How Selection Shapes Diversity in LLM Ecosystems

LLMs are increasingly deployed as agents within ecosystems where they compete for attention, are reused across contexts, and are reinforced through feedback loops. In such systems, behavioral traits spread not only because they are better, but because system design rewards some behaviors over others. We develop an analytical framework for LLM ecosystems, grounded in evolutionary dynamics, that links system-level selection to the evolution of trait distributions by treating exposure allocation as a source of selection pressure. We characterize the resulting regimes with stability-aware diagnostics (tangent stability and invasion exponents). We test the theoretical predictions in a minimal AI-native social platform in which LLM agents generate posts, evaluate one another, and compete for future visibility. The experiments support the theory: stronger selection increases concentration and pushes endpoints toward local instability; diversity support stabilizes specialization only within a bounded regime; and early trajectory fluctuations forecast later instability. We identify phantom diversity as a failure mode: endpoints can appear coexisting or specialized while remaining locally unstable or invadable. These results show that diversity audits should measure not only endpoint geometry, but also dynamic stability and early-warning signals.