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Following AI exposure across borders, and finding where the statistics go dark

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...
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The AI Boom's Invisible Balance Sheet

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...
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Six Questions for a Billion-Dollar Promise

A Skeptic’s Guide to Economic Impact Claims A Skeptic’s Guide to Economic Impact Claims Six questions anyone can ask about the economic promises attached to a big development — and what I learned building the model that produced them. When a large project comes to your county, it arrives with a number. Billions in economic impact. Thousands of jobs. Tens of millions in new revenue. That number is almost always paid for by the people who want the project approved, calculated using software whose workings are not public, and presented to a council as settled fact. Residents get to react to it. They almost never get to check it. Last year I built an economic model of Montgomery County, Maryland, from public data. It estimates what a given amount of spending actually does to the local economy. Building it taught me something more useful than any of its own numbers: the specific places where impact claims go wrong, and how to spot them without needing a mo...
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Tensions and Global Supply Chain Restructuring

Executive Summary Between 2017 and 2025, U.S. trade policy underwent two dramatic transformations. The 2018-19 trade war elevated tariff rates from 1.5% to 6% . Then in 2025, tariffs spiked to 52% before reversing to 13% . China's import share collapsed from 21.6% to 8.7% , while Mexico and Vietnam captured market share across all categories. Part I: The Tariff Timeline 2017 Baseline: Era of Low Tariffs U.S. average tariff rate: 1.5% — lowest in modern history Reflected decades of trade liberalization through WTO framework 2018-19 Trade War: First Escalation July 2018: First Section 301 tariffs (25% on $34B Chinese imports) → rate jumps to 2.8% ...
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Pricing Carbon in Maryland: An Open-Source Toolkit for State Climate Policy

Maryland has committed to cutting greenhouse gas emissions 60% by 2031 and reaching net zero by 2045 — among the most ambitious climate targets of any U.S. state. But setting a target is the easy part. The hard part is designing a carbon pricing system that actually works: one that cuts emissions, generates revenue for the clean energy transition, and protects the communities that can least afford higher energy costs. That is what this toolkit is built to do. Figure 1: Maryland total CO 2 emissions, 2000–2021 (Source: FRED) What it does The Maryland Carbon Pricing Toolkit is a set of four Python scripts that take you from raw federal data to a complete policy analysis in minutes. It pulls real emissions data from two public APIs — the Federal Reserve Economic Data (FRED) service for state-level CO 2 t...
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