays and rapid changes in technology3

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. Christopher Sheldon emphasized this disparity: "If you didn't have exposure to these hyperscalers in the equity market, you'd be massively underperforming. If you think about the credit markets, you're not necessarily getting paid to take that concentrated bet right now"1

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. Many AI borrowers require heavy upfront investment before generating reliable cash flow, making their debt harder to absorb for the biggest buyers of leveraged loans—managers of collateralized loan obligations (CLOs)2

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Revenue Requirements Raise Questions About Return Viability

The enormous scale of AI infrastructure investments raises fundamental questions about whether returns will justify the outlays. Goldman Sachs analysts estimate that hyperscalers will need to generate annual AI revenues of roughly $300 billion in the next few years just to break even3

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. Assuming hyperscalers target a return on investment of 15%-20%, they would need to generate $570-$800 billion of additional profit, according to Oxford Economics3

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. Oxford Economics estimates AI will boost US GDP by $850 billion by 2032, meaning at a 15% return on investment, hyperscalers would need to capture around two-thirds of this additional GDP, or almost all of it assuming a 20% rate of return—a scenario the firm's economists described as "very unlikely"3

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Market Participants Reassess Concentration and Structural Terms

Investors are increasingly scrutinizing their AI credit market exposure and demanding better terms. "We are having more conversations with LPs [investors] and insurance companies about how much AI exposure is prudent," Sheldon said. "Investors should be mindful of overexposure and concentration because correlations are higher across this AI ecosystem"1

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. Even if the largest hyperscalers, including Oracle, Amazon, Meta, Google, Microsoft, and SpaceX, each hit a maximum index weighting of 3%—the typical single-issuer limit for a bond portfolio—they could only raise up to $1.7 trillion from the high-grade bond market, leaving more than $6 trillion of expected capital expenditure unfunded1

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. Investors need to pay close attention to structural details, particularly for deals traded in the secondary market where there might not be full visibility on contract terms, including underlying leases. "We've walked away from deals where we couldn't get comfortable with the lease terms and force majeure clauses," Sheldon said. "If the lease isn't well structured, [a counterparty's] credit quality only gets you so far"1

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. As the Federal Reserve has started hiking its policy rate and Treasury yields remain at their highest levels since before the global financial crisis, the interest burden on hyperscalers' borrowings—both on- and off-balance sheet—continues to grow heavier, amplifying concerns about the sustainability of the current AI financing model3

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