> Semantic ablation is the algorithmic erosion of high-entropy information. Technically, it is not a "bug" but a structural byproduct of greedy decoding and RLHF (reinforcement learning from human feedback).

> Domain-specific jargon and high-precision technical terms are sacrificed for "accessibility." The model performs a statistical substitution, replacing a 1-of-10,000 token with a 1-of-100 synonym, effectively diluting the semantic density and specific gravity of the argument.

> The logical flow – originally built on complex, non-linear reasoning – is forced into a predictable, low-perplexity template. Subtext and nuance are ablated to ensure the output satisfies a "standardized" readability score, leaving behind a syntactically perfect but intellectually void shell.

What a fantastic description of the mechanisms by LLMs erase and distort intelligence!

I agree that AI writing is generic, boring and dangerous. Further, I only think someone could feel this way if they don't have a genuine appreciation for writing.

I feel strongly that LLMs are positioned as an anti-literate technology, currently weaponized by imbeciles who have not and will never know the joy of language, and who intend to extinguish that joy for any of those around them who can still perceive it.

People haven't really spoken about the obvious token manipulation that will be on the horizon once any model producer has some semblance of lock-in.

If you thought Google's degredation of search quality was strategic manipulation, wait till you see what they do with tokens.