A recent analysis examines concerns about debt financing for the artificial-intelligence boom, noting that while private-credit lending to software companies exceeded $500 billion by the end of 2025, much AI infrastructure financing is harder to observe. This physical build-out—chips, data centers, power connections, servers, and specialized compute capacity—has mostly been financed off the consolidated balance sheets of the largest technology companies via project and construction financing, equipment-backed lending, leases, asset-backed securities, and private credit, including loans secured by GPUs. Unlike houses, GPUs are displaced every two or three years, and nobody yet knows what a used one is worth. Investors commonly measure AI-related leverage by looking at the balance sheets of the five big hyperscalers: Alphabet, Amazon, Meta, Microsoft, and Oracle, where conventional leverage remains manageable. However, a working paper stress-test on the financing system suggests today’s situation is not a rerun of the 2008–09 financial crisis, as first-loss positions sit mostly outside the regulated banking system, but losses could still reach $140 billion and could grow as AI-infrastructure credit expands. Through 2024, the largest technology companies financed most of their aggregate capital spending from operating cash flow, but that has changed: the big hyperscalers undertook approximately $380 billion of capital spending in 2025 and are expected to spend roughly double that this year, putting capital spending on course to overtake operating cash flow. Debt issuance has risen