Highlights from the USCO Report on the Economic Implications of Artificial Intelligence for Copyright Policy (Part 1: Output Phase)

Luca Schirru

About the Report

In February 2025, the U.S. Copyright Office released the report “Identifying the Economic Implications of Artificial Intelligence for Copyright Policy: Context and Direction for Economic Research”, edited by USCO’s chief economist, Brent Lutes.

The report was produced after months of research, interactions among scholars and technical experts, and the outcomes of a roundtable event. By identifying the most pressing economic issues related to copyright and artificial intelligence (AI), the roundtable “aimed to provide a structured and rigorous framework for considering economic evidence so that the broader economic research community can effectively answer specific questions and identify optimal policy choices.”

Considering the length of the report and the variety and complexity of the issues it addresses, we will split our analysis into two separate blog posts: one focusing on the output phase and the other on the input phase. Following the structure of the report, we will begin with the output-related topics: “Copyrightability of AI-Generated Works and Demand Displacement” and “Copyright Infringement by AI Output”, as these are most directly connected to copyright. For this reason, we will not summarize the section on “Commercial Exploitation of Name, Image, and Likeness”, and instead recommend that readers refer directly to the report for details on that topic. 

Copyrightability of AI-Generated Works and Demand Displacement

This chapter, whose principal contributors are Imke Reimers and Joel Waldfogel, proposes the following question: “how the emergence of generative AI technology affects the optimal provision of copyright protection?” When discussing whether AI-generated works should be copyrighted, it connects to whether they cause a net positive value, and that there would also be the need “to be weighed against the value of human-generated works displaced by the technology”. (p.10)

The substitution effect is also considered, not only in cases where AI-generated works substitute human-generated ones, but also when AI-generated works are verbatim or near-verbatim reproductions of pre-existing human-generated content. Similarly, some of these near-verbatim reproductions may decrease the value of the related human work when, for example, they provide misinformation. Such a decrease in value may also reduce interest in human-generated works. On the other hand, and from an economic perspective, the report also suggests that “all of its uses would supplant revenue for human creators. Some uses will reduce deadweight loss, replacing it with consumer surplus by allowing for additional consumption that otherwise would not occur”. (p.10)

One of the effects that may be seen in the long run relates to the fact that human experimentation leads to more radical stylistic innovation and experimentation, while it is not clear “whether AI-generated output can ever engage in the same sort of experimentation and innovation as humans”. (p.11) While the report acknowledges that there is a possibility that AI may reduce production costs and be a tool to promote creativity, increase productivity, and enhance quality, it warns about the risk of less experimentation, crowding out “more risky and costly experimental creations that sometimes lead to valuable innovation”. (p.11) Displacing human creators may even be harmful to the development of Gen AI, as these models are trained with human-generated works, according to the report.

A first conclusion that may be drawn from this section is that further research, including empirical research, needs to be carried out to better understand issues like the value created and displacement caused by GenAI, the decrease in the value of human-generated works, the “degree to which the fixed cost recovery problem exists for AI-generated works” (p.12), and “the demand curve and cost function for creative works”. (p.14) 

When it comes to offering copyright protection to these AI-generated outputs, the report suggests that it would incentivize their production and affect human output in both positive and negative ways. However, it also recalls that this may not be optimal, as “copyright inherently limits public access to existing works and thus produces a social cost”. (p.12) The report also notes that production costs may differ between human-generated and AI-generated works, and that “copyright protection only serves its economic objective if the social value of the former outweighs that of the latter. If the fixed production costs of AI-generated works are sufficiently low, the additional incentives of copyright are not necessary for reaching optimal production levels, thus, offering copyright protection would be suboptimal”. (p.12)

Copyright Infringement by AI Output

As previously mentioned, the report does not delve into legal issues, focusing instead on economic analysis. In the chapter primarily contributed by Joshua Gans, the author offers considerations from an economic perspective on defining the “optimal scope of what output is infringing,” noting that “copyright protection from infringement should balance the incentives to produce and the ability to consume creative works.”

The author begins by explaining one of the structural dynamics of copyright, where “the mechanism used for incentivizing the production of new works (exclusive rights pertaining to the usage of a work) also limits consumers’ access to existing works”, and that the “broadest possible scope of protection could also effectively hinder new creative output for fear of liability”. (p.16)

It argues that an important step in the analysis is to identify the “optimal level of market power that we wish to confer to rightsholders in the context of competing AI-generated works”, assuming this level to be the same as that used in infringement disputes involving human-generated works. The chapter proposes considering multiple, but not all, factors that may impact the balance mentioned above, and reflects on how this would be different in cases involving AI (pp.16-17)

Several factors may affect the market power of the rightsholders, including but not limited to the threshold for infringement (the higher the threshold, the lower the power) and the requirements to demonstrate that the copy was infringing. On the latter, it is also argued that in the cases concerning AI-generated work, “access [to the allegedly infringed work] may be harder to dispute”. (p.17) 

According to the study, these factors may be helpful to understand if a rightsholder may or may not exercise its market power, but the potential value related to the market power would depend on considering the available legal remedies and the related costs. (p.18) When it comes to some of the involved costs, the scenario can vary depending on the characteristics of the infringer: “[…] given a fixed number of infringements, pursuing a small number of high-frequency infringers will often be less costly than pursuing a large number of low-frequency infringers”. (p.18)

A liability regime concerning AI-generated works would assign responsibility either to users and/or to developers. This choice would affect transaction costs for rightsholders: increasing if liability is assigned to users, and decreasing if assigned to developers. It would also impact rightsholders’ level of enforcement and market power, decreasing when responsibility is assigned to users, and increasing if assigned to developers. In addition, developing costs for AI systems would also be impacted: they would be higher if liability is assigned to the developer and lower if assigned to users. (p.19)

Market power would also be affected by the cost of creating infringing works, which may include both the cost of producing these outputs (e.g., manufacturing costs or the cost of entering a prompt into a generative AI system) and the costs related to legal actions taken by rightsholders, especially if they are successful. The market power of rightsholders may be negatively impacted if generative AI significantly reduces the cost of creating and distributing infringing works. (pp. 19–20)

The last factor, titled “indirect value appropriation”, relates to the possibility of a content owner extracting value from future infringements. In the context of generative AI, the “value of future infringing output could, in principle, be captured through negotiations for access to training data” (pp. 20–21), although this depends on variables that are further explored in the report. (pp. 21–22) Other issues related to indirect appropriation include the transaction costs involved in licensing, which may vary depending on whether negotiations occur individually or with rightsholders representing large sets of works. Intermediation mechanisms, such as statutory licensing, may also affect the feasibility of indirect appropriation. (p. 22)

Similar to the section above, it is highlighted that further empirical evidence is needed to understand “ empirical characteristics of the market and technology” that would help to determine the optimal liability regime (p.19) and “whether generative AI technology reduces the costs of producing infringing works and the degree to which it may do so”. (p.20)