Indexing is a special case of AI. At the limit, optimal cutting and learning are equivalent problems. Non-trivial spatial representations push these two things much closer together than is normally desirable for e.g. indexing algorithms. Tractability becomes a real issue.

Practically, scalable indexing of complex spatial relationships requires what is essentially a type of learned indexing, albeit not neural network based.

> is essentially a type of learned indexing, albeit not neural network based.

NN is just function approximation, why do you think that could not be a valuable part of the solution?

It seems like a dynamically adjusted/learned function approximator is a good general tool to most of these hard problems.