8-minute read.
Key Takeaways
- The software subsector is experiencing rapid repricing as investors reconsider the resilience of traditional enterprise software business models in light of accelerating AI adoption.
- The core issue is structural. AI is challenging longstanding assumptions around seat-based pricing, feature-driven product differentiation, competitive barriers to entry, and the longevity of established software platforms.
- While large investment grade software companies have so far seen only modest credit spread widening, midsized and highly levered borrowers are experiencing pronounced pressure.
- Heavy selling comes as AI infrastructure investment accelerates, increasing competitive pressure faster than incumbents can adapt.
- The breadth of the selloff is likely to create opportunities for active investors who can distinguish between business models that are structurally exposed to AI substitution and those that remain inherently durable.
By Garrett Olson, CFA® | Head of Credit
Understanding the Current Stress in Software Securities
A recurring theme we have emphasized for years is the increasing speed of cycles, defined by rapid compression of sentiment shifts, valuation moves, and capital structure repricing. That dynamic is now playing out forcefully in the software subsector, emerging as one of the most important market stories of 2026.
Over the past several months, investors have begun to fundamentally reassess the outlook for the software subsector. The pace of change within the industry, driven largely by rapid advances in AI, has raised important questions about how traditional enterprise software business models may evolve.
What matters most is not simply that conditions are changing quickly, but what that speed represents: markets are attempting to price in the potential long-term impact of AI faster than companies can demonstrate how they will adapt. This acceleration in narrative, positioning, and repricing is itself part of the broader cycle-speed theme.
This reassessment is broad in scope. It influences how investors think about software across the entire capital structure, from equities to investment grade credit, leveraged loans, CLO exposures, and private credit portfolios.
Why Are Software Securities Being Reconsidered?
For much of the past decade, the software subsector has been viewed as one of the most stable, defensible, and highly valued areas across both equity and credit markets. Investors have treated software companies as structurally resilient businesses, built on subscription-based recurring revenue, low customer churn, predictable cash flows, and consistently high gross margin. Many business models were and still are priced per employee or per “seat,” creating a naturally expanding revenue base as companies grew. This stability supported high valuations and made the subsector attractive to lenders and private equity sponsors, allowing software businesses to take on meaningful leverage.
As a result, software became a sizable component of leveraged credit markets, making up 4.7% of the High Yield market and 14% of the Leveraged Loan market. But as AI capabilities accelerated in late 2025, investors began rethinking many of the assumptions that had supported software’s reputation for stability. Questions emerged about how well traditional models would hold up if AI changed how work was done, altered pricing dynamics, or enabled faster-moving competitors. This growing uncertainty about the business model’s foundation ultimately sets the stage for how markets will react next.
AI Challenges Traditional Software Business Models
As investors began questioning the durability of traditional enterprise software models, the most immediate evidence of this shift appeared in market pricing. In the fourth quarter of 2025, the S&P 500 Software Index (S5SOFT) fell over 10% between October 28th and year-end, dramatically underperforming the broader S&P 500 by 978 basis points over that period and by 623 basis points for the full year.
Moves of this magnitude, especially against the broader market, seemed to signal that investors were no longer treating these companies as insulated from disruption. This repricing was rooted in several structural concerns about how AI interacts with the core economics of software. The first relates to seat-based pricing, which has long been one of the industry’s most reliable revenue drivers. If AI allows firms to maintain or increase productivity with fewer employees, then software companies that monetize per user could see slower growth, or even contraction, in one of their most stable revenue streams. Investors are also focused on how AI can replace individual features or entire workflows that previously required dedicated software modules. Tasks like generating reports, summarizing information, or automating routine processes can now be handled by AI systems. Furthermore, AI can make it easier for enterprises to have internal software engineers use coding agents to quickly replicate features or create alternatives to third-party software. When AI can replicate functionality that once supported entire product lines, it raises questions about how essential some legacy tools will remain.
A third concern is the shift in competitive dynamics. AI lowers barriers to entry for new software competitors, enabling them to build sophisticated functionality far more quickly than in prior technology cycles. At the same time, incumbents must accelerate their own innovation efforts, often with complex legacy codebases and customer commitments that slow their ability to respond. This dynamic challenges the pricing power and market dominance that have historically supported premium valuations.
Finally, AI compresses product cycles, increasing the risk of displacement. Software platforms that once evolved over predictable, multiyear timelines may now face competition that iterates much faster. This raises the possibility of shorter product lifecycles and earlier obsolescence than investors previously assumed.
Since these pressures directly affect the very foundation of software business models: pricing, feature differentiation, competitive positioning, and the longevity of core products, the market has lowered growth expectations and increased the discount rate related to future cash flows, resulting in contracting multiples and falling securities prices. As the concerns about enterprise software’s underlying economics gained traction, the shift in investor behavior and positioning became more extreme.
According to Goldman Sachs, hedge fund net exposure to software fell to a new record low, making it the most net-sold subsector year-to-date. Heavy selling has driven the S&P 500 Software Index (S5SOFT) down 17.54% year to date, a stark contrast to the broader market backdrop. Over the same period, the S&P 500 Equal Weighted Index is up 4.60%, and the Russell 2000 is up 5.83%. This level of underperformance suggests more than a rotation or a risk-off move; it indicates a deep repricing of expectations for the subsector as a whole.
In the equity market, software subsector declines are not limited to marginal or speculative companies. Instead, the valuation reset is occurring in some of the largest, most diversified, and most competitively advantaged software businesses in the market. That said, the reaction in credit markets has been more measured at the top end of the quality spectrum. Investment-grade software credit has widened only marginally, indicating that while equity multiples have contracted and debt/enterprise value (EV) ratios have increased, the probability that near-to-medium-term cash flows reset in a way that jeopardizes balance sheets or access to capital for the highest-quality issuers is low. The picture becomes much more concerning, however, as you move down the quality ladder.
Where the Stress Is Most Acute: Highly Levered and Midsized Software Borrowers
Looking at smaller, less diversified, and higher-levered issuers, we see real pressure building. Software businesses have historically been able to support aggressive leverage, with the average Debt/EBITDA for leveraged loan technology borrowers at 5.29x as of Q3 2025. That level of leverage is manageable when cash flows are perceived as highly stable, but it becomes a point of vulnerability the moment investors begin to question earnings durability.
As AI-related concerns have intensified, this part of the market has deteriorated quickly. Software leveraged loan spreads have widened 132 bps year-to-date to 662 bps, the widest since June 2023. At the same time, nearly $18 billion of U.S. tech company loans have fallen to distressed trading levels over the past four weeks, the highest amount since October 2022. These are not small moves, and they signal that investors are already assigning a meaningful increase in expected default probabilities, even though reported earnings for most companies have not yet shown material deterioration.
How the Stress Spreads: CLOs, Private Credit, and Publicly Traded Alternative Managers
As concerns about highly leveraged software issuers intensify, the broader lender and investor ecosystem is beginning to feel the effects. Because leveraged software borrowers are a major component of CLO portfolios and private credit funds, weakness in software credit flows directly into these vehicles.
Public BDC stocks are down 7–9% since mid-January, a clear indication that investors are pricing in the possibility of future credit losses or portfolio impairments. The impact is even more pronounced among larger alternative asset managers with significant exposure to leveraged equity positions: some publicly traded private equity firms are down 16–21% over the same period. These moves are consistent with the idea that stress in leveraged loans is filtering into portfolios long before any actual defaults emerge.
Additionally, Bank of America estimates that the technology sector accounts for roughly 20% of the private credit market, suggesting the subsector’s challenges may have an outsized influence on portfolio-level risk. This sequence of stress in leveraged loans, followed by weakness in CLOs, BDCs, and private credit managers, illustrates why the pace of this cycle matters. Markets are not waiting for defaults or missed interest payments. Instead, they are looking ahead, repricing risk based on the rising probability of future stress.
AI Investment Is Accelerating
Despite the quick repricing seen across software equities and credit, nothing suggests that these concerns will fade quickly. In fact, the opposite seems true. Recent hyperscaler capex forecasts have been substantially higher than expected, underscoring just how quickly investment in AI compute, training, and infrastructure is accelerating. This surge in investment sends a clear signal about the direction of the broader technology landscape. AI innovation continues to accelerate, with new models, tools, and capabilities advancing faster than most companies can adapt. At the same time, enterprise adoption is deepening as organizations rapidly incorporate AI into core workflows to achieve cost savings and productivity gains. This reinforces the pressure on legacy software models, many of which were built around assumptions that AI is now challenging directly.
The takeaway is simple: competitive pressure on software incumbents won’t fade; it will persist and adapt as AI capability and adoption advance.
Where the Opportunity Exists
The breadth of the selloff is likely to create opportunities for active investors who can distinguish between business models that are structurally exposed to AI substitution and those that remain inherently durable. The most compelling opportunities lie in companies whose value comes not from isolated features but from deep system integration and critical functionality. These include software businesses that function as systems of record and store essential enterprise data; platforms tied to regulatory or compliance requirements; products with high switching costs and deep workflow integration; and tools that are truly mission-critical, rather than feature-driven. These models benefit from stronger competitive moats and more resilient demand, attributes that become even more important in a fast-moving innovation cycle. For some software businesses with access to critical data sets, incorporating AI into existing products may even serve as a tailwind as value for customers compounds rapidly.
In the end, the pressures appearing in equities, leveraged loans, CLOs, and private credit all stem from the same source: investors are grappling with how rapidly AI is altering the economics of traditional enterprise software. As AI continues to advance and enterprises integrate it more deeply, the questions confronting the software industry will not disappear; they will evolve.
And that leads to the broader question now facing investors: what do current software valuations imply about future growth, margins, and cash flows, and where are market expectations likely to differ from realized outcomes?
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