Friday Sep 25
STRATEGYOther

The Buildout Is Bigger Than The Railroads

25SEP
3.63% OF GDPPRIOR WAVESAI BUILDOUT

A Brookings paper puts AI infrastructure at $10.3 trillion through 2032. That is 3.63% of GDP a year, larger than railroads, electrification or the highways.

Stijn Van Nieuwerburgh of Columbia compared the AI buildout to every major US infrastructure wave. Railroads peaked near 2.2% of GDP annually. AI is projected at 3.63%.

The paper's real subject is not the size. It is where the debt is going. Joint ventures, private credit, securitization, SPVs, leases and loan guarantees, increasingly off the hyperscalers' balance sheets.

He does not cry bubble. He says correlated exposures may be hard to observe before a downturn, which is a more specific and more unsettling claim.

full brief & sources

⚡ Why this matters

  • Every AI product roadmap assumes compute keeps getting cheaper. That assumption is now financed by structures nobody can fully see.
  • Off-balance-sheet financing is not inherently bad. It is how you finance a railroad. It is also how 2007 happened. The difference is visibility.
  • For a product leader, this is a planning input: the cost curve you are betting on has a credit cycle attached to it.

🔍 What happened

  • Brookings published the paper through the Brookings Papers on Economic Activity on September 23, 2026. Author: Stijn Van Nieuwerburgh, Columbia Business School.
  • Projected AI infrastructure investment: $10.3 trillion between 2025 and 2032, averaging 3.63% of GDP per year.
  • Historical comparison: canals, railroads, electrification, the interstate highway system and telecom buildouts all peaked lower. Railroads, the closest analogue, peaked around 2.2% of GDP.
  • The paper tracks a migration of financing away from hyperscaler balance sheets toward joint ventures, private credit funds, asset-backed securitization, special purpose vehicles, long-dated leases and vendor loan guarantees.
  • Van Nieuwerburgh writes that "it would be premature to conclude that AI infrastructure already poses systemic risk comparable to earlier credit booms."
  • He also writes that off-balance sheet structures matter "because they may make correlated exposures hard to observe before a downturn."

💬 Smart takes

  • Stijn Van Nieuwerburgh, Columbia: the financial arrangements are "freaking complicated." His two written sentences do opposite work on purpose. Not a bubble call. A visibility call.
  • The scale comparison: beating the railroads is not automatically alarming. The railroads did get built, and they did also produce several panics.
  • Counterpoint worth holding: hyperscaler cash flows are far stronger than any nineteenth-century railroad's. The equity cushion under this buildout is real.

🧭 Where this goes

  1. Likelymore papers dissect the SPV and private credit exposure specifically, now that the framing exists.
  2. Possiblea ratings agency publishes methodology for AI datacenter asset-backed paper, which would be the first real pricing signal.
  3. Wild Cardone large private credit fund marks down datacenter exposure and the visibility problem resolves itself the hard way.

🥄 The Spoon Take

Read the hedge, not the headline. A Columbia finance professor writing 'hard to observe before a downturn' in a Brookings paper is saying he cannot see the risk, not that there isn't one. That sentence is the whole paper. Anyone planning multi-year compute costs should file it.

🤔 Pushback

Eight-year infrastructure projections are close to guesses. The $10.3 trillion figure depends on demand assumptions that could halve, and the GDP-share comparison flatters AI by using different accounting eras.

·Brookings - Financing the AI buildout·US News / Reuters - Financing of historic AI buildout raises systemic risks, researcher says·Quartz - AI buildout on track to be biggest economic bet in US history