Wolfram Alpha already does that. But that's because Wolfram Alpha is built as a model whose purpose is "recognize what kind of problem this natural language query requires, then pass it on to the problem engine for that kind of problem", where each problem engine is an actual solution model for that kind of problem, based on actual facts about the world.

ChatGPT, though, is built as a completely different type of model, whose purpose is "find a pattern that this natural language query matches, then generate a greatest probability sequence of natural language words for that pattern based on the training data set". That's a completely different structure.