Data Guilds, Synthetic Labor Tax, and Recursive Legitimacy: Designing Fiscal and Institutional Mechanisms for an AI Social Contract

This paper develops a fiscal and institutional architecture for an AI social contract. It argues that existing tax and labor regimes neither internalize the externalities created when AI systems extract value from data and automate human tasks, nor sustain the legitimacy of the institutions that govern them. The paper proposes four linked mechanisms. Data guilds reconstitute individual data producers as collective bargaining agents, correcting a data externality that leaves consent-only models without leverage. The Synthetic Labor Tax (SLT) defines a tax base on the value created by synthetic labor and indexes its rate to the degree of synthetic-labor dominance, so that fiscal capacity expands as displacement deepens. SLCred, a programmable public asset issued against SLT revenue, channels those resources to displaced workers, communities, and public goods under transparent rules, while Proof of Useful Friction rewards governance interventions that verifiably reduce harm. Recursive-legitimacy institutions-citizens' assemblies with binding authority, multistakeholder allocation boards, and tiered reversible experimentation-provide the scaffolding that reproduces public consent over time rather than assuming it. Using simple bargaining and tax models and a comparative discussion of European and East Asian instruments, the paper argues that the fiscal and institutional pillars of an AI social contract must be designed together, and built while democratic leverage over AI infrastructures still exists.