Policy analysis framework demonstrates dynamic diagnostic governance for artificial intelligence systems, suggesting static regulatory models fail to track rapid technological changes.
This paper addresses the systemic mismatch between rapid AI capability growth and static governance frameworks. Existing approaches—responsible innovation, agile governance, and risk-based regulation—share a common flaw: they treat governance as a destination of fixed rules rather than an ongoing process of dynamic adaptation. Drawing on the ti-xiang diagnostic toolkit derived from Dynamic Sustenance Theory (DST v15.0, DOI: 10.5281/zenodo.20372186), this paper distinguishes between three underlying dynamic forces (Three Waves) and their observable emergent states (Three Phases). The framework introduces a standardized diagnostic grammar for identifying systemic pathologies and guiding sequenced interventions. Applying this toolkit, the paper diagnoses current global AI governance as suffering from a "Three-Wave comprehensive fracture with overlapping syntax lock-ins," validated through an in-depth analysis of the 2023 OpenAI crisis. It proposes an operational AI Governance Health Index (K-value) and a phased intervention pathway. This paper makes three contributions: (1) introducing a standardized diagnostic grammar for AI governance pathologies; (2) demonstrating the principle that phase-level fixes require wave-level interventions; and (3) providing an operational K-value with dynamic adjustment mechanisms. The framework offers a practical alternative to static principle-based governance models. This is a preprint of an original research article. It has been shared to facilitate academic discussion and feedback. Comments and critiques are warmly welcomed.
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一介叔声 (2026) studied this question.