# AI detector arms race

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. [[no-ai-statements|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

- [[no-ai-statements]] — the source case and the subreddit story
- [[human-made-disclosure]] — the response
- [[credibility-as-slop-test]] — the same surface-is-uninformative premise, different remedy
- [[readme-not]] and [[slop-marker-convention]] — the machine-side declaration