The stock market has reacted swiftly to the artificial intelligence revolution, quickly penalizing software firms and other companies perceived as vulnerable to disruption. However, the credit markets may soon be the next arena where the impact of AI-driven changes becomes evident, according to UBS analyst Matthew Mish.
In a recent research note, Mish projected that tens of billions of dollars in corporate loans could face default within the next year. This heightened risk, he explained, is particularly concentrated among software and data services companies owned by private equity firms, which he believes are most exposed to the disruptive potential of AI.
“We are factoring in a portion of what we term a rapid, aggressive disruption scenario,” Mish, who heads credit strategy at UBS, told CNBC. He indicated that recent advancements from leading AI developers such as Anthropic and OpenAI have accelerated expectations for AI’s widespread impact, prompting a quicker recalibration of forecasts.
“The market has been slow to adapt because they didn’t anticipate the speed of this transformation,” Mish noted. “There’s a necessity to re-evaluate how credit risk is assessed in light of this impending disruption, as it’s no longer a concern for the distant future, say 2027 or 2028.”
Investor sentiment surrounding AI has intensified this month, shifting from a narrative of broad technological uplift to a more pronounced “winner-take-all” dynamic. This suggests that emerging AI leaders like Anthropic and OpenAI could pose a significant threat to established players. While software companies bore the initial brunt of this market reassessment, a cascade of sell-offs has subsequently affected diverse sectors, including finance, real estate, and even trucking.
Mish and his colleagues at UBS outlined a baseline scenario predicting between $75 billion and $120 billion in new defaults across leveraged loans and private credit markets by the end of 2026. These figures are based on Mish’s estimates of potential default rate increases of up to 2.5% for leveraged loans and 4% for private credit, markets he estimates to be valued at $1.5 trillion and $2 trillion, respectively.
### A Potential “Credit Crunch”?
Beyond the baseline, Mish also highlighted the possibility of a more abrupt and severe AI-induced transition. In such a “tail risk” scenario, defaults could potentially double his base estimates, leading to a significant contraction in funding availability for numerous companies.
“The resulting contagion could lead to a credit crunch in loan markets,” he explained. “This would trigger a broad repricing of leveraged credit and a systemic shock originating from the credit sector.”
The actualization of these risks, Mish cautioned, will depend on a confluence of factors, including the pace of AI adoption by major corporations, the continued evolution of AI models, and other unpredictable variables. “While we are not currently forecasting this tail-risk scenario, we are observing trends that move in that direction,” he stated.
Leveraged loans and private credit are generally recognized as occupying the more volatile segments of the corporate credit landscape. These markets frequently provide financing to companies with below-investment-grade ratings, often those supported by private equity sponsors and carrying substantial debt burdens.
Mish categorizes companies’ positioning within the AI transition into three main groups. The first comprises the developers of foundational large language models, such as Anthropic and OpenAI. Although currently startups, these entities possess the potential to rapidly scale into significant publicly traded corporations.
The second group includes established investment-grade software firms like Salesforce and Adobe. These companies are characterized by robust balance sheets and possess the capacity to integrate AI technologies to fortify their competitive positions against emerging threats.
The final cohort consists of software and data services companies, predominantly owned by private equity, which typically carry higher debt loads. “The ultimate beneficiaries of this entire transformation,” Mish posited, “if it indeed unfolds as a rapid and profoundly disruptive shift, are least likely to emerge from this third category.”
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