A recent study led by Anil Menon of the University of California, Merced and Baobao Zhang of Syracuse University found that warnings about artificial intelligence (AI) potentially disrupting jobs in the near future do not significantly change public attitudes toward automation risks or policy responses.
The research surveyed 2,440 adults in the United States. Participants were presented with scenarios predicting when “transformative AI” could arrive—ranging from as soon as 2026 to as late as 2060—or received no timeline at all. The vignettes described expert predictions that advances in machine learning and robotics could replace workers across various professions, including software engineers, legal clerks, teachers, and nurses.
According to the findings, shorter timelines for AI’s arrival caused only a slight increase in anxiety about job loss due to automation. However, these concerns did not translate into significant changes in participants’ expectations regarding when job losses would occur or their support for government measures such as retraining programs or universal basic income.
“These results suggest that Americans’ beliefs about automation risks are stubborn,” said Menon and Zhang. “Even when told that human-level AI could arrive within just a few years, people don’t dramatically revise their expectations or demand new policies.”
The study used construal level theory to explore how people’s perceptions of time affect their risk assessments. It found that respondents who were told AI breakthroughs were imminent were not much more alarmed than those given more distant forecasts. Notably, only the group exposed to a 2060 timeline showed a significant rise in worry about job loss—a result researchers suggested might be because this forecast seemed more credible than predictions of immediate disruption.
Menon and Zhang argue that their findings challenge assumptions that making technological threats feel more urgent will lead to greater public support for regulatory action or social safety nets. They also note several limitations: the study focused on timeline cues rather than other psychological factors like economic trade-offs or trust in expert predictions; it also did not track changes over time.
“The public’s expectations about automation appear remarkably stable,” they said. “Understanding why they are so resistant to change is crucial for anticipating how societies will navigate the labor disruptions of the AI era.”
The study was published in The Journal of Politics amid ongoing debate over how large language models and generative systems may reshape employment landscapes.
