Beyond Human–AI Symbiosis: Toward a Population Ecology of Cognitive Hybrids

As prolonged interaction with artificial intelligence becomes increasingly integrated into human intellectual activity, the relevant unit of analysis may extend beyond individual users, AI models, and human–AI dyads. This paper proposes a population-level perspective in which persistent human–AI cognitive hybrids are treated as historically differentiated configurations shaped by the human component, the AI system, accumulated interaction context, and the trajectory of interaction over time. An individual configuration is represented as O(i) = [H(i), A(i), M(i), T(i)], while an ensemble of such configurations forms a population P = {O(1), O(2), ..., O(n)}. This framework distinguishes diversity among AI models from diversity among human–AI cognitive configurations and introduces the concept of hybrid cognitive monoculture to describe population-level convergence toward a limited set of functionally similar modes of interaction. On this basis, the paper formulates the Peripheral Heterogeneity Hypothesis: under certain conditions, preserving a functionally significant set of heterogeneous, incompletely standardized, and partially unpredictable configurations may increase population resilience by reducing correlated errors and preserving alternative responses to unforeseen epistemic disturbances. The hypothesis does not assume that diversity is inherently beneficial or that decentralization is necessarily preferable to standardization. Instead, it distinguishes individual optimization from population resilience and generates empirically testable predictions concerning error correlation, diversity of independent solutions, error detection, and recovery following epistemic shocks.