The Institutional Scaling Law: Non-Monotonic Fitness, Capability-Trust Divergence, and Symbiogenetic Scaling in Generative AI

Researchers propose the Institutional Scaling Law, challenging the assumption that AI performance improves monotonically with model size. The framework shows that institutional fitness (capability, trust, affordability, sovereignty) has an optimal scale beyond which capability and trust diverge, suggesting orchestrated domain-specific models may outperform large generalist models.

• →The Institutional Scaling Law demonstrates non-monotonic AI performance with an environment-dependent optimal model size N*(epsilon).
• →Capability and trust formally diverge beyond critical scale, challenging the 'bigger is better' paradigm in AI development.
• →Symbiogenetic Scaling shows orchestrated systems of domain-specific models can outperform frontier generalist models in their deployment environments.
• →The research predicts the next AI phase transition will be driven by better-orchestrated specialized systems rather than larger models.
• →The framework extends sustainability analysis from hardware-level to ecosystem-level AI deployment considerations.