{
  "id": 7235,
  "url": "https://arxiv.org/abs/2603.14126v1",
  "title": "The Institutional Scaling Law: Non-Monotonic Fitness, Capability-Trust Divergence, and Symbiogenetic Scaling in Generative AI",
  "summary": "Classical scaling laws model AI performance as monotonically improving with model size. We challenge this assumption by deriving the Institutional Scaling Law, showing that institutional fitness -- jointly measuring capability, trust, affordability, and sovereignty -- is non-monotonic in model scale, with an environment-dependent optimum N*(epsilon). Our framework extends the Sustainability Index of Han et al. (2025) from hardware-level to ecosystem-level analysis, proving that capability and tr",
  "authors": "Mark Baciak, Thomas A. Cellucci",
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-14T21:25:48.000Z",
  "fetched_at": "2026-07-14T16:33:03.574Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7235",
  "original_url": "https://arxiv.org/abs/2603.14126v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}