UBS Expert: AI Disruption Risks $120B Credit Defaults

The rapid evolution of artificial intelligence is surpassing the expectations of numerous industry observers, positioning it as a potential catalyst for significant turbulence within credit markets, as highlighted by UBS analyst Matthew Mish.

According to Mish, the sectors of leveraged loans and private credit, which together represent a substantial market value of $3.5 trillion, are particularly vulnerable to an impending surge in defaults. He projects that between $75 billion and $120 billion in fresh defaults could materialize by the close of 2026. Among the enterprises facing the greatest peril are those in the software and data services industries owned by private equity firms, which are grappling with substantial challenges in adapting to the transformative disruptions driven by artificial intelligence technologies.

In an interview with CNBC, Mish emphasized, “The pace of this development caught the market completely off guard.” He further advised that investors must fundamentally reassess their methodologies for evaluating credit risks amid this accelerated technological transformation.

Recent downturns observed in software stock valuations have already begun to underscore the initial phases of AI-induced market disruptions. Mish cautions that should the adoption of these AI technologies accelerate beyond current projections, the volume of defaults could escalate dramatically, possibly precipitating a more extensive credit market contraction. This scenario, he warns, could deliver a severe jolt to credit markets, compelling adjustments in the pricing dynamics of leveraged loans and exerting considerable strain on corporations burdened with high levels of debt.

Mish categorizes companies into three distinct cohorts based on their positioning relative to the AI revolution: those pioneering the development of AI models, such as OpenAI and Anthropic; established software powerhouses like Salesforce and Adobe that are well-equipped to leverage these advancements; and the third group comprising private equity-backed firms laden with substantial debt. The initial two categories stand to gain significant advantages from AI integration, whereas the latter group confronts the most acute vulnerabilities.

Although the precise timing and magnitude of AI’s impact remain uncertain, Mish asserts that the swift proliferation of these technologies holds the potential to destabilize not only individual technology enterprises but also the broader architecture of the financial system. Consequently, investors and lending institutions will likely need to overhaul their risk management frameworks to navigate the evolving terrain effectively.

This analysis draws attention to the unforeseen velocity at which artificial intelligence is infiltrating various sectors, particularly those reliant on legacy software infrastructures. The $3.5 trillion in leveraged loans and private credit represents a colossal pool of capital that has been extended under assumptions of stable growth trajectories, assumptions now potentially invalidated by AI’s disruptive force.

Private equity-owned software and data services companies, often characterized by leveraged buyouts and aggressive debt financing, may find themselves unable to pivot quickly enough. Their business models, predicated on predictable revenue streams from traditional software licensing and services, could erode rapidly as AI automates routine tasks, enhances data processing capabilities, and introduces novel competitive paradigms.

Mish’s projection of $75 billion to $120 billion in defaults by 2026 underscores a scenario where a significant portion of these debt-laden entities fail to generate sufficient cash flows to service their obligations. This could manifest through missed interest payments, covenant breaches, or outright insolvencies, triggering a domino effect across interconnected credit portfolios held by banks, institutional investors, and alternative asset managers.

The market’s initial oversight of this pace, as Mish noted, stems from a historical underestimation of technological inflection points. Investors accustomed to gradual digital transformations were unprepared for AI’s exponential progress, fueled by breakthroughs in large language models, generative capabilities, and scalable computing infrastructures.

Sell-offs in software stocks serve as an early warning indicator. Companies perceived as slower to integrate AI or vulnerable to obsolescence have experienced sharp declines in market capitalization, reflecting investor flight toward AI-native or AI-adaptive leaders. This shift in equity valuations often precedes credit deterioration, as reduced enterprise values impair collateral positions and heighten default probabilities.

Should AI adoption outpace expectations—driven perhaps by further reductions in computational costs, regulatory green lights, or enterprise-wide implementations—the default wave could intensify. Mish envisions leveraged loan prices compressing as spreads widen to compensate for elevated risks, while private credit funds face redemption pressures and liquidity squeezes.

Heavily indebted companies, particularly those in the private equity sphere, would bear the brunt. These firms, often acquired at premium valuations during lower interest rate environments, now confront refinancing challenges amid higher borrowing costs and disrupted cash flows. AI’s capacity to commoditize software functionalities exacerbates this, rendering proprietary solutions less defensible.

Mish’s tripartite classification provides a strategic lens for stakeholders. AI model creators like OpenAI and Anthropic are at the vanguard, monetizing foundational technologies through APIs, partnerships, and enterprise licenses. Their growth trajectories position them as net beneficiaries, potentially attracting fresh capital to fuel expansion.

Robust software incumbents such as Salesforce and Adobe exemplify adaptive resilience. These entities are embedding AI into core products—think AI-powered CRM enhancements or generative design tools—thereby safeguarding revenue bases while unlocking new monetization avenues. Their balance sheets, typically less encumbered by private equity leverage, afford maneuverability.

Conversely, debt-heavy private equity-owned firms languish in peril. Many operate in niches like enterprise resource planning, data analytics, or vertical-specific applications, where AI promises superior alternatives at lower costs. Inability to retrain workforces, rearchitect systems, or secure AI partnerships could precipitate revenue cliffs and covenant violations.

The uncertainty surrounding AI’s rollout timeline introduces additional complexity. Optimistic scenarios posit seamless integration with minimal disruptions; pessimistic outlooks foresee chaotic transitions akin to past tech shifts like cloud computing or mobile adoption. Mish leans toward the latter, advocating proactive risk recalibration.

Beyond individual firms, systemic reverberations loom. Credit markets underpin broader economic activity, funding expansions, acquisitions, and operational liquidity. A cascade of defaults could impair financial institutions’ balance sheets, curtailing lending capacity and amplifying recessionary pressures.

Investors must accordingly refine underwriting criteria, incorporating AI exposure metrics, adaptability scores, and scenario analyses. Lenders might demand enhanced covenants, equity cures, or AI transition plans. Portfolio diversification toward AI beneficiaries becomes imperative.

Mish’s commentary arrives at a pivotal juncture, as central banks navigate inflation battles and AI hype permeates boardrooms. Regulators, too, may scrutinize private credit’s opacity, potentially imposing disclosure mandates or stress tests attuned to technological risks.

Ultimately, this UBS perspective illuminates AI not merely as an efficiency booster but as a credit cycle disruptor. Stakeholders ignoring this dynamic risk obsolescence; those adapting proactively stand to capitalize on the forthcoming paradigm shift. As Mish implores, rethinking credit evaluation under rapid technological flux is no longer optional but essential for survival in the AI era.