Abstract Artificial intelligence (AI) presents unique regulatory challenges due to its rapid evolution and broad societal impact. Traditional ex ante regulatory approaches struggle to keep pace with AI development, exacerbating the “pacing problem” and the Collingridge dilemma. In response, experimentalist governance–particularly through regulatory sandboxes (RSs)–has emerged as a potential solution. This paper examines AI RSs within the European Union’s Artificial Intelligence Act (AI Act) from a law and economics perspective, investigating their capacity to address market and government failures and enhance regulatory efficiency compared to traditional command-and-control mechanisms. Applying an economic analysis of law framework, the paper evaluates how RSs can mitigate information asymmetries, reduce negative externalities, and facilitate iterative regulatory learning while promoting responsible AI innovation. It further analyses how RSs may correct specific government failures, including regulatory capture, rent-seeking, and knowledge gaps. Drawing comparative insights from FinTech, the paper identifies the institutional design features necessary to ensure their effectiveness and resilience. While RSs offer a flexible and innovation-friendly governance model, their success ultimately depends on sound institutional safeguards, proportionality, and alignment with broader policy objectives. The paper contributes to ongoing debates on experimentalism in AI governance by proposing design principles for effective, accountable, and adaptive sandboxes.
Antonella Zarra (2026) studied this question.