Private credit's AI exposure: SaaSpocalypse or contained sector reset?

Summary

Private credit funds face a sector-specific AI disruption risk rather than a systemic financial crisis. JPMorgan reports private credit holds 21% exposure to software, rising to 40% when including broader tech and business services—the highest concentration among extended credit markets. Blue Owl recently sold $1.4 billion in private loans and shifted one fund's redemption policy away from regular quarterly payouts to liquidity tied to asset sales and other events, a move that prompted comparisons to BNP Paribas's 2007 suspension of redemptions ahead of the financial crisis.

The structural risk profile differs fundamentally from 2008. Private credit comprises straightforward bilateral loans concentrated within private equity-backed companies and dedicated credit funds, not the opaque, tightly interconnected securitized derivatives that amplified the financial crisis. JPMorgan and UBS both frame software exposure as a sector-led reset rather than a macro default cycle. UBS's stress scenario assumes AI disruption could push private credit defaults to 14–15%, with leverage loans at 8–10% and high-yield at 3–6%. Material but localized, not systemic.

The underlying threat to vertical SaaS companies is structural. Rising interest rates have doubled debt burdens since the zero-rate era, and AI-enabled coding tools now let companies build bespoke solutions in days or weeks instead of months or years. This collapses the buy versus build calculus. A company no longer needs to pay a SaaS vendor for a point solution; it can build one internally at far lower cost. Established PE-backed firms face a particular disadvantage because institutional momentum in their workflows and internal development processes makes adoption of AI tools costly. Newer entrants or companies building processes from scratch have lower barriers to integrating AI into their engineering from day one.

The same AI productivity gains that threaten vertical SaaS also expand the set of economically viable software solutions across smaller firms. Job postings for software developers have actually increased with AI tool availability, suggesting a net productivity effect rather than pure job destruction. Nobel laureate Atholif Aghion's framing applies: automation modernizes production, makes firms more competitive, and opens new markets, enabling net job growth. The risk to PE-backed software companies is not that AI destroys demand for software, but that the firms best positioned to survive the transition are those that internalize AI tools early, not those locked into pre-AI cost structures and workflows.