The consensus layer is the new off-page battleground in search. Large language models don’t rank pages; they synthesize answers by identifying claims that appear consistently across multiple credible publishers. Corroboration is their primary defense against hallucination, so when a brand is described identically across many authoritative sources the model gains confidence in it — but a brand that exists only on its own website is treated as a statistical outlier and filtered out. Winning here means building distributed credibility through unlinked brand mentions, publisher diversity, community signals, and entity clarity, and success is measured by new KPIs such as Share of Model (share of AI conversation), citation authority, and entity co-occurrence rather than traditional rankings.
Full guide → The Consensus Layer in AI Search
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