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Stock markets have been quick to punish software companies and other perceived losers in the artificial intelligence boom in recent weeks, but credit markets are likely to be the next place to see AI disruption risks, the magazine said. UBS Analyst Matthew Misch.
Misch said in a research note on Wednesday that companies, particularly private equity-owned software and data services companies, are being squeezed by the AI threat, with tens of billions of dollars in corporate loans likely to default over the next year.
“We’re pricing in some of the so-called rapid and aggressive disruption scenarios,” Misch, head of credit strategy at UBS, said in an interview on CNBC.
The UBS analyst said he and his colleagues are rushing to update their forecasts for this year and beyond, as the latest models from Anthropic and OpenAI accelerate expectations for the arrival of AI disruption.
“The market was slow to react because we really didn’t expect things to happen this quickly,” Misch said. “People need to recalibrate the whole way they assess credit for this disruption risk, because this is not a ’27 or ’28 issue.”
Investor concerns around AI boiled over this month as the market moved from viewing the technology as a rising tide story for tech companies to a winner-take-all picture where Anthropic, OpenAI and others threaten incumbents. Software companies were the first to be hit hardest, but a series of stock declines hit sectors as disparate as finance, real estate and trucking.
In a note, Misch and other UBS analysts laid out a base case of new defaults by leveraged loan and private credit borrowers totaling between $75 billion and $120 billion by the end of this year.
CNBC calculated these numbers using Misch’s estimates that leveraged loan and private credit defaults will increase by up to 2.5% and up to 4%, respectively, by the end of 2026. He estimates the size of these markets to be between $1.5 trillion and $2 trillion.
“Credit contraction”?
But Misch also highlighted the possibility of a more abrupt and painful transition to AI, saying default rates would jump twice as high as his baseline estimates and cut off financing for many companies. This scenario is known in Wall Street jargon as “tail risk.”
“The ripple effect will be a credit crunch in the lending market,” he said. “The price of leveraged credit is going to change significantly, and credit is going to cause a shock to the system.”
Analysts at UBS say the risks are elevated, but will depend on the timing of AI adoption by large companies, the pace of improvement of AI models, and other uncertain factors.
“We are not looking for a tail risk scenario yet, but we are moving in that direction,” he said.
Leveraged loans and private credit are generally considered a riskier part of corporate credit because they often lend to below-investment-grade companies, many of which are backed by private equity and carry large amounts of debt.
According to Misch, when it comes to the AI industry, companies fall into three broad categories. The first is the creators of fundamental large-scale language models such as Anthropic and OpenAI, which are startups but could soon become large public companies.
The second is an investment grade software company such as: sales force and adobe Companies with strong balance sheets and the ability to implement AI to fend off challengers.
The final category is a group of private equity-owned software and data services companies with relatively high debt burdens.
“The winners of this whole transformation, if it’s going to be rapid and very disruptive or profound (change), as we increasingly believe, the winners are least likely to come from that third bucket,” Misch said.
