IDEAS home Printed from https://ideas.repec.org/p/bea/papers/0134.html
   My bibliography  Save this paper

Concepts and Challenges of Measuring Production of Artificial Intelligence in the U.S. Economy

Author

Listed:
  • Tina Highfill
  • David Wasshausen
  • Gregory Prunchak

Abstract

Much of the current literature on the economic impact of Artificial Intelligence (AI) focuses on the uses of AI, but little is known about the production of AI and its contribution to economic growth. In this paper, we discuss basic concepts and challenges related to measuring the production of AI within a standard national accounting framework. We first present a variety of examples that illustrate how both the production and use of AI software are currently reflected in macroeconomic statistics like Gross Domestic Product and the Supply and Use Tables. We then discuss a broader approach to measurement using a thematic satellite account framework that highlights production of AI across foundational areas, including manufacturing, software publishing, computer and data services, and research & development. The challenges of identifying and quantifying AI production in the national accounts using existing data sources are discussed and some possible solutions for the future are offered.

Suggested Citation

  • Tina Highfill & David Wasshausen & Gregory Prunchak, 2025. "Concepts and Challenges of Measuring Production of Artificial Intelligence in the U.S. Economy," BEA Papers 0134, Bureau of Economic Analysis.
    • Handle: RePEc:bea:papers:0134
    as

    Download full text from publisher

    File URL: https://www.bea.gov/system/files/papers/BEA-WP2025-1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2025. "Generative AI at Work," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(2), pages 889-942.
    2. Daron Acemoglu, 2025. "The simple macroeconomics of AI," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 40(121), pages 13-58.
    3. Flavio Calvino & Chiara Criscuolo & Hélène Dernis & Lea Samek, 2023. "What technologies are at the core of AI?: An exploration based on patent data," OECD Artificial Intelligence Papers 6, OECD Publishing.
    4. Bruce T. Grimm & Brent R. Moulton & David B. Wasshausen, 2005. "Information-Processing Equipment and Software in the National Accounts," NBER Chapters, in: Measuring Capital in the New Economy, pages 363-402, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Riccardo Zanardelli, 2025. "Navigating the safe harbor paradox in human-machine systems," Papers 2509.14057, arXiv.org, revised Jan 2026.
    2. Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025. "Macroeconomic and Labor Market Drivers of AI Adoption in Europe: A Machine Learning and Panel Data Approach," Economies, MDPI, vol. 13(8), pages 1-62, August.
    3. Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025. "Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences," Working Papers hal-05102974, HAL.
    4. Nida Çakır Melek, 2025. "Marshall B. Reinsdorf and Louise Sheiner (editors): The Measure of Economies: Measuring Productivity in an Age of Technological Change," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 60(3), pages 175-178, July.
    5. Matthew O. Jackson & Qiaozhu Me & Stephanie W. Wang & Yutong Xie & Walter Yuan & Seth Benzell & Erik Brynjolfsson & Colin F. Camerer & James Evans & Brian Jabarian & Jon Kleinberg & Juanjuan Meng & Se, 2025. "AI Behavioral Science," Papers 2509.13323, arXiv.org.
    6. Nickel, Christiane & Kilponen, Juha & Moral-Benito, Enrique & Koester, Gerrit & Ciccarelli, Matteo & Enders, Almira & Holton, Sarah & Landau, Bettina & Venditti, Fabrizio & Bobeica, Elena & Brand, Cla, 2025. "A strategic view on the economic and inflation environment in the euro area," Occasional Paper Series 371, European Central Bank.
    7. James Bono & Alec Xu, 2024. "Randomized Controlled Trials for Security Copilot for IT Administrators," Papers 2411.01067, arXiv.org, revised Nov 2024.
    8. L. Elisa Celis & Lingxiao Huang & Nisheeth K. Vishnoi, 2025. "A Mathematical Framework for AI-Human Integration in Work," Papers 2505.23432, arXiv.org, revised May 2025.
    9. James Bono & Beibei Cheng & Joaquin Lozano, 2025. "Randomized Controlled Trials for Conditional Access Optimization Agent," Papers 2511.13865, arXiv.org.
    10. Fasheng Xu & Xiaoyu Wang & Wei Chen & Karen Xie, 2025. "The Economics of AI Foundation Models: Openness, Competition, and Governance," Papers 2510.15200, arXiv.org.
    11. Yongheng Hu, 2025. "Heterogeneous Agents in the Data Economy," Papers 2509.09656, arXiv.org.
    12. Lu Fang & Zhe Yuan & Kaifu Zhang & Dante Donati & Miklos Sarvary, 2025. "Generative AI and Firm Productivity: Field Experiments in Online Retail," Papers 2510.12049, arXiv.org, revised Oct 2025.
    13. Xienan Cheng & Mustafa Dogan & Pinar Yildirim, 2025. "Artificial Intelligence in Team Dynamics: Who Gets Replaced and Why?," Papers 2506.12337, arXiv.org.
    14. Frantz Rowe & Raphaël Suire & Myriam Raymond & Florence Jacob, 2024. "Beliefs, Controversies, and Innovation Diffusion: The case of Generative AI in a Large Technological Firm," Post-Print hal-04973613, HAL.
    15. Yongheng Hu, 2025. "Analysis Theory of Data Economy: Dataization, Technological Progress and Dynamic General Equilibrium," Papers 2507.13274, arXiv.org, revised Jul 2025.
    16. James Bono, 2025. "Randomized Controlled Trials for Phishing Triage Agent," Papers 2511.13860, arXiv.org.
    17. Anais Galdin & Jesse Silbert, 2025. "Making Talk Cheap: Generative AI and Labor Market Signaling," Papers 2511.08785, arXiv.org.
    18. Lu Fang & Zhe Yuan & Kaifu Zhang & Dante Donati & Miklos Sarvary, 2025. "Generative AI and Firm Productivity: Field Experiments in Online Retail," CESifo Working Paper Series 12201, CESifo.
    19. Jorgenson, Dale W. & Nomura, Koji, 2005. "The industry origins of Japanese economic growth," Journal of the Japanese and International Economies, Elsevier, vol. 19(4), pages 482-542, December.
    20. Abendroth Dias Kulani & Arias Patricia & Bacco F. Manlio & Bassani Elias & Bertoletti Alice & Bertolini Lorenzo & Bertrand Astrid & Bili Danai & Boucher Philip & Cachia Romina & Ceresa Mario & Chaslot, 2025. "Generative AI Outlook Report," JRC Research Reports JRC142598, Joint Research Centre.

    More about this item

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bea:papers:0134. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Andrea Batch (email available below). General contact details of provider: https://edirc.repec.org/data/beagvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.