Your Private Credit Portfolio Has a Software Problem. Do You Know How Big It Is?

The private credit industry spent five years building a $3 trillion market on the back of one of the most favourable lending environments in modern history: low interest rates, expanding multiples, and a software sector whose recurring revenue model made it the ideal private credit borrower. Predictable cash flows. Asset-light structures. Subscription pricing. Covenant headroom. It seemed, for a time, almost too good to be true.

It was.

AI disruption has now arrived as an existential challenge to the legacy software business models that underpin a material portion of private credit portfolios. And the analytical frameworks most allocators rely on — opaque valuations, infrequent reporting, sector classifications that conceal true exposure — were not built to catch it in time.

The Hidden Concentration Problem

The first challenge is measurement. Most allocators believe they understand their software exposure. Most are wrong — not because they have been careless, but because the industry classifications that private credit managers use systematically understate true technology concentration.

Companies categorised as "business services," "commercial services," or even segments within healthcare and financial services are frequently software-driven enterprises at their core. Their revenue model, cost structure, and credit risk profile are those of a SaaS business — but they do not appear in technology allocations on a portfolio report. According to Bloomberg, fund managers apply industry categories in varying ways, meaning companies that are fundamentally software sellers may be misclassified as anything from retailers to food producers.

The consequence is that allocators reviewing their private credit portfolio on the basis of manager-reported sector breakdowns are, in many cases, looking at an incomplete picture. The actual concentration of AI-disruption risk is materially higher than the headline numbers suggest.

A Market Built for Different Conditions

To understand why the risk is so concentrated, it is necessary to understand how private credit grew into software so decisively. Between 2020 and 2023, direct lenders funded 40–70% of leveraged buyouts, up sharply from 15–25% pre-pandemic. Enterprise software companies were the preferred borrower: high margins, low capital intensity, and sticky revenue that looked like bond-equivalent cash flows.

Private credit lenders competed aggressively for access. Spreads compressed. Structures loosened. Covenant protections were diluted. Many loans were originated on ARR-based underwriting — a model that assumed the subscription revenue stream would remain durable. What it did not assume was that AI would arrive capable of performing the core functions that software companies charge subscription fees to provide.

The credit market, as UBS analysts noted in January 2026, has been slow to reflect what equity markets already priced in months earlier. Loan prices remained clustered near par even as software stocks fell 20–30%, and as bankruptcy filings in technology and business services rose. The gap between how equity markets and credit markets are pricing AI disruption risk represents one of the most significant analytical blind spots in private markets today.

What the Valuation Framework Is Missing

Private credit's fundamental challenge in this environment is that its valuation methodology was designed for stability, not for rapid fundamental change. Loans are typically marked near par — the price at which they were originated — regardless of deteriorating borrower fundamentals, because there is no transparent secondary market to provide a current price signal. Quarterly reporting cycles mean that stress can build for months before it is visible in portfolio data.

This structural opacity is not a flaw — it is a known feature of the asset class. But it becomes acutely dangerous when the disruption is rapid and sector-wide. By the time markdowns appear in NAV, the window to reposition has often already closed.

The practical implication: allocators who rely solely on manager-reported NAVs and quarterly updates are, by definition, operating with a significant lag. The information they receive reflects conditions that existed three to six months earlier. In a period of rapid AI-driven disruption, that lag is not a minor inconvenience — it is a material risk.

What Rigorous Monitoring Actually Requires

The response to this environment cannot be to avoid private credit. The asset class continues to offer attractive risk-adjusted yields, and the structural factors that drove its growth — bank retrenchment, demand for bespoke financing structures, private equity sponsor activity — remain intact. The response must be to upgrade the analytical framework used to monitor it.

Rigorous private credit monitoring in an AI-disruption environment requires four things that most allocators currently lack:

The asset managers who have fared best in this environment share a common characteristic: they maintained direct borrower engagement, applied scepticism to ARR-based valuations during the origination phase, and built portfolios with structural protections — senior secured positions, first-lien priority, stronger covenants — that create recovery optionality even in a deteriorating fundamental environment.

The Due Diligence Upgrade That Is Now Non-Optional

This environment has also fundamentally changed what rigorous due diligence on private credit managers looks like. The questions allocators need to ask have shifted from vintage-year return analysis to something more granular and forward-looking:

• What is the manager's true software and technology exposure, including adjacent sectors with software-driven business models?
• What proportion of investment income is coming from PIK structures, and is that share increasing?
• How did the manager underwrite ARR-based loans, and what assumptions were embedded about the durability of recurring revenue streams?
• What is the structural seniority and covenant quality of the portfolio — and how does recovery value hold up in an asset-light borrower stress scenario?
• How frequently does the manager engage directly with borrowers, and what are the early warning indicators they monitor?
• How does the manager's portfolio overlap with other private credit funds in the allocator's book?

These are not questions that can be answered by reviewing a quarterly report or a standard DDQ. They require active monitoring capability, direct manager engagement, and an analytical infrastructure capable of aggregating and interrogating data across an entire private credit allocation — not fund by fund in isolation.

The Bottom Line

Private credit is not broken. But the era of uniform performance across managers — when rising tides lifted all boats and the difference between a good manager and a mediocre one was obscured by benign conditions — is over. What is replacing it is a period of sharply widening dispersion, where the difference between disciplined underwriting and aggressive origination will be measured in default rates and recovery values rather than spread compression and deal volume.

For allocators, the imperative is clear: the analytical framework applied to private credit must now match the complexity and opacity of the asset class. That means moving beyond manager-reported data, building true sector exposure maps, stress testing against AI-specific disruption scenarios, and monitoring leading indicators of borrower stress continuously rather than quarterly.

The allocators who do that work now will be the ones who can navigate the dispersion ahead with confidence. Those who rely on the frameworks built for a different environment will find out about the problem in their portfolio at the same time as everyone else — which is almost certainly too late.