The rapid spread of large language models (LLMs) has raised concerns about the social reactions they provoke. Prior research documents negative attitudes toward AI users, but it remains unclear whether such disapproval translates into costly action. We address this question in a two-phase online experiment (N = 491 Phase II participants; Phase I provided targets) where participants could spend part of their own endowment to reduce the earnings of peers who had previously completed a real-effort task with or without LLM support. On average, participants destroyed 36% of the earnings of those who relied exclusively on the model, with punishment increasing monotonically with actual LLM use. Disclosure about LLM use created a credibility gap: self-reported null use was punished more harshly than actual null use, suggesting that declarations of "no use" are treated with suspicion. Conversely, at high levels of use, actual reliance on the model was punished more strongly than self-reported reliance. Taken together, these findings provide the first behavioral evidence that the efficiency gains of LLMs come at the cost of social sanctions.

翻译：大型语言模型（LLMs）的快速普及引发了对其所引发社会反应的担忧。现有研究记录了人们对人工智能使用者持有的负面态度，但此类反对是否会转化为代价高昂的实际行动仍不明确。本研究通过一个两阶段在线实验（第二阶段参与者N=491；第一阶段提供实验对象）探讨该问题，实验中参与者可花费自身部分初始资金来削减先前在完成真实努力型任务时是否使用LLM支持的同伴收益。平均而言，参与者销毁了完全依赖模型者36%的收益，且惩罚力度随实际LLM使用程度呈单调递增态势。关于LLM使用的信息披露产生了可信度鸿沟：自我报告未使用模型者比实际未使用者遭受更严厉惩罚，这表明"未使用"声明会受到质疑。反之，在高使用水平下，实际依赖模型者比自我报告依赖者受到更强惩罚。综合来看，这些发现首次提供了行为证据，表明LLMs的效率提升是以遭受社会制裁为代价的。