Because the embedding model was trained to place related content near each other, chunks about the same topic form clusters in the vector space. A new chunk about a topic already present lands close to that cluster rather than in empty space. This is the property that makes search by meaning possible: closeness in the vector space stands for similarity in meaning, not similarity in wording.
How AI Answers Questions About Documents It Never Learned