Financing the AI Buildout

Stijn Van Nieuwerburgh

No 35865, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Artificial intelligence is driving a large expansion in data center capacity, power infrastructure, and specialized computing equipment. We estimate that a 1 GW AI campus costs about $41 billion. A 188 GW U.S. buildout completed by 2032 would require total investment of nearly$9 trillion and amount to annual investment of 3.2 percent of GDP over 2025–2032. As hyperscaler capital expenditures outgrow internal cash flow, financing is shifting toward leases, joint ventures, project debt, private credit, securitization, and special-purpose vehicles. These structures expand funding capacity but also raise asset-level leverage, obscure contingent obligations, and expose investors to tenant concentration, technological obsolescence, execution delays, and uncertain residual values.

JEL-codes: E01 E22 G1 G31 R33 (search for similar items in EconPapers)
Date: 2026-10
Note: AP CF EFG PR
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.nber.org/papers/w35865.pdf (application/pdf)
Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:35865

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w35865
The price is Paper copy available by mail.

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().