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Private credit funds are exposed on two fronts: they are financing the massive debt rounds for AI infrastructure and also hold debt for traditional SaaS companies. As AI companies pitch a future where they render SaaS obsolete, it creates instability and default risk across these private credit portfolios.
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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.
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.
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.
The systemic risk from a major AI company failing isn't the loss of its technology. It's the potential for its debt default to cascade through an opaque network of private credit and other lenders, triggering a financial crisis.
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.
Tech giants are no longer funding AI capital expenditures solely with their massive free cash flow. They are increasingly turning to debt issuance, which fundamentally alters their risk profile. This introduces default risk and requires a repricing of their credit spreads and equity valuations.
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.
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.
