Most "AEO experts" are bluffing

Answer engine optimization is presented as a promising but highly difficult-to-verify marketing discipline because no reliable public data shows which prompts people actually submit to large language models, making common metrics such as visibility, citation rate, and average rank dependent on selected prompt sets and methodological assumptions. While AEO can produce genuine business value, including sign-ups, referral traffic, and self-reported attribution, the author argues that public claims of success can be easily manufactured by choosing favorable prompts, competitors, models, dates, samples, and metrics, then implying causation from loosely related changes. The piece distinguishes brand visibility, which measures whether a brand is mentioned, from citation rate, which measures whether a brand’s URL is linked, and average rank, which measures placement among recommended options. It advocates for greater skepticism, clearer methodological context, and greater reliance on measurable business outcomes despite their attribution limitations, while noting that emerging approaches such as agent-level measurement and improved data reconciliation may eventually make AEO reporting more trustworthy.

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