Why 90% Accuracy in Text-to-SQL is 100% Useless

Text-to-SQL applications using LLMs promise to democratize data access, but enterprise deployment requires 100% accuracy—not 80% or 90%—because hallucinations destroy user trust. Traditional benchmarks like Spider 1.0 create false confidence with clean datasets, while Spider 2.0 reveals the harsh reality: enterprise databases with massive schemas, diverse dialects, and external business logic cause model accuracy to plummet to 10-20%.