NextFin News - European lenders are finding it harder to keep software borrowers on ordinary repayment schedules as AI fears, weak underwriting and a refinancing-led loan market push credit terms in a more lender-protective direction. The broad shift is not a one-off glitch. It looks more like a structural repricing of software credit, where lenders are asking for tougher repayment mechanics because they no longer trust the old growth assumptions that once justified looser terms.
The immediate backdrop is a European leveraged finance market that has been dominated by refinancings, amendments and repricings rather than fresh risk-taking. CreditSights said its European leveraged finance covenant review for the second quarter of 2026 found that documentation continues to evolve amid record issuance volumes and a refinancing-driven market, with a growing imbalance between borrower and lender negotiating power. The same week, the Bank of England warned that some highly leveraged corporate borrowers reliant on riskier credit markets are especially exposed to tighter financing conditions, while noting that refinancing walls for riskier debt are steeper and that around 20% of riskier debt needs refinancing by the end of next year.
That combination matters because software borrowers sit at the intersection of those two pressures. Their cash flows are often valued on the assumption that growth, retention and margin expansion will stay intact long enough to support generous leverage. But when public investors start retreating from leveraged software issuers because of uncertainty around AI’s effect on incumbent business models, the burden shifts toward private credit lenders and specialist loan investors who can still underwrite the names. The result is a market that may still be liquid, but is clearly less forgiving. Lenders are not just charging more for risk; they are trying to change how risk gets repaid.
The unusual part is not that lenders want protection. It is that repayment mechanics, which are usually routine and formulaic, are becoming a negotiation point again. In a healthier credit cycle, borrowers can often roll loans, extend maturities or amend terms with limited friction. In this market, lenders appear to be using tighter repayment language to preserve optionality if earnings disappoint or if software valuations fall further. That suggests a shift in bargaining power, not merely a momentary wobble in sentiment.
This is why the story should be read as more than a software-sector headache. It is a test of whether Europe’s leveraged loan market is moving from a cyclical period of easy refinancing into a more structural phase where lender caution, AI uncertainty and weaker underwriting together force borrowers to accept terms that would have been harder to impose only a year ago.
Why Software Credit Is Losing Its Old Cushion
The central question is not whether lenders have become more cautious. They always do when underwriting quality weakens. The real question is why software borrowers, in particular, are losing the cushion that once let them borrow aggressively against sticky recurring revenue and stable growth narratives.
One answer is that the market’s prior framework has stopped working cleanly. For years, software debt was often justified by the sector’s high recurring revenue, high gross margins and relatively low capital intensity. That made leverage look manageable even when headline earnings were thin. But AI has complicated the premise. The Bank of England said the outlook has worsened because of uncertainty around the impact of widespread AI adoption on incumbent companies’ business models and profitability, with software singled out as a sector where risky credit markets have significant concentration. In plain terms, the old assumption that software distributions and margins would simply normalize upward is now more doubtful.
That is a structural point, not a cyclical one. A cyclical credit tightening would usually ease when growth returns, spreads compress or funding conditions improve. Here, the market is reacting to a possible regime change in how software companies create value. If AI reduces the durability of subscription pricing, weakens switching costs or shortens product cycles, then the credit profile changes at the source. That is the sort of shift lenders cannot solve by waiting for the next liquidity upswing.
There is also a direct transmission channel from equity sentiment to loan terms. When public-market investors retreat from leveraged software issuers, lenders face a higher probability that the borrower’s valuation, access to capital and strategic flexibility all deteriorate at once. That is why tighter repayment terms matter. They reduce the lender’s dependence on a clean exit through refinancing, and they create a faster path to cash recovery if the borrower underperforms. In other words, repayment terms are becoming a substitute for certainty that the business model no longer provides.
The market has already begun to price that uncertainty. S&P Global Ratings said in its July 2026 European leveraged finance monthly that uncertainty around the impact of AI is prompting public investors to retreat from leveraged software issuers, creating opportunities for private credit lenders. That sentence is important because it shows the trade is no longer simply about spread. It is about who is willing to own the risk at all. When the buyer base narrows, the remaining lenders can ask for more favorable terms, including tighter repayment features, because borrowers have fewer alternatives.
“Uncertainty around the impacts of AI is prompting public investors to retreat from leveraged software issuers, creating opportunities for private credit lenders.”
That is the first-order effect. The second-order effect is more interesting: as lenders demand more protection, the market may actually accelerate the divergence between strong and weak software credits. Better names can still refinance, but weaker ones may be forced into earlier restructuring conversations or more restrictive schedules. The terms themselves can become a sorting mechanism.
The strongest counter-thesis is that this is just another temporary tightening phase. Credit cycles are cyclical by nature, and once rate volatility fades and software growth stabilizes, lenders may revert to looser structures. That view has merit. The European leveraged loan market has repeatedly shifted from caution to competition when capital floods back in, and the current environment still features abundant liquidity for higher-quality borrowers. But the falsifying signal for the structural thesis is clear: if lender-friendly repayment terms stop spreading beyond a few stressed software credits and if refinancing spreads revert without a sustained improvement in software earnings quality, then this will have been a short-lived squeeze, not a regime shift.
Why Repayment Terms Matter More Than Spread
The real market signal is not just wider pricing. It is the return of structure as a negotiating lever.
In leveraged finance, spread is the visible price of risk, but repayment terms determine how quickly that risk is realized. A borrower can survive an extra 50 or 75 basis points if the business is still growing. It is much harder to absorb tougher amortization, tighter cash-sweep features, or repayment schedules that leave less room to kick the can down the road. That is why repayment terms are rare when credit is easy: lenders do not need them. They become more common only when lenders fear that the borrower’s future earnings may not be there when refinancing time comes.
The Bank of England’s warning about refinancing walls is useful here because it highlights the timing pressure behind the market change. If around 20% of riskier debt needs refinancing by the end of next year, then lenders know that a meaningful share of the market will come back to them under less favorable conditions. In that setting, taking a harder line now can be rational. It protects lender economics before the next maturity wall arrives. It also reduces the chance that a borrower uses the next refinancing window to escape weak current terms.
That logic is especially relevant in software because valuation and credit quality can move together. When software multiples compress, debt investors are not just looking at leverage ratios; they are looking at how much room remains before the equity cushion disappears. If the business model is being questioned at the same time as refinancing conditions tighten, then lenders may insist on repayment terms that force deleveraging sooner. The market is effectively saying: prove the earnings quality now, or pay down debt faster.
This is where the second-order implication becomes broader than software. Once lenders start using repayment mechanics as a defense against business-model uncertainty, the practice can spread into adjacent sectors with similar growth dependence: IT services, business services, even parts of healthcare technology. The trigger is not sector label alone. It is the combination of intangible value, refinancing dependence and a market story that may no longer command automatic trust.
CreditSights’ view that documentation continues to evolve amid record issuance volumes and a refinancing-driven market fits this pattern. Its covenant review describes a growing imbalance between borrower and lender negotiating power. That imbalance is not abstract. It tends to show up in exactly these places: extension rights, amortization schedules, cash traps, portability, add-backs and other provisions that determine who gets the benefit of time.
“Documentation continues to evolve amid record issuance volumes and a refinancing-driven market, with a growing imbalance between borrower and lender negotiating power.”
The argument against that interpretation is that stronger repayment terms may simply reflect cleverer underwriting, not a regime shift. If lenders learned from a few bad software credits and are now demanding better protection only in the weakest names, then the broader market could remain healthy. But that thesis weakens if borrower-friendly structures keep shrinking even for credits with stable recurring revenue and solid cash conversion. That would show the change is not just about bad actors. It would show lenders now doubt the sector-wide script.
What Would Prove This Is A Cycle, Not A Regime Shift
The cyclical case rests on mean reversion. The structural case rests on a changed underwriting framework. The evidence currently leans toward the latter, but not decisively enough to close the debate.
A cyclical reading would need three things: a short-lived liquidity squeeze, a temporary widening of spreads and a quick restoration of confidence once AI fears cool. That is plausible. European credit markets have often swung from caution to competition within a few quarters. The presence of private credit also gives borrowers an alternate funding channel that can soften pressure. If public lenders retreat, private lenders may step in, keeping issuance flowing even if terms are tougher.
But the structural evidence is stronger because it hits the model at its core. First, AI uncertainty is not just affecting valuations; it is altering the perceived durability of software revenue. Second, lenders are reacting not by merely repricing loans but by pushing for more restrictive repayment mechanics. Third, the Bank of England explicitly tied the risk to highly leveraged borrowers in riskier credit markets, where refinancing walls are steeper and investor demand can weaken fast. Those are features of a more durable re-underwriting cycle.
There is a practical analogy here. Spread is the toll charge. Repayment terms are the road design. If lenders are redesigning the road, they are no longer just asking for more money at the booth; they are deciding how fast the borrower can travel and whether the route has exits. That is a deeper response than a normal rate adjustment.
The strongest counter-thesis deserves a serious hearing. A large share of the market still believes software will emerge as an AI beneficiary rather than a casualty. If AI boosts productivity, strengthens product offerings and raises barriers to entry for the best platforms, then today’s lender caution could look overdone. In that scenario, tougher repayment terms would be a temporary overreaction to uncertainty, and once the winners separate from the losers, credit terms could normalize. That is not a fringe view; it is a mainstream bullish case for software equity and a plausible one for top-tier credit.
The signal that would falsify the structural view is concrete: if over the next two to three refinancing windows, lenders stop demanding tighter repayment mechanics and the spread differential between leveraged software credits and comparable non-software issuers narrows back to pre-AI levels without a rebound in software earnings durability, then the market has merely overcorrected. If, instead, repayment flexibility keeps shrinking while refinancing remains lender-favored, the structural verdict will be hard to escape.
For now, the balance of evidence says lenders are not just worried about the next quarter. They are questioning the next version of the business model.
What Happens Next
In the short term, the beneficiaries are private credit funds and specialist lenders that can price bespoke risk and negotiate tighter controls. The exposed names are software borrowers with higher leverage, weaker free cash flow and heavier refinancing needs. Medium term, the key question is whether this pressure stays contained to lower-quality credits or spreads into the broader European leveraged loan market. Long term, the outcome hinges on whether AI becomes a productivity tailwind for incumbents or a permanent impairment to the revenue durability lenders once assumed was safe.
The base case is that lender-friendly repayment terms remain more common in software and adjacent growth sectors through the next refinancing cycle, especially where the equity cushion is thin and the borrower depends on continued multiple support. The upside case for borrowers is a rapid improvement in software operating performance and AI monetization that restores confidence and relaxes terms. The downside case is a broader repricing of growth credit, where repayment protection spreads from software into other intangibles-heavy sectors and borrower optionality shrinks further.
The key falsifying signals are clear. If software earnings and free cash flow stabilize while lenders still insist on unusually restrictive repayment language, then the market will have overreacted to AI anxiety. If, however, lenders continue to tighten structure even as spreads compress, that would mean the negotiation has moved from pricing risk to rewriting it.
The market is no longer just asking what software is worth. It is asking how fast it should be paid back if the answer changes.
Explore more exclusive insights at nextfin.ai.
