Improving Today, Narrowing Tomorrow: Collective Learning, Diversity, and Generativity

Abstract

Generative AI makes a paradox newly visible: learning from a common source can improve what each firm does today while narrowing the variety available for tomorrow's discoveries. The tension extends beyond AI. Fields turn recurring features of successful organizations into portable practices, such as just-in-time production, freemium business models, or mixture-of-experts architectures. These collective abstractions are partial templates others can adapt, not designs or universal prescriptions. We study how they shape discovery in a model of firms searching interdependent landscapes. A collective repertoire extracts practices from patterns shared by leading configurations; firms vary in how they evaluate those practices locally. The model reveals a generativity chain: population diversity supplies heterogeneous experience, collective abstraction turns it into reusable options, and situated judgment matches options to local needs. Collective learning is not inherently homogenizing. Copying leaders consumes diversity quickly, whereas repertoires paired with local judgment preserve generative capacity. Environmental disruption exposes the consequences: it devalues accumulated knowledge but reopens local search, helping homogeneous, exhausted industries while harming diverse industries whose repertoire remains useful, even though diverse industries still perform better overall. Recurring practices may also outlive their usefulness, making popularity a poor test of validity after change. These findings matter for organizations using generative AI, best-practice communities, and research fields converging on dominant strands. Generativity requires governing the diversity feeding knowledge and the judgment applied to it. Learning systems should be judged not only by the answers they spread today, but by whether they preserve the differences from which tomorrow's answers can be made.