Generative AI May Be Good For Artists: Understanding the Impact of Artistic Style on Preference and Willingness to Pay

Abstract

Generative AI tools, such as MidJourney, Stable Diffusion, and DALL-E, allow users to generate high-quality images using simple text prompts and have seen wide adoption.

However, the ability of these tools to mimic the styles of specific artists has raised ethical and economic concerns. Artists argue that their intellectual property is being

exploited without consent or compensation, fueling calls for fair remuneration. In this paper, we propose a constructive alternative: generative AI as a means for artists to

monetize their unique styles, transforming a perceived threat into an economic opportunity. Using a multi-method approach, including large-scale prompt analysis, deep

learning models, and conjoint choice experiments, we demonstrate that artist styles can enhance image quality, increase consumer preference, and drive higher willingness

to pay (WTP) even under conditions of incentive alignment. Importantly, consumers express strong support for fair compensation schemes, creating a viable pathway for

artists to participate meaningfully in the generative AI ecosystem. These findings have implications for technology developers, artists, and policymakers seeking to balance

innovation with fairness.

Keywords: Generative AI, Human Brands, Image Analysis, Deep Learning Models, Conjoint Analysis