Executive summary
- Rapid advances in artificial intelligence are driving a reassessment of software credit risk and igniting market volatility. Credit investors are now evaluating broader balance sheet implications in the software space.
- While the software sector represents as much as 13.9% of the leveraged loan market, according to Morningstar LSTA US Leveraged Loan Index, it is a much smaller component of the US investment grade and high yield market at approximately 2.2% and 3.4%, respectively (ICE Data, JPMorgan).
Figure 1: Software exposure in index
Data as of January 31, 2026
Source: ICE Data, Bloomberg, JPMorgan.
- In our view, displacement scenarios appear unlikely in the near term as we see AI development and innovation as a multi-year cycle. However, margin pressure, IT budget reallocation, and seat-based revenue erosion represent credible medium-to-long term risks for certain issuers.
- While we remain broadly underweight software, we prefer to lean into infrastructure-related software and compliance-critical platforms which appear to be better positioned from a credit perspective due to higher switching costs and mission-critical use cases.
AI as a catalyst for software credit repricing
The proliferation of accessible AI tools has introduced a new structural variable in the software credit landscape. After several years of robust growth driven by a historic investment upcycle, investors are increasingly questioning whether legacy software business models can sustain cash flows as AI expands the range of tasks that can be automated or internally developed.
More recently, market participants have become more discriminating, particularly toward issuers lacking a credible AI narrative. Equity declines have been followed by widening credit spreads, reflecting concerns around medium-term earnings durability, refinancing risk, and leverage trajectories rather than near-term liquidity stress.
In this environment, credit markets are prioritizing downside protection over upside optionality, leading to greater dispersion across the sector.
Key credit risks to consider
The most extreme bear cases assume enterprises increasingly rely on AI tools to build internal software solutions, reducing demand for third-party vendors. While AI coding assistants have meaningfully improved developer productivity, several risks limit near-term displacement including:
- Pressure on seat-based and usage-based pricing models if AI adoption reduces enterprise headcount
- IT budget reallocation toward AI infrastructure, limiting pricing power and elongating sales cycles
- Margin compression as vendors invest to remain competitive while facing capped pricing
- Reduced deleveraging capacity, particularly for leveraged issuers reliant on EBITDA growth assumptions
Infrastructure vs. application software: credit implications
Software issuers can broadly be categorized as infrastructure or application providers. Infrastructure software underpins core enterprise operations and carries high switching costs, whereas Application software is more modular and discretionary, making it easier to replace, consolidate, or eliminate.
From a credit standpoint, application-layer issuers face greater downside risk, particularly where products are not embedded in mission-critical or regulated workflows. Infrastructure providers are generally more insulated due to revenue stickiness and operational dependency. For this reason, we see both better value and potential downside protection through the Application subsector.
What to watch: investment implications for credit investors
As we evaluate individual credits in this environment, we’re focused on several key indicators that will signal whether these risks are materializing.
Revenue durability remains paramount. We’re closely monitoring exposure to seat-based pricing models, churn trends, and renewal rates as enterprises reassess software spend. Any deterioration in net revenue retention or elongation in sales cycles would be an early warning sign.
Margin trajectory provides insight into competitive positioning. We’re watching whether issuers can absorb AI related investments without structurally impairing EBITDA, or whether margin compression becomes persistent rather than cyclical.
Figure 2: % of BDC sector exposures by fair value
Source: Barclays
Capital intensity is increasing for many software companies. Rising capex and R&D requirements to maintain competitiveness signal potential cash flow pressure, particularly for companies that historically operated asset-light models.
Leverage and covenant headroom deserve heightened attention. Slower growth assumptions embedded in capital structures may prove optimistic, creating refinancing risk for issuers approaching maturities. We’re particularly focused on maintenance covenant cushions for revolving credit facilities.
Finally, business criticality remains the most important qualitative factor. We continue to prefer issuers tied to infrastructure or compliance-sensitive workflows where switching costs are high and the software is genuinely mission-critical to enterprise operations.
Conclusion
We expect continued pressure across software credit as AI adoption reshapes investor perceptions of long-term cash flow stability. Risks are unevenly distributed and infrastructure and mission-critical software providers appear relatively better positioned. Application-layer issuers with discretionary use cases warrant closer scrutiny in portfolios. Market dislocations of this nature also create opportunities and we remain vigilant for issuers that are being mispriced relative to their fundamental resilience.
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