Analysis projects AI infrastructure investment could exceed U.S.$10 trillion over the build-out cycle, framing it as significant to U.S. GDP growth.

A forecast suggesting that U.S. AI infrastructure investment could exceed $10 trillion over the next eight years has drawn commentary from Tesla CEO Elon Musk, who contextualized the figure using the Kardashev Scale. Musk did not directly assess whether the scale of investment is excessive, but wrote on X that "an Earth economy is less than a trillionth the size of a K2 economy"—referring to a Type II civilization capable of harnessing nearly all energy emitted by its parent star. Musk's comparison is his own interpretation and not a research finding.

The forecast comes from the Brookings Papers on Economic Activity paper "Financing AI Buildout," authored by Columbia University professor Stijn Van Nieuwerburgh. The study estimates that U.S. investment in data center infrastructure, power systems, networks, and AI semiconductors will total $10.3 trillion between 2025 and 2032. On an annual average basis, this represents 3.63 percent of U.S. gross domestic product—substantially higher than comparable historical infrastructure booms. Railroad investment averaged 2.24 percent of GDP from 1870 to 1890; the interstate highway program, 1.13 percent from 1956 to 1973; and telecoms and fiber optics, approximately 1.1 percent from 1996 to 2003. Should the forecast materialize, AI investment relative to the economy's size would eclipse those earlier booms.

A significant risk factor centers on financing structures. Beyond direct corporate investment, off-balance-sheet financing is growing, including joint ventures, private credit, securitization, special purpose vehicles, and leasing arrangements. These structures remain vulnerable to shifts in AI demand, technological change, power and semiconductor supply constraints, and the creditworthiness of a concentrated base of data center tenants.

Van Nieuwerburgh cautioned that it remains too early to conclude AI infrastructure investment has created systemic risk comparable to past credit bubbles. He emphasized instead that interconnected risk exposures can be difficult to identify before an economic downturn, and that improved measurement and transparency of funding flows are essential.