Normative Renormalization in Distributed Monetary Dissolution: A Thought Experiment on AI Production Networks

This discussion paper examines a proposed multiscale method for representing economically and normatively heterogeneous value circuits in future AI-operated production networks. The thought experiment assumes near-universal artificial labour, extensive shared production information, and potentially scarce resources governed by differentiated rights. Recursive and overlapping circuits can generate large, and under repetition formally unbounded, families of possible pathways. Their effective material and institutional influence may diminish with path length, while their number, coupling, and dependence on historically situated normative appraisals complicate any direct truncation. The paper proposes normative renormalization as an explicitly provisional programme of scale-dependent circuit abstraction that seeks to reduce coordination complexity while preserving specified claims, contestation rights, distributional burdens, and material regeneration constraints. It distinguishes weighted-walk attenuation from the cancellation of liabilities and from the legitimacy of excluding or modifying a participant's claim. The hypothesis is that sufficiently capable AI, supported by shared but fallible production data, could assist in discovering effective interfaces, estimating approximation errors, detecting rights-sensitive exceptions, and proposing revisable allocation protocols. The paper also examines how model certification and protocol administration can generate new concentrated power. The objective is to clarify the conditions, limits, and possible tests of normatively constrained abstraction during spatiotemporally distributed monetary dissolution, without presuming moral consensus or complete dynamical knowledge.