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AI Barely Drove Votes. Voters Still Wanted a Crackdown.

Newsweek’s "Politics of AI" webinar examined data center opposition, the need for oversight and the struggle to protect jobs.

The 2026 midterms saw a significant pushback against AI and data center development, with opposition prevailing in 40 out of 41 local ballot measures. However, a Newsweek webinar revealed that voters' concerns about AI and data centers weren't rooted in specific issues, but rather a sense of losing control over an increasingly fast-paced world.

Brent Buchanan, founder of Cygnal, noted that AI ranked last among voter priorities, with data centers and AI trailing behind health insurers and pharmaceutical companies as issues to address. Nathaniel Persily, a Stanford Law professor, suggested that voters' frustrations stemmed from a distrust of major technology companies, particularly social media platforms, and warnings about job losses and other potential risks.

Despite these concerns, Buchanan found that voters were indifferent to the specifics of AI's impact. Some even questioned the necessity of data centers, seeing them as "soulless" buildings without significant benefits. Texas Governor Greg Abbott and Pennsylvania Governor Josh Shapiro both expressed greater skepticism about data center development during the election, potentially reflecting growing public resistance.

However, experts warned that technology executives had not adequately communicated the benefits of AI to ordinary people, while also publicly expressing concerns about job displacement. Younger union members in Michigan voiced worries about AI's potential impact on their careers, fearing that their investments in education and training would become obsolete.

While policymakers grappled with the issue, Mamo suggested that workforce training and possible rules around AI-related layoffs could help alleviate worker fears, but acknowledged the challenges in implementing and enforcing such measures.

Written by urgent.news from Newsweek's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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