AI Unease Leads Software Firms to Pause Debt Deals

Software makers are reportedly delaying debt deals amid steeper borrowing costs and tighter lender scrutiny.

This trend is happening amid mounting pressure from artificial intelligence, which has threatened the business model of these software companies, Reuters reported Monday (Feb. 23), citing industry sources.

According to the report, software companies have already paused or held off on fundraising because lenders and investors expect industry upheaval from AI.

“We expect AI disruption risk to be increasingly reflected over 2026 to early 2027, particularly for lower‑quality ​credit sectors with elevated refinancing needs — and more so in the U.S. than in Europe,” Matthew Mish, UBS’ head of credit strategy, told Reuters.

Leveraged loans, particularly for American tech companies, have begun to price modestly higher defaults, the report said. UBS expects defaults to tick up 3% to 5% in a scenario of faster market disruptions, compared with market expectations of a 1% to 2% increase.

“The disruption is going to play out over two years,” Mish said. “We ultimately think that the market will price in a majority, but not all of the defaults that we’re forecasting.”

An example of the way AI appears to be disrupting the software sector played out last week, when Anthropic released a new security feature for its Claude model, and several cybersecurity firms saw their share prices fall.

A report on this trend from Bloomberg News noted that investors are concerned that the ability to “vibe code,” or use AI to write software code, will allow software customers to develop their own applications, weakening demand for legacy products.

But as PYMNTS covered last year, research has found that vibe coding is not about to supplant human software developers anytime sooner.

According to that report, researchers have shown that agentic AI models like Claude operated best when developers reviewed outputs after once key checkpoints had been reached, instead of running fully autonomous sessions.

“Without those checkpoints, the models produced longer, less maintainable codebases and missed security constraints,” PYMNTS added.

Meanwhile, PYMNTS wrote recently about the role AI agents are playing in the software-as-a-service space. As these agents perform the duties of human employees, the connection between worker headcount and software revenue is growing weaker.

“Instead of charging per user, vendors are increasingly experimenting with pricing tied to tokens consumed, workflows executed, transactions processed, or measurable business outcomes delivered,” PYMNTS wrote. “The shift represents a structural recalibration of SaaS economics rather than its demise.”