Maybe I'm just a cynical data scientist, but this is how we get people using and interpreting models that they don't necessarily understand the complexities of. If some data violates an underlying assumption or has some complexities around representation and meaning then there's nothing really stopping someone getting a model that appears to fit correctly but gives answers that are meaningless or just wrong.

In traditional ML you need to a) define model to do prediction A -> B b) train the model, which may take minutes c) then do the predictions form A -> B, which takes (1, 10, 100) microseconds

With predictive queries, you: a) Ask prediction for any X based on any A, B and C and expect answers in (1, 10, 100) milliseconds

You basically trade throughput and latency to get higher productivity, faster iteration and simplified overall sysstem