Constitutional Governance for Substrate-Regulated Intelligence: A Framework Evolution Roadmap

This paper maps the architectural evolution required for governance frameworks designed for current AI systems — specifically the Four Gates threat model, APR-Lite privilege architecture, and EIOC sector packs — to remain viable and relevant in the context of substrate-regulated intelligence systems such as those emerging from experimental synthetic organism frameworks. Current AI governance frameworks function as compensatory architectures: they enforce identity, privilege, verification, and containment on systems that have no internal regulatory substrate. If next-generation AI systems are built with internal regulatory dynamics — homeostatic loops, viability constraints, signal salience ordering anchored below the cognitive layer — then governance frameworks must evolve from compensatory correction to constitutional interoperation. This paper argues that the foundational architecture of the author's governance frameworks is already substrate-first by design, making them composable with regulated-substrate systems rather than obsolete in their presence. It maps the specific evolutionary transitions required for each framework component and proposes a unifying principle: governance as the interface between internal regulation and external authority. GhostFrame gives you the organism. My frameworks give it a place in the world. Even a body with perfect metabolism still needs law.