I also think there’s a lot of potential to build smaller internal tools with prolog. Prolog + LLMs are a really good match. I think the broad usefulness of prolog has been bottlenecked in the past by the finding/sourcing/formatting correct data, but even cheap LLMs can turn information into prolog facts. You can do all sorts of really neat stuff with a teeny prolog core, and a lot of LLM sourced data.
Like for instance, I made a very rough model of how certain data flows through our many services. Queues, indexers, caches. Came up with a few predicates that describe what a race condition is, what stale data is, then let Claude loose on the codebase to fill in all of the facts. Then in a couple queries, I indeed found a few race conditions and stale data!
Prolog is like a database. It is mechanical. AI is like web search (which it essentially is).
SQL is fine if there are interlinks supporting the direction of your search. But those relationships frequently end up one-way, unless the architect both fights for it, and actually exists.