AI disruption could hit credit markets next, UBS analyst says
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The stock market has been quick to punish software firms and other losers of the AI boom in recent weeks, but credit markets will likely be the next place where the risk of AI disruption emerges. UBS analyst Matthew Mish.
Tens of billions of dollars in corporate loans are likely to default next year as companies, especially private equity-owned software and data services companies, are squeezed by the threat of artificial intelligence, Mish said in a research note on Wednesday.
“We’re pricing in some of what we call a rapid, aggressive disruption scenario,” Mish, UBS’s head of credit strategy, said in an interview with 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 they didn’t really think it was going to happen this quickly,” Mish said. “People have to completely recalibrate their perspective on evaluating credit for this disruption risk because this is not a ’27 or ’28 problem.”
Investor concerns about AI have grown louder this month as the market shifts from seeing the technology as a rising tide story for tech companies to a winner-take-all dynamic where Anthropic, OpenAI and others threaten incumbents. Software companies were hit first and hardest, but the cascading selloffs also affected industries as diverse as finance, real estate and trucking.
In their notes, Mish and other UBS analysts lay out a base case scenario in which leveraged loans and private loan borrowers would see new defaults totaling $75 billion to $120 billion by the end of this year.
CNBC calculated these figures using Mish’s estimates of up to 2.5% and 4% increases in defaults on leveraged loans and private loans, respectively, by the end of 2026. These are markets that he estimates are between $1.5 trillion and $2 trillion in size.
‘Credit crisis’?
But Mish also highlighted the possibility of a more sudden and painful AI transition, in which defaults would rise by twice the baseline forecasts, cutting off financing for many companies. The scenario is what is known in Wall Street jargon as “tail risk.”
“The knock-on effect of this situation will be that you will have a credit crunch in the credit markets,” he said. “You will be subject to a broad repricing of leveraged credit and you will experience system shock from the credit.”
While risks are increasing, the timing of AI adoption by large companies will be governed by the pace of AI model improvements and other uncertain factors, according to the UBS analyst.
“We’re not calling for that tail risk scenario yet, but we’re moving in that direction,” he said.
Leveraged loans and private loans are generally considered among the riskier corners of corporate credit because they finance companies that are often below investment grade, many backed by private equity, and carry higher levels of debt.
According to Mish, when it comes to the AI business, companies can be divided into three broad categories: The first are startups but may soon become large, publicly traded companies, the creators of core big language models like Anthropic and OpenAI.
The second is software companies for investment purposes. sales force And Adobe Companies that have solid balance sheets and can apply AI to fend off competitors.
The final category is a group of private equity-owned software and data services companies with relatively high levels of debt.
“The winners of this whole transformation are if it really turns out to be rapid, very disruptive, or as serious as we increasingly believe.” [change] Mish said the winners were unlikely to come from the third group.
