Licensing requirements crowd out small businesses and reduce confidence
Brough and Nazeri 23 (Wayne Brough, Policy director for R Street’s Technology and Innovation team. He manages product flow on technology policy issues while also continuing his own research in competition policy and intellectual property, Ahmad Nazeri, Prometheus Fellow @ SFL, Former Google Public Policy Fellow, “Regulatory Comments Before the U.S. Copyright Office Library of Congress In the Matter of Artificial Intelligence and Copyright,” R-Street, October 27th, 2023, https://www.rstreet.org/outreach/regulatory-comments-before-the-u-s-copyright-office-library-of-congress-in-the-matter-of-artificial-intelligence-and-copyright//Accessed June 29th, 2024) cpsw-sns
Introducing a licensing requirement for the development and adoption of generative AI systems would have profound economic implications.
Barrier to Entry: Given the vast number of works an AI training dataset might need to use—and the fact that thousands or millions of individuals might own those works—obtaining licenses for all underlying content becomes a significant challenge. This could act as a barrier to entry for smaller companies or startups that lack the resources to negotiate and secure such licenses.
Increased Costs: The process of identifying, negotiating and securing licenses for every individual piece of content in a dataset would be resource-intensive. These increased costs could be passed on to consumers or could deter companies from pursuing certain AI-driven projects altogether.
Stifling Innovation: The sheer complexity and cost associated with obtaining licenses might discourage innovation. Companies might opt for safer, less ambitious projects to avoid potential copyright pitfalls, thereby limiting the advancement of AI technologies.
Monopoly Concerns: Only large entities, like tech giants, that have the resources to navigate the licensing landscape or have already amassed vast amounts of data might be able to compete effectively in the AI space. This could lead to a monopolistic environment where only a few players dominate, thereby reducing competition and potentially stifling innovation.
Economic Incentives for Litigation: Given the structure of copyright remedies, even small-value infringements can lead to lawsuits due to the potential for statutory damages. This could encourage opportunistic lawsuits, further increasing costs for AI developers.
Potential Negative Outcomes: While broader access to data can help mitigate some of the negative outcomes associated with AI (e.g., biases), restricting access through licensing could exacerbate these issues. For instance, limited data access might hinder the ability of AI systems to be trained on diverse datasets and lead to biased outcomes.
Impact on Broader Economy: The ripple effects of these challenges could extend beyond the AI industry. Reduced innovation in AI could slow advancements in sectors across the economy that rely on AI, such as health care, finance and transportation.