What LLMs can do is limited, they are superior to wet-wear in some tasks like finding and matching patterns in higher dimensional space, they are still fundamentally limited to a tiny class of problems outside of that pattern finding and matching.
LLMs will be tools for some math needs and even if we ever get quantum computers will be limited in what they can do.
LLMs, without pattern matching, can only do up to about integer division, and while they can calculate parity, they can't use it in their calculations.
There are several groups sitting on what are known limitations of LLMs, waiting to take advantage of those who don't understand the fundamental limitations, simplicity bias etc...
The hype will meet reality soon and we will figure out where they work and where they are problematic over the next few years.
But even the most celebrated achievements like proof finding with Lean, heavily depends on smart people producing hints that machines can use.
Basically lots of the fundamental hints of the limits of computation still hold.
Model logic may be an accessable way to approach the limits of statistical inference if you want to know one path yourself.
A lot of what is in this article relates to some the known fundamental limitations.
Remember that for all the amazing progress, one of the core founders of the perceptron, Pitts drank him self to death in the 50s because it was shown that they were insufficient to accurately model biological neurons.
Optimism is high, but reality will hit soon.
So think of it as new tools that will be available to your child, not a replacement.