Man Group Credit Team: This Credit Cycle Defined by Divergence, with AI Creating New Concentration Risks in the Investment-Grade Market
The Man Group credit team argues that the core characteristic of the current credit cycle is not broad-based deterioration but divergence—AI is exerting vastly different credit impacts on borrowers within the same industry, while bond issuance by hyperscale technology companies is creating new concentration risks in the investment-grade market. Meanwhile, compressed spreads are increasing the cost of misjudgment.
In its October 2026 credit cycle outlook, the Man Group credit team put forward a central judgment: this cycle is characterized by divergence rather than broad-based credit deterioration. This assessment is not based on optimistic assumptions but stems from a data-driven review of private credit, high yield, leveraged loans, emerging market corporate bonds, and the investment-grade market—each segment showing resilience, albeit distributed extremely unevenly.
AI acts as the most concrete amplifier of this divergence. In the private credit sector, high-profile defaults in the software and SaaS industries in 2026 sparked questions about the asset class's overall resilience. However, data from the Cliffwater Direct Lending Index shows that realized credit losses were only 0.47%, below the long-term annual average of approximately 0.98%. Nevertheless, the non-accrual rate rose from 1.27% at the end of 2025 to 1.85% in Q1 2026, implying a drop in recovery rates from roughly 53% to 49%. Man Group points out that the key lies in AI's disparate impact on different borrowers: mission-critical enterprise software deeply embedded in client operations and difficult to replace represents a fundamentally different credit proposition compared to commoditized, point-solution SaaS products. Lenders must determine whether AI adoption is supporting a borrower's pricing power and customer retention or threatening both simultaneously.
In the investment-grade market, AI creates risk in a different way—not through defaults, but through concentration. Hyperscale technology companies are estimated to spend up to $800 billion on AI infrastructure this year, financing these expenditures through corporate bonds and asset-backed structures, thereby generating significant supply of long-dated bonds. The spread on the JPMorgan USD Investment Grade Hyperscaler Index reached approximately 140 basis points by late August 2026, roughly double its level at the start of 2025. Man Group warns that if these investments fail to generate expected cash flows, hyperscalers could become a major source of pressure on investment-grade returns—particularly given their projected share of the investment-grade index is expected to reach 10% within four years. Notably, this exposure is not limited to USD portfolios: Alphabet has borrowed approximately $60 billion across six currencies, demonstrating how the same issuer can appear in portfolios diversified by currency or geography, while previously independent funding pools increasingly share the same underlying credit risks.