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AI detector arms race

Wiki conceptllm-skepticismwritingai-disclosure ↳ show in map Markdown
title
AI detector arms race
type
concept
summary
Surface-based AI detection fails in both directions, the tells change every model release, and the stable equilibrium is writers degrading their work to pass
tags
llm-skepticism, writing, ai-disclosure
created
2026-07-21
updated
2026-07-21

Automated and human detection of AI-generated text both rest on surface features — formatting, punctuation, fluency, structure. Every one of those features is either something a model can be told not to produce, or something a careful human does anyway. The result is a detector that misfires in both directions, and a feedback loop that punishes good writing.

Both error types are live

False positives are the better-documented side. James Zhan reports his Reddit comments being called ChatGPT output for being well-written and well-formatted, and his own prose scoring "80% written by AI" in commercial detectors. The em-dash panic is the canonical case: for a stretch in 2025-2026 the presence of an em dash was treated as sufficient evidence, which is a rule that flags every writer with a decent grasp of punctuation.

False negatives are quieter but larger. r/isthisAI draws millions of weekly visitors precisely because people can't tell on their own. Mixed human-and-model text ("AI-assisted") sits in a gap no surface rule handles at all.

The tells decay

Each model release removes some of the signals the previous generation left. A reader who learned the 2025 tells is calibrated against a model nobody runs anymore. So detection isn't a skill you acquire once — it's maintenance against a target that moves faster than most people track, and the maintenance is unpaid.

Two adaptations follow. Some readers flip the default to "assume generated unless declared otherwise," which is a rational response to a detector you can't trust but which convicts every honest writer who doesn't happen to post a disclaimer. Others rely on detector tools, which pushes the problem to the next stage.

The perverse equilibrium

The sharp consequence, and the reason this deserves its own page: when a detector uses quality as a proxy for machine authorship, the way to prove you're human is to write worse.

Zhan's worked example — a detailed, well-formatted subreddit post automatically removed as AI-generated, restored only after he cut the level of detail, stripped the bullets, headings and italics, and rewrote it "like someone word-vomited it on a phone." Every step there is a deliberate reduction in usefulness, performed to satisfy a machine. If the practice generalizes, the observable effect of AI detection is that public human writing gets flatter, shorter, and less organized, while model output — free to be as polished as it likes — keeps its formatting.

This is the inverse of the failure in llm-as-average-democratizer. There, cheap plausible output raises the floor so effort stops being visible. Here, the attempt to recover the effort signal from the surface actively lowers the ceiling.

Why the exit is disclosure, not better detection

Both sides of this wiki's disclosure cluster reach the same conclusion from opposite directions: the property you want to know (how was this made?) is not present in the artifact, so no amount of reading recovers it. slop-marker-convention gets it from the producer declaring machine authorship, human-made-disclosure gets it from the producer declaring human authorship, and credibility-as-slop-test gets it from the producer staking their reputation. None of them try to fix the detector, because the detector is not fixable — it's classifying on features that carry no information about production.

Cross-references

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