I imagine it would be fast, then you realise you've made a static content caching layer out of a neural network and replace it with Varnish cache and it would be hyper fast.
I just don't know how you can achieve that with static cache ... only if somebody else requested that exact mortgage calculation before and it is still in the cache.
Also, my idea of the "given input" from the earlier comment would have to include results of sql queries that would form the entire input to the LSTM.
But honestly I think over trained auto encoders can be used as hash maps. That would be an application more in line with what I think you are saying.
And then you're hoping that the NN would act like a JIT compiler/optimiser and run the same code faster. But if it was possible to process (compile? transpile? JIT compile?) the Python code to run faster, then writing a tool to do that sounds easier than writing an AI which contains such a tool within it.
So there's a handwave step where the AI develops its own innate Python-subset optimiser, without anyone having to know how to write such a thing, which would be awesome indeed .. is that possible?