Generative AI-Driven Publishing Transformation: A GAIP Framework for Human–AI Co-Creation and Dynamic Value Generation in the Publishing Ecosystem

Generative artificial intelligence is profoundly reshaping the content production methods, dissemination structures, and value distribution mechanisms of the publishing industry. The traditional publishing system is centered on a linear process of "author—editor—publishing institution—reader," but with the intervention of generative AI, content production is gradually shifting towards a new paradigm of "human-machine co-creation—dynamic generation—real-time distribution." This paper proposes the "Generative AI Publishing Framework (GAIP)," constructing a five-stage closed-loop model of "demand-driven—AI generation—multimodal editing—intelligent distribution—feedback evolution." This framework emphasizes the core role of generative AI in content creation, editorial optimization, publishing decisions, and reader interaction, and introduces a "publishing value co-creation function" to characterize the overall efficiency and value output mechanism of the publishing system. The research argues that generative AI not only improves publishing efficiency but also restructures the power structure and value network of knowledge production, transforming the publishing system from a linear chain into a dynamic ecological network.