Marxist theory gives us analytical tools for understanding AI’s political economy that liberal frameworks consistently avoid:

Surplus value and ownership: AI systems are means of production concentrated in a small number of corporations. Understanding who owns them, and how they are used to extract value, is fundamental to AI ethics.

Ideology: Techno-solutionism, the neutrality myth, meritocracy discourse, and disruption ideology serve the interests of dominant actors in the AI economy. Identifying them as ideology, rather than common sense, is the first step to questioning them.

Data as appropriation: Users who generate the data that powers AI platforms are providing labour without compensation. This is a Marxist analysis with concrete implications for data rights and platform governance.

Algorithmic management: AI is used to intensify and surveil labour, extending managerial control while depressing wages, stripping benefits, and increasing precarity.

Surveillance capitalism: Shoshana Zuboff’s framework, extended by Marxist analysis, reveals how human experience is converted into behavioural data, processed by AI, and sold as prediction products that modify human behaviour for commercial ends.

Class interests shape regulation: AI governance reflects the interests of those with the resources to shape it. Evaluating regulatory outcomes requires asking whose interests they actually serve.

Alternatives exist: Public ownership, data commons, democratic governance, cooperative enterprise, and worker rights provide concrete foundations for an AI future organised around human need rather than capital accumulation.