AP NORC survey finds 64 percent of Americans say AI too fast
TL;DR
An AP NORC poll published October 8, 2026 finds that 64 percent of U.S. adults believe artificial intelligence is developing “too fast,” while only 8 percent say it is moving “too slow.” Around eight in ten respondents want the U.S. government to prioritize keeping AI under human control and protecting workers, and most do not trust either major party or President Trump to handle AI policy effectively.
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This article aggregates reporting from 2 news sources. The TL;DR is AI-generated from original reporting. Race to AGI's analysis provides editorial context on implications for AGI development.
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Race to AGI Analysis
The AP NORC survey puts hard numbers on a trend everyone in the field has felt anecdotally: the public thinks AI is moving too fast and trusts almost no one in Washington to manage it. When roughly two thirds of Americans say development is too rapid and four in ten say they trust neither party on AI, labs lose political cover for a “move fast and fix it later” posture. That matters because frontier developers increasingly depend on permissive regulatory environments to run gigantic training runs, deploy agentic systems, and experiment with semi autonomous infrastructure.
The findings also suggest that AI is becoming a mainstream political issue rather than a niche tech topic. President Trump’s low marks on AI, despite his broader base of support, show that skepticism about industry influence and safety cuts across partisan lines. For companies like OpenAI, Anthropic, Google, Meta and Microsoft, this creates real downside if a high profile failure reinforces fears about loss of control or mass job loss. It also empowers more aggressive regulatory coalitions at the city and state levels, not just in D.C.
In the race to AGI, widespread belief that the technology is already moving too quickly is a warning sign. If labs push visible capabilities much further without equally visible governance and safety measures, they risk a political backlash that could manifest as moratoria, licensing regimes, or blunt bans on certain deployment patterns.
