A colleague cites Beyer et al. and argues that nearest...

The claim that nearest‑neighbor search breaks down in 768‑dimensional space rests on a theoretical result that assumes the data are uniformly distributed with no intrinsic clustering. In practice, contrastive training of the encoder deliberately shapes the embedding space into dense clusters that reflect semantic similarity. Because the theorem’s assumptions are violated, the empirical recall@10 of 0.82 does not refute the usefulness of semantic search; instead it provides information about how the actual data distribution behaves. The correct adjudication is to evaluate the hypothesis in the context of the learned embedding distribution, not to dismiss it based solely on the high‑dimensionality theorem.