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The emergence of large language models (LLMs) has made it possible for generative artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets in the U.S. and globally. Here, we investigate whether existing, openly-available LLMs can be used to create messages capable of influencing humans’ political attitudes. Across three pre-registered experiments (total N = 4,829), we find consistent evidence that assigning participants to read persuasive messages generated by LLMs can lead to attitude change across a range of policies, including highly polarized policies, such as an assault weapons ban, a carbon tax, and a paid parental-leave program. Overall, we found LLM-generated messages were similarly effective in influencing policy attitudes as were messages crafted by lay humans. These results demonstrate that recent developments in AI make it possible to create politically persuasive messages cheaply and at massive scale.
