AI Regulation

PSJ 54/3 2026-07-23 Original Corporate Quasi‐Sovereignty: Big Tech and the Politics of Sovereign Authority in the Digital Age

PNAS 123/30 pp. e2509768123-e2509768123 2026-07-20 Original The backfiring effect of weak AI safety regulation Benjamin Laufer, Jon Kleinberg, Hoda Heidari Abstract Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches. We present a strategic model that explores interactions between safety regulation, general-purpose AI technology creators, and domain specialists-those who adapt the technology for specific applications. Our analysis examines how regulatory measures targeting different parts of the AI development chain affect the outcome of this game. Our model assumes AI technology is characterized by two key attributes: safety and performance. The regulator first sets a minimum safety requirement that applies to one or both players. The general-purpose creator then invests in the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their use cases, updating the safety and performance levels and taking the product to market. Resulting revenue is shared between the specialist and generalist. Our analysis reveals two insights: first, weak safety regulation imposed predominantly on domain specialists can backfire. While it might seem logical to regulate AI use cases, our analysis shows that weak regulations targeting domain specialists alone can reduce safety in a large class of parameterizations. Second, in contrast to the previous finding, we observe that stronger, well-placed regulation can mutually benefit all players. When regulators impose appropriate safety standards on both general-purpose AI creators and domain specialists, the regulation can function as a commitment device, leading to safety and performance gains, surpassing what is achievable under no regulation or regulating only one player.

PSJ — 2026-03-29 Original Designing Integrated Policies for the Twin Transition: Challenges and Tradeoffs Martino Maggetti Abstract ABSTRACT Democratic governance faces the intertwined imperatives of managing the transformative risks and opportunities of digitalization, particularly stemming from artificial intelligence (AI), while achieving environmental sustainability within planetary boundaries. Policymakers worldwide, particularly those involved in the European Union's “twin transition” agenda, often depict these transformations as mutually reinforcing; nonetheless, complementarity cannot be assumed: for instance, digitalization's energy demands threaten climate targets, and, respectively, sustainability goals constrain the scope and direction of technological innovation. To contribute to this discussion, this article examines integrated policy approaches that address both transitions simultaneously. In addition to typical policy integration hurdles about coherence and consistency, three macrochallenges require consideration when designing integrated policies for the twin transition: (1) the misalignment of temporal dynamics between fast‐moving technological change and long‐term environmental crises; (2) the discrepancy in scientific knowledge, consisting of high uncertainty about AI regulation versus a strong consensus in climate science; and (3) the ambivalent nature of transitions, capable of strengthening or undermining democracy in opposite directions. This contribution discusses these challenges, points to existing tradeoffs, and outlines a research agenda. In doing so, it advances policy integration as a means of societal transformation and provides a foundation to connect it with transition policies more systematically.