Seems that either the NN memoizes all the inputs and outputs until the function is totally mapped - then functions as a memoized lookup table, or the NN has discerned what the mortgage calculation is, and is doing exactly the calculation your {Python} backend does, but migrated into an NN middleware layer instead, which sounds like it would be slower.

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?