The Bathtub of European AI Governance: Identifying Technical Sandboxes as the Micro-Foundation of Regulatory Learning

The EU AI Act adopts a horizontal and adaptive approach to govern AI technologies characterised by rapid development and unpredictable emerging capabilities. To maintain relevance, the Act embeds provisions for regulatory learning. However, these provisions operate within a complex network of actors and mechanisms that lack a clearly defined technical basis for scalable information flow. This paper addresses this gap by establishing a theoretical model of the regulatory learning space defined by the AI Act, decomposed into micro, meso, and macro levels. Drawing from this functional perspective of this model, we situate the diverse stakeholders -- ranging from the EU Commission at the macro level to AI developers at the micro level -- within the transitions of enforcement (macro-micro) and evidence aggregation (micro-macro). We identify AI Technical Sandboxes (AITSes) as the essential engine for evidence generation at the micro level, providing the necessary data to drive scalable learning across all levels of the model. By providing an extensive discussion of the requirements and challenges for AITSes to serve as this micro-level evidence generator, we aim to bridge the gap between legislative commands and technical operationalisation, thereby enabling a structured discourse between technical and legal experts.