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A significant portion of private credit is concentrated in software companies. Many of these loans were made when rates were low, often with high leverage and weak terms. The emergent threat of AI-driven disruption to their business models now adds a new layer of fundamental risk to this already vulnerable cohort.
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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.
The most significant risk in software-focused private credit isn't established companies but those underwritten on Annual Recurring Revenue (ARR) multiples instead of cash flow. These high-growth, non-cash-flowing businesses may never reach profitability if disrupted by AI, creating a major potential vulnerability.
The "canary in the coal mine" for private credit isn't SaaS debt but any over-leveraged company. A firm burdened by debt repayments lacks the capital to invest in AI and automation, making it vulnerable to disruption by less-leveraged, more innovative competitors in any industry, not just software.
An expert warns of a "mini bubble" where private credit funds lent heavily to PE firms buying unprofitable software companies based on high ARR multiples. With falling valuations, AI disruption, and a wall of debt maturing, a wave of defaults and restructurings is imminent.
Despite market fears about AI disrupting software companies, underlying private credit loans are structured defensively. They are often written at a 30% loan-to-value, meaning there is a 70% equity cushion before the lender's principal is at risk.
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.
Beyond the long-term threat of AI disruption, highly leveraged, lower-quality software companies funded by private credit face a more immediate problem: a $65 billion wall of debt maturing by 2028. They must refinance this debt amid high uncertainty, creating significant near-term risk separate from AI's eventual impact.
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.