Private Credit: Diligencing AI Risk in Software Loans

Over the past decade, private credit direct lenders rapidly expanded their allocations to enterprise software and Software-as-a-Service (SaaS) businesses. In the regulated business development company (BDC) market alone, direct lenders originated around $115 billion in loans to software firms, representing about a fifth of all their lending and over 80 percent of their technology portfolios. Software and technology borrowers account for roughly 20 percent of the more than 2,400 unique global middle-market sponsor-backed borrowers tracked by KBRA.

The investment thesis behind this heavy concentration rested on seemingly bulletproof fundamentals: predictable annual recurring revenue (ARR), net revenue retention rates exceeding 110 percent, gross margins hovering above 75 percent, and minimal capital expenditure requirements. Software businesses were long viewed as asset-light cash generators capable of comfortably servicing high leverage multiples across economic cycles.

The rapid emergence of generative artificial intelligence has introduced a structural catalyst that fundamentally challenges these historical underwriting assumptions. As AI lowers software development costs and accelerates code commoditisation, the durability of established software cash flows is no longer guaranteed. Private credit investment committees now face the urgent task of separating defensible software platforms from vulnerable point solutions across their existing loan books.

Evaluating borrower defensibility requires credit analysts to look far beyond top-line revenue growth and gross margins. In an environment where AI tooling can rapidly replicate basic user interfaces and intermediate logic, traditional software metrics fail to capture whether a borrower possesses a durable economic moat software moats.

Credit teams must evaluate the underlying architectural and operational layers that protect a borrower from rapid technological substitution. Software assets that command genuine defensibility typically demonstrate high workflow integration, proprietary data gravity, and significant operational friction against replacement.

• Mission-Critical Systems of Record: Core platforms such as enterprise resource planning (ERP), general ledger accounting, and core banking infrastructure maintain deep regulatory and operational ties that make wholesale replacement risky and cost-prohibitive.
• High Cost of Failure in Regulated Industries: Vertical software operating in healthcare, legal compliance, aviation, or financial reporting carries stringent regulatory audit standards where the liability of deploying untested autonomous AI tools creates substantial barrier to entry.
• Proprietary Data Loops and Workflow Embeddedness: Platforms that capture multi-year proprietary transaction histories, custom business logic, and bi-directional system integrations generate sustained user friction that cannot be replicated simply by querying an open foundation model.
• Domain-Specific Context and Deterministic Output: Enterprise buyers require auditability and zero-hallucination guarantees for mission-critical processes, protecting deeply embedded niche vendors from generic conversational interfaces.

This technological bifurcation is driving a pronounced K-shaped outcome across enterprise software. High-conviction platforms with defensible systems of record are consolidating market share by embedding generative capabilities directly into their existing distribution channels. Conversely, horizontal point solutions, workflow wrappers, and light automation tools face acute displacement, eroding the recurring cash flows that back middle-market debt.

The transmission of artificial intelligence disruption into private credit portfolios occurs through tangible structural mechanisms that degrade debt service capacity. A primary channel is the vulnerability of seat-based licensing models, which have formed the bedrock of enterprise SaaS monetization for two decades.

As autonomous AI agents automate operational tasks across customer support, marketing, engineering, and data analysis, enterprise customers require fewer human seats to execute equivalent workloads. This seat contraction directly compresses borrower contract values at renewal, transforming what was once assumed to be perpetual net revenue expansion into structural ARR churn.

• Direct Product Substitution: Generative AI creates revenue uncertainty for software borrowers, substituting for some existing products outright while lowering development costs and barriers to entry for challengers.
• Per-Seat Monetization Compression: Enterprise workforce reductions in automated functional areas lead directly to license de-provisioning during annual contract true-ups and renewals.
• Gross Margin Compression: Incumbent software borrowers must absorb escalating compute, token API, and infrastructure costs to incorporate AI features into existing products while facing competitive pricing pressure that prevents passing these expenses to customers.
• Elevated Defensive R&D Capital Intensity: Maintaining product competitiveness requires significant ongoing software engineering investment, depressing EBITDA margins and eroding free cash flow conversion needed for scheduled debt amortisation.

Because software companies possess asset-light balance sheets with limited tangible collateral, severe revenue erosion leaves direct lenders with depressed enterprise values in restructuring scenarios. Monitoring ARR durability requires dynamic cohort analysis to detect early signs of seat contraction and down-tiering before conventional payment defaults materialize.

The confluence of elevated base interest rates and software valuation multiple compression poses acute refinancing challenges for sponsor-backed borrowers. Many middle-market software buyouts structured between 2020 and 2022 were underwritten at peak revenue multiples and highly leveraged debt packages structured with floating-rate coupons.

In credit evaluations of 495 software and technology borrowers representing approximately 20 percent of middle-market direct lending portfolios, research by KBRA identified 165 companies with relatively elevated AI exposure. Crucially, 25 percent (41 companies) within this higher-risk cohort face debt maturities before the end of the second quarter of 2027, compared to an average of 19 percent across broader middle-market sectors.

When these loans approach maturity, direct lenders must recalibrate loan-to-value (LTV) ratios against compressed software exit multiples. A borrower underwritten at an enterprise valuation of 12 times recurring revenue that now commands a 6 times multiple cannot support its existing leverage stack without substantial equity injections from financial sponsors debt capacity.

KBRA's assessment is that AI poses diffuse and manageable credit risks, with the 41 near-term-maturity borrowers in its higher-risk cohort spread across more than 90 rated vehicles and 28 direct lenders, so any losses should be absorbed without significant ratings migration. Even so, lenders report early signs of AI-driven impacts, including shifts in customer behaviour, longer sales cycles, and budget reallocation toward AI initiatives, and are preparing for a modest increase in defaults.

In response to heightened technology disruption risks, private credit direct lenders are actively tightening documentation and adjusting structural covenant packages for both new originations and debt amendments covenant due diligence. Lenders are requiring additional protections and expanding pricing spreads by up to 100 basis points or more for software borrowers deemed susceptible to AI-driven competition.

Underwriters are moving away from permissive covenant-lite structures that became prevalent in the upper middle market, re-establishing financial maintenance tests that provide early governance rights before enterprise value deteriorates. Structural terms are being tailored specifically to address software-specific technology vulnerabilities.

• Tightened Intellectual Property Negative Covenants: Restricting the transfer, licensing, or pledge of core proprietary codebase, algorithmic IP, and proprietary customer training data to unrestricted subsidiaries or third-party joint ventures.
• Earlier Sunset of ARR-Based Covenants: Accelerating the mandatory transition from recurring revenue maintenance tests to recurring cash flow and debt-to-EBITDA leverage covenants, preventing loss-making borrowers from burning cash indefinitely.
• Strict Limits on AI R&D Add-Backs: Capping management EBITDA add-backs for capitalized software development, generative AI infrastructure experiments, and restructuring fees to maintain transparent accounting visibility into true operating cash flow.
• Enhanced Liquidity and Minimum Cash Thresholds: Imposing strict monthly liquidity tests and minimum liquidity covenants to ensure borrowers maintain adequate runway to absorb margin compression from increased compute overhead.
• Structured Sponsor Equity Cures: Restricting the frequency and consecutive use of sponsor equity cures to address covenant breaches, requiring new equity injections to be applied directly to pay down principal debt rather than artificially inflating reported EBITDA.

Sponsor commitment remains a decisive variable in debt performance. When a software borrower requires incremental capital to re-architect its tech stack or acquire complementary AI capabilities, lenders must evaluate whether the private equity sponsor possesses sufficient uncalled fund reserves and commercial conviction to defend the equity value of the asset.

Managing portfolio risk requires private credit managers to execute structured, proactive re-underwriting campaigns across their existing software loan books. Because private credit investments consist primarily of illiquid, unrated bilateral loans, lenders cannot reactively sell out of distressed positions in secondary markets when public sentiment shifts; risk mitigation must occur through proactive monitoring and structured credit workouts.

Credit teams should institute dynamic risk-scoring frameworks that systematically categorise software borrowers based on product defensibility, revenue durability, and technological obsolescence velocity relative to loan maturity profiles.

By establishing rigorous re-underwriting workstreams across these five analytical pillars, direct lending credit committees can identify underperforming credits well before quarterly payment defaults occur, enabling early interventions such as negotiated margin step-ups, principal paydowns, or structured recapitalizations.

The sheer volume of documentation required to rigorously re-underwrite dozens of software portfolio companies creates operational bottlenecks for credit investment professionals. Sifting through thousands of pages of credit agreements, billing logs, customer churn reports, and technical architecture whitepapers requires significant manual effort from deal teams.

To overcome these constraints, institutional private credit funds and private equity sponsors are deploying advanced diligence platforms to accelerate portfolio reviews and enhance new deal underwriting. Automated platforms ingest disparate data streams, cross-reference confidential financial models with legal terms, and maintain verifiable evidence trails back to primary source documentation Findings & Risk Intelligence.

• Data Room Ingestion: Rapidly ingests and indexes virtual data rooms, processing hundreds of credit agreements, financial spreadsheets, billing exports, and board decks within minutes to establish an auditable diligence repository.
• AI-Analysis Engine: Parses complex borrower management presentations, cross-references revenue cohort schedules against billing data, and evaluates contractual renewal provisions across enterprise customer contracts.
• Risk Radar: Continuously screens borrower disclosures and financial performance metrics, surfacing structural anomalies, hidden customer churn patterns, covenant headroom erosion, and tech stack liabilities.

Plausity equips private credit funds and M&A advisory teams with the specialized analytical infrastructure needed to evaluate borrower resilience in volatile market environments. By transforming raw portfolio data into structured, evidence-backed credit memos, direct lenders can systematically defend their loan portfolios against technology disruption while capturing attractive risk-adjusted returns across the evolving software ecosystem.

Plausity is an AI-native due diligence platform that helps M&A advisory firms, VC and PE funds, and corporate development teams structure evidence, findings and questions across a data room. It does not replace human advisers, does not guarantee deal outcomes, and does not provide legal, tax, audit or regulatory advice — all AI-generated findings, especially regulatory ones, require confirmation and advisor review by qualified professionals.

To explore the underlying capabilities, see the Plausity AI analysis engine and the findings and risk intelligence product page. For team-level workflows, see how VC and PE funds and M&A advisory firms use Plausity across live deals.

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