Recommender systems are not neutral tools; they are AI systems that actively shape human behaviour while being shaped by it in return. When platforms prioritise short-term revenue over content quality, these human-algorithm feedback loops can push ecosystems into self-reinforcing decline. This paper presents a game-theoretic model of co-adaptation between a platform deploying an algorithmic recommender and a population of creators responding to its incentives. The model yields two equilibria: a cooperative outcome with sustained high-quality content, and a defective outcome in which both parties prioritise short-term gains. The transition between them can be irreversible: a temporary relaxation of quality standards may shift the system beyond a tipping point from which restoring the original policy no longer recovers the prior equilibrium. The location of this tipping point depends on the initial health of the ecosystem, implying that early intervention is structurally more effective than delayed correction. The model is intentionally minimal, providing a baseline for future extensions while highlighting how human-AI system design can generate persistent societal effects.