Let There Be Compute!

The AI infrastructure boom will be determined not only by how much compute is built, but by whether credit markets can provide the right capital with sufficient structural protection and underwriting discipline.

• Hyperscaler balance sheets, public debt and equity cannot fund the entire cycle alone; asset-based finance, private investment grade, infrastructure capital and new financing structures will increasingly be required.
• The September FOMC raised rates by 25 basis points while long-term Treasury yields remained elevated, increasing the importance of matching financing structures, asset lives and expected cash flows.
• Credit-market dispersion is widening beneath stable indexes: the high-yield CCC-to-B spread ratio reached 3.5x, with distressed spreads near 2,600 basis points versus about 200 basis points for non-distressed high yield.
• The software repricing exposed concentration risk: software represents about 3% of the roughly $1.5 trillion U.S. high-yield market, 13% of leveraged loans and more than 20% of the approximately $1.9 trillion corporate private-credit market.
• AI infrastructure capital expenditure is projected at roughly $7.6–8 trillion through 2030, while AI-linked exposure could approach 20% of the investment-grade index, tying power, chips, cooling, land, leases and financing to a limited group of economic actors.

Takeaway: Investors should assess AI credit exposure by ultimate issuers, collateral, counterparties and economic drivers rather than by strategy labels, and favor seniority, covenants, collateral analysis and specialized underwriting as the cycle evolves.