AI Disruption Looms Over Credit Markets, Warns UBS Analyst

The stock market has rapidly penalized software firms and other companies perceived as vulnerable to the artificial intelligence boom in recent weeks. However, credit markets are likely to be the next arena where the disruptive impact of AI becomes evident, according to UBS analyst Matthew Mish. Tens of billions of dollars in corporate loans are at risk of defaulting over the next year, particularly among software and data services companies owned by private equity, as they face increasing pressure from AI advancements, Mish stated in a recent research note.

"We’re pricing in part of what we call a rapid, aggressive disruption scenario," Mish, UBS head of credit strategy, told CNBC in an interview. He explained that he and his colleagues have accelerated their forecasts for the current year and beyond due to the rapid advancements in AI models from companies like Anthropic and OpenAI, which have expedited expectations for AI-driven disruption.

"The market has been slow to react because they didn’t really think it was going to happen this fast," Mish observed. "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."

Investor apprehension surrounding AI intensified this month, as the market’s perception shifted from viewing the technology as a broad uplift for technology companies to a more pronounced "winner-take-all" dynamic. In this evolving landscape, companies like Anthropic and OpenAI are increasingly seen as posing a threat to established market players. Software firms were among the first and most significantly impacted, but a series of sell-offs has since spread to diverse sectors, including finance, real estate, and trucking, illustrating the pervasive nature of these AI-driven fears.

In their note, Mish and other UBS analysts outlined a baseline scenario projecting a combined default of $75 billion to $120 billion in leveraged loans and private credit by the end of the year. These figures were derived from Mish’s estimates of potential default increases of up to 2.5% for leveraged loans and up to 4% for private credit by late 2026. These markets are estimated to be valued at $1.5 trillion and $2 trillion, respectively.

‘Credit Crunch’ Concerns Rise

Mish also highlighted the possibility of a more abrupt and severe AI transition, where defaults could double his baseline assumptions, potentially leading to funding cutoffs for numerous companies. This scenario, referred to on Wall Street as "tail risk," could trigger a significant credit crunch in loan markets, a broad repricing of leveraged credit, and a systemic shock originating from the credit sector.

While acknowledging the escalating risks, Mish emphasized that the actual impact will be contingent on several uncertain factors, including the pace of AI adoption by large corporations and the speed of AI model improvements. "We’re not yet calling for that tail-risk scenario, but we are moving in that direction," he stated.

Leveraged loans and private credit are generally categorized as riskier segments of corporate credit. These markets often finance companies with below-investment-grade ratings, many of which are backed by private equity and carry substantial debt burdens.

Mish categorizes companies in relation to the AI trade into three broad groups. The first comprises creators of foundational large language models, such as Anthropic and OpenAI. While currently startups, these entities could rapidly evolve into major publicly traded corporations. The second category includes investment-grade software firms like Salesforce and Adobe, which possess strong balance sheets and the capacity to leverage AI to defend against emerging competitive threats. The third and most vulnerable cohort consists of private equity-owned software and data services companies burdened by relatively high debt levels.

"The winners of this entire transformation – if it really becomes, as we’re increasingly believing, a rapid and very disruptive or severe [change] – the winners are least likely to come from that third bucket," Mish concluded.