The AI revolution is a double-edged sword for software. While some firms will boost margins using AI for development, many others, particularly smaller, leveraged companies, face existential disruption. This creates significant shorting opportunities for investors who look beyond current earnings to future terminal value.

While over $40 billion in software loans are stressed, this reflects market perception of future AI disruption rather than current performance degradation. Key fundamentals like net retention and revenue growth remain relatively healthy. The real risk lies in a company's inability to adapt and its software's ease of replacement.

Historical analysis of distressed cycles in sectors like energy and retail shows that roughly one-third of the industry's debt defaulted over a two-year period. Applying this precedent to the software sector, which has approximately $300 billion in debt, suggests a potential default wave of around $100 billion if current pressures continue.

With fewer traditional credit cycles, the most fertile ground for distressed investing lies in industry-specific downturns caused by technological or policy shifts. These "microcycles" offer opportunities to invest in good companies working through temporary, concentrated disruption.

As over-leveraged software companies fail, a new investment class will emerge. "Software special situations" funds will acquire these distressed assets from creditors, abandon growth-at-all-costs, and focus on restructuring for profitability and dividends, akin to a Constellation Software model.

The recent software stock sell-off is rooted in investors' inability to confidently price long-term growth (terminal value). While near-term earnings might be strong, the uncertainty of future business models due to AI is causing a fundamental reassessment of what these companies are worth.

The sectors with the most distress are tech, healthcare, and services, specifically among companies taken private via leveraged buyouts. Many of these deals were predicated on aggressive synergies and growth that failed to materialize, leaving them far more levered than originally planned and vulnerable to downgrades.

A significant portion of private credit is concentrated in software companies. Many of these loans were made when rates were low, often with high leverage and weak terms. The emergent threat of AI-driven disruption to their business models now adds a new layer of fundamental risk to this already vulnerable cohort.

Recent financial distress in large, private equity-owned software companies is being misattributed to the threat of AI. The actual cause is over-leveraging when interest rates were low, followed by an inability to service that debt as rates rose and growth slowed. It's a credit problem, not a technology disruption problem.