US Banks: Expert Insights: AI Financing Cycle, Credit Dispersion and the Path of Fed Policy

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US Banks: Expert Insights: AI Financing Cycle, Credit Dispersion and the Path of Fed Policy

hat divergence is evident across high yield,

broadly syndicated loans and private credit, but also within sectors and even subsectors.

On AI-related disruption specifically, she argued that the market’s early reaction—

particularly in software—was initially quite sharp, but that investors are now becoming

somewhat more deliberate and nuanced in how they assess the risks. Even so, the

conclusion is not that the threat has gone away; rather, it is that underwriting AI

disruption requires name-by-name, bottom-up credit work, with some subsectors likely

to emerge relatively resilient while others remain exposed to material disintermediation

risk.

AI disruption lifting default risk, but unlikely systemic

Neha noted that BofA has revised its view of downgrade and default pressure across

credit modestly higher as AI disruption becomes more relevant, but she was careful to

distinguish that from a classic cyclical washout. Her framework is not one of double-

digit defaults or a broad credit crash, but one in which weaker issuers face a sustained

period of elevated pressure as AI changes competitive positioning and revenue durability

in certain industries.

She cited default rate expectations of roughly 2% in high yield, 6% in broadly syndicated

loans, and 8% in private credit, with private credit remaining the most vulnerable area of

the market. Importantly, though, she stopped short of arguing that private credit stress

is likely to transmit in a systemic way into public markets. The broader picture remains

gradually constructive, but with enough undercurrents developing that credit selection,

recovery assumptions and business-model durability matter more than they have in

years.