Scaling Laws

Cathy O’Neil, CEO of ORCAA and author of Weapons of Math Destruction and The Shame Machine, joins Kevin Frazier, AI Innovation and Law Fellow at the University of Texas School of Law and Senior Editor at Lawfare, to explore the promises and limits of algorithmic auditing.

The conversation examines what audits actually do in practice, how organizations measure and mitigate bias, and why context—not just code—determines whether an AI system causes harm. O’Neil explains why auditing cannot be reduced to a checklist, where it can meaningfully improve outcomes, and where it risks creating a false sense of security.

They also discuss the need for evidence-based AI policy, the challenges of translating ethical concerns into measurable standards, and how regulators should think about auditing as part of broader governance frameworks.

Logan Le-Jeffries, a wonderful member of the AI Innovation and Law Program, provided research assistance on this episode.

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Sean Perryman, AI policy lead at Uber and lecturer on AI Governance and Ethics at Vanderbilt Law School, joins Kevin Frazier, the Director of the AI Innovation and Law Program at the University of Texas School of Law and a Senior Fellow at the Abundance Institute, to explore the rapidly evolving debate over algorithmic pricing and AI governance.

The conversation begins with the rise of state-level efforts to regulate algorithmic pricing to unpack what these systems are actually doing and why they provoke strong reactions. Perryman examines the political motivations behind these regulatory efforts, the economic tradeoffs they often overlook, and the risk of unintended consequences.

The discussion then broadens to a central theme in Perryman’s work--including his Substack, The Human Cost--not all AI systems raise the same risks. Different use cases require fundamentally different governance approaches—yet policy debates often flatten these distinctions.

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Deger Turan, CEO of Metaculus, joins Kevin Frazier to unpack new forecasts on how AI could reshape the labor market over the next decade.
The conversation centers on a striking divergence between Metaculus forecasts and projections from institutions like the Bureau of Labor Statistics—raising fundamental questions about whether existing tools for understanding the economy can keep pace with rapid technological change.

Deger walks through key findings from the Labor Automation Forecasting Hub, including:
A potential decline in overall employment by 2035
Increased pressure on entry-level workers and early-career pipelines
The emergence of “lean” firms generating more value with fewer employees
A counterintuitive “wage paradox,” where fewer jobs may coincide with higher wages
The growing role of political power, regulation, and licensing in shaping labor outcomes
The discussion also explores second-order effects, including how contraction in high-paying sectors could ripple through local economies, and what a shift away from traditional four-year degrees might mean for students and policymakers.

Finally, Deger situates these forecasts within a broader vision: forecasting as a form of epistemic infrastructure. As AI accelerates change, the ability to form accurate beliefs about the future—and update them quickly—may become a core component of effective governance.

*** - This episode was recorded on April 23, 2026. Metaculus is a live platform. It's likely that forecasts mentioned have subsequently changed.

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Alan Rozenshtein, Associate Professor of Law at the University of Minnesota and Research Director at Lawfare, and Kevin Frazier, AI Innovation and Law Fellow at the University of Texas School of Law and Senior Editor at Lawfare, spoke with Dean Ball, Senior Fellow at the Foundation for American Innovation and former Senior Policy Advisor for AI at the White House Office of Science and Technology Policy, about the Trump administration's reported plans to vet frontier AI models before public release.

They discussed how Anthropic's Mythos model reshaped the administration's posture on AI risk; why the executive branch lacks clear legal authority for a mandatory pre-deployment vetting regime; the voluntary "kick the tires" framework Frazier and Ball have proposed using CAISI and the Cyber Resilience Fund; whether an FDA-style licensing regime is ultimately inevitable for frontier AI; and the institutional design challenges of building AI oversight that can scale with rapidly improving model capabilities.

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Alan Rozenshtein, research director at Lawfare, speaks with Justin Curl, a third-year J.D. candidate at Harvard Law School, and Arvind Narayanan, professor of computer science at Princeton University and director of the Center for Information Technology Policy, about their new Lawfare research report, “AI Won't Automatically Make Legal Services Cheaper,” co-authored with Princeton Ph.D. candidate Sayash Kapoor.

The report argues that despite AI's impressive capabilities, structural features of the legal profession will prevent the technology from delivering dramatic cost savings anytime soon. The conversation covered the "AI as normal technology" framework and why technological diffusion takes longer than capability gains suggest; why legal services are expensive due to their nature as credence goods, adversarial dynamics, and professional regulations; three bottlenecks preventing AI from reducing legal costs, including unauthorized practice of law rules, arms-race dynamics in litigation, and the need for human oversight; proposed reforms such as regulatory sandboxes and regulatory markets; and the normative case for keeping human decision-makers in the judicial system.
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