Introduction
Regulatory sandboxes have emerged as a preferred solution for addressing the challenges of traditional regulatory frameworks when applied to new technologies, particularly artificial intelligence (AI). The European Union AI Act imposed on EU Member States the obligation to establish at least one AI regulatory sandbox by August 2026.
This paper questions whether regulatory sandboxes truly deliver on their promises and proposes new pathways for regulators to incorporate agile decision-making. Additionally, it investigates how sandboxes often create bureaucratic overhead while not necessarily solving the regulatory challenges they claim to resolve. The model of relaxing certain regulations to allow for new products to be developed is misguided and increasingly in disuse, especially in high-risk scenarios.
Current regulatory sandbox literature focuses on perceived benefits, but few undertake systematic analysis of risks and lost opportunities.
The paper will address three core questions: First, under what circumstances do regulatory sandboxes provide genuine value over alternative approaches? Second, what are the opportunity costs of choosing sandboxes over other experimental regulatory tools? Third, how can regulators achieve the stated benefits through less bureaucratic means?
The paper draws on a critical comparative assessment of sandbox initiatives in Spain, Brazil, Singapore, and the United Kingdom. Rather than a systematic case study analysis, these four jurisdictions serve as illustrative reference points. A deeper empirical examination of documented outcomes across these jurisdictions, including analysis of stated objectives versus achieved results and stakeholder participation structures, is reserved for subsequent work after the sandboxes are concluded.
This research builds on responsive regulation theory while challenging its application to sandbox contexts. While Ayres and Braithwaite’s responsive regulation framework emphasizes regulatory flexibility and stakeholder engagement,
Regulatory sandboxes require extensive application processes and complex governance structures. Sandbox learnings often fail to translate into broader regulatory improvements, with insights rarely informing general regulatory frameworks. Participation bias emerges as sandbox participants typically represent well-resourced organizations, potentially excluding smaller innovators and affected communities.
These limitations point toward alternative regulatory approaches that achieve sandbox-like benefits with greater efficiency. Regulators can adjust enforcement priorities based on emerging technology characteristics through adaptive enforcement strategies. Regular stakeholder consultations and technical advisory committees can reduce information asymmetries more democratically than closed sandbox environments.
The paper is structured as follows. Section II provides background and context, tracing the emergence of regulatory sandboxes in the financial sector and situating the current enthusiasm for the tool within the broader debate on regulation and innovation in AI governance. It introduces the four jurisdictions addressed throughout the paper (Spain, Brazil, Singapore, and the United Kingdom) and outlines the key definitions and typologies that inform the analysis. Section III examines the principal benefits attributed to regulatory sandboxes from a regulator’s perspective, including regulatory learning, agility, stakeholder participation, and the signaling of commitment to innovation, while beginning to probe the gap between these promises and their realization in practice. Section IV then turns to the transaction costs and implementation challenges that regulators face when operationalizing a sandbox, addressing capacity and expertise constraints, firm-side participation barriers, accountability risks, and the question of whether the costs are proportionate to the benefits. Section V maps the spectrum of alternative experimental regulatory tools available to regulators—including innovation hubs, help desks, or structured dialogue and participation—and shows that for many objectives attributed to sandboxes, less resource-intensive instruments achieve comparable or superior outcomes. Section VI presents a decision framework to help regulators assess whether a sandbox is genuinely warranted before committing to one, and Section VII proposes an evaluation framework for ensuring that sandboxes, where chosen, contribute meaningfully to the broader regulatory environment rather than functioning as a compliance service for participating firms. The conclusion argues that what AI governance needs is not necessarily fewer sandboxes, but more rigorous use, monitoring, and evaluation of sandboxes and of the full range of experimental regulatory instruments in the regulators’ toolkit.
This project was made possible through the support of Grant 63641 from the John Templeton Foundation. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation. For more information, visit The Next Frontier: Rethinking Regulation in an Era of Rapid Innovation.