Private Credit Strategies for Software Lending in the Age of AI
Recurring revenue used to be the end of the software credit conversation. If the ARR was high-quality and sponsor-backed, the loan was "defensive" by default.
In an AI-driven market, that shortcut is dangerous.
Private credit strategies for software lending now have to answer a harder question: not just can this borrower service debt through a slowdown, but will this revenue still exist – at this price point, with these customers – when the debt needs to be refinanced?
Why the Old Software Credit Underwriting Playbook Is Breaking
How recurring revenue became shorthand for credit quality
For the last decade, software credit underwriting largely rode on one core assumption: recurring revenue equals resilience.
The logic was simple:
- Contracts renew.
- Churn is low.
- Sponsors defend the equity.
- The exit market stays open.
In that regime, underwriting sponsor-backed software looked straightforward:
- Pull the ARR metrics.
- Check retention and upsell.
- Flex leverage to squeeze returns.
- Rely on a deep refinancing and syndication market.
The result was an asset class treated as structurally defensive. Software loans commanded a safety premium on the basis of revenue characteristics alone.
The blind spot: underwriting stability while products are being disrupted
The problem is that recurring revenue measures contract continuity, not competitive position.
You can have:
- Stable ARR today.
- Satisfied customers on last year’s survey.
- A sponsor with dry powder.
…and still be underwriting a product that is sliding toward irrelevance under AI-driven competition.
That blind spot matters because lenders don’t get paid on last year’s contracts. They get paid on future cash flow and refinancing outcomes. When AI changes product expectations and pricing power, recurring revenue becomes a lagging indicator.
The old playbook priced software credit as if the product and customer priorities were static. They are not.
AI Disruption and the New Question for Software Lenders
From “can they service the debt?” to “will this revenue still exist?”
AI changes the underwriting question.
Historically, lenders asked: Can this company service its debt through a cyclical slowdown?
Now the question is more fundamental: Is this revenue durable as products and customer priorities shift under AI?
According to Bloomberg reporting, AI disruption is already raising questions around the creditworthiness of sponsor-backed software. That concern is not about one quarter’s EBITDA. It is about:
- How quickly AI enables credible alternatives.
- How fast features become commoditized.
- How procurement teams rethink value for money.
Serviceability in the next 12 months is no longer enough. Lenders must underwrite whether the business model still commands the same economics over the life of the loan.
Why sponsor-backed software credit is losing its safety premium
Sponsor backing and recurring revenue once combined into a powerful comfort story. Today, that story is fraying at the edges.
When AI shortens product cycles and lowers switching costs, sponsor-backed software credits can slide from “premium defensive” to structurally fragile without an obvious macro shock.
The chain looks like this:
- AI-powered competitors pressure pricing.
- Growth spend rises to defend share.
- Cash conversion weakens.
- Refinancing becomes more conditional.
- Debt trades down well before a payment default.
The safety premium in software credit is now conditional on adaptable products and real cash generation, not simply on recurring revenue and sponsor ownership.
Market Signals: What Recent Software Deals Are Telling Lenders
The market is already signaling caution. You don’t need a formal default cycle to see it.
Sophos: refinancing resistance in a supposedly defensive corner
Bloomberg has reported that cybersecurity firm Sophos sought to refinance roughly $2.5 billion of leveraged loans due next year. Several private credit lenders reportedly passed, and the debt traded around 95 cents on the dollar.
For a category once considered bulletproof, that price action is a message:
- Lenders are no longer willing to refinance every large software stack on autopilot.
- Even well-known names cannot assume par refis at tight spreads.
When recurring revenue alone could carry the story, this kind of hesitation was rare. It is now becoming a risk indicator: if a company with brand recognition and recurring revenue meets a more selective credit market, underwriting standards are shifting.
Medallia: when cash conversion and marks expose structural fragility
Medallia provides another illustration of the new regime.
As Bloomberg has reported:
- The company carried roughly $2.8 billion in private credit.
- Interest payments were delayed.
- There was add-on borrowing layered on top of the existing stack.
- Some BDCs marked the debt as low as 54 cents on the dollar before lenders took control earlier this year.
This is not a story of a single missed KPI. It is a case study in what happens when:
- Cash conversion can’t keep up with the capital structure.
- Refinancing assumptions baked into the original deal no longer hold.
On paper, the borrower still operates in a software category with recurring revenue. In practice, the combination of leverage, cash strain, and a less forgiving market turned the structure into an operator workout, not a passive hold.
A New Framework for Private Credit Strategies in AI Markets
Effective private credit strategies for software lending have to evolve from an ARR-centric growth lens to a downside-and-durability lens.
At Manhattan Private Credit, we think in three pillars.
Underwriting revenue durability in an AI-driven product cycle
The first question is not, “How fast is ARR growing?” It is, “How durable is this revenue under realistic AI scenarios?”
Key considerations include:
- Feature commoditization: Which core features are becoming table stakes under AI, and how will that affect pricing?
- Customer workflow integration: Is the product deeply embedded in critical workflows, or is it a replaceable layer?
- Switching costs in an AI world: Do AI tools make migration cheaper, faster, or less risky for customers?
- Roadmap credibility: Does the product roadmap show a defensible response to AI-driven competition, or is it marketing language masked as innovation?
Recurring revenue is useful. Durable, defendable revenue is what actually protects lenders.
Putting cash conversion at the center of the credit thesis
Growth stories can survive negative free cash flow for years. Credit structures cannot.
Underwriting now needs to prioritize cash conversion, not just adjusted EBITDA:
- How much of reported earnings converts to cash after customer acquisition and retention spend?
- What happens to cash conversion if top-line growth slows or sales efficiency deteriorates?
- How dependent is the model on continued sponsor equity injections to support growth?
In an AI-driven market where pricing and volume are both less predictable, lenders need downside cases that focus on cash:
- Lower growth, stable cost base.
- Lower pricing, modest cost cuts.
- Higher churn, higher acquisition spend.
The thesis should still hold in those scenarios. If it doesn’t, the loan is not defensive.
Refinancing resilience: assuming today’s window can close
Finally, software credit underwriting has to assume the refinancing window is conditional, not permanent.
That means pressure-testing:
- Maturity profiles: What real options exist 12–24 months before the wall?
- Capital structure flexibility: Are there levers – amortization, PIK toggles, equity cures – that preserve time without simply masking impairment?
- Lender base composition: Are current lenders structurally able to extend, or are they mark-to-market constrained and rotation-prone?
Refinancing resilience is not about hoping for a better syndication market later. It is about structuring and sizing deals so that the company is refinanceable in a more selective, AI-aware credit market.
Operator-Level Questions to Separate Durable Software from Future Impairments
Institutional software lenders now need to think like operators, not just analysts.
Product, pricing, and customer behavior tells
Useful questions include:
- Product: Which customer workflows would break if this software disappeared tomorrow? If the answer is vague, durability is weaker than the ARR suggests.
- AI posture: Is AI integrated into the core product in a way customers value, or is it a superficial add-on to satisfy investor narratives?
- Pricing power: When did the company last push price? How much pushback did they get? Are renewals increasingly tied to discounting or bundling?
- Customer concentration and health: Are the largest customers also the most exposed to AI-driven disruption in their own sectors?
These questions help separate appearing sticky from actually indispensable.
Capital structure and covenant tells
On the financing side, the underwriting lens should include:
- Leverage vs. realistic cash generation, not pro forma adjusted metrics.
- Covenant design that catches deterioration early rather than simply deferring recognition.
- Built-in incentives for sponsors to inject equity before assets become binary.
In a regime where AI can rapidly re-rate valuations, covenant-light structures anchored only on ARR leave lenders exposed to sudden, deep impairments.
What This Shift Means for Private Credit Strategies and Portfolios
Re-rating “high-quality” software exposure
For private credit allocators, the question is: What portion of the software book was priced on the old playbook?
Portfolios heavy in:
- Covenant-lite software loans,
- Underwritten primarily on recurring revenue and sponsor reputation,
- With aggressive leverage and back-ended maturities,
are implicitly long the assumption that AI will not materially change customer behavior within the loan life.
That is a large, often unexamined bet.
Re-rating doesn’t necessarily mean exiting the category. It does mean:
- Reassessing durability, cash, and refi profiles deal by deal.
- Factoring in the risk that loans trade down before any payment issues emerge.
Positioning ahead of further AI-driven repricing
The opportunity is on the other side of that adjustment.
Lenders who move first to an AI-aware underwriting regime can:
- Avoid structures most vulnerable to disruption.
- Price risk where others are still backward-looking.
- Provide capital solutions to borrowers and sponsors who need a partner capable of operator-level analysis, not just capital at scale.
In that world, software credit is not dead. It is simply returning to being a sector that has to be underwritten, not assumed safe.
FAQ: Private Credit Strategies for AI-Disrupted Software
Why is recurring software revenue no longer enough for credit underwriting?
Recurring revenue still reduces some volatility, but AI is accelerating product cycles and changing customer priorities. A borrower can show stable ARR while its product is being displaced, its pricing power erodes, and its refinancing options narrow. Lenders now need to underwrite whether that revenue will remain durable, not just whether it recurs today.
How does AI specifically impact software credit risk?
AI lowers switching costs, enables new competitors, and can commoditize features that once justified premium pricing. That can compress margins and weaken cash conversion before headline ARR rolls over. On the credit side, this raises questions about terminal value, exit scenarios, and the willingness of future lenders to refinance today’s structures on similar terms.
How should private credit strategies adapt when underwriting software deals?
Three pillars matter most: revenue durability under realistic AI-driven competition; cash conversion after growth spend and customer acquisition costs; and refinancing resilience given potential shifts in risk appetite. Recurring revenue is an input, not the thesis. Lenders should pressure-test downside cases where growth slows, pricing steps down, and the refinancing window is less friendly.
How do examples like Sophos and Medallia illustrate these risks?
According to Bloomberg reporting, Sophos faced muted lender appetite when seeking to refinance a large leveraged loan stack, and its debt traded below par. Medallia reportedly carried significant private credit balances, delayed interest payments, and required add-on borrowing, with some BDC marks far below par before lenders took control. Both show how seemingly solid software credits can become fragile when cash and refinancing collide with changing conditions.
What can equity sponsors do to keep their software credits financeable?
Sponsors can prioritize genuine product differentiation in an AI context, protect pricing power, and support investments that improve cash conversion rather than just headline growth. On capital structure, they can avoid over-levering on the assumption that recurring revenue will always command rich credit terms, and they can engage with lenders early around realistic refinancing paths.
About Manhattan Private Credit
Manhattan Private Credit focuses on event-driven and structurally complex credit where capital structure, governance, and real operating dynamics matter more than marketing narratives.
Our private credit strategies for sponsor-backed software emphasize deep underwriting of revenue durability, cash conversion, and refinancing resilience – and avoid reliance on outdated shortcuts like “recurring revenue = defensive.”
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