I'd like to see your proposal get a bit more concrete. Your request seems a bit more for "magic" than "AI".
You're not likely to succeed with "here's the query and schema" and have AI somehow figure out what the best plan there is. There's a lot of rules to observe for correctness, and you can't just try to execute arbitrary valid plans, because the initial query plans are going to be really bad. So you again need a model of what valid query plans are, and a cost model to evaluate how efficient a plan is, without having executed it.
I think there's plenty opportunities for ML type improvements, but they're in individual parts, less as a wholesale query planner replacement. I think the most likely beneficiaries are around statics collection and evaluation, incremental improvements to query plans, and some execution time -> plan time feedback loops.
What I'm saying is that I, as a human, can spot simple situations under which semantically-correct optimizations are available. I can also create queries where literally cutting and pasting IDs over and over again into a WHERE clause is quicker than joining or using an IN clause. These things are dumb.
I personally think there are massive opportunities for data description languages and query languages that make expressing a single set of semantics simpler. SQL is supposedly a declarative language, but almost always requires you to understand _all_ the underlying implementation details of a database to get good performance.
Beyond that, I'd be more than happy with ML that enhanced, heuristically, some parts of the query optimiser. Hell, I'd be happy with a query optimiser that just went away and optimised, 24 hours a day, or at least didn't just spend 19ms planning a query that is then going to run for 8 hours.