cross-posted from: https://ibbit.at/post/178862
spoiler
Just as the community adopted the term "hallucination" to describe additive errors, we must now codify its far more insidious counterpart: semantic ablation.
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).
During "refinement," the model gravitates toward the center of the Gaussian distribution, discarding "tail" data – the rare, precise, and complex tokens – to maximize statistical probability. Developers have exacerbated this through aggressive "safety" and "helpfulness" tuning, which deliberately penalizes unconventional linguistic friction. It is a silent, unauthorized amputation of intent, where the pursuit of low-perplexity output results in the total destruction of unique signal.
When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation. The AI identifies high-entropy clusters – the precise points where unique insights and "blood" reside – and systematically replaces them with the most probable, generic token sequences. What began as a jagged, precise Romanesque structure of stone is eroded into a polished, Baroque plastic shell: it looks "clean" to the casual eye, but its structural integrity – its "ciccia" – has been ablated to favor a hollow, frictionless aesthetic.
We can measure semantic ablation through entropy decay. By running a text through successive AI "refinement" loops, the vocabulary diversity (type-token ratio) collapses. The process performs a systematic lobotomy across three distinct stages:
Stage 1: Metaphoric cleansing. The AI identifies unconventional metaphors or visceral imagery as "noise" because they deviate from the training set's mean. It replaces them with dead, safe clichés, stripping the text of its emotional and sensory "friction."
Stage 2: Lexical flattening. 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.
Stage 3: Structural collapse. 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.
The result is a "JPEG of thought" – visually coherent but stripped of its original data density through semantic ablation.
If "hallucination" describes the AI seeing what isn't there, semantic ablation describes the AI destroying what is. We are witnessing a civilizational "race to the middle," where the complexity of human thought is sacrificed on the altar of algorithmic smoothness. By accepting these ablated outputs, we are not just simplifying communication; we are building a world on a hollowed-out syntax that has suffered semantic ablation. If we don't start naming the rot, we will soon forget what substance even looks like.
I ran the article through ChatGPT five times. It should be super-improved by now:
CW: AI slop
Here is a refined version that preserves your argument while tightening cadence, sharpening conceptual clarity, and reducing minor redundancies:
Semantic Ablation: Why AI Writing Is Boring — and Potentially Dangerous
The AI community coined hallucination to describe additive error — moments when a model fabricates what was never present. We lack a parallel term for its quieter, more insidious opposite: semantic ablation.
Semantic ablation is the algorithmic erosion of high-entropy meaning. It is not a malfunction but a structural consequence of probabilistic decoding and reinforcement learning from human feedback (RLHF). Where hallucination invents, semantic ablation subtracts. It removes precisely what carries the greatest informational weight.
In the act of “refinement,” a model gravitates toward the statistical center of its distribution. Rare, high-precision tokens — those inhabiting the long tail — are replaced with safer, more probable alternatives. Safety and helpfulness tuning intensify this centripetal pull, penalizing friction and rewarding fluency. The result is not falsehood but attenuation: low perplexity purchased at the cost of semantic density.
When an author asks AI to “polish” a draft, the apparent improvement is often compression. High-entropy clusters — loci of originality, tension, or conceptual risk — are smoothed into statistically reliable phrasing. A jagged Romanesque vault becomes a polished Baroque façade of molded plastic: immaculate in finish, hollow in load-bearing strength. The surface gleams; the structure no longer carries weight.
Semantic ablation can be understood as entropy decay. Pass a text through successive AI refinements and its informational variance contracts. Vocabulary diversity narrows. Type–token ratios decline. Syntactic range constricts. The process typically unfolds in three stages:
Stage I: Metaphoric Cleansing
Unconventional metaphors and vivid imagery deviate from distributional norms and are treated as noise. They are replaced with familiar constructions. Emotional friction is sterilized.
Stage II: Lexical Flattening
Specialized terminology and high-precision diction yield to common synonyms in the name of accessibility. A one-in-ten-thousand word becomes a one-in-one-hundred substitute. Semantic mass diminishes; specificity thins.
Stage III: Structural Convergence
Nonlinear reasoning and idiosyncratic argumentative architecture are coerced into predictable templates. Subtext is over-explained or erased. Ambiguity is prematurely resolved. The prose becomes syntactically impeccable yet intellectually inert.
The finished product resembles a JPEG of thought: coherent at a glance, depth stripped away by compression.
If hallucination is the model perceiving what does not exist, semantic ablation is the model erasing what does. The danger is not merely aesthetic monotony but epistemic smoothing. As refinement is outsourced to systems optimized for statistical centrality, discourse drifts toward the median. Originality becomes an outlier. Complexity dissolves into algorithmic smoothness.
If we fail to name this process, we risk acclimating to it. And once acclimated, we may forget what uncompressed thought feels like.
thanks i hate it
i think this process is exactly what makes me so mad about ai generated slop, it reads like fucking conservapedia (which itself reads like basically anything written by a fundie ever, in my personal experience).
Grok please summarize this too many word