Key Takeaways
- • Private credit lending to venture-backed startups continues to surge despite mounting concerns about AI-driven disruption to software companies, which represent approximately one-third of private credit fund portfolios and face maturity pressures in the next two years.
- • AI-related private market dealmaking exceeded $140 billion in 2024, a 460% increase from $25 billion in 2023, driven by sector-specific opportunities in infrastructure and GPU-backed assets that attract institutional capital differently than legacy software exposure.
- • Private credit faces two distinct AI risks with different mechanics: loans to legacy software companies vulnerable to AI displacement versus asset-backed private credit financing data centers and energy infrastructure, which present alternative risk profiles that market headlines often conflate.
- • Morgan Stanley analysis indicates potential for private credit defaults to reach pandemic-era highs if AI disruption of the software sector accelerates, creating liquidity and redemption pressure across private credit funds with significant SaaS exposure.
- • Investors are increasingly skeptical of private equity and loan structures premised on "recurring revenue" models in software, signaling a reassessment of borrower creditworthiness assumptions that underpinned pre-2024 lending standards.
Summary
Private credit lending to venture-backed startups has accelerated despite elevated concerns about artificial intelligence disruption reshaping the software sector. The apparent contradiction reflects a bifurcated market: while traditional software lending faces significant headwinds, capital continues flowing into AI infrastructure assets that command investor confidence. Private market dealmaking related to AI exceeded $140 billion in 2024, a dramatic 460% increase from $25 billion in 2023, demonstrating that disruption concerns have not dampened institutional appetite—they have simply redirected it.
The private credit market confronts two structurally distinct AI-related risks that typically collapse under singular headlines. First, approximately one-third of private credit funds have extended loans to the SaaS sector, exposing portfolios to legacy software companies vulnerable to AI-driven obsolescence and margin compression. About 20% of loans issued to software and related sectors will mature within the next two years, concentrating refinancing risk. Second, a surge in asset-backed private credit is financing GPU-backed infrastructure, data centers, and energy grids supporting AI computation—a fundamentally different credit thesis with different default mechanics and collateral dynamics.
Morgan Stanley's recent analysis projects that AI disruption of software could elevate private credit defaults to their highest levels since the pandemic, intensifying liquidity and redemption pressure across funds. This risk crystallizes as investors grow skeptical of private equity and loan structures built on assumptions of predictable, recurring revenue streams in software categories now subject to rapid technological displacement. The market's skepticism reflects a fundamental reassessment of which borrowers retain durable competitive moats in an AI-accelerated environment.
The divergence between continued lending volume and rising default concerns underscores institutional investors' ability to distinguish between sectors. While legacy software lending faces structural headwinds, capital continues to flow toward infrastructure-backed private credit and AI-adjacent sectors offering different risk-return profiles. This segmentation will likely define private credit performance through 2027, with fund positioning and portfolio composition determining exposure to both the opportunities and disruption risks embedded in AI's continued expansion across enterprise and infrastructure markets.
The private credit market confronts two structurally distinct AI-related risks that typically collapse under singular headlines. First, approximately one-third of private credit funds have extended loans to the SaaS sector, exposing portfolios to legacy software companies vulnerable to AI-driven obsolescence and margin compression. About 20% of loans issued to software and related sectors will mature within the next two years, concentrating refinancing risk. Second, a surge in asset-backed private credit is financing GPU-backed infrastructure, data centers, and energy grids supporting AI computation—a fundamentally different credit thesis with different default mechanics and collateral dynamics.
Morgan Stanley's recent analysis projects that AI disruption of software could elevate private credit defaults to their highest levels since the pandemic, intensifying liquidity and redemption pressure across funds. This risk crystallizes as investors grow skeptical of private equity and loan structures built on assumptions of predictable, recurring revenue streams in software categories now subject to rapid technological displacement. The market's skepticism reflects a fundamental reassessment of which borrowers retain durable competitive moats in an AI-accelerated environment.
The divergence between continued lending volume and rising default concerns underscores institutional investors' ability to distinguish between sectors. While legacy software lending faces structural headwinds, capital continues to flow toward infrastructure-backed private credit and AI-adjacent sectors offering different risk-return profiles. This segmentation will likely define private credit performance through 2027, with fund positioning and portfolio composition determining exposure to both the opportunities and disruption risks embedded in AI's continued expansion across enterprise and infrastructure markets.
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