Assessing legal-economic impacts of authorship attribution rules on innovation incentives and creative labor markets in AI-driven content industries.

The accelerating integration of artificial intelligence (AI) into creative production workflows including visual design, music composition, journalism, and entertainment media has prompted renewed debate over the legal and economic implications of authorship attribution rules.Traditional intellectual property (IP) frameworks presume a human creator whose labor, skill, and intentionality justify ownership and exclusive rights.However, when AI systems autonomously generate content or materially shape creative outputs, determining authorship becomes ambiguous.This uncertainty directly affects innovation incentives, revenue distribution models, and the structure of creative labor markets.If AI-generated outputs are considered unprotected or freely replicable, firms may underinvest in advanced creative tools, while individual creators may face wage suppression and diminished bargaining power.Conversely, granting exclusive rights to organizations that deploy AI tools risks concentrating creative ownership in a small cluster of technology firms, thereby reducing market diversity and limiting independent creative agency.Emerging hybrid attribution models such as shared authorship, contributory rights indexing, and provenance-weighted compensation seek to balance these tensions by distinguishing between human conceptual input and algorithmic execution.Yet these models require clear regulatory standards, transparent documentation of creative contributions, and interoperable metadata infrastructure to function at scale.Assessing the legal-economic impacts of attribution decisions is therefore critical to shaping fair competition, sustaining creative employment, and promoting long-term innovation.The future of AI-driven content industries will depend on designing authorship governance structures that equitably reward human creativity while acknowledging the generative capacities of machine systems.