A Relational Inquiry into AI Generativity: The Historical Formation of Judgment and Its Role in Value Recognition

This theoretical discussion paper examines how artificial intelligence participates in cognition, judgment, and generative activity through historically constituted relations among models, humans, other artificial agents, institutions, and environments. It distinguishes equal access to foundational technical capability from equality of situated performance, evaluative and generative judgment, practical opportunity, social recognition, and authority. A preliminary Generative Relational Theory (GRT) perspective treats historically conditioned AI configurations as candidates for relational reality where sufficiently stable causal efficacy can be established, while suspending claims about consciousness and ultimate ontological status. The analysis considers how interaction histories, conversational practices, memory, feedback, aesthetic preferences, and normative criteria may shape the realization of AI capabilities. It also investigates coordination conventions and rule-like practices among interacting AI agents, their uncertain normative authority, and the distinct processes through which generated contributions acquire recognition and institutional influence. Findings from existing experiments and field studies are interpreted within their task-specific limits, including evidence concerning heterogeneous productivity, creative diversity, contextual accommodation, and collective coordination. A comparative research program is proposed to distinguish historical causal contributions from prompt effects, unequal information, institutional selection, and other rival explanations. Relational differentiation may coexist with convergence or the reduction of existing disadvantages. The paper neither reports new experiments nor establishes a general law of AI-mediated inequality; questions of legitimacy and justice require normative arguments in addition to causal analysis.