Market Context: The Evolution of AI Credit Risk
AI credit risk due diligence is the systematic process by which direct lenders, private credit funds, and leveraged finance teams evaluate a borrower's susceptibility to artificial intelligence disruption before extending debt. It assesses how generative AI substitution risks, surging compute and API expenses, and shifting software pricing models threaten cash flows, contract durability, and debt service coverage. Unlike traditional credit underwriting that relies primarily on historical software revenue retention and high gross margins, AI credit risk due diligence tests whether recurring revenues will endure, whether margins will erode under compute heavy workloads, and whether assets can survive upcoming refinancing events.
For years, software-as-a-service (SaaS) businesses were considered gold-standard credit profiles in direct lending. Private credit funds favored software targets because of their predictable recurring revenue streams, high gross margins, low capital intensity, and sticky multi-year customer contracts. High enterprise valuations and robust sponsor equity cushions provided further reassurance, allowing lenders to underwrite high debt multiples with modest cash flow volatility. Artificial intelligence has since introduced a structural reassessment of software credit risk across private credit portfolios, with public software valuations de-rating from peak 2021 multiples and spreads on software loans in both the broadly syndicated and direct lending markets adjusting to the new environment. The repricing has been visible in public markets: software company stocks fell by almost 30% between October 2025 and February 2026, while business development company (BDC) share prices dropped by about 10% on average and discounts to net asset value deepened. Spread compression had already thinned the cushion beforehand, with average spreads on US LBOs financed by direct lending contracting 161 basis points between 2022 and 2024.
That reassessment matters because SaaS lending became one of private credit's most concentrated theses: outstanding loans to SaaS firms grew from roughly $8 billion in 2015 to over $500 billion, about 19% of total direct loans, by end-2025, with a third of all private credit funds carrying SaaS exposure. In practice, credit teams now split software and technology exposure into two distinct risk vectors: software substitution risk and GPU-backed infrastructure risk. Software substitution risk affects application layer software borrowers whose core features can be automated, replicated, or bypassed by AI native tools and broad foundation models. When evaluating these targets, lenders must conduct thorough AI disruption due diligence for software targets to verify whether product moats remain defensible.
Conversely, GPU-backed infrastructure debt involves financing hardware, data centers, and compute capacity tied to AI model training and inference. Private credit funds have layered this exposure on top of already rising lending to big US tech firms and other AI companies, and BDCs with above-median software exposure have underperformed lower-exposure peers by roughly five percentage points since October 2025. While these credits often appear asset backed, they present distinct risks including rapid hardware depreciation, shifting cloud provider terms, high capital intensity, and counterparty exposure to early stage AI developers. Lenders evaluating compute exposure must perform rigorous AI infrastructure exposure due diligence to separate contractually secured cash flows from speculative capacity commitments.
The Practical Framework: Cash Flow and Cost Pressures
To underwrite cash flow vulnerabilities under AI disruption, direct lenders must look beyond aggregate revenue numbers. J.P. Morgan Asset Management's research on software exposure in private credit portfolios finds that AI disruption transmits to credit impairment through four channels, revenue erosion, margin compression, valuation compression and refinancing seizure, and that traditional fundamental metrics are inadequate for analyzing software credit risks. Historical annual recurring revenue (ARR) and gross retention metrics no longer guarantee future debt service capability if end-users can switch to lower-cost AI alternatives or build custom internal tools using open-source models.
Lenders must evaluate the structural tension between historical ARR durability and emerging AI substitution risk. In traditional SaaS, long-term contracts provided multi-year visibility. Under AI pressure, seat-based licensing models are particularly vulnerable as client headcounts contract or tasks are automated by autonomous agents. Performing disciplined SaaS revenue quality due diligence allows credit analysts to decouple true workflow embedding from vulnerable seat-based billings.
On the expense side, AI integration introduces cost volatility that directly impacts cash available for debt service (CADS). Traditional software companies operated with very high gross margins because incremental distribution costs were negligible. When software applications incorporate third-party LLM APIs or host proprietary inference models, cost of goods sold (COGS) rises with continuous cloud compute and token usage. That is why lenders analyzing software credits are told that pricing model context is critical to interpreting margins, rather than reading a reported gross margin at face value.
This margin compression directly weakens debt service coverage ratios (DSCR). If a borrower cannot pass rising compute costs onto customers through outcome-based pricing, operating margins shrink even as top-line revenue appears stable. Underwriters must perform detailed sensitivity audits on compute and API economics to model cash flow generation across varying inference volumes and third-party model pricing scenarios.
Structural Risks: Refinancing, Maturities, and PIK
Beyond operational cash flow pressures, AI disruption heightens structural debt risks around maturities and leverage resets. Because private credit loans are held at par and borrowers do not publicly disclose earnings, there is no mechanism by which deterioration in a borrower's business model surfaces in stated valuations until a hard event, a covenant breach, a missed payment or a maturity wall, forces recognition, and by the time marks move the remediation options available to lenders have already narrowed; in software, that early warning was weakened further by ARR-based rather than EBITDA-based underwriting. Software loans originated during periods of peak valuation multiples face severe refinancing walls if enterprise values de-rate before loan maturity. When terminal valuation multiples compress, refinancing existing principal requires either significant sponsor equity injections or aggressive debt paydowns.
Lenders navigating these maturities must evaluate SaaS exit risk due diligence frameworks to stress test whether borrowers can refinance under normalized valuation multiples. A borrower unable to refinance at maturity is typically pushed toward an amend-and-extend transaction, and extending maturity without fixing the underlying performance issues tends to delay a restructuring rather than prevent it.
Payment-in-kind (PIK) interest elections and liquidity concessions serve as critical early warning signs of underlying stress. Reed Smith's analysis of shadow defaults describes maturity extensions, PIK toggles and covenant relief (loosened or suspended covenants, reset leverage tests, fees paid in exchange for waivers) as the mechanisms that widen the gap between reported performance and economic reality, and argues that sophisticated limited partners should ask how many credits would fail to refinance today and how many satisfied covenants only because the sponsor intervened. That signal has been strengthening: as of Q4 2025, 6.4% of loans in Lincoln International's proprietary database carried interest deferred mid-loan because of liquidity strain rather than elected at origination, up from 2.5% in Q4 2021. When combined with covenant relief or leverage test resets, reliance on un-negotiated mid-term PIK elections indicates that operating cash flows are failing to cover cash interest obligations.
Covenant quality and collateral protection also require critical re-examination in software and AI credit deals. Traditional software loans are backed primarily by intangible assets, including proprietary code, customer lists, and brand equity. In a default scenario triggered by AI obsolescence, the enterprise value supporting those intangibles can fall quickly, leaving senior lenders with limited liquidation recovery. Unlike physical asset collateral, obsolete software code cannot be easily liquidated to recover loan principal.
What Lenders Are Testing vs. What Borrowers Must Show
When underwriting software and compute exposures, lenders focus on fundamental credit signals that indicate long-term survival, while borrowers must present verifiable operational data room evidence. Credit committees seek clear evidence that the borrower possesses defensible moats, strong sponsor backing, and sufficient equity cushions to absorb cash flow volatility.
Lenders evaluate three core structural signals during credit analysis:
- Sponsor Support and Capital Commitment: Determining whether the private equity sponsor has the financial capacity and willingness to inject fresh equity cures if cash flows dip during an AI product transition.
- Equity Cushion and Realized Leverage: Testing original loan-to-value (LTV) ratios against realistic, current enterprise valuations rather than legacy purchase price multiples.
- Downside Case and Recovery Assumptions: Modeling downside liquidations under conservative recovery assumptions that assume zero collateral value for superseded software code.
Borrowers are correspondingly expected to provide clear, verifiable evidence of customer retention, contract durability and product defensibility, because lenders are trying to separate software companies that are genuinely vulnerable from those simply impacted by an indiscriminate de-rating, a distinction that requires a robust analytical framework and in which sponsor quality is increasingly important. Merely showcasing high gross retention is insufficient; borrowers must present cohort-level analysis showing how net revenue retention (NRR) trends among customers who have adopted AI alternatives. Borrowers must also undergo comprehensive customer due diligence churn analysis to prove that core product usage remains mission-critical.
The AI Credit Risk Red-Flag Matrix
To help credit committees identify emerging vulnerabilities early, direct lenders use structured red-flag matrices during initial underwriting and ongoing portfolio monitoring. The matrix below outlines primary signals, their underlying credit significance, and recommended lender actions.
| Signal | Why It Matters | Action |
|---|---|---|
| Surging Infrastructure COGS | Escalating cloud compute and API token costs erode gross margins and reduce cash available for debt service. | Audit API unit economics and model DSCR under higher inference usage scenarios. |
| Declining Net Revenue Retention | Indicates customer contract downsizings or migration toward competing AI native point solutions. | Conduct cohort level churn analysis and review renewal rates among key enterprise accounts. |
| Over-Reliance on Wrappers | Product functions as a light UI wrapper around third party LLMs with low defensibility and zero moat. | Require independent technical diligence on proprietary data assets and workflow integration. |
| Unexpected Mid-Loan PIK Election | Signals operational cash flow deficits preventing full cash interest service, compounding debt burden. | Restrict further revolver draws, request updated cash flow forecasts, and engage sponsor. |
| Maturity Wall Under De-rated Multiples | Borrower cannot refinance existing debt principal given lower prevailing software valuation multiples. | Stress test enterprise value under lower exit multiples and negotiate early equity paydowns. |
| Concentrated Model Provider Risk | Dependence on a single AI model vendor creates vulnerability to price hikes, API changes, or outage risk. | Verify multi-model fallback architecture and review third party vendor SLAs and pricing terms. |
Virtual Data Room Evidence Checklist
Private credit underwriters and deal teams require specific documented evidence before finalizing credit agreements for software or compute exposed borrowers. The virtual data room (VDR) must contain granular operational, financial, and technical documentation to support credit approval.
- Granular API and Cloud Compute Cost Breakdown: Multi-year historical and projected COGS detailing model inference, hosting, and third-party vendor token expenses.
- Customer Churn and Retention by Cohort: Detailed customer retention data segmented before and after the launch of generative AI product updates.
- AI Infrastructure Vendor Contracts: Service agreements with cloud providers and model vendors, including pricing terms, volume commitments, and SLA guarantees.
- Unit Economic Sensitivity Models: Financial models demonstrating DSCR and leverage under varying compute cost increases and seat contraction scenarios.
- Technical Architecture and Data Moat Audit: Third-party technical evaluations confirming proprietary dataset ownership and deep workflow integrations.
- Detailed Covenant Compliance and PIK Forecasts: Historical compliance certificates and multi-period projections modeling covenant headroom under downside cases.
- Sponsor Equity Support and Cure History: Capital call structure, remaining fund reserves, and historical record of sponsor equity contributions across portfolio companies.
- Downside Recovery and Collateral Appraisal: Independent liquidation valuation assessing recovery values for intellectual property, enterprise contracts, and hardware assets.
Practical Implications and Deal Workflow Execution
The evolution of AI credit risk requires adjustments across all transaction participants. For lenders, underwriting standards must shift from relying on historical revenue stability to testing technical defensibility and margin resilience. For private equity sponsors, securing debt financing requires providing greater operational transparency and preparing stronger equity cushions. For borrowers, demonstrating clear product moats and unit economic discipline is now mandatory to access direct lending markets.
How to use this in your next diligence workflow
Credit analysts and deal leads should integrate this analytical framework into every stage of their transaction review. Start by categorizing the credit risk vector during screening, separating application software substitution from compute infrastructure exposure. During preliminary underwriting, audit compute COGS trends and cohort retention data to verify that gross margins can withstand model inference costs. Prior to credit committee approval, run downside sensitivity models that incorporate lower valuation multiples at maturity.
Modern transaction teams leverage dedicated software platforms to automate document review and risk extraction during intensive deal timelines. Deploying an AI-native due diligence platform enables deal teams to quickly analyze complex data rooms, surface contract anomalies, and cross-reference financial metrics.
Deal intelligence tooling built for institutional investment and lending workflows can shorten the mechanical part of this work. Ingestion and analysis layers let credit teams process thousands of data room PDFs, contracts, and financial models in a fraction of the time manual review takes. Automated risk detection helps flag materiality thresholds, legal exposures, and financial discrepancies, while report generation and shared workspaces let teams convert findings into investment committee memos IC memo automation with full source traceability.
Plausity is an AI-native due diligence and deal intelligence platform that helps deal teams analyze company information, structure findings, surface red flags, compare documents, and convert diligence work into investment-ready outputs. Plausity helps investors, founders, and management teams prepare clearer, evidence-backed diligence materials; it does not replace professional judgment, independently provide legal, financial, tax, commercial, or technical advice, or guarantee credit approval, valuations, or investment outcomes.
Data Room Checklist and Practical Implications
Credit committees evaluating software or AI-infrastructure borrowers should treat this evidence set as a working checklist rather than a one-time review, since AI substitution risk and compute-cost pressure evolve quickly.
- ARR schedule with cohort retention and true net revenue retention over multiple periods
- Customer contract book showing multi-year commitments, termination-for-convenience clauses, and pricing power
- Compute and API vendor contracts with committed spend, pricing escalation, and switching costs
- Documented margin evolution under AI-related cost pressure, aligned with AI pricing model due diligence
- Analysis of substitution exposure to AI disruption and defensibility per AI moat due diligence
- Findings from a software technology due diligence review of the product architecture
- Financial statements and quality-of-earnings evidence aligned with a financial due diligence checklist and commercial evidence per a commercial due diligence checklist
- Debt maturity schedule, covenant compliance history, and any PIK election history
In practice, private credit teams and direct lenders benefit from tooling built for diligence for PE and VC funds, combining findings and risk intelligence with AI-powered diligence analysis to cross-reference credit agreements, customer contracts, and financial models at speed. Structured cross-referencing helps ensure that a lender's recovery assumptions align with realistic exit-side valuation views.
How to use this in your next diligence workflow
Credit teams can apply this framework directly in a live underwriting process: use the red-flag matrix above to score every software or AI-infrastructure credit at screening, then convert covenant, PIK, and refinancing findings into a single risk register tied to specific documents before the memo is drafted.
Use Plausity to organize borrower evidence, pressure-test risk themes and convert credit findings into memo-ready outputs, supported by risk register automation and consistent investment committee memo workflows so that a lean credit team can run several borrowers in parallel without losing the audit trail.
