Generative AI (GenAI) systems raise fundamental challenges for copyright law at both the input and output stages. On the input side, legal uncertainty surrounds the large-scale scraping of copyrighted data for model training, with divergent rules across jurisdictions and limited transparency on how data is sourced. On the output side, courts struggle to determine when AI-assisted creations are sufficiently human to merit protection, leading to inconsistent or unclear legal outcomes. This paper outlines the "AI copyright conundrum" and examines its impact on the incentives to create, the accessibility of high-quality datasets, and the sustainability of cultural production. We discuss policy options and open questions for research.

In this paper, we investigate the effects of non-exclusive agreements between networks of mobile money agents on mobile network operator choices, using survey data from Tanzania conducted in 2017. By combining survey responses with geo-location data and information on agent proximity, we employ discrete choice models to analyze consumers' decisions in subscribing to mobile network operators and their corresponding mobile money providers. Our findings highlight the significant influence of the distance to mobile money agents on consumers' subscription choices. To explore the impact of interoperability (non-exclusivity) at the mobile money agent level, where consumers can use the nearest agent from any mobile money provider, we assess its effects on market shares of mobile network operators. Our results indicate that interoperability at the agent level has only a minor impact on market shares. Smaller operators experience marginal gains as their consumers can now utilize agents of larger providers, which are often closer in proximity. In conclusion, we find that interoperability at the agent level does not considerably alter the market structure in the context Tanzania during the period under consideration.