AI Strains Software Firms, Reviving Risks in $3 Trillion Private Credit Market

Artificial intelligence is changing how software is built, priced, and bought—and the speed of that shift is starting to stress parts of the software industry that have long been considered dependable. As revenue models evolve from seat-based subscriptions to usage-based pricing, and as AI features disrupt product roadmaps, some software firms are facing slower renewals, heavier R&D costs, margin pressure, and uncertain customer demand. That strain matters well beyond Silicon Valley.

Why? Because software companies have become some of the biggest borrowers in the $3 trillion private credit market. For years, private lenders have provided financing to sponsor-backed software businesses using predictable recurring revenue as the backbone for leverage. Now, AI is injecting new volatility into those stable cash flows—reviving concerns about underwriting standards, covenant protections, and default risk.

Why software became a private credit favorite

Private credit—loans provided by non-bank lenders such as private debt funds—grew rapidly as regulation tightened bank lending and investors chased higher yields. Software fit the private credit playbook well because many companies offered:

• Recurring revenue through subscription contracts
• High gross margins and scalable unit economics
• Predictable retention via embedded workflows
• Strong cash conversion in mature SaaS models

That combination helped justify larger loan sizes and higher leverage multiples, especially for private-equity-owned companies. Lenders often underwrote deals using metrics like annual recurring revenue (ARR), net dollar retention (NDR), customer concentration, churn, and free cash flow.

But AI is chipping away at the assumptions that made many software borrowers look bond-like.

How AI is straining software company fundamentals

1) Pricing shifts are undermining predictability

Traditional SaaS relies on seat-based pricing: more users equal more revenue. AI features, however, often push software toward usage-based models (per API call, per token, per workflow). While usage pricing can increase revenue in some cases, it can also make revenue more cyclical, harder to forecast, and sensitive to customer cost-cutting.

For lenders, that can translate into more uncertainty around the very metrics that support leverage—especially when a borrower’s debt service depends on stable monthly recurring revenue.

2) AI-driven competition is accelerating product disruption

AI lowers barriers for new entrants and allows good enough competitors to ship quickly. It also enables horizontal platforms to bundle features that used to be premium add-ons. As a result, some incumbents face:

• Feature commoditization (AI makes previously complex tasks trivial)
• Shorter product cycles that demand constant reinvestment
• Higher churn risk if customers can replace tools with AI-native alternatives

This matters in private credit because underwriting often assumes that churn and retention remain within a tight historical range. AI makes that history less reliable as a guide.

3) Costs rise before revenues catch up

Building and operating AI is not free. Even software firms that integrate third-party models can face higher costs from infrastructure, inference, data pipelines, monitoring, security, and compliance. Companies that build proprietary models face even larger R&D and compute demands.

Many firms are seeing a J-curve effect: investment spikes first, while monetization and efficiency benefits arrive later—if they arrive at all. That can squeeze EBITDA and reduce cushion for interest payments, especially for highly levered borrowers.

4) Customer buying behavior is changing

Enterprise buyers are reevaluating software stacks with a new question: Can AI consolidate what we currently pay for? This can lead to:

• Longer sales cycles as customers pause to reassess tooling
• Downsell risk when customers reduce seats or move to cheaper tiers
• Procurement pressure to prove ROI and productivity gains

For private credit, weaker bookings and slower renewals can quickly translate into lower cash flow—particularly for companies with high fixed costs or aggressive debt amortization schedules.

What this means for the $3 trillion private credit market

Private credit is not monolithic. It includes senior secured direct lending, unitranche loans, mezzanine structures, and opportunistic credit. But the software-heavy portion of the market shares common exposure: a large number of deals were underwritten during years of low rates and premium valuations, using optimistic growth assumptions.

As rates rose and growth expectations normalized, lenders already had to adjust. AI adds a fresh layer of uncertainty that may revive risks that seemed manageable when SaaS performance appeared steady.

Key risk areas re-emerging

• Higher leverage against softer earnings: If EBITDA falls due to AI-related investment or pricing pressure, leverage ratios jump.
• Refinancing risk: Companies that borrowed at low rates may face tighter terms and higher coupons at maturity.
• Weaker covenant protection: Some deals included covenant-lite terms, reducing early warning signals for lenders.
• Concentration risk: Many private credit portfolios are heavy in sponsor-backed tech and software.
• Valuation uncertainty: If exit multiples compress, private equity owners may be less able to inject equity or refinance debt.

In short: when software stops behaving predictably, private credit loses one of its most trusted pillars.

Why defaults could rise even if AI is good for tech

AI can be transformative and still cause near-term credit stress. Credit performance depends less on headlines and more on cash flow timing, debt structure, and operational execution. A company can have a compelling AI roadmap and still face trouble if:

• AI initiatives take longer to monetize than expected
• Customers demand discounts to pay for AI features
• Margins compress due to compute costs
• Competition forces higher sales and marketing spend

When a borrower’s loan terms were designed around steady ARR growth and expanding margins, even modest deviations can matter—especially for firms with thin interest coverage.

How private lenders are adapting their underwriting

Private credit funds are not standing still. Many are tightening diligence and adjusting deal structures to account for AI-related volatility. Common responses include:

• More scrutiny on revenue quality, including the mix of usage vs. subscription and renewal cohorts
• Adjusted margin assumptions to reflect ongoing AI infrastructure costs
• Stronger covenants or enhanced reporting requirements
• Higher pricing (wider spreads, fees) to compensate for uncertainty
• Lower leverage or larger equity cushions from sponsors

Some lenders are also differentiating between AI winners and AI exposed borrowers. A mission-critical platform that embeds deeply into customer workflows may remain resilient, while a point-solution vendor vulnerable to AI feature bundling may face more pressure.

What software firms can do to reduce credit stress

For software companies—especially those financed through private credit—AI strategy is now intertwined with capital structure health. Practical steps that can improve resilience include:

• Clarify monetization pathways: Define pricing, packaging, and cost-to-serve for AI features.
• Protect gross margins: Optimize inference costs and renegotiate cloud spend where possible.
• Focus on retention: Prove measurable ROI and reduce churn risk during renewals.
• Avoid overbuilding: Prioritize AI features that directly support core workflows and revenue.
• Maintain liquidity: Extend runway and keep covenant headroom where possible.

Ultimately, lenders want evidence that AI is not just an innovation story—but a repeatable, profitable delivery model that can support debt obligations.

The bottom line: AI is reshaping credit risk in software

The private credit boom was built partly on the idea that software is durable, recurring, and predictable. AI is challenging that premise by changing pricing, intensifying competition, and pulling forward investment costs. For a market approaching $3 trillion, even small shifts in default expectations or recovery values can have outsized consequences.

Private lenders and software borrowers that adapt fastest—through tighter underwriting, more realistic cash flow planning, and clearer AI monetization—may navigate the transition successfully. Those that rely on yesterday’s SaaS assumptions could find that AI doesn’t just disrupt products; it disrupts balance sheets, too.

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