Curated and analyzed by the JobGoneToAI team. Original reporting by CNBC.
AI Disruption Expected to Cause Up to $120 Billion in Corporate Loan Defaults, UBS Analyst Warns

— CNBC
Key Takeaway
UBS analyst Matthew Mish warns that the rapid adoption of AI could lead to significant defaults in corporate loans, particularly affecting software and data services firms. He estimates that defaults could reach between $75 billion to $120 billion by the end of the year due to the disruptive impact of AI.
From the Original Report
LivestreamMenuMake ItselectUSAINTLLivestreamSearch quotes, news & videosLivestreamWatchlistSIGN INCreate free accountMarketsBusinessInvestingTechPoliticsVideoWatchlistInvesting ClubPROLivestreamMenu The stock market has been quick to punish software firms and other perceived losers from the artificial intelligence boom in recent weeks, but credit markets are likely to be the next place where AI disruption risk shows up, according to UBS analyst Matthew Mish. Tens of billions of dollars in corporate loans are likely to default over the next year as companies, especially software and data services firms owned by private equity, get squeezed by the AI threat, Mish said in a Wednesday research note. "We're pricing in part of what we call a rapid, aggressive disruption scenario," Mish, UBS head of credit strategy, told CNBC in an interview. The UBS analyst said he and his colleagues have rushed to update their forecasts for this year and beyond because the latest models from Anthropic and OpenAI have sped up expectations of the arrival of AI disruption. "The market has been slow to react because they didn't really think it was going to happen this fast," Mish 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." Investor concerns around AI boiled over this month as the market shifted from viewing the technology as a rising tide story for technology companies to more of a winner-take-all dynamic where Anthropic, OpenAI and others threaten incumbents. Software firms were hit first and hardest, but a rolling series of sell-offs hit sectors as disparate as finance, real estate and trucking. In his note, Mish and other UBS analysts lay out a baseline scenario in which borrowers of leveraged loans and private credit see a combined $75 billion to $120 billion in fresh defaults by the end of this year. CNBC calculated those figures by using Mish's estimates for increases of up to 2.5% and up to 4% in defaults for leveraged loans and private credit, respectively, by late 2026. Those are markets which he estimates to be $1.5 trillion and $2 trillion in size. But Mish also highlighted the possibility of a more sudden, painful AI transition in which defaults jump by twice the estimates for his base assumption, cutting off funding for many companies, he said. The scenario is what's known in Wall Street jargon as a "tail risk." "The knock-on effect will be that you will have a credit crunch in loan markets," he said. "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 rising, they will be governed by the timing of AI adoption by large corporations, the pace of AI model improvements and other uncertain factors, according to the UBS analyst. "We're not yet calling for that tail-risk scenario, but we are moving in that direction," he said. Leveraged loans and private credit are generally considered among the riskier corners of corporate credit, since they often finance below-investment-grade companies, many of them backed by private equity and carrying higher levels of debt. When it comes to the AI trade, companies can be placed into three broad categories, according to Mish: The first are creators of the foundational large language models such as Anthropic and OpenAI, which are startups but could soon be large, publicly traded companies. The second are investment-grade software firms like Salesforce and Adobe that have robust balance sheets and can implement AI to fend off challengers. The last category is the cohort of private equity-owned software and data services companies with relatively high levels of debt. "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. Got a confidential news tip? We want to hear from you. Sign up for free newsletters and get more CNBC delivered to your inbox Get this delivered to your inbox, and more info about our products and services. © 2026 Versant Media, LLC. All Rights Reserved. A Versant Media Company. Data is a real-time snapshot *Data is delayed at least 15 minutes.
Global Business and Financial News, Stock Quotes, and Market Data
This is an excerpt. Read the full article at CNBC.
Original Source
Read original reporting at CNBCJobGoneToAI curates, verifies, and adds original analysis to third-party reporting. We link to the original source so you can verify the facts yourself.
Related Stories
AI Eating Itself: How AI Companies Cut Costs Using Their Own Tools
Meta, Anthropic, and other AI companies are using their own AI tools to automate internal operations and eliminate jobs. The irony: AI builders cutting costs by replacing their own workers.
The Skills Gap Widening: AI Specialists in Demand, Adjacent Roles Disappearing
While tech companies cut 50,000+ jobs, AI specialists remain in desperate demand with a 3.2:1 shortage ratio. But training programs can't keep up, creating a widening skills chasm between AI experts and everyone else.
Q1 2026: 39,000+ Tech Jobs Lost in 3 Months
An unprecedented 39,000 to 51,000 tech jobs were eliminated in Q1 2026. Our data-driven analysis breaks down the geography, companies, and job functions hit hardest by this wave of AI-driven layoffs.