How Generative AI Transforms the Artistic Process

Reinventing Authorship: How Generative AI Transforms the Artistic Process

Art history is marked by technological shifts. The advent of photography in the 19th century sparked fears that painting would lose its relevance; yet, the traditional medium discovered new languages through Impressionism and abstraction. Today, the debate centers on neural networks and generative image-diffusion models (such as Midjourney, Stable Diffusion, and DALL-E), raising a fundamental question: does artificial intelligence create art, or merely reorganize the human cultural repository?

To understand AI in visual production, it is necessary to distinguish between technical generation and poetic intentionality:

The role of the prompt as a conceptual score: A visual artist using algorithms acts much like a composer or a curator. Selecting lexical commands, iterating through random seeds, and configuring model parameters require a refined aesthetic repertoire to transform a generic output into a work with a distinct identity.

Media hybridity: In contemporary workflows, AI rarely concludes the production process with a single click. It functions as a tool for rapid prototyping, ideation, or background texturing, combining with digital painting software, 3D modeling, and analog interventions.

The absence of machine subjectivity: Algorithms possess no sensibility, consciousness, or existential motivation. The generated work carries the semantic weight and ambiguities projected onto it by the observer and the operator.

Rather than negating the figure of the author, artificial intelligence expands the concept of conceptual authorship, shifting the focus from purely manual dexterity to intention, critical vision, and visual storytelling.