The disruption caused by Artificial Intelligence has shifted from a product debate to a funding issue. After weeks of stock market downturns, the software sector is beginning to feel the next domino fall: debt is also becoming more complicated. Several companies are delaying credit operations because they now face a dual filter: still-high interest rates and, more importantly, a much more rigorous assessment by banks and debt buyers, who no longer finance “stories” but actual capacity to withstand AI-related challenges.
The market’s message is clear: if part of the software industry becomes more replaceable — through automation, generative tools, or generalist platforms — then the risk isn’t just “growing less.” It’s worse repayment conditions, refinancing under greater strain, and a risk premium that didn’t previously exist.
From stock market hit to credit premium
The first phase of this story was visible on the stock exchange: the S&P 500 Software & Services index has shed around $2 trillion since its October high, according to Reuters. This adjustment isn’t solely explained by macroeconomics or interest rates. It stems from an uncomfortable question now dominating investment committees: what portion of software revenues is protected when AI reduces costs for functions that used to be charged at a premium?
The second phase involves credit. The same logic that compresses multiples in the stock market now pushes up the cost of money. If the market perceives your future growth as more fragile, it demands:
- greater profitability (higher interest or more discounting at issuance),
- more collateral (stricter clauses),
- and increased cash visibility (less trust and more metrics).
Why the impact is felt first in leveraged loans
Not all credit reacts equally. The focus is particularly on leveraged loans, where the tech sector — especially software — has a larger share. Here, the market is more sensitive to any structural threat because the margin of error is small: high debt levels, weaker ratings, and reliance on refinancing.
Reuters reports that the tech exposure in leveraged loans in the U.S. amounts to $260 billion, compared to around $60 billion in high-yield bonds. This size difference matters because it concentrates risk “where it hurts most”: in instruments where a small confidence drop can quickly lead to worse conditions.
Table — Where “AI Fear” First Affects Debt
| Market | Why it’s more sensitive | Common developments |
|---|---|---|
| Leveraged loans | More tech, weaker ratings, dependence on refinancing | Spreads widening, covenants tightening, deal pauses |
| High yield | Less software concentration, more diversified base | Gradual adjustments, increased issuer selectivity |
UBS: risk begins to be “priced and labeled” between 2026 and 2027
The key point isn’t just that AI “scares” investors. It’s that financial houses are already factoring in timeframes. UBS warns that disruption risk due to AI could increasingly be reflected between 2026 and early 2027, and in a faster-disruption scenario, default rates could jump to 3%–5%, exceeding what consensus expects, according to Reuters.
This doesn’t mean the sector will collapse. It indicates that the market is starting to treat them as “businesses in transition”, not as machines with inherently protected recurring revenues.
The real trigger: “Replaceability” and pricing power
In credit, risk is measured with ratios. But the root of the problem is more conceptual: if software is no longer perceived as unique, its ability to command prices diminishes. And if pricing power weakens, margins compress. And if margins shrink, the cost of capital rises because the market perceives less cushion for debt repayment.
This is why the impact is most pronounced on lower ratings. Reuters highlights that approximately half of software exposure in leveraged loans is in tranches B- or below: exactly where investors become allergic to any structural threat. A “B-” with near maturity and uncertain growth doesn’t need to go into crisis; it’s enough for the market not to want to refinance cheaply.
The symptom: deal freezes and pipeline drying up
When a market begins to distrust, the first thing that stalls is the flow. Reuters points out that, at that moment, there were no ongoing leveraged loan deals for software companies, partly because issuers and banks expect credit prices to stabilize after declines since late January.
Additionally, large acquisition-related packages become stress tests. Reuters cites the example of Qualtrics’ $5.3 billion financing as a thermometer of appetite. If debt buyers demand too much, the cost shifts to the deal… or the deal slows down.
In Europe, the pattern is similar: Reuters mentioned that Team.blue postponed covenant changes and loan extensions because of market deterioration.
Secondary effect: deeper discounts to sustain revenues
The “product” consequence sneaks back in: if financing becomes more expensive, the pressure to maintain revenues intensifies. In software, this often leads to discounts, value-added packages, “bundles,” and in some cases, a shift toward hybrid models where software is sold as a platform with integrated AI to prevent customers from perceiving they can do the same “for free” or with fewer licenses.
This puts the industry at a delicate juncture: lowering prices to protect volume can erode margins; maintaining prices with declining demand weakens cash visibility; and weaker visibility leads to more credit tightening. It’s a cycle the market is already beginning to anticipate.
What changes from now on: AI as an invisible loan clause
By 2026, the tech sector will see a new form of due diligence emerge: beyond churn, ARR, and gross margin, there’s an assessment of which parts of the product could be automated by AI and how much of the value proposition depends on functions that could become “commoditized.”
The conclusion is uncomfortable but clear: AI isn’t just rewriting software; it’s rewriting the software’s cost of capital. For many companies, this could be more disruptive than any new feature.
Frequently Asked Questions
Why are investors demanding higher returns on software debt because of AI?
Because they perceive more structural risk: if AI reduces differentiation and pricing power, revenue predictability drops, and safety margins for debt repayment shrink.
Which companies will fare worst in 2026?
Those with low ratings (B- or below), high leverage, and short maturities, especially if their products are easily substitutable by alternatives (other suites, platforms, or AI automation).
What metrics will banks look at now before financing software?
Beyond ARR, they will consider churn, net retention, operating margin, cost discipline, cash visibility… and signs of competitive defense against AI (integrations, switching costs, proprietary data, compliance).
Can this improve if interest rates fall or the market stabilizes?
It might ease the pressure, but the core issue is structural: the perception of replaceability. Even if rates fall, credit will continue to differentiate more between “defensive software” and “disposable software.”