Big AI-Based Mathematics and the Redistribution of Epistemic Labor
Bibliographic Data
| ID | 24227486 |
|---|---|
| Authors | M Friedman (0000-0002-2905-0170, Mathematical Institute, University of Bonn, Germany), Kati Kish Bar-On (0000-0001-6867-3701, Boston University, MA, USA) |
| Year | 2026 |
| Pages | 3063127261482050-3063127261482050 |
| Publication date | 2026-10-07 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Social Studies of Science (JOURNAL) |
| Journal identifiers | ISSN: 0306-3127 • E-ISSN: 1460-3659 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/03063127261482050 |
| PMID | 42844720 |
| OpenAlex | W7220954875 |
| Language | EN |
| References cited | 52 |
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.
credit attribution · Division of labor · epistemic authority · epistemic labor · human-machine collaboration · large-scale cooperation · resource concentration · socio-technical regimes · Cybernetics and Technology in Society · Ethics and Social Impacts of AI · Research, Science, and Academia
Symbolic Capital or Social Glue? Recognition in Collaborative High-Energy Physics Research
The Creative Mind
The Constitution of Algorithms
Energy and Policy Considerations for Deep Learning in NLP
Exploring the Stratified Nature of Artificial Intelligence Research Funding in United States Educational Systems
Generative AI Glitches
Polyhedra and the Abominations of Leviticus
AI as Agency without Intelligence
A group theory of group theory
Opacity thought through
From Euclidean geometry to knots and nets
Epistemic Trust in Science
Artificial Epistemic Authorities
Experts-Part II
Big Science
Understanding Artificial Agency
The Mangle of Practice
Some Elements of a Sociology of Translation
Epistemic Cultures
The Meaning of Data
| Highly cited | No |
|---|