What is the ultimate conceptual limitation of similarity-based learning?

Similarity-based learning assumes distance equals meaning, which often breaks in real-world data.

All KNN-like methods rely on the assumption that proximity implies semantic similarity. However, in real systems, meaningful structure may be non-geometric or require learned representations, making raw distance unreliable.