Information Gain

The term gained currency from a Google patent describing scoring documents by the new information they provide relative to documents a user has already seen. As an editorial principle it is simple: the web does not need an eleventh article saying the same ten things, and ranking systems increasingly have ways to detect redundancy through semantic similarity.

Sources of genuine gain include proprietary data, original research, first-hand testing and experience, expert positions that depart from consensus, and synthesis that connects previously separate information. Comprehensiveness alone is not gain if every point already exists elsewhere.

AI engines make redundancy brutally visible. An LLM already encodes the consensus on most topics — content repeating that consensus gives the model nothing it must cite, because it can generate the same substance itself. What forces attribution is information the model cannot produce: your benchmark numbers, your survey findings, your documented test results. Citations flow to content that functions as a primary source.

This reshapes content strategy under generative engine optimization: the question shifts from did we cover the topic to what does this page know that nothing else knows. Unique claims, clearly stated and attributed, are the citable surface area of a page.

Audit existing pages for me-too sections and either differentiate or consolidate them. Systematically mine your unique assets — product usage data, customer patterns, internal benchmarks, practitioner experience — and lead with them rather than burying them under restated basics. State unique findings in quotable, self-contained sentences so they survive passage extraction. Tracking which of your claims actually appear in AI answers shows where your gain is real; Geonimo's citation tracking connects specific pages to the prompts they win.