Private equity and private credit groups' big bets on SaaS companies, the biggest area of PE activity in the past decade, risk being derailed by the rise of AI
Dealmakers and lenders are facing a ‘Darwinian moment’ as digital services risk being made obsolete by new technologies
Context & Ripple Effects
Private capital’s exposure to enterprise software had already become a credit concern: Apollo reportedly cut exposure and shorted enterprise-software loans, while Nomura found software debt lagging other sectors in CLO returns amid AI fears. This report broadens that warning from individual portfolios to the PE-and-private-credit model built around SaaS.
The pressure also sits alongside enterprise AI vendors’ work with PE firms, which has created a competitive challenge for Indian IT services as more services become automatable. Capital is increasingly being directed toward AI capacity and control points rather than assuming incumbent digital-service businesses retain durable cash flows.
First-order effects
- PE owners and private-credit lenders must reassess SaaS portfolio companies whose products or service layers can be displaced, repriced, or commoditized by AI.
- Software borrowers face more scrutiny of recurring revenue durability and debt-service capacity, particularly where AI can change a customer’s build-versus-buy decision.
Second-order effects
- New SaaS deals and refinancings may require tighter underwriting, shifting attention from historical growth metrics toward evidence that a product remains differentiated in an AI-enabled market.
- PE-backed software vendors face a stronger incentive to incorporate AI into their offerings or defend narrower workflows; lenders’ caution can constrain the capital available for that transition.
Third-order effects
- If AI disruption continues to weaken confidence in legacy SaaS cash flows, private-capital returns may depend less on financial engineering and more on whether owners can identify defensible software positions early.
- The financing market could increasingly distinguish between software exposed to automation and the infrastructure and assets supporting AI deployment, reinforcing the large-scale financing buildout for AI infrastructure.
The trend: AI is forcing private markets to reprice software assets according to technological defensibility rather than the historical predictability of subscription revenue.