Over the last 7 decades, human–AI collaboration in art has evolved from simple tools to dynamic partnerships.
Today, AI acts as a generator, instrument, co-creator, muse, curator, and vacuum cleaner, shaped by both technical progress and artistic intent.
In its earliest stages, AI functioned as an instrument, executing processes strictly defined by the human artist.
It offered no ideas or interpretation of its own, but extended human creativity through formal systems like rule-based logic or randomness.
These early experiments weren’t about delegating authorship, but about testing whether creativity itself could be systematized, blurring the line between artistic intuition and algorithmic procedure.
However, soon AI began producing autonomous outputs with minimal human input beyond initial setup.
In this mode, works like Annie Dorsen’s Hello Hi There (2010) emerged, where two chatbots delivered unscripted onstage dialogue, showcasing AI’s potential as a performer within human-defined boundaries.
In the art project, The Next Rembrandt (2016), a team used AI trained on Rembrandt’s works to produce a new painting in his style.
AI can also serve as a muse, not producing the final work, but sparking new creative directions through its unexpected outputs. For instance, Google’s DeepDream (2015) generated surreal, dreamlike images by amplifying patterns in existing photos.
While the results were often chaotic, artists used them as inspiration.
In co-creative modes, humans and AI influence each other in real-time or through iteration. In 2002, Lynn Hershman Leeson’s Agent Ruby, an AI web character for SFMOMA, learned and evolved through conversations with users, blending interactive art with cinema.
Beyond creation, AI also plays an important role in restoration and completion, helping artists reconstruct unfinished works.
A notable example is the 2021 project to complete Beethoven’s unfinished 10th Symphony.
It was trained on Beethoven’s compositions to generate stylistically consistent passages, but human musicologists curated the material, arranged the movements, and ensured historical and musical coherence.
A less direct but increasingly controversial form of “collaboration” occurs when data-driven AI systems learn from large collections of human-created work.
In this mode, AI builds its generative abilities by absorbing patterns and styles from existing art.
What unites these different modes of cooperation is the shifting distribution of creative agency. AI challenges artists to rethink their role in the creative process—and forces audiences to reconsider what art means.