Paper short abstract:

This paper explores whether emerging markets in generative AI can disrupt big tech's assetization practices and break techno-economic lock-ins.

Paper long abstract:

We live in a society where surveillance has become a twenty-first-century culture and the common good no more than an imaginary (Stoddart, 2021). Over the last two decades, tech companies have created a new ‘normal’ whereby data have become dispossessed from its owners, gathered in highly creative ways (Geiger and Gross, 2021; Zuboff, 2019) and sold for significant profits on the market (Birch, Cochrane and Ward, 2021). Big tech’s surveillance capitalist practices and monopolistic positions have created asymmetries in market power and techno-economic lock-ins (Birch and Bronson, 2022; Zuboff, 2019). Start-up businesses are often said to have no choice but to become part of the surveillance culture (Stoddart, 2021; Zuboff, 2019) and consumers cannot fight the choiceless created in and through digital markets (Dholakia et al, 2021; Sahota, 2020). Despite repeated calls for data justice (Dencik et al, 2022; Taylor, 2017), increasingly stringent regulation (e.g. GDPR) and significant fines - the big 5 received combined fines of €3.04bn in 2023 (Koch, 2024)- the powerful position of big tech has become increasingly difficult to dislodge. In many experts’ view, this tendency will only be exacerbated with the advent of Artificial Intelligence (AI) becoming a pivotal market technology. Against this pessimistic intuition, the current paper examines if and how the recent launch and proliferation of generative AI presents a unique opportunity to in fact challenge the techno-economic lock-ins created in and by data assetization. Can data be ‘freed’ by AI technologies, and can data justice principles be baked into emerging AI markets? We endeavour to present tentative answers to these questions by using a multi-method qualitative research method (Gross and Geiger, 2023) – a document analysis (grey and policy literature), 18 semi-structured interviews (with AI experts, policy experts, NGOs, and advocacy groups) and selected ethnographic insights. In this empirical work, we address the following questions: What are the assetization practices of AI companies? What impact is AI making and doing when it comes to transforming surveillance capitalism? and What can be done to build more moral data markets?