Anthropic AI Rattles Software Loans — KKR & Co.'s Private Credit at Increased Risk
- KKR’s large private-credit arm faces AI risk potentially reducing software borrowers’ revenues and cash flows.
- For KKR and peers, AI-driven defaults could increase amendments, extensions, and liquidity stress in unitranche and LBO loans.
- Analysts urge KKR to stress-test portfolios and engage borrowers about product roadmaps and cost structures.
Opening: AI tools rattle software-backed private credit
Private credit markets face renewed uncertainty after Anthropic unveils advanced AI tools that analysts say can perform complex professional tasks traditionally sold by enterprise software vendors. The development triggers immediate concern across lenders that have concentrated exposure to software borrowers, a cohort that has been a favored target for private credit since 2020. Market data firm PitchBook and other observers note that unitranche and other direct-loan structures have been heavily used to finance software deals, leaving a roughly $3 trillion private credit market sensitive to rapid shifts in software business models.
KKR’s private credit franchise confronts software disruption
KKR & Co., which operates one of the largest private credit platforms, is navigating the risk that AI adoption could undercut revenue and cash flows at many of its software borrowers. Analysts say Anthropic’s models accelerate the potential for incumbents’ offerings to be displaced or commoditised, raising immediate questions about borrower valuations, covenant coverage and the resilience of cash flows that support interest and amortisation schedules. For KKR and peers, higher default risk in the software cohort could translate into more loan amendments, extensions and liquidity stress in illiquid unitranche and leveraged buyout financings.
The concern is not limited to a single loan type: unitranche structures that blend senior and subordinated risk often lack the trading liquidity of public bonds, and their covenant packages can vary markedly across deals, complicating workout prospects. UBS warns that in an aggressive disruption scenario U.S. private credit default rates could rise toward 13%, a level well above defaults in leveraged loans or high-yield bonds, heightening potential losses for managers with concentrated tech exposure. Jeffrey C. Hooke of Johns Hopkins adds that many private credit portfolios were already showing strains from liquidity mismatches and loan extensions, so AI represents an added stressor rather than an isolated shock.
Market structure and lender reactions
Industry participants say lenders are revisiting underwriting assumptions, tightening covenants where possible and increasing diligence on AI-related disruption risk when evaluating new software loans. PitchBook data show software accounts for about 17% of U.S. business development company deal counts, underlining how widespread the exposure is across private credit channels.
Analyst warnings and next steps
Analysts urge asset managers, including KKR, to stress-test portfolios for accelerated AI adoption scenarios and to engage proactively with borrowers on product roadmaps and cost structures. How managers respond in coming weeks will shape whether the market absorbs the disruption or faces broader credit strain.
