AI Disruption Could Trigger ‘Shock to the System’ in Credit Markets, UBS Warns

The artificial intelligence boom, which has already reshaped equity markets, may soon rattle corporate credit markets, according to a senior analyst at UBS.

Matthew Mish, head of credit strategy at UBS, warned that disruption from AI could lead to tens of billions of dollars in loan defaults, particularly in the leveraged loan and private credit markets, sectors that together represent roughly $3.5 trillion in outstanding debt.

In an interview with CNBC, Mish said the speed of AI development has exceeded earlier expectations, prompting UBS analysts to revise their outlook for corporate credit risk.

“We’re pricing in part of what we call a rapid, aggressive disruption scenario,” Mish said.

Defaults Could Reach $120 Billion

UBS projects that between $75 billion and $120 billion in fresh defaults could emerge across leveraged loans and private credit by the end of the year under its baseline scenario.

Leveraged loans, estimated at approximately $1.5 trillion, and private credit markets, valued at around $2 trillion, are particularly exposed because they typically finance below-investment-grade companies, many of which are backed by private equity and carry higher debt loads.

Mish estimates default rates could rise by up to 2.5% in leveraged loans and up to 4% in private credit by late 2026, driven largely by pressure on companies vulnerable to AI-driven disruption.

Software and data services firms, especially those owned by private equity groups, are seen as among the most exposed.

“The market has been slow to react because they didn’t really think it was going to happen this fast,” Mish said. “People are having to recalibrate the whole way that they look at evaluating credit for this disruption risk, because it’s not a ’27 or ’28 issue.”

From Equity Shock to Credit Risk

Investor anxiety around AI has intensified in recent weeks as markets shift from viewing artificial intelligence as a broad-based growth story to one increasingly defined by winner-take-all dynamics.

Companies such as OpenAI and Anthropic are seen as leading the development of foundational large language models, potentially threatening established software providers.

While publicly traded investment-grade software firms such as Salesforce and Adobe may have the balance-sheet strength to adapt, heavily indebted private firms could struggle to compete or refinance their obligations.

“The winners of this entire transformation, if it really becomes a rapid and very disruptive change, the winners are least likely to come from that third bucket,” Mish said, referring to private equity-owned companies with high leverage.

Tail Risk of a Credit Crunch

Beyond its baseline forecast, UBS outlined a more severe “tail risk” scenario in which defaults could double relative to base projections. Such an outcome could trigger a broad repricing of leveraged credit and restrict funding for many firms.

“The knock-on effect will be that you will have a credit crunch in loan markets,” Mish said. “You will have a broad repricing of leveraged credit, and you will have a shock to the system coming from credit.”

The trajectory of AI adoption, model improvement and corporate integration will ultimately determine the scale and timing of credit stress, Mish noted. While UBS is not yet calling for the worst-case outcome, the direction of risk is shifting.

“We’re not yet calling for that tail-risk scenario, but we are moving in that direction,” he said.

As artificial intelligence accelerates transformation across industries, attention is now turning from equity valuations to debt sustainability, a shift that could define the next phase of market volatility.