Representational Sequestration and Discursive Admissibility: A Structural Analysis of Non-Deployable AI Governance Frameworks in Professional Network Discourse

This paper investigates the structural gap between the high volume of AI governance frameworks circulating in professional network discourse and their operational viability in live execution environments. The research question addresses why frameworks distributed via professional networking platforms fail to demonstrate the properties required for deployment under real-world execution constraints. Using qualitative discourse analysis, a corpus of publicly accessible content from large-scale professional platforms between February and early April 2026 was coded against the Execution Viability Framework (EVF), a technical baseline for assessing governance system viability. Content was assessed using a binary presence/absence framework for each EVF criterion. Findings identify four recurring archetypes of non-deployable governance production: the Curatorial Parasite, the Credit Diffusion Agent, Premature Architectural Commitment, and the Optimisation LARP. These archetypes operate within a Discursive Admissibility layer, achieving Representational Velocity while remaining structurally sequestered from execution. None of the frameworks in the corpus demonstrated a compliance forcing function, incentive-inversion testing, runtime authorisation validation, or live deployment evidence. The paper proposes a procurement verification framework to assist organisations in distinguishing representational artefacts from deployable architecture. The structural failures documented here are not inherent to the field of AI governance. They are a function of a distribution channel that rewards Representational Velocity over execution evidence, and of a procurement landscape that has not yet developed the tools to distinguish between the two.