AI Jobs Risk Index Flags 9.3 Million U.S. Jobs as Vulnerable, With Losses Reaching $1.5 Trillion
Updated
Updated · Fortune · May 12
AI Jobs Risk Index Flags 9.3 Million U.S. Jobs as Vulnerable, With Losses Reaching $1.5 Trillion
2 articles · Updated · Fortune · May 12
9.3 million U.S. jobs are vulnerable to AI automation under Bhaskar Chakravorti’s American AI Jobs Risk Index, with projected income losses of about $200 billion and up to $1.5 trillion in an extreme scenario.
14 knowledge-driven metros—including San Jose, New York, Seattle, Boston and Raleigh-Durham—face the heaviest hit, with 3.6 times the job loss and more than five times the income loss of traditional manufacturing areas.
49,123 layoffs have been tied to AI automation so far this year, versus roughly 55,000 in all of 2025, though some cuts also reflect tech companies redirecting cash toward AI infrastructure.
Tech unemployment rose to 3.8% last month but stayed below the overall 4.3% rate, suggesting the broader labor market remains steady even as white-collar displacement risks build.
Chakravorti argues this new "Wired Belt" could echo the political fallout of the China shock, while others such as Apollo’s Torsten Slok say AI may ultimately boost productivity and business formation.
Why do some experts predict AI will create jobs, even as AI-linked layoffs are currently on the rise?
Will AI amplify white-collar workers' skills, or will it simply make their jobs the first to be automated?
Past reskilling efforts failed Rust Belt workers. Can new policies protect the 'wired belt' from a similar fate?
AI’s Immediate Threat to US Jobs: Insights from the American AI Jobs Risk Index and the Urgent Need for Policy Action
Overview
The American AI Jobs Risk Index warns that artificial intelligence is set to cause significant job and income disruption across the US, with major urban centers and university towns facing the highest risk. High-skill, high-wage roles—especially in technical, analytical, and creative fields—are more vulnerable to AI-driven displacement than previously thought, and the pressure for these workers to transition may come faster than expected. This geographic and occupational specificity highlights the need for targeted policy interventions, rather than broad solutions, to help the most affected regions and professions adapt to the rapid changes brought by AI.