Listen: Information Gain
How much new information a document adds beyond what is already known; defined in information theory as a drop in entropy, and applied in search to reward content that adds rather than repeats.
Transcript
Information gain is a measure of how much new value a piece of content adds, compared to what is already known. In information theory, it refers to the reduction in uncertainty when we learn something new. Today, this concept is becoming crucial for search engines and artificial intelligence.
A web page scores well on information gain when it brings something unique to the table, rather than just repeating the consensus. Google even holds a patent on ranking documents by how much new information they provide relative to what a reader has already seen. While this is a working hypothesis and not a confirmed ranking signal, the logic behind it is clear.
AI systems that retrieve and cite sources have every reason to prefer pages with unique information. If your content only echoes what a language model already knows, or what other sites are already saying, there is no reason for an AI to cite you.
To stand out, you need to maximize your information gain. You do this with original data, first-hand testing, and specific, unique findings. That is the key to staying visible in an AI-driven world.