UBS Group has sounded a new alarm: the credit market may be the ‘invisible powder keg’ that remains insufficiently priced in the wave of AI disruption.
While Wall Street is still reeling from the sharp decline in software stocks, UBS Group has sounded a new alarm: the credit market may be the ‘invisible powder keg’ that remains insufficiently priced in the wave of AI disruption. As artificial intelligence technology is iterating at a pace far exceeding expectations, those heavily indebted software and data service companies—especially those held by private equity—are teetering on the brink of default.
Matthew Mish, Head of Credit Strategy at UBS, stated bluntly in a research report released on Wednesday (February 12) that the market is repricing for a ‘rapid and aggressive disruption.’ He projected that by the end of next year, there could be an additional $75 billion to $1.2 trillion in defaults in just the leveraged loan and private credit sectors. This estimate is based on UBS’s baseline scenario: a 2.5-percentage-point increase in leveraged loan default rates, representing a scale of approximately $1.5 trillion; and a 4-percentage-point rise in private credit default rates, with a scale of about $2 trillion.
“The market response has been sluggish because they really didn’t expect it to happen so quickly,” Mish said in an interview with CNBC. He pointed out that as companies like Anthropic and OpenAI release their latest models, the market’s timeline expectations for AI disruption have been sharply compressed. “People have to readjust their entire approach to assessing the risk of such disruptions in credit because this isn’t a 2027 or 2028 issue.”
From ‘Growth Stories’ to ‘Life-and-Death Races’
Since the beginning of this month, investors’ narrative logic regarding AI has undergone a fundamental shift: the market no longer views this technology as a universal dividend for all tech companies but rather as a brutal reshuffle where only the winners take all. While software stocks were the first to bear the brunt of the sell-off, panic has quickly spilled over into seemingly unrelated industries such as finance, real estate, and trucking.
Mish emphasized that under the impact of AI, companies can be clearly divided into three tiers:
· Tier One: Creators of foundational large models like Anthropic and OpenAI. These are currently startups but are highly likely to rapidly emerge as the next generation of large public companies.
· Tier Two: Investment-grade software companies like Salesforce and Adobe. They possess robust balance sheets and ample cash flow, enabling them to withstand challengers through rapid AI deployment.
· Tier Three: Software and data service companies owned by private equity. These enterprises generally have high debt levels and heavily rely on traditional business models, making them the most vulnerable to AI disruption.
Tail Risk: What if the credit market ‘freezes’?
In addition to the baseline scenario, UBS Group has also outlined a more painful ‘tail risk’ scenario. Under this situation, default rates would reach twice the baseline estimate, and a large number of companies would lose access to financing.
“The chain reaction would be a credit crunch in the loan market,” Mish described. “You would see a broad repricing of leveraged credit, and credit would have a shock impact on the system.” This scenario is similar to the junk bond sell-off in the energy sector a decade ago or the credit freeze during the bursting of the internet bubble more than two decades ago.
UBS analysts pointed out that although risks are accumulating, the actual trajectory still depends on several key variables: the pace at which large enterprises adopt AI, the speed of improvements in AI models themselves, and the refinancing needs of the market. Currently, approximately 20% of leveraged loans and private credit face refinancing pressures by 2028, meaning risks will continue to build over the next two years.
“We are not yet calling for a tail risk scenario, but we are moving in that direction,” Mish admitted frankly.
Who is paying for the AI revolution?
Notably, the leveraged loans and private credit highlighted in this warning are the riskiest segments in the corporate credit space. They typically provide financing to companies below investment grade, many of which are backed by private equity and carry high leverage.
As AI tools begin to erode the traditional Software-as-a-Service (SaaS) business model, the cash flows of these highly leveraged companies are facing unprecedented pressure. Market participants worry that if these firms fail to adapt quickly in the wave of technological iteration, they will become the first ‘casualties’ of this technological revolution, and ultimately, the credit markets holding their massive debts will bear the cost.
