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Paper short abstract

By advancing an infrastructural approach to Critical AI studies, we seek to unravel and challenge the ‘thingness’ of AI. Offering a critical empirical intervention, we analyse the infrastructural dependencies and market structures shaping public AI projects in Denmark.

Paper long abstract

Responding to calls within Critical AI Studies to move beyond the ‘thingness’ of AI, this paper develops an infrastructural and empirical approach to public-sector AI. Rather than treating AI systems as discrete technological artefacts, we examine the layered infrastructural dependencies and market structures through which they are assembled, maintained, and governed. Public AI projects across the EU are increasingly promoted as strengthening national IT sectors and envisioned as ways to uphold digital sovereignty (Coletti et al., 2025). Yet such claims remain undertheorized and empirically underexplored.

Drawing on a dataset of 229 AI projects rolled out across the public sector in Denmark, we develop a methodological framework for identifying key market actors supplying public AI infrastructures. First, we analyse procurement documents to map official suppliers, revealing a complex landscape of domestic IT companies and public–private partnerships (Laage-Thomsen et al., 2025). We then zoom in on the technological design of these AI systems, their cloud infrastructures, model architectures, software libraries, APIs, and more, to show how global platform corporations persist as de facto infrastructural sub-contractors for the public sector (Luitse, 2024).

This approach allows us to cut across local “small tech” suppliers assembling and maintaining particular services, and global “big tech” corporations supplying the underlying infrastructural building blocks, foregrounding embedded power asymmetries. By situating local AI systems within their wider global ecosystem of infrastructural dependencies, the paper offers a critical empirical intervention to ongoing debates about digital sovereignty and a methodological pathway for studying AI as a socio-technical and political assemblage.