How to Prove a Text Was Written by a Human

Proving that a text was written by a human is a question of provenance, not style, and the evidence hierarchy matters: an archive capture from before modern AI writing is strong evidence; a detector score or a reader's impression is not proof.

To test our AI Content Detector on human writing, we took text only from archives that saved each page at the time, such as Wayback Machine and Common Crawl captures. We needed human text that modern AI writing tools could not have touched, and that forced one rule for every document: the capture date counts, not the publish date. Websites rewrite old posts and keep the old dates, so the date on a live page decides nothing.

The hardest case is text from 2022 onward. In one payments company's blog, 7 of 50 posts from January-October 2022 already read like GPT-3 or Jasper output, so we stop human labels at 2021. For newer writing, the available evidence shifts to process records: version histories, dated drafts and session logs kept by the writing tool.

Detector readings sit lower in that hierarchy. Ours pairs a ModernBERT classifier with a separate predictability check from a pair of open Qwen2.5 models, and across 9,963 verified human documents it called 4 AI. The sections below explain how each kind of evidence holds up and where it stops.

What will matter most for proving human authorship in the next 12 to 24 months?

We expect writing records to gain weight over finished-text scores, detector-based accusations to face more pushback, and humanizing tools to keep weakening what style can show. None of this is certain.

One open question is whether detectors will become accurate enough to settle these disputes on their own. Our data gives reasons for caution. Even under a strict false-positive rule, formal human writing is the easiest to misread: the previous version of our detector called 8 of 637 US federal agency documents AI before we retrained it, and the open desklib detector called 4.1% of them AI at its 1% threshold. Training runs also vary: retraining the identical recipe moved "not called" on human documents from 0.22% to 0.66% and pipeline recall at the strict threshold from 97.8% to 92.4%. Evidence of who wrote a text, and when, will keep carrying weight that a reading of the finished text cannot.

Why can't style alone prove that text was written by a human?

Because AI models learned from human writing, and people are poor judges of the difference. In a Stanford study, readers told human from AI-written profiles apart with only 50-52% accuracy.

According to Stanford HAI research by 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. Those cues are easy for a model to produce, so an authorship claim built on them rests on very little, as of September 2026.

The provenance-versus-style test separates two questions that authorship discussions often run together. Provenance asks: who produced this text, and is there independent evidence it existed before modern AI tools were in use? Style asks: how does this text read, and does it resemble AI output? Both questions are worth asking. They need different evidence, and answering one does not answer the other. Keeping them apart is the clearest step toward a defensible authorship claim.

A common assumption is that AI text gives itself away by sounding mechanical. Research points elsewhere. Reinhart et al. (PNAS, February 2025) found that language models write in a noun-heavy, information-dense style, and that the gap from human writing is larger for instruction-tuned models than for base models. One plausible explanation for some false positives is that careful, formal human writing sits close to that style. In our own testing, the human writing most often mistaken for AI was neutral, well-organised institutional prose: of the 4 of 2,868 pre-2021 Medium posts that the previous version of our detector called AI, 3 were institutional prose and the fourth was a list of titles.

Our September 2026 detector called 0 of 4,546 verified human documents AI, with a 95% upper bound of 0.08%. We set a rule that at most 0.5% of human documents may be called AI in every register, not just on average, and confirmed the model on data that took no part in choosing it: on 1,401 fresh Medium posts and 511 documents from 8 agencies never used anywhere, it made 0 false positives on each, against 3 on each for the previous detector. We publish the limits alongside the results because a false-positive rate is the number that tells you what a detector reading is worth.

Our content data shows the same split from the other side. On one sports equipment manufacturer's site, AI-drafted article sections built on the company's own sales and shipping records read AI 37% of the time to our detector, against 71% for the rest of the same articles. The limit matters: for a payments company whose articles used general industry figures, the split was 98% against 100%. AI-drafted text can read more human when it carries specific records, which is one more reason how a text reads cannot settle who wrote it.

Style is how a text reads. Provenance is where it came from.

A detector, even one that reports origin separately from style, still reads only the finished text. Independent records, such as an archive capture or a writing history, show where the text came from.

Why can't a publish date prove text existed before AI writing tools?

Because websites rewrite old posts but keep the old dates, and AI writing tools reached marketing before ChatGPT. In one company blog, 7 of 50 posts from January-October 2022 already read like GPT-3 output.

Those 7 posts, which read like GPT-3 or Jasper output, came from a payments company's blog. That is why we treat 2022 as contaminated and stop human labels at 2021. The archive side is stricter still: we take human text only from Wayback Machine snapshots and Common Crawl captures up to 31 December 2020. The capture date proves the words existed before modern AI writing; the date field on the page proves nothing on its own.

Rebuilt websites are the other trap. When a site is migrated, articles dated "2016" may carry new text, so we use archived snapshots, never the live page, as proof of human authorship. ChatGPT's release in November 2022 is not a clean line either, because AI writing tools were in use in marketing well before it.

The capture-date rule is only the first filter. These are the rules we apply when building a verified human set:

• Use the capture date, not the publish date. Wayback Machine and Common Crawl captures up to 31 December 2020.
• Treat 2022 as contaminated. Human labels stop at 2021.
• One post per author. Every post in our 2,868-post Medium sample has a different author, so no single writer's style dominates the result.
• Exclude writing about AI text. We removed 38 posts about GPT-2, GPT-3 or "written by AI" experiments, which were common on Medium in 2019-2020.
• Exclude essay mills. We removed 60 SEO essay-mill pages, which were often spun or machine-assembled even before modern AI.
• Check the text is complete. In 2019-2020 Medium served member-only posts to crawlers in full: of 1,605 member-only posts we parsed, only 25 were preview-only teasers, and we dropped those.
• Keep the author's words only. We strip images, code blocks, captions, repeated titles and publication footers.

For our train-test split, we held out whole sources rather than individual documents. A website, an author group or a government agency is used either to train the detector or to test it, never both. Our agency test used 9 agencies for testing and 11 for training, plus 8 more that we collected only after choosing the model.

We also prefer licensed text for training. Works of the US federal government are public domain, so we used agency prose to teach the detector what formal human writing looks like. Medium posts, which are "all rights reserved" in 98% of cases, were used only to test, never to train.

A displayed date can be edited after publication. An archive capture records what a page said on the day it was saved. That asymmetry is why capture date is the provenance marker we rely on.

What can a writer actually do to prove their text predates AI tools?

Gather three kinds of evidence, strongest first: independent archive captures, records of the writing process kept while drafting, and detector readings from tools that report their false-positive rates.

The strongest is an existing Wayback Machine or Common Crawl capture of the page at or before 31 December 2020. Writers who published before that date may already have captures without doing anything, and a URL search in the Wayback Machine shows whether one exists. If a capture matches the text in question, that record is stronger evidence than any detector score or stylistic analysis.

For text written after 2020, an archive capture shows when the words existed but cannot rule out AI help, because the tools were already in use. The evidence shifts to process: version history in a collaborative editor such as Google Docs, dated draft exports, or a record from a tool that watches the writing itself. In the r/writers thread How trustworthy are these AI text detectors?, commenters named edit history, version history and document metadata as the only reliable proof of human authorship.

Tools built for this are starting to appear. This Tool Listens to You Type to Prove Your Writing Is Human, published in Generative AI in the Newsroom on Medium, describes OKhuman, which monitors typing, including the sound of keystrokes through the microphone, and publishes a "stamp" verifying that writing is human. At the time it was in a pre-release testing program open only to US applicants, and the author noted that being watched changed how she prepared to write.

Detector readings are the weakest of the three, and we would not rely on them as primary proof. A detector reads only the finished text. What makes a reading more useful is the error rate behind it: how often the tool calls verified human writing AI, measured by kind of writing. A human-looking score from a tool that has never reported that number tells you little.

Here is what those numbers look like for our own detector. It splits text into passages of about 300 words (up to 6 per document), calls a document AI only when at least one passage scores 0.998 or higher, and calls it human only when no passage reaches 0.5; everything in between is not called. Across 9,963 verified human documents it made 4 false positives, and it called 99.6% of held-out long-form articles from our own AI pipeline AI (222 of 223). Those figures are internal evaluation on held-out data, not an independent audit.

The limits matter as much as the results. Paraphrase attacks remain the hard case: GPT-4 text rewritten sentence by sentence with the DIPPER paraphraser was called AI only 31% of the time by the current version. Short passages mislead too: two of three false alarms in one 2019 blog were 47- and 62-word call-to-action tails, so we now drop tails under 150 words.

The one-post-per-author rule shaped the human side of those numbers. In the 2,868-post Medium sample every post has a different author, so no single writer's style dominates the false-positive rate. Add 1,401 fresh Medium posts from authors not in the first sample, 4,546 documents across 17 registers, 637 agency documents and 511 documents from 8 agencies collected after the model was chosen, and that is the 9,963.

Archive capture shows the words existed. Process records are strong evidence. Detector readings are context.

The gap between those three is wider than any single score suggests.

The limits of this method matter as much as its results. Our verified human sets come from text that was published and archived, so writing that never went online, such as manuscripts, internal drafts and unsubmitted papers, falls outside them. Language models used for scoring have also likely read old public text during their own training, which can make human text look more predictable to them. Human writing that was never published anywhere is the strongest proof, and it is the next dataset we are adding.

For writers, a practical order follows. As guidance: check whether an archive capture of your page exists, keep version history and dated drafts for anything new, and treat any detector score, including a human-looking one, as context rather than a verdict. If you want a reading anyway, the free AEO Content AI Detector shows one for each passage of about 300 words it scores.

The same principle, that specific records carry more weight than polish, shapes how we produce content. The AEO Content Engine builds 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.