Public Insights on Private Credit: Making Sense of AI Disruption in Credit

AI is causing a selective repricing of software credit rather than a broad rejection of technology exposure, making borrower-level moat analysis more important.

• Agentic AI can automate workflows, compress labor-arbitrage economics and challenge seat-based pricing, while systems of record, regulated workflows and proprietary data may remain defensible.
• As of July 2, 2026, software loan spreads had reached crisis-era levels, wider than on all but nine days in the previous seven years, while most sectors remained closer to historical norms.
• Lincoln International data showed TMT private-company enterprise values fell 7.8% in Q1 2026, more than twice the sector's worst COVID-quarter decline.
• Upper-middle-market software direct-lending deals experienced greater yield widening than core or lower-middle-market deals, exposing risks in 2021-vintage LBOs financed on earlier growth, margin and valuation assumptions.
• Investors should segment technology exposure by business model and moat, stress coverage under severe AI-substitution scenarios, and monitor renewal behavior, usage data, hiring signals and customer-level information between valuation dates.

Takeaway: Private credit should not be avoided solely because of AI, but managers must underwrite software borrowers individually, reassess refinancing durability and use liquidity and monitoring tools early; this creates both risks for homogeneous sector exposure and opportunities for selective lenders.