Talking to Your Data: What MCP Doesn't Solve

Every AI platform is racing to ship data connectors, but "talk to your data" hides five distinct jobs — and only one of them is the AI writing SQL. This episode traces what actually happens between a question and a valid query: schema ingestion, semantic grounding, query generation, execution with error feedback, and interpretation back to language. We look at why a semantic layer beats raw text-to-SQL by sixteen points on the same model, how agentic loops with actor-critic and execution feedback close the correctness gap, and whether specialized NL-to-SQL models still earn their place against frontier general models. Then we ask the uncomfortable question underneath all the leaderboard numbers: if roughly a third of the reference answers in the standard benchmarks are wrong, what exactly are we measuring? Episode #758615 — open it directly at myweirdprompts.com/758615