There are no lossless transformations of natural-language text
Summary
Sophie Alpert argues that there are no lossless transformations of natural-language text, so engineers should stand behind every idea and sentence when using AI to help write docs, and not disclaim AI-generated content.
View Cached Full Text
Cached at: 08/12/26, 08:20 AM
Similar Articles
There are no lossless transformations of natural-language text
Simon Willison highlights Sophie Alpert's essay arguing there are no lossless transformations of natural-language text; AI-assisted rewrites can lose the author's intended meaning, so engineers must stand behind every sentence they publish.
@lagerskoy: A SINGLE GITHUB REPO JUST COLLAPSED THE “AI SOUNDS LIKE AI” PROBLEM. Most AI text fails for one reason: it’s too perfec…
A GitHub repo called Humanizer solves the 'AI sounds like AI' problem by adding sentence variation, natural pacing, and small imperfections to AI-generated text, making it indistinguishable from human writing. This tool promises to compress the editing layer into seconds, disrupting content creation workflows for SEO, newsletters, scripts, and ghostwriting.
“No AI” Statements Are Much More Than Mere Statements
The article argues that 'No AI' statements are necessary and useful for labeling human-made content, countering the view that such disclaimers are unnecessary. It emphasizes the difficulty of distinguishing AI-generated from human-made content, especially as AI improves.
This text was all written by AI. Can you prove it?
The article critiques the concept of AI-generated text and the effectiveness of watermarking for proving AI involvement, arguing that it may not capture human contribution and highlights ambiguity in defining AI slop.
Optimization Is Not All You Need
This essay analyzes the alignment of language models through the lens of 'optimization culture,' arguing that the focus on measurable improvement has shifted AI from exploratory engagement to administrative tedium, and that optimization procedures cannot distinguish between error and invention.