Pre-registered AI visibility study finds no technical signals predict repeated brand mentions

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A developer conducted a pre-registered study to test whether businesses repeatedly named by AI assistants like ChatGPT, Gemini, and Perplexity differ on observable technical signals from those named only once. The study examined four signals — AI crawler access, llms.txt presence, JSON-LD structured data, and offers availability — across 455 home-decor businesses drawn from 264 AI-generated answers per test arm. No signal separated repeatedly mentioned businesses from one-time mentions in the direction commonly assumed by the SEO and AI-visibility field, and the pre-defined refutation threshold was not reached in any cut. A notable finding emerged around llms.txt adoption: 71% of detected files were identical boilerplate from a single generator, inflating apparent adoption rates from a true 9% to a misleading 32%. The researcher emphasized that writing and publishing a failure condition before collecting data was key to producing a result that could not be manipulated after the fact.

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