<p>This article asks a deep and urgent question for the age of artificial intelligence: how will the gains from algorithmically mediated production be distributed across society? We reconceptualize algorithmic surplus value (ASV) not as a break with Marx’s labour theory of value but as a digital-era intensification of relative surplus value. On our account, AI systems—still constant capital or “dead labour”—reorganise production and circulation by compressing socially necessary labour time and by enclosing informational and infrastructural rents; they do not autonomously create value. Building on this theoretical repositioning, we bridge principles and practice by proposing operational tools—value-based filters and culturally responsive value repositories—that parameterize fairness, accountability, and pluralism within algorithmic pipelines. We clarify the role of large language models as assistants, not oracles, suitable for analysis and scenario generation but not for moral adjudication. We then outline institutional pathways—public data funds, transnational minimum-threshold and options-menu compacts, and policy sandboxes with auditable metrics—through which ASV’s gains can be steered toward public ends. Frontier modalities in humanoid care robotics, quantum machine learning, and neuromorphic and edge computing are used as stress tests to show how coordination speedups and rent enclosure can widen distributive asymmetries absent governance. The contribution is both analytical and practical: we couple a Marxian account of value extraction to implementable mechanisms and institutions so that, as AI scales, algorithmic wealth becomes a common good rather than a private windfall.</p>