The Em Dash Is Not the AI Tell. The Dash Rate Is.

A March 2026 arXiv study, "The Last Fingerprint," measured em dash rates across 12 AI models and about 240,000 words of generated text, as summarized by Jordan Gibbs on Medium. GPT-4.1 produced 10.62 em dashes per 1,000 words and GPT-5.4 produced 1.43, below a human baseline of 3.23 taken from eight published essays.

Those numbers make a single-character rule unreliable. The human essays in the same comparison ranged from 0.33 to 17.12 em dashes per 1,000 words, so the most dash-heavy essay used the mark more often than GPT-4.1 (10.62) or Claude Opus (9.09). GPT-5.4 used it less than half as often as the human baseline. A rule that flags any em dash accuses writers like that essayist and passes a model like GPT-5.4.

The character also responds quickly to instructions. In a suppression test Gibbs cites, Claude fell from about 9 em dashes per 1,000 words to 0.19 when told to "write plain prose," and to zero when told "no em dashes." Gibbs also reports that OpenAI shipped a setting to reduce em dashes in late 2025, and that Sam Altman attributed the earlier rate to RLHF tuning. A mark that one instruction or one setting can remove is a weak basis for judging who wrote a text.

Gibbs's own rule is blunt: "No single tell proves anything." He argues that the signal that remains is density, the accumulation of tells in a text rather than any one of them. Our data, below, supports a narrower version of that point: when writing rules produced asides, the rate survived a character swap, and only rewriting the asides removed it.

What is the construction-level tell that outlasts any character substitution?

The aside itself. When writing rules ask for dash-punctuated asides, swapping the em dash for a spaced hyphen keeps the same interrupting rhythm, and the dash rate per 1,000 words stays high.

We measured this in September 2026 on one company's blog: its 663 live pages and a fleet of AI-drafted articles. The AI-drafted articles contained zero em dash characters, because a sanitizer replaced every one with a spaced hyphen. They still carried 12.9 dash-punctuation marks per 1,000 words. The company's own pre-AI blog carried 0.8.

An outside AI-detection review of that site reported "about 14 em-dashes per 1,000 words" on pages that contained no em dash at all. The review was right in substance. It treated the em dash, the en dash, the spaced hyphen and the double hyphen as one signal, so the swap had changed the glyph and kept the tell.

The cause sat in the writing instructions. One rule told the model to use dashes for parenthetical asides. Another said no em dashes, use a spaced hyphen. The model followed both: it kept the rhythm and changed the character. In our work, instructions that recommend a device and ban its best-known form at the same time produced the device. A "humanizer technique" we audited showed the same problem from another angle: it mandated negative parallelism ("it's not X, it's Y"), which is itself one of the best-known AI tells.

The fix was a budget, not a new character. We replaced the rule with a budget of 2 dash marks per 1,000 words and rewrote asides as commas, parentheses or separate sentences. New articles then measured 1.69 dash marks per 1,000 words, down 86% from 11.99, with no replacement tell. The budget is 2; the measured result was 1.69.

A second site shows how far apart two authorships can sit. On a sports equipment manufacturer's blog, 288 posts written by one human author between 2009 and 2018 averaged 0.0 em dashes per 1,000 words and 492 words each. The AI-drafted buying guides on the same site averaged 7.2 em dashes per 1,000 words and 2,927 words each.

The same site shows why we keep origin and style as separate readings. Our detector flagged 2 of the 288 human posts (0.7%) and 73% of 30 AI-drafted buying guides at the time, while both groups scored in a similar style band, 57 against 68 on our style scale. Style overlapped; origin did not.

Dashes are not the only habit that survives a surface edit. Rules execute literally. When we banned one stock phrase as a heading, the model moved the phrase into the first sentence. Our first per-site phrasing generator, built to vary wording, put the same phrase back into one site's pool until we added a phrase-level guard. A fix has to name the pattern it removes and then check that the pattern does not return in a new position.

None of this turns a dash budget into a disguise. Our detector does not count dashes, and removing them did not change its reading: in our September test, 81-100% of generated prose sections still read AI to a detector trained on that pipeline. A budget removes a pattern that readers and outside reviewers notice. It does not change who wrote the text.

Which AI models actually use the em dash, and does it matter for detection?

Usage differs sharply by model and version, from heavy use to none, so the character alone is a poor authorship test. Our own detector does not count dashes at all.

The published comparisons do not even agree on a single model, which is informative in itself. In the six-model test Nina Harris reports, Claude used two em dashes on the shared prompt. In "The Last Fingerprint," Claude Opus averaged 9.09 per 1,000 words, close to GPT-4.1 at 10.62, while GPT-5.4 dropped to 1.43. One prompt, one model version or one setting can move the result a long way.

The idea itself has a short, traceable history. After the lifestyle writer known as LindyMan tweeted that frequent em dashes were a sign of AI writing, Kate Lindsay, writing in Embedded, traced the rumor to around November 2024, when a user in the OpenAI Developer Community forum could not get ChatGPT to stop adding dashes: "I will even remind ChatGPT not to use it and it will agree, and then do it immediately again." It began as a complaint about one product's habit.

Human use is moving too. Jordan Gibbs reports that em dash use tripled in tech subreddits in a single year and more than doubled in ecology paper abstracts between 2021 and 2025. His reading is that "a lot of it is humans absorbing the style." That is a plausible explanation, not a measured cause. Either way, a mark that more human writers are using is a poor basis for accusing any one of them.

Across AI-drafted articles we measured, the character was far from universal. In a July 2026 corpus of 816 AI-drafted articles from 24 websites, em dash characters appeared in 34% of articles. One stock opening phrase appeared in 74% of the same articles, so shared phrasing was a much more common pattern in that corpus than the em dash. One scripted sentence appeared in 362 articles on 18 different sites, and in a 30 July snapshot of 843 articles the most common article skeleton was shared by 95 articles on 11 sites.

Whether the character matters for detection depends on what is doing the detecting. A reader, or an outside review that counts dashes, will notice a high rate of asides whichever glyph carries them. Our detector works differently. It combines a ModernBERT classifier with a separate predictability check from a pair of open Qwen2.5 models, scores passages of about 300 words, and calls AI only at 0.998 or higher and human only below 0.5; anything in between is not called. It does not count dashes, and removing them did not change its reading.

Model style also runs deeper than punctuation. Sun et al. (ICML 2025) told apart text from ChatGPT, Claude, Grok, Gemini and DeepSeek with 97.1% accuracy, and the distinction persisted after rewriting, translation and summarization. Reinhart et al. (PNAS, February 2025) described a noun-heavy, information-dense model style, with a larger gap from human writing for instruction-tuned models than for base models. Neither finding is about dashes.

For editors, the practical reading is to free the character and budget the asides. Keep an em dash where it earns its place, count every dash form together, and rewrite asides as commas, parentheses or separate sentences when the rate climbs. None of that turns an AI draft into human writing. It removes one surface pattern that readers and reviewers notice.

What will change about AI writing detection in the next 12-24 months?

We expect style guides to move from banning the em dash to budgeting all dash marks, named tells to keep rotating, and false accusations over punctuation to stay a real risk.

Rate budgets can be gamed as well. Anything a reader can count, a writing tool can be told to avoid. In one r/ChatGPTPro thread, a user describes a list of "380-ish overused words" as the only way they have managed to avoid detection by tools like GPTZero, and adds that getting around AI detection is "much harder than people think." Surface tells rotate because they are easy to change. Model style, as the attribution research above shows, is much harder to remove, which is why we treat a dash budget as an editing standard rather than a way past a detector.

Why does the em dash debate keep getting the detection question wrong?

The debate treats one character as evidence of authorship. A character is easy to add or remove, and the error rate of that judgment is almost never measured against verified human writing.

Detection claims mean little without a known false-positive rate. We test our own detector on text we can show a person wrote, dated 2021 or earlier or captured by web archives before 2021: 9,963 verified human documents in total. It called 4 of them AI. On a 4,546-document pool of client archives, marketing blogs, open-web text and sports and health copy, it called none (95% upper bound 0.08%), as of September 2026.

The mistakes are instructive. An earlier version of our detector called 4 of 2,868 pre-2021 Medium posts AI. Three were institutional prose: a US government aid programme update, a policy analysis of an Affordable Care Act bill and a corporate design case study. The fourth was a list of 100 headline-style post titles. The current version called 2 of the 2,868. One plausible explanation for the pattern is that neutral, well-organised institutional writing sits close to the register AI models imitate by default.

Human readers do no better with surface cues. In a Stanford HAI study (Hancock et al., March 2023), people judging dating, professional and hospitality profiles told human from AI text with 50-52% accuracy. They wrongly read grammatical correctness, first-person pronouns, family references and informal language as signs of a human writer. Punctuation is a cue of the same kind: visible, easy to change and unrelated to who did the writing.

The cost of that cue falls on real writers. Jaime Hoerricks, an autistic and ADHD writer, opens The Em Dash Is Not AI with a LinkedIn comment left on one of their posts: "This looks like AI-generated writing. All those em dashes. No one actually writes like this." In an r/grammar thread, writers describe rewriting sentences to avoid the mark. One says the head of a non-profit warned that readers would think a newsletter was AI if it used em dashes, and another says an app would not post a comment until the em dash was changed to a double hyphen.

The figures below are the dash measurements this article relies on, with their sources.

Practical checklist for editors (guidance, not a detection test)

• Count every dash form together: em dash, en dash, spaced hyphen and double hyphen.
• Set a budget instead of a ban. Ours is 2 dash marks per 1,000 words.
• Rewrite asides as commas, parentheses or separate sentences rather than swapping the character.
• Check writing rules for conflicts, such as one rule that asks for dash asides and another that bans em dashes.
• Name the exact phrases or constructions a rule removes, and guard against a generator putting them back.
• Never accuse a writer on punctuation alone. Ask for a measured false-positive rate before trusting any authorship check.

The em dash is not the tell. A high rate of dash-punctuated asides is a tell readers and reviewers notice, and a budget removes it, but neither the character nor the rate settles who wrote the text.

The em dash is a weak tell, and the dash rate is a stronger one, but neither is proof of authorship. On one company's blog, a sanitizer removed every em dash and the asides stayed at 12.9 per 1,000 words; a budget of 2 and rewritten asides brought new articles to 1.69. A rule that banned the character and kept the aside is the clearest case we have of an edit that changed the glyph and left the tell in place.

What this data cannot tell me is how long rate-based cues will stay useful. Models are already being tuned away from the em dash, and anything a reader can count, a writing tool can be told to avoid. Deeper style is another matter: Sun et al. still told five model families apart with 97.1% accuracy after rewriting, translation and summarization.

Substance moved our readings more clearly than punctuation did. On one sports equipment manufacturer's site, article sections built on the company's own sales and shipping records read AI 37% of the time, against 71% for the other sections of the same articles. The limit: for a payments company whose articles used general industry figures, the same comparison came out at 98% against 100%.

So free the character, budget the asides, and put the effort into what the article contains. That is what the AEO Content Engine is built for: articles grounded in each company's own records, written to read like expert human writing and structured so AI answer engines can cite them.

This article is part of our research series on how AI writes and how humans write. The overview of the whole series is How AI Writes vs How Humans Write.

To see which passages of a draft still read machine-written after the dashes are budgeted, run it through the free AI Content Detector.