Large language model (LLM) "answer engines" such as ChatGPT now send measurable referral traffic to the open web, and a practice analogous to search engine optimization, here called Answer Engine Optimization (AEO), has emerged. Public AEO success stories typically quote large raw growth multiples, but raw referral growth is confounded by the rapid platform-level growth of the answer engines themselves. We report a longitudinal field study on a single high-traffic domain (glasp.co) whose corpus of hundreds of thousands of YouTube question-and-answer pages received a defined bundle of AEO interventions in January 2026 (detailed in Section 4). Because the interventions were concentrated on one subset of the site, the untreated remainder of the same domain acts as a contemporaneous control that absorbs the platform tailwind. Using first-party analytics and server logs rather than probabilistic third-party estimators, we find: (1) raw growth is dominated by the platform tailwind: on monthly aggregates total ChatGPT referrals grew 5.7x while untreated pages on the same domain grew 3.5x over the same window; (2) an interrupted time-series model on the weekly treated/control ratio estimates a discrete, intervention-aligned level increase of 1.82x (95% CI 1.31-2.54, HAC p=0.001), robust across engagement-filtered traffic (2.27x) and alternative specifications; (3) however, a conservative placebo-in-time permutation test yields p=0.16, so the effect is suggestive, not conclusive, given a short and noisy pre-period; and (4) Google organic clicks to treated pages did not fall beyond the ambient site-wide trend and indexation was preserved, consistent with the SEO-protection rule. The methodological message, separating treatment from platform tailwind with an on-domain control, matters more than any single multiple, and implies that headline AEO multiples substantially overstate causal effect.

翻译：大型语言模型（LLM）“答案引擎”（如ChatGPT）现已向开放网络发送可测量的推荐流量，一种类似于搜索引擎优化的实践——此处称为答案引擎优化（AEO）——应运而生。公开的AEO成功案例通常引用巨大的原始增长倍数，但原始推荐增长与答案引擎自身的快速平台级增长相混淆。我们针对单一高流量域名（glasp.co）开展了一项纵向实地研究，其包含数十万个YouTube问答页面的语料库于2026年1月接受了一套明确的AEO干预措施（详见第4节）。由于干预措施集中于该网站的一个子集，同一域名未经处理的剩余部分作为同期对照组吸收了平台增长尾风。利用第一方分析工具和服务器日志而非概率性第三方估计量，我们发现：(1) 原始增长由平台尾风主导：按月汇总，ChatGPT总推荐量增长5.7倍，而同一域名未经处理的页面在同一时间段内增长3.5倍；(2) 基于每周处理/对照组比率的间断时间序列模型估计出离散的、与干预对齐的水平增长为1.82倍（95%置信区间1.31-2.54，HAC p=0.001），该结果在经参与度过滤的流量（2.27倍）及替代规格下均具有稳健性；(3) 然而，保守的按时间排列的安慰剂检验得出p=0.16，因此在前期数据短且嘈杂的条件下，该效应仅为提示性而非结论性；(4) 谷歌自然点击至处理页面的数量未超出站点整体趋势下降，且索引得以保留，与SEO保护规则一致。方法论上的启示——通过域内对照组将处理效应与平台尾风分离——比任何单一倍数更重要，并暗示头条AEO倍数大幅高估了因果效应。