Marxist analysis of AI is not only critical. It is also generative, pointing toward alternative ways of organising AI development and governance that serve broader human interests rather than narrow capital accumulation.

Public ownership of AI infrastructure. Just as public utilities, electricity, water, telecommunications, have in many countries been recognised as too important to leave entirely to private control, AI infrastructure could be publicly owned or regulated as a public good. Public investment in AI research and development, publicly owned computational infrastructure, publicly mandated data sharing requirements, these are not radical propositions. They are extensions of existing public interest regulation to a new domain.

Data commons. Rather than allowing data to be captured and controlled by private platforms, data commons models propose that data generated by communities should be owned collectively and governed democratically. This would break the data moats that give large platforms their structural advantage and allow communities to decide how data about their members is used.

Democratic AI governance. Worker representation on the boards of companies deploying AI, elected representatives on AI regulatory bodies, community oversight of AI systems deployed in public services, these are governance mechanisms that embed democratic accountability in AI decision-making rather than leaving it to market forces and corporate self-regulation.

Cooperative and community-owned AI. Alternatives to corporate AI development exist, cooperative enterprises, public institutions, community-owned platforms, that operate on different principles. They do not eliminate the technical challenges of building AI systems, but they change the interests that shape design decisions.

Worker rights in AI transitions. For workers displaced or intensively managed by AI, Marxist perspectives support robust labour protections: meaningful notice and support for displaced workers, collective bargaining rights over algorithmic management, the right to contest algorithmic decisions that affect employment, and participation in decisions about how AI is introduced into workplaces.

These alternatives are not utopian. They are grounded in existing institutional practices, public utilities, cooperative enterprises, labour law, extended to the domain of AI. They are also contested. The political forces that would resist them are substantial, precisely because the interests they challenge are powerful. Marxist analysis does not promise that these alternatives will be easily achieved. It argues that they are necessary, and that the alternative is an AI future shaped by the interests of capital rather than the needs of people.