What A.I. Is Actually Doing to the Economy

Key takeaways

AI's impact on the economy is complex and currently subtle, but potentially profound, unfolding gradually like historic technological shifts.
Historical patterns show that slow adoption of new technology allows societies to adapt and reduces the severity of economic shocks.
Rapid, concentrated disruptions like the China shock can devastate communities and economies, highlighting the importance of policy preparedness.
Policymakers should improve data tracking, bolster social safety nets, and consider innovative policies to manage AI's economic effects.
Individuals, especially young workers, face uncertainty in choosing futures, underscoring the need for adaptable, forward-looking strategies.

Summary

The Uncertain Impact of AI on the Economy

The podcast opens with concerns about AI's effects on jobs and the economy, highlighting public anxiety and the difficulty in measuring AI's immediate impact. Unlike past technological shifts, AI's rapid development makes tracking its effects in real-time challenging due to outdated government data systems and inconsistent private sector reports. Experts suggest that while some companies benefit from AI and even report job growth, others claim AI-induced layoffs, revealing a complex and unpredictable landscape. The discussion emphasizes the importance of understanding AI's subtle, gradual effects and the historical patterns of technological disruption.

Historical Lessons from the Internet Revolution and China Shock

Ben Casselman draws parallels between current AI developments and past technological and economic upheavals, specifically the 1990s internet boom and the China trade shock. He explains that the internet, despite its disruptive nature, caused gradual job shifts rather than mass layoffs, thanks to the slow pace of adoption and adaptation. Conversely, the China shock caused rapid and concentrated factory closures, devastating entire communities with immediate job losses and social consequences. These historical examples illustrate how the speed and distribution of disruption determine whether societies experience gradual transition or catastrophic upheaval, informing current policy considerations.

Policy Responses and Individual Adaptation Strategies

The discussion stresses that policymakers are beginning to grasp lessons from past disruptions but have yet to implement comprehensive solutions. Key recommendations include improving real-time data collection, strengthening existing social safety nets like unemployment insurance, and exploring new policies such as universal basic income or government stakes in AI-enabled companies. The conversation emphasizes the uncertainty faced by individuals, especially young people, in choosing educational or career paths amidst unpredictable AI-driven changes. Ultimately, the podcast advocates for embracing uncertainty and pushing for policies that can mitigate potential social and economic damages.

Show notes

As artificial intelligence becomes more advanced, people are getting more nervous about how it could change the economy and their jobs.

Ben Casselman, the chief economics correspondent for The New York Times, explains why A.I.’s impact is so hard to pin down and what we can learn from the tech disruptions of the past.

Guest: Ben Casselman, the chief economics correspondent for The New York Times.

Background reading:

Ben Casselman, the chief economics correspondent for The New York Times.

Photo: George Wylesol

For more information on today’s episode, visit nytimes.com/thedaily. Transcripts of each episode will be made available by the next workday.

Subscribe today at nytimes.com/podcasts or on Apple Podcasts, Spotify and Amazon Music. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.