Scaling Laws AI and the Future of Work: Joshua Gans on Navigating Job Displacement
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Sep 11, 2025 Joshua Gans, a professor at the University of Toronto and co-author of "Power and Prediction," discusses the complexities of AI-induced job displacement. He analyzes how recent regulations, like updates to New York's WARN Act, impact transparency in layoffs. Gans speculates on AI's influence on entry-level jobs and emphasizes the essential human skills still needed in an AI-driven world. He advocates for adaptable AI regulations that foster innovation while addressing ethical concerns, ultimately revealing the nuanced dynamics of technology and employment.
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Mass Unemployment Requires Unreal GDP Growth
- Massive GDP gains would be required for AI to cause mass unemployment at scale, which Joshua Gans finds unlikely.
- Technological change creates distributional winners and losers but rarely ends all jobs overnight.
Taxi Example Illustrates Systemic Change
- Gans uses the taxi/ rideshare disruption to show how a systems change expanded supply and restructured the industry.
- The change lowered incomes for some but created large consumer benefits and new distributional questions.
Jobs Contain Hard-to-Automate Elements
- Many jobs bundle driving with security, handling, or social tasks that are hard to automate fully.
- Full replacement often requires new systems (depots, remote monitors) rather than just swapping in technology.
