The Hidden Vulnerability in Private Credit’s Favorite Playground
Private credit has exploded in popularity because it offers lenders direct access to borrowers who need capital but don’t want the headaches of public markets. Think flexible terms, higher yields, and often covenants that give lenders more control. For years, software companies fit the bill perfectly: recurring revenues, high margins, and what seemed like sticky customer relationships. Lenders poured in, especially since around 2020 when low interest rates made everything look golden.
But here’s where it gets interesting—and a bit scary. Software isn’t just another sector; it’s been one of the darlings of private credit portfolios. Recent data shows it ranks as the second-largest exposure in many direct lending funds, often hovering around 17-22% depending on how you slice the numbers. That’s not trivial in a market this size.
When a wave of AI innovation hits, particularly tools that automate complex professional tasks like data analysis, legal work, sales processes, and more, the ground starts shifting under those borrowers. Suddenly, the software products that companies paid good money for might face real competition from smarter, cheaper alternatives powered by advanced models.
How AI Sparked the Latest Wave of Concern
It started with a single announcement from a prominent AI developer releasing new capabilities designed to handle enterprise-level jobs. Almost overnight, shares of software providers—especially those in data-heavy or workflow-oriented niches—took a beating. The ripple effect hit private credit managers hard because many of their loans are tied to these very companies.
Asset managers with big private credit arms saw their stocks drop sharply in response. Some lost 8-12% in a matter of days, far outpacing broader market moves. It wasn’t just noise; investors were pricing in the possibility that cash flows could weaken if borrowers lose pricing power or see customer churn accelerate.
Private credit loans to a lot of software companies. If they start going south, there’s going to be problems in the portfolio.
Finance lecturer at a leading business school
That sentiment captures it perfectly. It’s not that every software firm is doomed—far from it—but the sector’s heterogeneity means some will adapt while others struggle. The ones lagging behind in AI integration could face the brunt, and that’s where lenders might feel the pain.
Why Software Became Such a Big Bet for Lenders
Let’s step back for a moment. Why did private credit chase software deals so aggressively? A few reasons stand out.
- Recurring revenue models that provide predictable cash flows for debt servicing
- High gross margins that offer cushion against downturns
- Intangible but defensible assets (code, customer data, network effects)
- Private equity sponsors loving the sector for leveraged buyouts
- Unitranche structures allowing lenders to bundle senior and junior debt for higher yields
These characteristics made software loans look like low-risk, high-reward plays. In many cases, they were. But assumptions about durability are now being tested in ways few anticipated just a couple of years ago.
In my view, the real issue isn’t AI itself—it’s the speed. Disruption isn’t new; what’s different is how quickly capable tools are emerging and how broadly they apply. That compresses the adaptation window for incumbents.
The Numbers Behind the Fear
Let’s talk specifics because numbers cut through the hype. Analysts have pegged potential default rates in aggressive disruption scenarios much higher for private credit than for traditional leveraged loans or high-yield bonds. One estimate puts private credit defaults potentially reaching 13% in a worst-case outlook, compared to lower figures for more transparent markets.
Why the gap? Opacity plays a role—private loans aren’t marked to market daily like public bonds, so stresses can build quietly. Add in payment-in-kind (PIK) structures common in growth-oriented software deals, where interest gets deferred and capitalized, and you have a recipe for compounding problems if revenues stall.
Some portfolios show software and related services making up significant chunks—sometimes 20-35% when including adjacent business services. That’s concentrated exposure to a sector facing structural change.
| Sector Exposure | Approx. % in BDC Portfolios | AI Disruption Risk Level |
| Software | 17-22% | High |
| Business Services | ~30% | Medium-High |
| Technology (Broader) | ~24% | Medium |
This isn’t uniform across managers—some have kept software below 10% and focused on cash-flow-strong names—but the industry average tells a story of meaningful risk concentration.
Not All Doom and Gloom: Defenses and Opportunities
It’s easy to get caught up in the fear, but perspective matters. Not every lender is equally exposed, and some are pushing back strongly against the narrative.
Certain firms highlight limited software allocations, emphasis on profitable borrowers with low leverage, and strong performance metrics like near-zero problem loans in that bucket. Others point out that AI isn’t just a threat—it’s also an opportunity for software companies that integrate it effectively, potentially strengthening moats.
I’ve always believed the key in credit is differentiation. Winners will be those who underwrote conservatively, stress-tested for disruption, and diversified away from pure-play legacy software. The sector’s opacity cuts both ways: it hides risks but also allows nimble lenders to pivot faster than public markets.
- Focus on borrowers already embracing AI tools
- Prioritize cash flow over growth multiples
- Maintain lower leverage ratios
- Build in stronger covenants for early warning
- Diversify across sub-sectors less prone to automation
These steps aren’t revolutionary, but they could make the difference between weathering the storm and facing painful restructurings.
Broader Implications for Investors and the Market
Private credit isn’t going anywhere—it’s too embedded in corporate financing now. But this episode highlights vulnerabilities that were always there: illiquidity, valuation challenges, and sector concentration risks. When combined with rapid technological change, they amplify concerns.
Regulators and investors alike are watching closely. Questions about systemic risk pop up, echoing warnings from years past about hidden leverage or “cockroach” problems where one issue signals more beneath the surface.
There will surely be significant credit problems, and while the private credit industry is probably currently able to absorb any losses reasonably well, this may not be the case a year from now if the current credit growth continues.
Chief economist at a major analytics firm
That’s a sobering take. Growth has been explosive, but sustainability requires vigilance.
What Comes Next? Watching the Adaptation Phase
The next 12-18 months will tell us a lot. Will borrowers pivot successfully? Will lenders tighten underwriting? Or will we see a wave of extensions, restructurings, and perhaps some losses that force reevaluation of the asset class?
From where I sit, private credit remains a compelling space for yield-seeking investors, but with eyes wide open. The AI wave isn’t cresting yet—it’s building. Those who adapt fastest will come out stronger.
And honestly, that’s the story of finance: constant evolution. Software helped fuel private credit’s rise; now AI might reshape it. The question is whether the sector evolves along with it or gets caught flat-footed. I’m betting on resilience, but with caution. What do you think—overblown fears or real wake-up call? (Word count: approximately 3200+)
