Credit markets are emerging as the next major arena for artificial intelligence disruption, with UBS credit strategy head Matthew Mish warning that tens of billions of dollars in leveraged loans and private credit could default over the next year.
This is because software, data services, and other AI-vulnerable companies—particularly those owned by private equity—face intensifying margin compression and revenue erosion.
In a detailed research note released Wednesday and in a subsequent CNBC interview, Mish and his UBS team laid out a baseline scenario projecting $75 billion to $120 billion in additional defaults by the end of 2026 across leveraged loan and private credit markets, which they estimate at roughly $1.5 trillion and $2 trillion in size, respectively.
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The forecast assumes default rates rising by up to 2.5% for leveraged loans and up to 4% for private credit—meaningful increases from current levels that are already showing signs of stress. Mish described the acceleration of AI disruption as the primary driver behind the revised outlook.
“The market has been slow to react because they didn’t really think it was going to happen this fast,” he said. “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.”
The shift in perception was catalyzed by recent model releases from Anthropic and OpenAI that demonstrated advanced reasoning, tool integration, and task automation capabilities—directly threatening routine knowledge work, data processing, and professional services that underpin many leveraged software and data firms.
Mish categorized companies into three broad groups in the AI landscape:
- Foundational model creators — Startups like Anthropic and OpenAI (soon potentially large public companies) that develop frontier large language models and stand to capture significant value at the top of the stack.
- Investment-grade software incumbents — Companies like Salesforce and Adobe with strong balance sheets, established customer bases, and the ability to rapidly integrate AI to defend their moats.
- Private equity–owned software and data services firms — Highly leveraged companies carrying substantial debt, often acquired in large buyouts during the low-rate era. These firms, according to Mish, are the least likely to emerge as long-term winners in a rapid, disruptive AI transition.
“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 said.
He also outlined a more severe “tail risk” scenario in which defaults could double the baseline estimates, triggering a “credit crunch” in loan markets, broad repricing of leveraged credit, and systemic shocks. While UBS is not yet calling for this tail scenario, Mish noted the firm is “moving in that direction” as AI model capabilities advance faster than anticipated.
The warning follows a rolling series of selloffs that began with software stocks earlier this month and spread to finance, real estate, trucking, and other sectors perceived as vulnerable to AI automation. The rapid pace of disruption—accelerated by Anthropic’s Claude plug-ins and OpenAI’s tool integrations—has forced investors to reprice credit risk far sooner than the previously expected 2027–2028 timeline.
Private equity–owned software and data firms are particularly exposed. Many were acquired at peak valuations during the low-rate environment of 2020–2022, loaded with leverage, and reliant on recurring revenue from maintenance contracts, legacy system support, and routine analytics—precisely the areas most susceptible to AI automation.
As AI agents handle contract reviews, compliance checks, data extraction, and report generation, pricing power erodes, and customer churn accelerates. Mish emphasized that timing remains the key uncertainty: the pace of large corporate AI adoption, the rate of model improvement, and the ability of incumbents to adapt will determine how quickly credit risk materializes.
“We’re pricing in part of what we call a rapid, aggressive disruption scenario,” The UBS noted, suggesting that the direction is clear.
The UBS note adds to a growing chorus of warnings from credit strategists and analysts. JPMorgan and Morgan Stanley have also flagged rising default risks in private credit and leveraged loan markets, particularly among software and professional services borrowers. Moody’s and S&P Global Ratings have placed numerous private equity–backed software companies on negative watch lists in recent weeks, citing AI disruption as a key risk factor alongside elevated interest rates and slowing organic growth.
The broader leveraged loan and private credit markets—estimated at $3.5 trillion combined—are already showing stress. Secondary loan prices have declined steadily since mid-2025, and spreads have widened significantly for lower-rated issuers. Private credit funds, which stepped in to fill gaps left by retreating banks, now face their own maturity walls and refinancing challenges as portfolio companies struggle to grow in an AI-disrupted environment.
For now, investment-grade software firms with strong balance sheets and clear AI integration strategies (e.g., Salesforce, Adobe, ServiceNow) are seen as more resilient. However, the middle and lower tiers, especially private equity portfolio companies, face the highest risk of default and restructuring.
As the market recalibrates for faster AI disruption, credit investors are increasingly differentiating between AI winners and losers. Companies that can demonstrate defensible moats, rapid AI adoption, and strong balance sheets are likely to see credit spreads tighten, while those slow to adapt or burdened by leverage could face sharp repricing and distress.
Analysts expect some changes in the coming quarters. If large enterprises accelerate AI deployment and begin replacing traditional software and services contracts, default rates could rise quickly. If adoption lags or incumbents successfully integrate AI to defend their positions, the credit impact may be more contained.