发表机构
Princeton University; New York University（普林斯顿大学; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过语言模型市场模拟，发现个人AI助手的节省收益部分被卖方适应所侵蚀，且无助手消费者负担未显著增加，强调应在市场层面评估助手效果。
AI 中文摘要
个人AI助手开始为消费者进行交易，早期采用者获得了实际节省。这些节省能否持续，以及没有助手的消费者会遭遇什么，取决于卖方如何应对——这是一个单用户证据无法回答的问题。我们构建了一个基于智能体的租赁市场，其中语言模型扮演消费者、助手和由算法定价工具引导的六个自适应卖方。一半人口在咨询或执行授权下获得助手；合同将仅通过租户物理行动避免的费用与预先选定的附加服务配对，授权助手可在线取消该附加服务。一个分析基准和一个行为校准规则市场提供事前预测，配对分支（冻结与自适应卖方）将采纳效应与市场反馈分开。在三十个模拟市场中，当卖方被冻结时，执行助手将采纳者的支出削减了每租户日13.7美元；适应收回约三分之一，留下8.7，收益既以较低账单形式也以完成租赁形式出现。卖方提高标价率同时削减费用，对无助手消费者负担的校准预测（+3.6）未转移：他们的平均支出变化为+0.4，置信区间为-0.6至+1.3。卖方模型交换和市场内费用设定向定价工具的转移表明，费用行为以及随之而来的收益分配由卖方侧决定。我们得出结论，助手应在市场层面评估——包括完成、总支出和非用户——比较层准确指出了校准行为预测在语言模型市场中失败的确切位置。
英文摘要
Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no assistant, depends on how sellers respond -- a question single-user evidence cannot answer. We build an agent-based rental market in which language models play consumers, assistants, and six adaptive sellers guided by an algorithmic pricing tool. Half the population receives an assistant under an advisory or an executing mandate; the contract pairs a fee only the renter's physical action avoids with a pre-selected add-on an authorised assistant can cancel online. An analytical benchmark and a behaviourally calibrated rule market supply ex-ante predictions, and paired branches with frozen versus adaptive sellers separate adoption effects from market feedback. Across thirty simulated markets, executing assistants cut adopters' spending by 13.7 USD per renter-day when sellers are frozen; adaptation claws back about a third, leaving 8.7, with the gains arriving both as lower bills and as rentals completed at all. Sellers raise headline rates while cutting fees, and the calibrated forecast of the burden on unassisted consumers (+3.6) does not transfer: their mean spending change is +0.4, confidence interval -0.6 to +1.3. Seller-model swaps and a within-market transfer of fee-setting to the pricing tool show that fee conduct, and with it the division of the gains, is decided on the seller side. Assistants, we conclude, should be evaluated at market level -- completion, total spending, and non-users included -- and the comparison layers locate exactly where a calibrated behavioural forecast fails in a language-model market.
