{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:32:20Z","timestamp":1779327140493,"version":"3.51.4"},"reference-count":74,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Platform Digitale Infrastructuur Social Science and Humanities (PDI-SSH)","award":["Call Digital Infrastructure SSH 2020"],"award-info":[{"award-number":["Call Digital Infrastructure SSH 2020"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:p>A recent innovation in the field of machine learning has been the creation of very large pre-trained models, also referred to as \u2018foundation models\u2019, that draw on much larger and broader sets of data than typical deep learning systems and can be applied to a wide variety of tasks. Underpinning text-based systems such as OpenAI's ChatGPT and image generators such as Midjourney, these models have received extraordinary amounts of public attention, in part due to their reliance on prompting as the main technique to direct and apply them. This paper thus uses prompting as an entry point into the critical study of foundation models and their implications. The paper proceeds as follows: In the first section, we introduce foundation models in more detail, outline some of the main critiques, and present our general approach. We then discuss prompting as an algorithmic technique, show how it makes foundation models programmable, and explain how it enables different audiences to use these models as (computational) platforms. In the third section, we link the material properties of the technologies under scrutiny to questions of political economy, discussing, in turn, deep user interactions, reordered cost structures, and centralization and lock-in. We conclude by arguing that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging our capacities for critical appraisal and regulatory response.<\/jats:p>","DOI":"10.1177\/20539517241247839","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T04:53:06Z","timestamp":1713761586000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":38,"title":["Foundation models are platform models: Prompting and the political economy of AI"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1686-3700","authenticated-orcid":false,"given":"Sarah","family":"Burkhardt","sequence":"first","affiliation":[{"name":"Department of Media Studies, University of Amsterdam, Amsterdam, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2404-9277","authenticated-orcid":false,"given":"Bernhard","family":"Rieder","sequence":"additional","affiliation":[{"name":"Department of Media Studies, University of Amsterdam, Amsterdam, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"bibr1-20539517241247839","unstructured":"Ahmed N, Wahed M (2020) The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research. arXiv:2010.15581."},{"issue":"3","key":"bibr2-20539517241247839","first-page":"671","volume":"104","author":"Barocas S","year":"2016","journal-title":"California Law Review"},{"key":"bibr3-20539517241247839","volume-title":"The Search","author":"Battelle J","year":"2011"},{"key":"bibr4-20539517241247839","first-page":"610","volume-title":"Conference on Fairness, Accountability, and Transparency","author":"Bender EM","year":"2021"},{"key":"bibr5-20539517241247839","unstructured":"Bertuzzi L (2023) AI Act: MEPs close in on rules for general purpose AI, foundation models.\n                      EURACTIV\n                      , 20 April. 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Now they walk dogs and fix air conditioners.\n                      Washington Post\n                      , 5 June. Available at: https:\/\/www.washingtonpost.com\/technology\/2023\/06\/02\/ai-taking-jobs\/."},{"key":"bibr69-20539517241247839","doi-asserted-by":"crossref","unstructured":"Wang S, Liu Y, Xu Y, et al. (2021) Want To Reduce Labeling Cost? GPT-3 Can Help. arXiv:2108.13487.","DOI":"10.18653\/v1\/2021.findings-emnlp.354"},{"key":"bibr70-20539517241247839","unstructured":"Wei J, Wang X, Schuurmans D, et al. 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