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Carvao, Paulo

The accelerated adoption of Artificial Intelligence marks a pivotal moment in technological progress. AI is reshaping industries, redefining labor markets, and prompting critical societal reflections on intelligence, reasoning, and the dissemination of information. While AI offers opportunities for economic growth, it also presents risks that must be managed to avoid adverse societal and geopolitical outcomes, making effective and transparent governance more urgent than ever.

This paper explores the potential of dynamic, collaborative public-private governance to foster safe innovation. Drawing from primary research, including interviews with tech industry leaders, U.S. Members of Congress, and staff, and an analysis of 150 AI-related bills introduced by the 118th U.S. Congress, this work identifies emerging areas of alignment between policymakers and industry stakeholders. It also highlights opportunities for a unified national approach, despite the challenges of a fragmented legislative environment.

The authors propose a dynamic governance approach that brings government and industry together while combining the foresight of ex-ante measures with the adaptability needed to respond to technological advancements. Coupled with existing ex-post mechanisms, the Dynamic Governance Model creates a comprehensive framework to promote competition, innovation, and accountability. It represents a policy-agnostic extra-regulatory framework, including a public-private partnership for standards setting and a market-based ecosystem for audit and compliance.

The rapid expansion of artificial intelligence infrastructure, including data centers and the energy, land, water, and labor systems that support them, presents regional policymakers with trade-offs that are poorly captured by the prevailing “innovation versus regulation” frame. This article develops the AI Infrastructure Triad as a conceptual framework for analyzing three competing priorities in regional AI infrastructure governance: Progress, Sustainability, and Equity. We argue that regions are unlikely to maximize all three simultaneously under current technological, institutional, and resource conditions. Drawing on prior work on the economic, physical, and moral limits of AI development, a previously coded dataset of 10,068 public comments submitted to the 2025 U.S. AI Action Plan and illustrative regional cases, the article interprets stakeholder and regional positions as different ways of prioritizing the triad’s frontiers. The evidence is used illustratively rather than as a full causal test. The paper’s contribution is to clarify the trade-offs that infrastructure decisions often obscure, distinguish deliberate triad governance from default allocation by market power or regulatory inertia, and propose a Deliberate Triad Choice Framework for policymakers considering AI infrastructure decisions of significant scale.

This review situates Rewiring Democracy within debates on artificial intelligence, democratic governance, and institutional design. In contrast to work that emphasizes technological determinism or narrowly defined risk mitigation, the review engages Bruce Schneier and Nathan E. Sanders’ argument that AI is becoming embedded in democratic institutions and everyday civic practices, shaping how governments process information, allocate authority, deliver public services, and mediate civic participation.

The book is positioned alongside scholarship on surveillance capitalism, infrastructural power, and sociotechnical governance, while highlighting its distinctive contribution: a pragmatic framework for examining how AI may reconfigure legislatures, administrative agencies, courts, and civic life under conditions of democratic constraint. The review argues that the book is strongest when it treats AI as a governance challenge rather than merely a technical tool, but less complete when it moves from plausible use cases to durable institutional reform. It also assesses the book’s methodological orientation, noting both its strengths in conceptual synthesis and its limits in institutional specification, political economy, and democratic theory.

The rapid expansion of artificial intelligence infrastructure, including data centers and the energy, land, water, and labor systems that support them, presents regional policymakers with trade-offs that are poorly captured by the prevailing “innovation versus regulation” frame. This article develops the AI Infrastructure Triad as a conceptual framework for analyzing three competing priorities in regional AI infrastructure governance: Progress, Sustainability, and Equity. We argue that regions are unlikely to maximize all three simultaneously under current technological, institutional, and resource conditions. Drawing on prior work on the economic, physical, and moral limits of AI development, a previously coded dataset of 10,068 public comments submitted to the 2025 U.S. AI Action Plan and illustrative regional cases, the article interprets stakeholder and regional positions as different ways of prioritizing the triad’s frontiers. The evidence is used illustratively rather than as a full causal test. The paper’s contribution is to clarify the trade-offs that infrastructure decisions often obscure, distinguish deliberate triad governance from default allocation by market power or regulatory inertia, and propose a Deliberate Triad Choice Framework for policymakers considering AI infrastructure decisions of significant scale.