Here’s something that hasn’t happened in nearly two decades: European lenders are telling software companies they need to start repaying their loans on a schedule. Not at maturity. Not whenever they choose. Now.

The return of amortization requirements to Europe’s leveraged finance market marks a notable shift in how creditors are assessing the software sector. The last time these repayment terms were widely used in Europe was during the global financial crisis. This time, the trigger is not a banking meltdown. It is artificial intelligence.

What is happening

Lenders in Europe’s private credit and leveraged finance markets are increasingly asking software borrowers to commit to gradual principal repayments over the life of their loans, rather than relying on bullet-maturity structures that have dominated the market for years.

Paysafe Ltd. provides one example. The payments company has proposed 5% annual repayments on its original loan principal in exchange for a two-year extension to its loan maturity.

Investors are also reportedly preparing to seek similar amortization terms in upcoming negotiations with think-cell Software GmbH, a company backed by private equity firm Cinven.

The concern driving this change is direct: lenders are worried that AI could reshape the software industry before the loans come due. If a product can be replicated or rendered obsolete by an AI system, a borrower’s revenue stream becomes less predictable.

That matters because the market’s long-standing comfort with software lending was built on the assumption that recurring subscriptions, sticky customers, and high margins would support refinancing years down the line. Amortization does not remove that risk, but it does give lenders a way to recover capital progressively instead of waiting for a single repayment event at maturity.

Why lenders are concerned

For years, software companies were favored borrowers in leveraged finance. Recurring revenue models, high margins, and sticky customer bases made lenders comfortable extending large credit facilities with limited repayment requirements.

AI has complicated that view. Lenders are now questioning whether the advantages that made software businesses attractive — including proprietary algorithms, user interface advantages, and switching costs — will remain durable as AI tools become more accessible and cheaper to deploy.

The issue is fundamentally one of refinancing risk. A lender that provides a bullet loan to a software company is betting that the company will still be creditworthy enough to refinance when the loan matures. If AI disruption weakens the borrower’s competitive position, the lender could be left with debt that is difficult to refinance. Amortization reduces that risk by returning some capital before maturity.

This shift also reflects a broader change in how creditors are underwriting technology businesses. Rather than treating software as a relatively uniform lending category, lenders are increasingly distinguishing between companies with defensible data, deep enterprise integration, or other barriers to replacement and those whose products may be easier to imitate or substitute.

What it means for investors

For software companies backed by private equity, amortization requirements reduce free cash flow available for growth investment, dividends, or additional leveraged acquisitions. A 5% annual repayment on a large loan can amount to a significant cash burden that must be funded somewhere.

Companies with thinner margins or slower growth are likely to feel the pressure most sharply.

The tighter financing environment could also create a split within the software sector. Companies that can demonstrate real protection against AI disruption — through proprietary data assets, deep enterprise integrations, or regulatory advantages — are more likely to keep access to favorable financing terms. Those that cannot make that case convincingly may face higher borrowing costs.

For borrowers, the immediate issue is not just cost but structure: lenders are using repayment schedules to reserve more downside protection in a market where AI has introduced a new layer of uncertainty into familiar credit assumptions.