This is true, I had omitted it for simplicity; It is still the same approach applied to scaled problems. Humans don't execute it perfectly, but computers do.
With humans, and any other fallible but "true" math system, the rate of errors is roughly linear to the size of the problem. (Linear to the # of steps, that is)
With LLMs and likewise systems, this is different. There is an "exponential" dropoff in accuracy after some point. The problem-solving approach simply does not scale.
> you will see that its approach will not be dissimilar to how a human would reason about the question internally.
"Not dissimilar", but nevertheless a mere approximation. It doesn't apply strict logic to the problem, but guesses what steps should be followed.
This looks like reason, but is not reason.