>It's not thinking



  >it compressed the internet into a clever, lossy format with nice interface and it retrieves stuff from there.

Humans do both, why can't LLM's?

  >Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

More like pulling out a deep-fried meme, looking for context, then searching google images until you find the most "original" JPG representation with the least amount of artifacts.

There is more data to add confidently, it just has to re-think about it with a renewed perspective, and an abstracted-away higher-level context/attention mechanism.

> Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

Empirically speaking, I have a set of evals with an objective pass/fail result and a prompt. I'm doing codegen, so I'm using syntax linting, tests passing, etc. to determine success. With chain-of-thought included in the prompting, the evals pass at a significantly higher rate. A lot of research has been done demonstrating the same in various domains.

If chain-of-thought can't improve quality, how do you explain the empirical results which appear to contradict you?

I have no idea how accurate it actually is, But I've had the process used by LLM described as the following: "Think of if like a form of UV Mapping, applied to language constructs rather than 3D points in space, and the limitations and approximations you experience are similar to those emerging when having to project a 2D image over a 3D surface."

These kind of reductive thought-terminating cliches are not helpful. You are using a tautology (it doesn't think because it is retrieving data and retrieving data is not thinking) without addressing the why (why does this preclude thinking) or the how (is it doing anything else to generate results).

Maybe if you bury your head in the sand AI will go away. Good luck!