What it means
Semantic similarity measures how close two pieces of text are in meaning, not how many words they share. In AI search, content and queries are turned into embeddings, and the system compares them by distance (often cosine similarity) to decide what is relevant. It replaces exact keyword matching as the ranking signal. The question is no longer "does my page contain the query," it is "does my page sit close to the query in meaning-space."
Why it matters
Google has been moving toward meaning over literal strings since Hummingbird in 2013, which let it match semantics instead of taking queries literally. Retrieval in AI search runs on the same idea: a passage gets pulled because its meaning is near the query, even if it never uses the exact phrase. So stuffing a target keyword does little, while covering the concept clearly and completely does a lot. The same math also exposes a risk, because two of your own pages that sit too close in meaning-space read as near-duplicates and cannibalize each other.
Take a payroll brand, PayCadence, writing about "contractor vs employee classification." A page that explains the concept with concrete tests, examples, and edge cases sits close to the many ways people phrase that question, so it gets retrieved across variations. A thin page that just repeats the keyword sits far from most real phrasings and rarely surfaces.
How to use this knowledge
Write for the concept. Cover the topic and its related ideas thoroughly so your content sits near the full range of ways people ask about it.
Use natural phrasing. Include the questions and language real users employ, since meaning-based matching rewards coverage over exact-keyword repetition.
Check for cannibalization. Compare pages by similarity and merge or differentiate any pair above roughly 0.7, where they read as near-duplicates.
Strengthen entity coverage. Name the people, products, and concepts tied to a topic so the model places your content in the right meaning neighborhood.
Growth Memo guidance
Post-Hummingbird, Google could better match the meaning (semantics) of search terms with results. The machine was able to get what people really meant.
"Degree" means how similar the content across two or more pages is, expressed in cosine or content similarity. Though not an exact science, in my experience, a cosine similarity higher than 0.7 classifies as "high", while it's "low" below a value of 0.5.
Embeddings — the meaning vectors that semantic similarity is computed over.
Vector database — the index that stores embeddings and ranks them by similarity.
Retriever — the component that uses similarity scores to pull relevant chunks.
Content cannibalization — what happens when two of your pages sit too close in meaning-space.
Entities — the named objects that anchor content in the right semantic neighborhood.