Public Insights on Private Credit: Making Sense of AI Disruption in Credit

Artificial intelligence (AI) is forcing credit investors to revisit one of the market's most familiar assumptions: that software exposure can be treated as a single, high-quality bucket. That shorthand worked when many enterprise software credits were underwritten around recurring revenue, customer retention and high switching costs. It is less useful in a market where agentic AI can automate certain workflows, compress labor-arbitrage economics and challenge seat-based pricing models, while other businesses remain deeply embedded in systems of record, regulated workflows or proprietary data environments.

That distinction matters because credit markets are already moving from broad concern to name-level repricing. The result has not been wholesale rejection of technology risk. It has been dispersion. Public markets move quickly and often bluntly, but credit investors need to ask more precise questions about which software borrowers are vulnerable, which can use AI to improve margins or customer retention, and which are being mispriced by a sector-level selloff.

Exhibit 1: AI Is Repricing Moats, Not Eliminating Technology Credit

The first step is separating borrower models, not simply reducing a headline software allocation. Systems of record, deeply embedded enterprise platforms, AI deployers and physical-moat borrowers should not be analyzed the same way as narrow workflow tools, labor-arbitrage services, seat-based software or levered legacy technology. In this market, recurring revenue alone is no longer a complete moat; the more important question is whether the customer relationship, data asset and workflow position remain defensible as AI capabilities improve.

Exhibit 2: Software Is Trading in Its Own Far Tail
Most Sectors Sit at or Below Their Historical Medians

Source: JPM Leveraged Loan index export. Technology column used as the software proxy. DM – Forward to three years. Industrial median computed across the 21 sector indexes.

The broadly syndicated loan market is already differentiating. As of July 2, 2026, software loan spreads had widened to crisis-era levels, wider than on all but nine days in the past seven years, while most sectors were trading much closer to normal ranges. Technology, media and telecommunications (TMT) has been a notable outlier, with spreads moving well beyond the historical range experienced by most other sectors. That differs from prior risk-off periods such as COVID-19 and the 2022 rate shock, when spread widening was more synchronized across sectors. AI is not producing a simple beta drawdown; it is repricing business-model risk inside a sector that used to trade more uniformly.

Exhibit 3: Private Tech Values Have Begun to Crack from Their Own Far Tail

Source: Lincoln International, Lincoln Private Market Index data – quarterly change in enterprise value of ~1,800 PE-owned private companies (EBITDA < $250M). TMT used as the technology proxy

Private credit tends to adjust more slowly, but it is not insulated. Valuations can lag public-market moves, and par marks can mask deterioration until a refinancing, amendment or revenue event forces the issue. Lincoln International data shows the technology, media and telecommunications (TMT) values declined 7.8% in Q1 2026, more than twice the sector's worst COVID quarter. That does not mean every software borrower is impaired, but it does suggest that AI-related repricing is beginning to appear in private valuations as well.

Exhibit 4: The Size Premium Has Inverted in TMT Private Credit

Average Implied Yield to Maturity for USD TMT Senior Debt, by Borrower EBITDA Size

Source: Lincoln International private market database.

The pressure also appears uneven within private credit. Lincoln International valuation data shows greater yield widening for upper middle market software direct lending deals than for core or lower-middle-market software deals in the same sector. That distinction is important for underwriting: 2021-vintage software leveraged buyouts (LBOs) were often financed around growth, margins and valuation assumptions built for a different technology cycle. Where AI threatens the revenue model or pricing power, debt investors need to revisit both enterprise value cushion and refinancing durability.

Manager Response

The right investor question is less, "How much technology exposure do you own?" and more, "What kind of technology exposure do you own, and how have you stressed it?" Investors should ask managers to break software exposure down by sub-sector, business model and moat durability; distinguish application-layer tools from systems of record; evaluate customer concentration and contract duration; and stress coverage ratios under severe AI-substitution scenarios, not just modest revenue headwinds.

For liquid credit managers, liquidity allows earlier repositioning, although stressed-loan liquidity can thin quickly. For private lenders, the hold period is longer, documentation matters more and monitoring needs to happen faster than the quarterly valuation cycle. Usage telemetry, hiring signals, web traffic, renewal behavior and customer-level data may become increasingly important early-warning indicators.

Conclusion AI disruption is not a reason to avoid credit. It is a reason to be more precise. Over the next several renewal and refinancing cycles, we believe the market will continue to separate durable software borrowers and AI beneficiaries from businesses whose revenue is closer to the automation target. That creates both risk and opportunity—risk for managers that treat software as a homogeneous sector, and opportunity for managers that underwrite moats name by name, use liquidity early and avoid relying on simple mean reversion.