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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.
‘Credit crunch’?
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.
