Information gain is how much new information a page adds relative to what is already available for the same query. A page that restates the consensus everyone else already published has low information gain; a page with a proprietary statistic, a first-hand test, or a detail no competitor covers has high information gain.
The term comes from a Google patent, "Contextual estimation of link information gain" (publication US11354342B2, filed 2018), which describes scoring a document by "the additional information" it contains beyond documents a user has already seen, then reranking follow-up results accordingly. Google has never confirmed it runs this exact mechanism in production, so treat information gain as a well-evidenced concept, not a named ranking factor.
It matters for AI visibility because engines retrieve and cite at the passage level. When a model assembles an answer, a page holding a specific, corroborated datapoint that no higher-ranked page provides can be pulled in even when it ranks outside the top results — while commodity content that duplicates the field gets skipped. Original evidence is the durable lever; restated summaries decay first.