The bond market takes the stage after the stock market: UBS says AI "kill list" update, $120 billion in corporate loans face default risk

While Wall Street is still nervously watching software stocks plummet, UBS has sounded a new alarm: the credit market may be the “hidden powder keg” that hasn’t been fully priced in amid the AI disruption wave. As the pace of artificial intelligence technology advances far beyond expectations, those heavily indebted software and data service companies—especially those owned by private equity—are on the brink of default.

UBS credit strategist Matthew Mish stated plainly in a research report released on Wednesday (the 12th) that the market is re-pricing itself for a “rapid and aggressive upheaval.” He estimates that by the end of next year, defaults could add up to $75 billion to $120 billion in the leveraged loan and private credit sectors. This projection is based on UBS’s baseline scenario: leveraged loan default rates rising by 2.5 percentage points, amounting to roughly $1.5 trillion; private credit default rates increasing by 4 percentage points, totaling about $2 trillion.

“The market’s response has been slow because they really didn’t expect it to happen so quickly,” Mish told CNBC. He pointed out that with companies like Anthropic and OpenAI releasing new models, market expectations for the timing of AI disruption have been sharply compressed. “People have to readjust their entire way of assessing this kind of credit risk because this isn’t a problem for 2027 or 2028 anymore.”

From “Growth Story” to “Life-or-Death Race”

Since this month, investors’ narrative around AI has undergone a fundamental shift: the market no longer views this technology as a universal boon for all tech companies, but as a brutal reshuffle where winners take all. Although software stocks have been the first to be sold off in panic, the fear has quickly spilled over into seemingly unrelated sectors like finance, real estate, and trucking.

Mish emphasized that under the impact of AI, companies can be clearly divided into three tiers:

· First Tier: Creators of foundational large models like Anthropic and OpenAI. They are still startups but are highly likely to rapidly emerge as next-generation large public companies.

· Second Tier: Investment-grade software firms such as Salesforce and Adobe. They have solid balance sheets and ample cash flow, capable of deploying AI swiftly to fend off challengers.

· Third Tier: Software and data service companies owned by private equity. These firms generally carry high debt levels and rely heavily on traditional business models, making them the most vulnerable to AI disruption.

Tail Risks: What if the credit market “freezes”?

Beyond the baseline scenario, UBS also sketches a more painful “tail risk” picture. In this scenario, default rates could double the baseline estimate, cutting off financing channels for many companies.

“A chain reaction would lead to credit tightening in the loan market,” Mish described. “You would see widespread repricing of leveraged loans, and credit would impact the system.” This scenario resembles the junk bond sell-off of energy companies a decade ago or the credit freeze during the internet bubble burst over twenty years ago.

UBS analysts note that although risks are building, the actual evolution depends on several key variables: the pace of large companies adopting AI, the speed of improvements in AI models themselves, and market refinancing needs. Currently, about 20% of leveraged loans and private credit face refinancing pressures by 2028, meaning risks will continue to ferment over the next two years.

“We’re not yet calling for a tail risk scenario, but we’re heading in that direction,” Mish admitted.

Who is paying for the AI revolution?

It’s noteworthy that this warning focuses on leveraged loans and private credit, which are among the riskiest segments in corporate lending. They typically finance below-investment-grade companies, many backed by private equity and carrying high leverage.

As AI tools begin to erode traditional SaaS (Software as a Service) business models, the cash flows of these highly indebted companies are under unprecedented pressure. Market concerns are that if these companies cannot adapt quickly during the technological shift, they will become the first “sacrifices” in this wave of innovation—and ultimately, the creditors holding these massive debts will bear the cost.