Look at the algorithmic tools used in ML and automated theorem proving for example: ML uses gradient descent (and related numerical methods) for local optimization, while constraint satisfaction/optimization/Boolean satisfiability, SAT modulo-theories, Quantified Boolean Optimization, etc., rely on combinatorial optimization. Mathematically, combinatorial optimization is far more problematic compared to numerical methods and much more difficult, largely because modern computers and NVidia gaming cards are really fast in crunching floating point numbers and also largely that most problems in combinatorial optimization NP-hard or harder.
Now thing of what LLM and local optimization is doing: it is essentially searching/combining sequences of words from Wikipedia and books. But search is not necessarily a difficult problem, it is actually an O(1) problem. While multiplying numbers is an O(n^2.8 (or whatever constant they came up with)) problem while factorization is (God knows what class of complexity) when you take quantum computing into the game).
Great, these are my 2 cents for the day, good luck to the OpenAI investors (I am also investing there a bit as a Bay Area citizen). You guys will certainly make help desk support cheaper...