AI data center opponents are cheering midterm results, but they say the fight isn’t over
New York — Texas resident Maggie Elizabeth Hill stayed up late on election night to watch the midterm results trickle in. But she was up early again the next morning, already thinking about how she could work with the state’s newly elected Democratic leaders to slow the development of AI data centers. “I was so excited … The post AI data center opponents are cheering midterm results, but they say…
On election night, Maggie Elizabeth Hill in Texas anxiously followed the results, already contemplating how to work with her new Democratic leaders to curb AI data center development. Hill founded Don’t Drain Texas, a group rallying rural communities against data center projects. The rapid expansion of AI data centers emerged as a pivotal issue in the lead-up to the midterms.
Grassroots movements sprang up nationwide, with concerns ranging from local economic impacts to broader distrust of AI and tech billionaires' influence. Following the midterms, data center opponents across the country view the election results as a referendum on their cause, seeing the surge of populist, Democratic candidates as a significant victory.
A CNN poll found that about a quarter of voters made AI and data centers a "crucial" factor in their vote, with roughly three to one opposition to data center construction within communities. While some oppose data centers outright, other opponents seek more transparency and benefits for local residents. Notable victories include Democratic Sens.
Sherrod Brown and Annie Andrews, who ran on data center reform platforms. In Texas, Democrats gained control of key statewide offices, with Hill asserting that the midterms may lead to a shift in political responsiveness. Data center opponents are now focused on holding newly elected officials accountable for their promises to regulate or limit data center construction.
Written by urgent.news from Egypt Independent's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
