Key Takeaways

• AI exposure is not the same as AI vulnerability. The strength of the individual business model remains decisive.
• Mission-critical workflows, proprietary data and high switching costs can provide meaningful defenses against disruption.
• Valuation markdowns should not automatically be treated as evidence of deteriorating credit quality.
• Sponsor equity provides the greatest protection when it combines financial capacity with operational expertise.
• Redemption pressure could strengthen lender protections by reducing competition for some transactions.
• PIK structured at origination carries a different risk signal from PIK introduced when a borrower cannot service cash interest.

AI Risk Goes Beyond Sector Labels

Software is central to the AI and private credit debate. At year-end, Hercules Capital reported 24.3% of its portfolio in application software and 10.6% in system software; Blue Owl also described varying software exposure across its BDCs. Exposure alone, however, does not determine credit quality.

The panelists rejected treating software as a single AI-obsolescence category. Cybersecurity, defense technology, regulatory technology, fintech, healthcare IT and specialized platforms differ in competitive position, customer need and tolerance for error.

Instead, lenders assess borrower defenses: differentiated data access, integrated distribution, specialized workflows, pricing power and products embedded in customer operations. These features can make displacement harder as AI advances.

AI risk belongs within fundamental analysis. Analysts should test its effect on revenue durability, customer retention, margins, competitive advantage and product value using sector expertise and borrower-level evidence.

What Makes Software AI-Resilient?

The most defensible software credits are businesses whose products perform essential functions customers cannot easily replace.

Blue Owl emphasized recurring, mission-critical solutions with durable advantages, pricing power and high switching costs. Systems of record embedded in workflows are especially resilient where errors, downtime or security failures cannot be tolerated.

Large language models are predictive systems. They may excel at interpretation and communication, but payroll calculations and bank transfers demand deterministic accuracy: nearly right is still wrong.

Established software companies are not automatically protected. Their resilience depends on integrating AI without weakening controls or reliability, so underwriting must assess both disruption risk and the capacity to adapt.

The Value of Sponsor Equity

The panel challenged the idea that software lending produces inevitably binary outcomes, pointing to the depth and active role of sponsor equity beneath senior lenders.

Blue Owl said technology portfolio LTVs were typically in the low-to-mid-30% range. Its software borrowers had weighted average EBITDA of about $300 million and revenue above $1.25 billion, providing substantial equity beneath the debt.

That equity can absorb volatility before credit impairment, but sponsor support is not only financial.

Operationally engaged sponsors can adjust pricing, costs, go-to-market strategy and acquisitions as AI, competition or economic conditions reshape markets.

Credit teams should therefore assess sponsors on financial capacity, willingness to invest and operational ability, not simply the size of the equity check.

Valuation Pressure Versus Credit Risk

Market volatility can reduce the reported value of a private credit investment without changing the borrower’s capacity to repay.

Portfolio companies are re-underwritten quarterly, with credit ratings and discount factors reviewed. Liquid software debt comparables and spread movements can change valuation marks even when the underlying private asset is stable.

A markdown estimating a third-party sale price is therefore not automatically a credit event. The key test is whether operating performance, liquidity or debt-servicing capacity has deteriorated.

Credit professionals should explain this distinction clearly while investigating repeated or severe markdowns. Market signals matter, but they must be tested against borrower-level evidence through continuous monitoring and re-underwriting.

Redemptions Could Strengthen Protections

Elevated redemptions in perpetual private credit vehicles have intensified liquidity scrutiny. The panel described a disciplined, board-level decision balancing available liquidity, pro forma leverage, market conditions, portfolio effects and fairness to both exiting and remaining investors.

The liquidity mechanism also reflects a fundamental feature of the asset class: investors receive a premium for holding assets that cannot always be converted into cash immediately.

Redemptions may also rebalance supply and demand. With less capital competing for deals, lenders may gain leverage; the panel reported early, uneven signs of wider spreads and more constructive documentation discussions. Covenants, structure and protections may improve before headline pricing.

For credit teams, strong documentation matters because pricing moves with markets, while well-built protections determine lender options if performance weakens.

Good PIK, Bad PIK

Headline PIK figures can conceal important differences.

The panel distinguished PIK structured into the original transaction from PIK introduced through an amendment. In the first case, a limited PIK component may form part of a deliberate capital structure designed to preserve borrower liquidity. In the second, a switch to PIK because the borrower cannot service cash interest is a more serious credit signal.

The practical lesson is straightforward: analysts must identify why PIK exists, when it was introduced and how it is expected to be repaid. The presence of PIK should trigger analysis, not an automatic conclusion.

Implications for Credit Professionals

Private credit analysis increasingly demands the ability to separate broad narratives from borrower-specific evidence.

Credit professionals must understand technology risk without treating every software borrower alike. They must distinguish valuation movements from operational deterioration, evaluate the real strength of sponsor support and interpret PIK according to its origin. They also need to understand how fund-level liquidity and market competition can affect borrower-level terms.

Questions for Firms

1. Can our underwriting distinguish AI-enabled business models from businesses genuinely vulnerable to AI displacement?
2. Do our portfolio reviews separate market-driven valuation changes from borrower-level credit deterioration?
3. Are we assessing sponsor support on operational capability as well as committed capital?
4. Do our documents provide sufficient protections if performance weakens or refinancing conditions tighten?
5. Can we clearly distinguish PIK designed at origination from PIK introduced because of financial stress?

Frequently Asked Questions

Does AI make all software lending riskier?

No. The panel argued that software exposures differ significantly. Mission-critical platforms with proprietary data, pricing power and high switching costs may be more resilient than less differentiated products.

Is a private credit valuation markdown a credit event?

Not necessarily. Market comparables and spread movements can affect valuations even when the borrower’s performance remains stable. Analysts must assess the underlying business before concluding that credit quality has weakened.

Why are covenants important in direct lending?

Covenants and documentation determine the protections and options available to lenders if borrower performance deteriorates. The panel suggested that improving market conditions for lenders may appear in documentation before pricing.

Is PIK interest always a sign of distress?

No. PIK included at origination may be a deliberate liquidity feature. PIK introduced because a borrower cannot meet cash interest is a more concerning signal.

Building Modern Credit Skills

The Global Credit Certificate helps professionals strengthen the practical and analytical capabilities required in this more demanding credit environment.

The GCC includes dedicated coverage of private credit and AI in modern credit analysis, helping learners connect core credit principles with emerging challenges in underwriting, monitoring and decision-making. It develops the judgment required to evaluate business models, capital structures, risk signals and changing market conditions without relying on broad assumptions.

For firms, it provides a structured way to build consistent credit capability across teams. For practitioners, it supports the transition from understanding individual risk indicators to making well-reasoned, defensible credit decisions.

Explore how the Global Credit Certificate develops modern credit skills and supports stronger decision-making across private and public credit markets.