Enterprise AI Analysis

A Co-Evolutionary Theory of Human-AI Coexistence: Mutualism, Governance, and Dynamics in Complex Societies

Author: Somyajit Chakraborty

Abstract: Classical robot ethics is often framed around obedience, including Asimov's laws. This framing is insufficient for contemporary AI systems, which are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social environments. This paper proposes conditional mutualism under governance as a framework for human-AI coexistence: a co-evolutionary relationship in which humans and AI systems develop, specialize, and coordinate under institutional conditions that preserve reciprocity, reversibility, psychological safety, and social legitimacy. We synthesize work from computability, automata theory, statistical machine learning, neural networks, deep learning, transformers, foundation models, world models, embodied AI, alignment, human-robot interaction, ecological mutualism, biological markets, coevolution, and polycentric governance. We formalize coexistence as a multiplex dynamical system across physical, psychological, and social layers, incorporating reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization. The framework yields conditions for existence, uniqueness, and global asymptotic stability of coexistence equilibria. We complement the analytical results with deterministic ODE simulations, basin-of-attraction sweeps, sensitivity analyses, governance-regime comparisons, shock tests, and local stability checks. The simulations indicate that governed mutualism reaches a high coexistence index with negligible domination, whereas insufficient or excessive governance can produce domination, weak-benefit lock-in, or suppressed developmental freedom. The results suggest that reciprocal complementarity can support stable coexistence, while ungoverned coupling can increase fragility, lock-in, polarization, and domination risk. Human-AI coexistence should therefore be treated as a co-evolutionary governance problem rather than a static obedience problem. This reframing provides an analytical basis for coexistence principles that permit bounded AI development while preserving human dignity, contestability, collective safety, and fair distribution of gains.