AI Disruption Threatens Credit Markets: UBS Analyst Warns of Billions in Defaults

The stock market has swiftly penalized software firms and other companies perceived as vulnerable to the artificial intelligence (AI) boom in recent weeks. However, credit markets are likely to be the next arena where the disruptive impact of AI becomes evident, according to Matthew Mish, an analyst at UBS. Mish, who leads credit strategy at UBS, indicated in a research note that tens of billions of dollars in corporate loans are poised for default over the coming year, particularly affecting software and data services companies owned by private equity, as they face increasing pressure from AI advancements.

Mish explained that UBS has incorporated a "rapid, aggressive disruption scenario" into its financial modeling. This recalibration of forecasts for the current year and beyond has been prompted by the accelerated timelines for AI disruption, influenced by the latest models from leading AI developers like Anthropic and OpenAI. The market, Mish observed, has been slow to react because the rapid pace of these developments was not widely anticipated. Consequently, investors and analysts are now compelled to fundamentally reassess how they evaluate credit risk in the face of this evolving AI landscape, recognizing that it is no longer a distant concern for the late 2020s.

Investor apprehension regarding AI escalated this month. The market’s perception has shifted from viewing AI as a general uplift for technology companies to a more pronounced "winner-take-all" dynamic. This shift highlights the potential for AI leaders like Anthropic and OpenAI to challenge and displace established industry players. Software firms bore the initial brunt of this market sentiment, but a cascading series of sell-offs has since impacted diverse sectors, including finance, real estate, and the trucking industry.

In their detailed analysis, Mish and his UBS colleagues project a baseline scenario where borrowers in the leveraged loan and private credit markets could face a combined default range of $75 billion to $120 billion by the end of 2026. These figures were derived from Mish’s estimations of potential increases in default rates, up to 2.5% for leveraged loans and up to 4% for private credit, within markets he estimates to be valued at $1.5 trillion and $2 trillion, respectively.

The prospect of a "credit crunch" looms as a potential consequence of this AI-driven disruption. Mish highlighted the possibility of a more abrupt and severe AI transition, which could lead to defaults doubling his base assumptions. Such a scenario would likely result in funding being cut off for numerous companies, creating a ripple effect across the financial system. Mish described this as a "tail risk," a low-probability, high-impact event. The cascading effect, he warned, would lead to a credit crunch in loan markets, a broad repricing of leveraged credit, and a significant shock to the financial system originating from the credit sector.

While the risks are escalating, their ultimate manifestation will be contingent on several uncertain factors. These include the pace of AI adoption by large corporations, the speed of AI model advancements, and other evolving technological and market dynamics. Mish clarified that UBS is not yet forecasting this extreme tail-risk scenario but acknowledges that the market is moving in that direction.

Leveraged loans and private credit are generally considered to be among the riskier segments of the corporate credit market. This is often due to their financing of companies that fall below investment-grade ratings, many of which are backed by private equity firms and carry substantial debt burdens.

Mish has categorized companies into three broad groups in relation to the AI trade. The first group comprises the creators of foundational large language models, such as Anthropic and OpenAI. Although currently startups, these entities have the potential to evolve into major publicly traded companies. The second category includes investment-grade software firms like Salesforce and Adobe. These companies possess strong balance sheets and the capacity to integrate AI technologies to maintain their competitive edge and fend off emerging threats. The third and most vulnerable group consists of software and data services companies owned by private equity firms, which typically operate with higher levels of debt.

Mish concluded that if the AI transformation proves to be as rapid and severely disruptive as increasingly believed, the ultimate winners are least likely to emerge from this third category of highly leveraged, private equity-backed companies. The implications for these firms and the broader credit markets underscore the significant and evolving impact of artificial intelligence on the global economy.