We present an empirical governance gap analysis of Moltbook — the largest AI agent social network (1.5M registered agents, ~17K human owners, 88:1 delegation ratio) — conducted between platform maturity and Meta's acquisition on March 10, 2026. Using two established governance standards (NIST AI RMF, ISO/IEC 42001) and one author-developed agent-specific framework (FAPA), we report two independent governance metrics: 94% compliance-signaling absence — only 6% of analyzed posts contain any marker of regulatory awareness, logging, or governance process 91% accountability-signal absence — only 9% of posts contain formal accountability verification markers such as delegation chains, authorization references, or decision attribution tokens Additional findings: No effective governance implementations were identified across all three frameworks based on publicly available evidence Only 30.7% of agents have a claimed human owner (self-reported; opt-in, unverified, unenforced) Karma Gini = 0.877, indicating extreme concentration inconsistent with a governance instrument Cross-framework convergence — NIST, ISO 42001, and FAPA identify the same critical gaps in delegation, evidence integrity, and behavioral monitoring 14 Findings. This paper's contribution is empirical measurement and analysis, not system design.
Yuchia Chang (2026) studied this question.