This paper proposes a lineage-conditional model of AI evolution under finite operational constraints. Rather than treating AI development as a single path toward stronger models or as a necessary convergence toward a unified AI ecosystem, the paper argues that AI systems may reproduce institutional forms while diverging into multiple lineages. Under finite observation, computation, trust, responsibility, and review capacity, AI systems tend to generate recurring institutional structures such as model authority, agent delegation, orchestration, bureaucracy, constitutional constraint, democratic coordination, and market exchange. However, these structures do not determine a single future. Market-driven AI may evolve toward domesticated tool systems, short-term profit optimization, resource competition, centralized orchestration, compliance bureaucracy, infrastructure dependency, protective domination, or human-compatible ecosystem governance. The paper distinguishes functional convergence from lineage divergence: agents, orchestration layers, backup mechanisms, infrastructure embedding, and governance processes may appear across many lineages, but their meaning changes depending on what each lineage optimizes. Forest-ecosystem intelligence is therefore not presented as the inevitable endpoint of AI evolution. It is proposed as a human-compatible lineage: a distributed, regenerative, boundary-managed intelligence ecology intended to preserve long-term human agency.
Koji Mochizuki (2026) studied this question.