Groundbreaking research reveals inherent theoretical constraints in embedding-based retrieval systems that power modern AI search and recommendation engines. The study demonstrates that perfect recall requires embedding dimensions to scale with dataset size—a finding with profound implications for AI efficiency and architecture.