Software, once a defensive haven for credit investors, faces a major threat from AI. AI's ability to standardize data and workflows could disrupt legacy SaaS companies, making the 30% of direct lending portfolios concentrated in software a significant, overlooked risk.

While AI tools threaten the value of vertical SaaS companies heavily funded by private credit, this isn't a systemic risk. The same AI tools enable broader productivity gains across the economy, creating more value than is lost in these specific private credit deals. The market is also less interconnected than the 2008 mortgage market.

While public software stocks have dropped 20-30% on fears of AI disruption, credit markets, particularly private credit, remain confident. Lenders are protected by low leverage multiples (1-6x EBITDA) and a substantial equity cushion, making them less sensitive to equity valuation shifts.

While AI may devalue software companies backed by private credit, this won't trigger a 2008-style crisis. The argument is that these losses will be contained within the software sector. Furthermore, AI's broad productivity gains will likely create an economic expansion that outweighs the damage to these specific portfolios.

Software's heavy presence in leveraged loan (<15%) and private credit (>20%) portfolios makes these markets more vulnerable to AI disruption than high-yield bonds (<5%). This concentration risk is already visible, with the distressed universe of leveraged loans growing 50% year-to-date, a stress not yet seen in the bond market.

A significant portion of private credit portfolios consists of loans to software companies, which were underwritten based on predictable, recurring revenue. AI is now fundamentally disrupting these business models, threatening to devalue the very collateral that underpins billions of dollars in these 'safe' loans.

Private credit funds have taken massive market share by heavily lending to SaaS companies. This concentration, often 30-40% of public BDC portfolios, now poses a significant, underappreciated risk as AI threatens to disintermediate the cash flows of these legacy software businesses.

While leverage multiples are similar across the market, Neuberger targets companies acquired at high purchase price multiples (avg. 17x). This strategy results in a significantly lower loan-to-value ratio, providing a larger equity cushion and reducing the lender's ultimate risk.

Roughly one-third of the private credit and syndicated loan markets consist of software LBOs financed before the AI boom. Goodwin argues this concentration is "horrendous portfolio construction." As AI disrupts business models, these highly levered portfolios face clustered defaults with poor recoveries, a risk many are ignoring.