The effort heuristic in the generative age: Why consumers aesthetically appreciate AI art but refuse to pay

The rapid proliferation of generative artificial intelligence (AI) has disrupted creative industries, sparking intense debate regarding consumer responses to algorithmic creativity. To reconcile conflicting narratives of algorithm appreciation and aversion, this research draws on the effort heuristic to conceptualize a distinct decoupling of aesthetic utility from economic worth in AI art. Across one field experiment and four online scenario-based studies, we demonstrate that while AI-generated art elicits aesthetic pleasure and interest comparable to human-created works, it suffers a severe penalty in economic valuation (Studies 1a & 1b). This devaluation is driven primarily by the effort heuristic—the perception of low production effort—rather than essentialist concerns regarding the soul of art (Study 2). Consequently, explicitly disclosing human-in-the-loop effort (e.g., iterative tuning and curation) significantly mitigates this discount by elevating perceived effort (Study 3). Furthermore, we identify payment mode as a critical boundary condition: a voluntary tipping model, which frames payment as rewarding human labor, is far more effective than a traditional fixed-price purchase model in restoring economic value (Study 4). These findings advance the theoretical understanding of value construction in the generative age and provide actionable, process-centric strategies for monetizing AI-generated content.