Generative AI, Human Creativity, and Art

Abstract

Recent artificial intelligence (AI) tools have demonstrated their ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g., Midjourney, Stable Diffusion, Dall-E), which automates humans’ execution to generate high-quality digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that text-to-image AI substantially enhances human creative productivity by 25% and increases the value as measured by the likelihood of receiving a favorite per view by 50% over time. While peak artwork content novelty (focal objects and object relationships) increases over time, average content novelty declines, suggesting an expanding but inefficient creative space. Additionally, there is a consistent reduction in both peak and average visual novelty (pixel-level stylistic elements). Importantly, AI-assisted artists who can produce more novel content ideas, regardless of overall novelty before adoption, produce artworks that their peers evaluate more favorably. The results imply that ideation and likely filtering are necessary skills in the text-to-image process, thus giving rise to “generative synesthesia” - the harmonious blending of human senses and AI mechanics to discover new creative workflow. Lastly, AI adoption decreased value capture (favorites earned) concentration among the adopted.

Keywords: Generative AI, Human-AI Collaboration, Creative Workflow, AI Adoption Impact, Art