February 17, 2026
Who sanded down my sentences?
Why AI writing is so generic, boring, and dangerous: Semantic ablation
From sharp to bland: writers say AI sands your voice while skeptics want proof
TLDR: A viral essay claims AI “polishing” erases the sharp, specific parts of writing—dubbed “semantic ablation.” Commenters largely agree, sharing tales of bland makeovers, while skeptics ask for real examples and others beg for an anti-polish “chaos mode.” It matters because it shapes how we think, create, and read.
The internet is buzzing over a fiery essay that coins a new villain in AI writing: “semantic ablation,” the idea that AI “polish” scrubs away the weird, specific, human bits and leaves a clean, empty shell. The piece calls it a “JPEG of thought”—pretty on the surface, but missing data. Fans say it nails the feeling of AI’s “helpfulness” training (the kind where humans give thumbs-up to safe answers) nudging everything to the middle. One commenter sighed, “Race to the middle really sums up how I feel about AI,” while another mourned how the “pointiness” of prose gets sanded away into nothing.
Writers piled on with war stories: one said AI editing “wanted to replace all the little bits of me.” The drama spiked when a commenter asked if we could “invert a sign” and get the opposite—basically, an anti-polish mode that amps up the spice (cue the aside: would any lab ever release that?). Skeptics, led by a request for receipts—“I’d like to see some concrete examples”—pushed for proof beyond vibes. Meanwhile, the memes wrote themselves: “from serrated knife to butter knife,” “smooth brain prose,” and that haunting “JPEG of thought” tagline. Whether you’re pro-chaos or pro-proof, the crowd agrees on one thing: nobody wants their voice turned into oatmeal.
Key Points
- •The article defines “semantic ablation” as the algorithmic erosion of high-entropy information in AI-written or AI-refined text.
- •It attributes this effect to greedy decoding and RLHF, intensified by safety/helpfulness tuning that penalizes unconventional language.
- •The author proposes measuring semantic ablation via entropy decay and collapsing type-token ratios across successive AI refinement loops.
- •A three-stage process is described: metaphoric cleansing, lexical flattening, and structural collapse toward standardized readability.
- •Semantic ablation is contrasted with hallucination, warning of a broader drift toward generic, low-perplexity outputs in written communication.