BIZD: Private Credit Is Still In Trouble

The persistent stress in private credit markets represents an underappreciated risk for the AI infrastructure buildout, particularly for mid-tier semiconductor equipment suppliers and data center developers that have increasingly relied on private debt to finance capital-intensive expansions. While the hyperscalers like Microsoft, Google, and Meta can tap public debt markets at favorable rates, the ecosystem of smaller players supporting AI infrastructure lacks that luxury.

Private credit has been a crucial financing mechanism for companies in the $500 million to $5 billion revenue range that are scaling production capacity to meet AI chip demand. These include specialty cooling system manufacturers, advanced packaging facilities, and regional data center operators. Many of these firms turned to private credit over the past three years precisely because it offered speed and flexibility that traditional bank lending couldn't match during the AI boom. The problem is that private credit funds themselves are now facing redemption pressures and mark-to-market losses on existing portfolios, making them considerably more selective about new commitments.

The implications are most acute for semiconductor capital equipment and materials suppliers. Companies expanding manufacturing capacity for advanced packaging, high-bandwidth memory production, or specialized substrates often need $200 million to $800 million in financing for new facilities. These projects have 18 to 24 month payback periods and are predicated on sustained AI chip demand. If private credit availability contracts, these companies face a difficult choice: delay expansion plans, dilute existing shareholders through equity raises, or accept more restrictive terms from strategic investors who may be competitors or customers.

We're already seeing evidence of this dynamic. Several second-tier foundries and packaging specialists have pushed out capacity expansion timelines in recent months, citing financing conditions rather than demand concerns. This creates a potential bottleneck in the AI supply chain that could emerge in late 2025 or 2026, precisely when demand for inference chips and custom AI accelerators is expected to accelerate beyond current training-focused buildouts.

The valuation impact extends beyond direct financing constraints. Private credit stress typically correlates with wider credit spreads across risk assets, which pressures the multiples of high-growth, cash-flow-negative AI companies. Many AI infrastructure plays trade at 15 to 25 times forward revenue with negative EBITDA, justified by growth narratives. If the cost of capital rises materially, these valuations become harder to defend, particularly for companies more than two years away from profitability.

There's also a competitive angle worth considering. Large, cash-rich semiconductor companies like TSMC, Samsung, and Intel could exploit tighter credit conditions to strengthen their ecosystem control. If smaller suppliers struggle to finance capacity expansions, the integrated players can selectively provide capital in exchange for long-term supply agreements or equity stakes, effectively consolidating supply chains. This might be strategically sound for the giants but reduces competitive intensity and optionality for chip designers and hyperscalers who benefit from a diverse supplier base.

The bull case for AI infrastructure spending assumes frictionless capital formation to support the massive physical buildout required. Private credit stress introduces friction that could slow deployment timelines and shift more economics toward companies with fortress balance sheets. For investors, this argues for overweighting established players with strong cash generation and underweighting high-growth stories dependent on continued easy access to growth capital. The AI thesis remains intact, but the financing environment increasingly favors incumbents over challengers.