This is how all thinking actually works.

It is very much absolutely positively 100% not.

When I’m solving a problem, im not inventing a solution de novo every time I do it. I’m using heuristics.

You are using LONG TERM heuristics. Your world model is of the world, generalized across your life-long experience. The ruleset for Tai Chi and the ruleset for ballroom dance have overlap with the ruleset you learned skipping rope and the ruleset you learned playing hopscotch. The quote you listed above illustrates that there are no heuristics that even apply to the whole board of Othello. The LLM didn't even learn that all squares on the board are equal.

PEMDAS as order or operations, the math operations being basically procedures.

This, again, is a world model. If you train an LLM on math, it will not come up with PEMDAS. It will come up with thousands of patchwork rules covering individual numbers because there is no methodology to markov chains that gives you an overall picture.

THIS IS THE IMPORTANT BIT. It all goes back to autocomplete on your keyboard, which all goes back to Robert Mercer, which all goes back to Renaissance Capital, which all goes back to Markov chains, which CAN. NOT. BE. complete sets.

"Informally, this may be thought of as, "What happens next depends only on the state of affairs now."

Your heuristics are "here's how to play chess." The LLM's heuristics are "if this was the last move, here are the list of legal next moves" times literally every possible permutation of the board. Your heuristics of "here's how to play chess" can be extrapolated to "here's how you would probably play 3d chess" and "here's what the rules for 'battle chess' might be" and "here are the similarities and differences between chess and checkers." The LLM's heuristics are "I have no training data for that" three ways.

To be Frank, even a world model is basically a systematically constructed bag of heuristics.

The key phrase there is "systematically." That makes it a set of heuristics. They are interrelated, interdependent and extensible. They are portable from situation to situation and they can be generalized. The word "bag" is used instead of "set" because there is no system. There are no interrelations, there is no interdependency and there is no extensibility. LLMs never learned that there are only five fingers on a hand. Six-fingered men had to be hand-coded out of the model. LLMs never learned about perspective. Perspective had to be (pain-stakingly) coded into the model. There is no adaptability to LLMs. In order to solve their blind alleys they have to be hand-coded around them - you cannot teach an LLM "do not reproduce copyrighted material" you have to give it a laundry list of the parts of training data it cannot reproduce within a certain percentage match. It cannot go "I must not draw Mario, therefore I must not draw Luigi."

It covers more domains than your ad hoc heuristics as AI is using them today, but the difference isn’t the approach, it’s the scale.

It's not the scale, it's the approach. Any creature that thinks will create generalizations. That's how T-mazes work: will the rat associate the left turn with a reward, and take that left turn reward to the next maze. LLMs do not create generalizations, they create ad-hoc frameworks to report the stochastic mean of the problem in front of them right now. Here's Manhattan as mapped by an LLM:

Will it navigate? No. Will it give mostly-accurate turn-by-turn directions based on the inputs and outputs of the training data? Yes. But it will NEVER generalize to "street intersections are almost always ninety degrees."