Proposes Collective Discovery Capacity, examining group dynamics and AI's role in enriching discovery processes.
This paper proposes Collective Discovery Capacity as the ability of groups to generate discoveries that surpass individual observation through the diversity, retention, translation, and structural integration of observations. Building upon Discovery Continuity, Continuity Mismatch, and Reward Organizations and Discovery Organizations, the paper explores how discoveries may emerge not only from individual observation but from the interaction of multiple observers across different domains, experiences, and environments. The paper further examines how AI systems may increasingly function as discovery infrastructure—not by replacing human observation, but by helping human discoveries find one another. In the age of AI, the central challenge may shift from automating discovery to building infrastructures that help human observations become connectable.
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N Tanaka (2026) studied this question.
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