So basically, just like back in CNNs, when we made it use multiple filters hoping that it would mimic our human-designed filter banks (one edge detector, one this, one that), we found that instead each of the filters was nonsensical interpretability-wise, but in the end it gave us the same or better answer, LLMs CoT is BS but it gives the same or better answer compared to when it actually makes sense. [I'm not making a human comparison, very subjective, just comparing LLM with BS CoT vs LLM with makes-sense CoT]

Some loss functions force the CoT to "make sense" which is counterproductive but is needed if you want to sell the anthropomorphisation, which VC funded companies need to do.

There is no need to fall back to anthropomorphisation either to explain why long CoTs lead to better answers -- LLMs are a fixed amount of compute. Complexity theory says that for harder problems we need more correlated compute. Only way for an LLM to compute "more" is to produce more and more tokens. Note that due to previous computations coming as input, it is correlated compute, just what we need.

What you observed would happen anyways, to be clear, just pointed out an interesting tangent. Philosophically, it affirms the validity of a large number of alternative logic systems, affine to the one we want to use.