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The stock market has been quick to punish software companies and other perceived losers from the artificial intelligence boom in recent weeks, but credit markets will likely be the next place the risk of AI disruption appears, according to UBS analyst Matthew Mish.
Tens of billions of dollars in business loans are at risk of defaulting over the next year as companies, particularly software and data services companies owned by private equity funds, are crushed by the threat of AI, Mish said in a research note released Wednesday.
“Part of what we’re assessing is what we call a rapid and aggressive disruption scenario,” Mish, UBS’s head of credit strategy, told CNBC in an interview.
The UBS analyst said he and his colleagues were quick to update their forecasts for this year and beyond as the latest models from Anthropic and OpenAI have accelerated expectations that AI disruption is coming.
“The market was slow to react because they didn’t really think it was going to happen that quickly,” Mish said. “People need to recalibrate their entire way of assessing credit for this disruption risk, because this is not a 2027 or 28 problem.”
Investor concerns around AI have gained momentum this month as the market has shifted from viewing the technology as a rising tide for tech companies to a “win-win” dynamic in which Anthropic, OpenAI and others threaten incumbents. Software companies were hit first and hardest, but a continuing series of selloffs affected industries as disparate as finance, real estate and trucking.
In his note, Mish and other UBS analysts lay out a base case scenario in which borrowers of leveraged loans and private credit would see a total of $75 billion to $120 billion in new defaults by the end of this year.
CNBC calculated these numbers using Mish’s estimates of increases of up to 2.5% and up to 4%, respectively, in defaults for leveraged loans and private credit by the end of 2026. These are markets he estimates to be worth $1.5 trillion and $2 trillion.
“Credit crisis”?
But Mish also highlighted the possibility of a more sudden and painful AI transition, in which defaults would increase twice as much as his base case estimates, cutting off funding for many companies, he said. The scenario is what is known in Wall Street jargon as “extreme risk.”
“The knock-on effect will be a credit crunch in the lending markets,” he said. “You will have a large revaluation of leveraged credit, and you will have a shock to the system from credit.”
Even as risks increase, they will be governed by the timing of AI adoption by large companies, the pace of improvements in AI models and other uncertain factors, according to the UBS analyst.
“We’re not looking at that extreme risk scenario yet, but we’re moving in that direction,” he said.
Leveraged loans and private credit are generally considered among the riskiest aspects of business credit because they often finance lower-quality companies, many of which are backed by private capital and carry higher levels of debt.
When it comes to the AI business, companies can be classified into three broad categories, according to Mish: The first are the creators of the large, foundational language models such as Anthropic and OpenAI, which are startups but could soon become large, publicly traded companies.
The latter are investment software companies like Sales force And Adobe who have strong balance sheets and can implement AI to fend off challengers.
The final category is the cohort of software and data services private equity firms with relatively high debt levels.
“The winners of this whole transformation — if it actually becomes, as we increasingly believe, a rapid and very disruptive or serious transformation.” [change] – winners are least likely to come from this third group,” Mish said.
