See, here's the thing, AI is effectively a last chance effort to revive developed-nation economies and stave off stasis/entropy/death. It's a gamble. And the best shot we have.
But if we fail, then the whole world, including the United States, including all that we have known and cared for, will sink into the abyss of a new Dark Age made more sinister, and perhaps more protracted, by the lights of perverted science.
Oops, sorry, I don't know why Churchill is popping in. That's the penultimate sentence of his "Their finest hour" speech. It was immediately preceded by:
Hitler knows that he will have to break us in this Island or lose the war. If we can stand up to him, all Europe may be free and the life of the world may move forward into broad, sunlit uplands.
Which reminds me there is one other option to goose developed economies short of hyper-inflationary money-printing; the one Germany and the EU have chosen, massive investment in arms and armaments.
From the University of Chicago, Booth School of Business, Chicago Booth Review, August 7:
Investors should look past software to the financing of AI infrastructure.
For much of the past year, alarm about the credit taken on to fund the boom in artificial intelligence has centered on software—a concern I examined in June. The focus is understandable. Private-credit lending to software companies had exceeded $500 billion by the end of 2025, and fears that AI-related debt could crowd out other borrowers have pressured valuations and software-heavy business development companies alike.
This exposure is relatively visible because many private-credit vehicles disclose their portfolios. But other AI infrastructure financing, however, is much harder to observe.
The physical build-out of AI—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, yes, private credit.
There are even loans secured by GPUs, with the chips that run AI themselves pledged as collateral. As it works with a mortgage secured by a house, if a borrower stops paying, its lender takes the chips and sells them. But while most houses hold their value for decades, GPUs are being displaced every two or three years as faster ones are developed, and nobody yet knows what a used one is worth.
Investors commonly measure AI-related leverage by looking at the balance sheets of the largest cloud-computing companies, the five big hyperscalers: Alphabet, Amazon, Meta, Microsoft, and Oracle. For these companies, conventional leverage remains manageable, but that is only one part of the financing system. An institution may hold technology equities, infrastructure funds, private-credit vehicles, securities issued by insurers, and real estate debt, and regard those exposures as diversified. Legally, they are separate investments, but economically, they may share risks related to future AI demand.
In a working paper, I mapped this financing system and performed a stress-test on it. The question I had is how much financing depends on the same underlying cash flows—and where the associated risk ultimately resides.
The results of this test suggest that today’s situation is not a rerun of the 2008–09 financial crisis: 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.
Are the hyperscalers overextended?
Through 2024, the largest technology companies financed most of their aggregate capital spending from operating cash flow. 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 in step. The hyperscalers issued approximately $120 billion of corporate bonds last year, compared to an average of about $28 billion annually between 2020 and 2024. Issuance in the first half of 2026 has already exceeded the 2025 amount.
Let’s be clear: The hyperscalers aren’t in financial difficulty. But the marginal financing of AI infrastructure has begun to extend well beyond their own balance sheets.
A hyperscaler may lease a data center from a developer that has financed the property, construction, and equipment separately, borrowing from a range of private-credit funds, insurers, pension funds, and retail-oriented vehicles.
Commitments also extend beyond funded debt: S&P Global Ratings, the credit rating agency, has identified about $675 billion of signed but not yet commenced lease obligations across the hyperscalers—an amount that’s excluded from the funded-debt totals in my analysis but that demonstrates why reported corporate debt is an incomplete measure.
Where the financing sits....
....MUCH MORE
Also at the CBR, July 13:
