IDEAS home Printed from https://ideas.repec.org/b/bis/bisbps/154.html
   My bibliography  Save this book

The AI supply chain

Author

Listed:
  • Leonardo Gambacorta
  • Vatsala Shreeti

Abstract

The rapid advancement of artificial intelligence (AI) relies on a complex supply chain comprising five key layers: hardware, cloud infrastructure, training data, foundation models and AI applications. This paper examines the market structure of each layer and highlights the economic forces shaping them: rapid technological change, high fixed costs, economies of scale, network effects and, in some cases, strategic behaviour by dominant firms. We also highlight the expanding influence of big tech companies across the AI supply chain. We discuss the challenges for consumer choice, innovation, operational resilience, cyber security and financial stability.

Suggested Citation

  • Leonardo Gambacorta & Vatsala Shreeti, 2025. "The AI supply chain," BIS Papers, Bank for International Settlements, number 154.
  • Handle: RePEc:bis:bisbps:154
    as

    Download full text from publisher

    File URL: http://www.bis.org/publ/bppdf/bispap154.pdf
    File Function: Full PDF document
    Download Restriction: no

    File URL: http://www.bis.org/publ/bppdf/bispap154.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Iñaki Aldasoro & Leonardo Gambacorta & Anton Korinek & Vatsala Shreeti & Merlin Stein, 2024. "Intelligent financial system: how AI is transforming finance," BIS Working Papers 1194, Bank for International Settlements.
    2. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
    3. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
    4. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2024. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 723-771.
    5. Andrei Hagiu & Julian Wright, 2023. "Data‐enabled learning, network effects, and competitive advantage," RAND Journal of Economics, RAND Corporation, vol. 54(4), pages 638-667, December.
    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. Eshwar Ram Arunachaleswaran & Natalie Collina & Sampath Kannan & Aaron Roth & Juba Ziani, 2024. "Algorithmic Collusion Without Threats," Papers 2409.03956, arXiv.org, revised Dec 2024.
    2. Abada, Ibrahim & Lambin, Xavier & Tchakarov, Nikolay, 2024. "Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?," European Journal of Operational Research, Elsevier, vol. 318(3), pages 927-953.
    3. Jon Danielsson & Andreas Uthemann, 2024. "Artificial intelligence and financial crises," Papers 2407.17048, arXiv.org.
    4. Bruno Carballa Smichowski & Yassine Lefouili & Andrea Mantovani & Carlo Reggiani, 2025. "Data Sharing or Analytics Sharing ?," Working Papers hal-04956937, HAL.
    5. Suzie Grondin & Arthur Charpentier & Philipp Ratz, 2025. "Beyond Human Intervention: Algorithmic Collusion through Multi-Agent Learning Strategies," Papers 2501.16935, arXiv.org.
    6. Harrington, Joseph E., 2024. "The effect of demand variability on the adoption and design of a third party’s pricing algorithm," Economics Letters, Elsevier, vol. 244(C).
    7. Shi, Ziyi & Xu, Meng & Song, Yancun & Zhu, Zheng, 2024. "Multi-Platform dynamic game and operation of hybrid Bike-Sharing systems based on reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    8. Inkoo Cho & Noah Williams, 2024. "Collusive Outcomes Without Collusion," Papers 2403.07177, arXiv.org.
    9. Kopalle, Praveen K. & Pauwels, Koen & Akella, Laxminarayana Yashaswy & Gangwar, Manish, 2023. "Dynamic pricing: Definition, implications for managers, and future research directions," Journal of Retailing, Elsevier, vol. 99(4), pages 580-593.
    10. Ding, Shasha & Sun, Hao & Sun, Panfei & Han, Weibin, 2022. "Dynamic outcome of coopetition duopoly with implicit collusion," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    11. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    12. Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2022. "Data‐driven mergers and personalization," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 3-31, March.
    13. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2019. "Algorithmic Pricing What Implications for Competition Policy?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(1), pages 155-171, August.
    14. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2019. "Exclusive Data, Price Manipulation and Market Leadership," CESifo Working Paper Series 7853, CESifo.
    15. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
    16. Stefano Colombo & Aldo Pignataro, 2022. "Information accuracy and collusion," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(3), pages 638-656, August.
    17. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.
    18. Hanna Halaburda & Jeffrey Prince & D. Daniel Sokol & Feng Zhu, 2024. "The business revolution: Economy‐wide impacts of artificial intelligence and digital platforms," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 269-275, March.
    19. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
    20. Böheim, René & Hackl, Franz & Hölzl-Leitner, Michael, 2021. "The impact of price adjustment costs on price dispersion in e-commerce," International Journal of Industrial Organization, Elsevier, vol. 77(C).

    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:bis:bisbps:154. 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: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.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.