The Governance of Beauty: Access to Visual Art Encounters under Generative AI

Generative AI raises a conditional question about the political economy of visual art: when producing some representations becomes easier, which conditions continue to govern access to meaningful encounters, and who can benefit from controlling them? This paper distinguishes aesthetic significance, judgment, symbolic articulation, institutional attribution and economic returns. It develops the retained proposal that value can depend on historically formed relations whose contributors and controllers differ. Established work on aesthetic experience, social formation of taste, commercial experience and practical access supplies the philosophical context. Bounded empirical studies inform the research question while leaving a general migration of economic returns open. A small presentation-capacity model shows how an increase in eligible images can make a fixed downstream capacity binding; its assumptions and counterexample separate that result from claims about beauty, market power or profit. The paper then considers historically formed susceptibility, personalization and the evidence required to connect them. A separate normative argument examines the maintenance of shared conditions, fair terms and participant revision. Hypothetical cases include a remembered sunset, controlled access, a revenue-supported venue and an open digital initiative. Fees, generated imagery and curation each permit enabling and restrictive arrangements. The contribution is a scoped method for investigating changes in encounter conditions, with empirical causation, normative allocation and conceptual priority left open.