I agree there's some relation.
Everything ChatGPT does is in a single evaluation of its neural network. It is like being asked to look at a chess board and immediately name the first best move that comes to your mind instinctively, rather than trying to progress through the game tree iteratively and see where it goes.
This is also why it fails at multiplication, which is what computers scientists call an Order-N-squared problem, meaning the number of steps an algorithm must takes to perform multiplication grows roughly by N^2 as the input size of the multiplicands grows by N.
Since ChatGPT always uses a constant number of steps in it's processing, there is some size N where it necessarily fails at multiplication. This is true for any machine/algorithm that use a constant number of steps.
The solution for ChatGPT, as well as for humans, is to break the problem down into manageable steps and work on them piece meal until the problem is solved. This is why mathematicians need chalkboards to do what they do, multiplying big numbers or solving proofs, often requires many steps and can't be solved by intuition alone.
Google discovered this with their alpha zero chess/go AI: It's single network evaluation of the best move had an ELO score of around 3000, still better than the best humans, but not by much. But by letting it iterate through different top moves and see which course of action faired best overtime, it increased it's level of play to 5000 ELO, well into super human territory.
For reference a beginner chess player has an ELO of 1000, a decent amateur around 1200, a chess master 1800, grand masters start around 2400, while the best human players in history have been around 2800.
So I think we need to make a "meta ChatGPT wrapper" that asks ChatGPT how to break down a problem into smaller steps, query ChatGPT multiple times to solve each step and also double check it got the right result along each step in the process, if it second guesses itself, ask it to break down that intermediate step, and so on recursively, until the problem is solved.
I think this replicates the thought processes for a wide range of human intellectual activity and I believe it would widen the class of problems ChatGPT could effectively solve.