Stock Markets February 25, 2026

UBS Maps Out How a Sudden AI Shock Could Ripple Through Private Credit

Analyst warns of elevated defaults, sharply reduced issuance and concentrated sector vulnerabilities in a tail disruption scenario

By Nina Shah ARCC
UBS Maps Out How a Sudden AI Shock Could Ripple Through Private Credit
ARCC

UBS analyst Matthew Mish set out a tail-risk scenario in which a rapid, severe AI disruption would materially worsen defaults and tighten credit across U.S. high-yield, leveraged loan and private credit markets. The bank quantified potential default rates, estimated losses and drawn exposures, and highlighted concentrations and stress signals that could amplify a broad credit cycle.

Key Points

  • UBS models a tail scenario where defaults could rise to 3-6% for U.S. high-yield, 8-10% for leveraged loans and 14-15% for private credit, with cumulative defaults and losses approaching $420bn and $300bn.
  • Private credit now represents 6% of U.S. GDP; stress signals include defaults of 3-5%, leverage up to 7.5-8x in certain sectors, and pressured interest coverage ratios - raising concerns about underwriting and funding resilience.
  • Concentration in services, technology and healthcare - particularly technology - increases vulnerability, and the linkage between private and public credit markets means stress could spill across market segments.

UBS analyst Matthew Mish said in a note on Wednesday that investor attention is increasingly turning to the prospect of a "rapid, severe AI disruption," and the bank provided updated modelling to show how such an event might propagate through private credit and the wider leveraged finance universe.

UBS emphasized that this is "not our baseline," but said the refreshed analysis clarifies the potential catalyst and supplies revised forecasts for defaults and spreads under a tail scenario. The bank's estimates indicate a marked deterioration in credit quality across distinct segments of the market.

Specifically, UBS projects that, in this stress case, defaults could rise to 3-6% in U.S. high-yield, 8-10% in leveraged loans and 14-15% in private credit. Aggregated across these markets, the bank estimates defaults and losses would approach $420 billion and $300 billion, respectively. Credit provision would tighten significantly, with private credit and loan issuance potentially falling 50-75% year over year.

UBS also flagged the exposure of financial institutions through nonfinancial corporate borrowing and related facilities - described in the note as NFBI loans, which currently total $2.5 trillion. Under the severe disruption scenario, the bank estimates $1.6-1.8 trillion in total drawn exposures, and it notes that 30-40% of those drawn amounts are tied to private equity, private credit, business development companies or structured vehicles that UBS views as higher risk.

The note underscores that private credit has expanded quickly relative to the size of the economy, now accounting for 6% of U.S. GDP. UBS said stress indicators are present: defaults are running between 3% and 5%, leverage has reached 7.5-8x in some sectors, and interest coverage ratios remain under pressure.

Concentration risks also figure prominently in the bank's analysis. Portfolios are often heavily weighted toward services, technology and healthcare, and UBS singled out technology as "especially vulnerable to disruption from AI adoption or rapid retrenchment." Because private and public credit markets are interconnected, the bank warned stress in one area is unlikely to remain isolated.

While UBS did not declare the market to be in crisis, it cautioned that "the ingredients are present for a severe credit cycle," even as single-name, idiosyncratic risks may still create specific investment opportunities.


Analyst context - The modelling is presented as a tail-risk construct rather than the bank's base case. It is intended to illuminate how a concentrated, fast-moving technological disruption could translate into higher defaults, material losses and a sharp contraction in market issuance and liquidity.

Risks

  • A rapid, severe AI disruption could trigger sharply higher defaults and losses across private credit and leveraged finance, affecting banks, asset managers and credit investors - sectors directly tied to corporate lending and structured vehicles.
  • Credit availability may contract materially, with private credit and loan issuance potentially dropping 50-75% year over year, which would strain borrowers reliant on non-bank financing and reduce liquidity in secondary markets.
  • High drawn exposures - an estimated $1.6-1.8tn in a severe scenario out of $2.5tn of NFBI loans - with 30-40% linked to higher-risk conduits such as private equity, private credit, BDCs and structured vehicles, raise funding and counterparty risks for financial institutions.

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