What changes when your customers stop searching
Search rewarded the page that ranked. AI assistants reward the brand the model can describe: a different mechanism, and a different thing to measure.
Notes on AI search visibility: how assistants pick the brands they recommend, and what moves the answer.
Search rewarded the page that ranked. AI assistants reward the brand the model can describe: a different mechanism, and a different thing to measure.
Assistants lean on retailers, reviews, forums and video for the sources behind a recommendation. Your own site is a minority of the evidence they use.
Training bots and search bots are different tokens with different consequences, and most robots.txt files block the wrong one. What each control actually does.
Which technical measures platforms actually say they use, which they say they ignore, and the one new control that genuinely changes whether you appear in AI answers.
What the research on position bias and retrieval actually supports about structure, and where the evidence runs out and vendor advice takes over.
The best evidence we have on changing how a generative engine describes you comes from one 2024 paper. Its findings are useful, specific, and smaller than the headlines.
Presence, perception and displacement. What each one answers, how to compute it defensibly, and why a single AI visibility score usually hides the useful part.
Same question, same settings, different answer. The research on variance says diversify phrasings, languages and engines rather than repeating one prompt.
Assistants cite about four sources where a search page lists ten, and roughly a third of the domains they cite never appear in the matching organic results.
Two different mechanisms decide whether your brand appears in an AI answer: what the model retrieved, and what it already believed. They need different work.