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Abstract We investigate whether large language models (LLMs) threaten democracy through their persuasive capabilities. Using two survey experiments (N = 10, 417) and real-world simulations, we compare the cost-effectiveness of LLM chatbots against traditional campaign tactics, taking into account both the “receive” and “accept” steps in the persuasion process. Our design advances prior research by assessing extended human-LLM interactions and measuring short- and long-term effects across three political domains. We find that while LLMs are comparably persuasive to campaign ads once seen, real-world impact depends on both message reception and acceptance. Simulations estimate LLM-based persuasion costs 48–75 per voter versus 100 for traditional methods. However, traditional methods currently scale more effectively. While LLMs do not yet offer substantially greater potential for large-scale persuasion, this may shift as capabilities improve and techniques for scalable exposure become feasible.
Chen et al. (Thu,) studied this question.