IDEAS home Printed from https://ideas.repec.org/a/taf/rripxx/v32y2025i2p512-528.html
   My bibliography  Save this article

Asset manager capitalism and the political economy of artificial intelligence

Author

Listed:
  • Andrea Lagna

Abstract

Artificial intelligence (AI) represents the next technological frontier in asset management. Drawing on the asset manager capitalism literature in International Political Economy (IPE) and the scholarship on machine learning (ML) in Social Studies of Finance (SSF), I propose a framework to guide research on how AI is redefining competition in the asset management sector and augmenting the power of investment management giants. This framework comprises three interrelated thematic areas: (a) the intersections of asset management and AI; (b) the intelligent technologies of asset management; and (c) the power of asset management firms in the data economy. This research agenda encourages greater collaboration between IPE and SSF scholars in exploring the emerging AI-driven power dynamics in asset management. Additionally, it enables IPE scholars to enrich interdisciplinary conversations on the political economy of AI.

Suggested Citation

  • Andrea Lagna, 2025. "Asset manager capitalism and the political economy of artificial intelligence," Review of International Political Economy, Taylor & Francis Journals, vol. 32(2), pages 512-528, March.
    • Handle: RePEc:taf:rripxx:v:32:y:2025:i:2:p:512-528
    DOI: 10.1080/09692290.2024.2432393
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09692290.2024.2432393
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/09692290.2024.2432393?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    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:taf:rripxx:v:32:y:2025:i:2:p:512-528. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rrip20 .

    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.