Big AI-Based Mathematics and the Redistribution of Epistemic Labor

The use of AI-based technologies is reshaping mathematics in ways that extend but also strain the classic Big Science analogy. As in Big Science, the new AI-based mathematical work relies on concentrated computation, specialized and hierarchical teams, sociopolitical justification, and large-scale collaboration. But AI also changes how epistemic labor is distributed and attributed: Systems now generate constructions, explore proof spaces, and formalize arguments, prompting mathematicians to renegotiate how credit, authority, and accountability are assigned across hybrid human-machine assemblages. This reconfiguration redistributes epistemic authority toward corporate labs and elite hubs that control models, data, compute, and narrative, thereby gating which problems appear tractable and legitimate. Analyzing four dimensions (resource concentration, division of labor, political embedding, and collaboration), and examining recent cases, we argue that AI-based mathematics constitutes a novel sociotechnical regime we call 'Big AI Mathematics', in which truth claims are increasingly mediated by opaque pipelines and institutional power, making the future of mathematical knowledge as much a governance problem as an epistemic one.