This is the same reasoning behind why Yann Lecun thought test-time scaling would not work for LLMs: compounding error.

Instead, the more tokens LLMs use, the better their performance on many tasks. LLMs can self-correct, evidenced by the power of getting models to question themselves by emitting "Wait," in S1. https://arxiv.org/abs/2501.19393

You wouldn't believe the amount of reasoning I saw these past few months that was correct until the stochastic parrot decided that a "wait" token should now be used and everything steered off a cliff.

Yeah came here to comment exactly this. And this is generally why I dislike/avoid this type of first principle analysis: it can make very convincing arguments that are just totally wrong due to some misleading assumption