Lelo Nxumalo

Earlier this month I wrote about Stijn Van Nieuwerburgh’s NBER paper, “Financing the AI Buildout” . His numbers are hard to forget. A one-gigawatt AI campus costs about $41 billion, and a U.S. buildout of 188 gigawatts by 2032 would need nearly $9 trillion of investment, about 3.2 percent of GDP a year. As the hyperscalers’ capital spending outgrows their cash flow, the financing is moving into leases, joint ventures, project debt, private credit, securitization and special-purpose vehicles. In “The AI Boom’s Invisible Balance Sheet” , I mapped part of that financing network from public filings. My conclusion was that the main problem is visibility: the people best placed to judge the risk cannot yet see the whole picture. That post asked who owes whom inside the United States. This one asks the next question: who outside the United States holds the AI boom, and how much of that can official statistics actually see? I am writing the answer up as a working paper. What follows is a pr...

What public filings reveal about who funds AI data centers and who bears the risk, and what oversight should look like Stijn Van Nieuwerburgh's “Financing the AI Buildout” paper describes an investment cycle of nearly $9 trillion over 2025–32 that is increasingly funded through leases, joint ventures, project bonds, private credit and special-purpose vehicles. These structures expand funding capacity, but they also raise leverage at the level of the asset and keep much of it off the balance sheets of the firms that use the capacity. The template is Meta's Hyperion campus in Louisiana. A joint venture, 80 percent owned by Blue Owl funds and 20 percent by Meta, owns the campus and leases it back to Meta; Beignet Investor, the Blue Owl vehicle that holds the 80 percent stake, sold about $27 billion of bonds to pay for it. The paper's central worry is that the resulting exposures are layered, correlated and hard to observe. This post asks how hard. I tried to map the fina...