AI disruption could hit credit markets next, UBS analyst says

The stock market has been quick to punish software companies and other losers of the AI ​​boom in recent weeks, but credit markets are likely to be the next place where risks of AI disruption emerge, according to UPS Analyst Matthew Misch.

Tens of billions of dollars in corporate loans are likely to default over the next year, as companies, especially software and data services companies owned by private equity, come under pressure from the threat of artificial intelligence, Misch said in a research note on Wednesday.

“We are pricing in part of what we call a rapid and aggressive disruption scenario,” Misch, head of credit strategy at UBS, told CNBC in an interview.

The UBS analyst said he and his colleagues were quick to update their forecasts for this year and beyond because the latest models from Anthropic and OpenAI accelerated forecasts of the arrival of AI disruption.

“The market was slow to react because they didn’t really think it would happen that quickly,” Misch said. “People have to recalibrate the whole way they look at credit assessment of this disruption risk, because it’s not a 27 or 28 issue.”

Investor concerns about artificial intelligence have escalated this month as the market has shifted from viewing the technology as a story of a rising tide of tech companies to a winner-take-all dynamic as Anthropic, OpenAI and others threaten incumbents. Software companies were the first and hardest hit, but a continuous series of sales hit sectors as disparate as finance, real estate, and trucking.

In his note, Misch and other UBS analysts laid out a baseline scenario in which leveraged and private credit borrowers see $75 billion to $120 billion of new defaults by the end of this year.

CNBC calculated these numbers using Misch’s estimates of increases of up to 2.5% and up to 4% in defaults for leveraged loans and private credit, respectively, by late 2026. These are markets estimated to be $1.5 trillion and $2 trillion in size.

But Misch also highlighted the potential for a sudden and painful turnaround in AI, with defaults jumping by twice the estimates of his underlying assumption, cutting off financing for many companies, he said. This scenario is what is known in Wall Street parlance as “tail risk.”

“The indirect effect will be that you will have a credit crunch in the loan markets,” he said. “You’re going to have a massive repricing of leveraged credit, and you’re going to have a shock to the system coming from credit.”

While risks are rising, they will be governed by the timing of AI adoption by large companies, the pace of AI model improvements and other uncertain factors, according to the UBS analyst.

“We are not yet calling for a risk scenario, but we are moving in that direction,” he said.

Leveraged loans and private credit are generally considered among the riskier angles of corporate credit, because they often finance below-investment-grade companies, many of which are backed by private equity and carry higher levels of debt.

When it comes to AI trading, companies can be classified into three broad categories, according to Misch: The first category is the builders of large, foundational language models like Anthropic and OpenAI, which are startups but may soon become large, publicly traded companies.

The second is investment grade software companies such as Sales force and Adobe Which have strong balance sheets and can apply artificial intelligence to fend off competitors.

The final category is a group of software and data services companies owned by private equity that have relatively high levels of debt.

“The winners of this whole transformation – if it really becomes, as we increasingly believe, a very rapid and destructive or extreme transformation… [change] “The winners are unlikely to come from that third bucket,” Misch said.