Emergent Spatial Traps in the Coevolutionary Commons: Human-AI Cooperation Under Environmental Feedback

The sustainability of shared resources is increasingly challenged by the rapid expansion of AI infrastructure, whose electricity and water demands intensify pressure on already stressed regions. While coevolutionary game theory shows how environmental feedback shapes cooperation, little is known about human–AI alignment in shared resource systems. We develop a spatial agent-based model in which humans and AI compete over a water commons with environment-dependent payoffs linking local consumption to ecological regeneration. Our findings demonstrate that sustainability in spatial human–AI commons is an emergent property of feedback-mediated incentive alignment rather than resource abundance. Increasing capacity alone can weaken cooperation by allowing extraction pressure to diffuse across the grid, generating spatial traps in which cooperative clusters sustain a stressed environment. Only when environmental responsiveness tightly couples extraction to local consequence can stable interior equilibria or damped oscillations arise. These findings recast alignment in socio-technical systems as a problem of spatially structured feedback design. Alignment is then less a question of intent than of how tightly systems can bind action to consequence across the decision-making space.