A Generative Grammar of Value Circuits in AI Production Networks: A Thought Experiment on Future Production Relations

This preliminary discussion paper explores a generative language for describing recurrent resource and service relations in hypothetical production networks operated predominantly by highly capable artificial-intelligence systems. It distinguishes simple graph cycles, repeated closed walks, and physically executable histories with typed inputs, time-indexed events, shared capacities, obligations, and differentiated access permissions. Unbounded recursion in a fixed finite graph yields a potentially infinite but regular language of closed walks; greater expressive claims require precisely specified representations and additional assumptions. A proposed grammar of production circuits would generate candidate event structures through serial, parallel, alternative, nested, and recurrent composition, subject to independently evaluated material, temporal, institutional, and regenerative conditions. The study addresses the ambiguity introduced by overlapping circuits and shared events, examines state-dependent participation, conditional cascade closure and serial fragility, and identifies possible interfaces for AI-assisted recognition from extensive, fallible production records. It develops a normative dossier for authorization, claims, review and affected interests, and demonstrates why material accounts alone underdetermine rule-specific findings. It also examines why a syntactically generated circuit need not sustain productive capacities, justify resource claims, or imply the disappearance of monetary coordination. The paper concludes by identifying bounded formal results, inspectable AI-assisted research uses, and the evidential and institutional conditions required for any economic deployment. Its proposed economic and normative semantics remain hypothetical.