AI disruption could spark a ‘shock to the system’ in credit markets, UBS analyst says

The stock market has recently reacted swiftly against software companies and others viewed as vulnerable amidst the artificial intelligence (AI) surge. However, UBS analyst Matthew Mish indicates that the credit markets could soon feel the impact of AI disruptions. He forecasts that tens of billions in corporate loans may face defaults over the coming year, particularly affecting software and data services firms under private equity ownership, as they navigate the mounting AI threats. In a research note released on Wednesday, Mish highlighted the urgency with which his team has revised their forecasts for this year and the future, citing rapid advancements from AI models developed by Anthropic and OpenAI. "We're pricing in part of what we call a rapid, aggressive disruption scenario," Mish stated in an interview with CNBC. He observed that the market has been slow to adapt, as many did not anticipate this level of disruption occurring so quickly. Investor anxiety surrounding AI intensified this month as the narrative shifted. The technology, once seen as a rising tide benefiting all tech companies, is now perceived to favor a select few players, with Anthropic and OpenAI posing significant threats to established firms. This shift has led to a series of sell-offs across various sectors, including finance, real estate, and trucking, with software companies facing the brunt of the downturn. Mish and his colleagues at UBS outlined a fundamental scenario where borrowers of leveraged loans and private credit could experience a combined $75 billion to $120 billion in new defaults by the end of this year. These estimates were derived from Mish's projections, suggesting increases in defaults of up to 2.5% for leveraged loans and up to 4% for private credit by late 2026, impacting markets valued at $1.5 trillion and $2 trillion, respectively. Moreover, Mish pointed to the potential for a more abrupt and severe transition driven by AI, where defaults could double beyond his baseline estimates, leading to funding shortages for numerous companies. This situation is often referred to in Wall Street terminology as a "tail risk." He warned, "The knock-on effect will be that you will have a credit crunch in loan markets. You will have a broad repricing of leveraged credit, and you will have a shock to the system coming from credit." While the risks are escalating, Mish emphasized that their manifestation will depend on the timing of AI adoption by large corporations, the pace of AI model advancements, and other unpredictable elements. "We're not yet calling for that tail-risk scenario, but we are moving in that direction," he noted. Leveraged loans and private credit are typically seen as riskier segments of corporate credit, often financing below-investment-grade firms, many of which are backed by private equity and carry higher debt levels. Mish categorizes companies within the AI landscape into three main groups: first, the creators of foundational large language models like Anthropic and OpenAI, which are emerging as powerful entities; second, investment-grade software firms such as Salesforce and Adobe, which possess strong balance sheets and can leverage AI to stay competitive; and finally, private equity-owned software and data services firms burdened with significant debt. "The winners of this entire transformation — if it indeed unfolds as a rapid and disruptive change — are least likely to emerge from that third category," Mish concluded.