Where AI money worked in the midterms
AI-funded super PACs saw mixed effectiveness in Tuesday’s midterm elections.
In the recent midterms, AI-funded super PACs displayed varied levels of success. The Guardrails Alliance PAC, which advocates for AI regulations, achieved a perfect 5-0 record in its spending. Leading the Future, an AI advocacy group, poured $4.8 million into supporting Republican Darline Graham's bid for a Senate seat. However, Graham ultimately lost the election, and her opponent, Annie Andrews, famously dubbed Graham "Data Center Darline" during the campaign.
Despite this setback, Leading the Future's other spending played a role in Rep. Jared Moskowitz's victory in Florida, despite redistricting concerns. On the Republican side, super PACs backed candidates who ultimately lost, according to Wired. The other significant AI-related super PAC, Public First Action, spent $20 million in the general election with a more mixed outcome.
It successfully backed winners such as Rep. Valerie Foushee in North Carolina and Rep.-elect Scott Wiener in California, but the Republicans it supported lost. Guardrails Alliance allocated over $3.1 million to oppose five candidates who ultimately lost. Yet, money couldn't sway voters on the most impactful AI-related issue: local ballot measures limiting data center development.
These measures, which appeared on about 40 ballots across the country, overwhelmingly passed. A GOP operative attributed the unexpected success of these measures to a lack of opposition from Leading the Future. The group's spokesman, Jesse Hunt, emphasized that their investments aimed to build a coalition for a national regulatory framework, foster innovation, and protect workers, families, and communities.
Hunt called for a multi-cycle effort, predicting that the 2028 election will reveal which candidates genuinely prioritize a national AI regulatory framework versus those who politicize the issue.
Written by urgent.news from Semafor's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
