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OpenAI announced on October 5, 2026, that it will begin adding text watermarks to eligible ChatGPT and Codex output in the European Union over the coming weeks. The watermark, called textGrain, is a statistical pattern in the wording—not hidden characters or invisible spaces. OpenAI’s own tests show that synonym edits can sharply reduce detection, but they do not establish that every watermark can be removed or that any particular rewrite will evade detection.
What ChatGPT’s text watermark is
OpenAI says textGrain subtly influences the distribution of word and word-piece choices as a model generates text. A detector can then look for the resulting statistical pattern across a passage. The mark is therefore embedded in the wording’s pattern, rather than stored as a separate visible or hidden element. OpenAI explains the mechanism in its text-watermarking help page.
Copying and pasting does not add or preserve a hidden watermark payload: OpenAI says the system does not insert hidden characters, extra tokens, invisible spaces, or unusual punctuation. The signal is in the statistical choices of words.
Who gets watermarked output, and when
OpenAI’s October 5, 2026 announcement said the EU rollout would cover eligible ChatGPT and Codex output across all plans, phased in over the coming weeks. That is an announcement-date plan, not confirmation that rollout is complete for every eligible account or output. OpenAI also said it was not making watermarking a global default at launch. API customers globally could opt in for select models; API watermarking was off by default at announcement. See OpenAI’s announcement for its rollout statement.
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OpenAI’s help page says the applicable Code of Practice does not require watermarking for outputs shorter than 200 tokens—about 150 English words—or for code snippets. Eligibility and exceptions can depend on the output and implementation, so the announcement should not be read as a guarantee that every ChatGPT response in the EU will carry a detectable signal.
Why edits can make the watermark harder to detect
Because textGrain relies on patterns in word choice, changing enough wording can weaken the signal. OpenAI’s 2026 evaluation reports that, for 400-token passages, synonym substitutions reduced detection from about 92% to 66% when 10% of words were replaced, and to 17% when 25% were replaced. These are company-reported results under the evaluation’s conditions—not a guarantee that a particular edit will remove a watermark or defeat a detector.
Detection also varied with passage length and content in OpenAI’s evaluation: for psychology passages, the reported rate was about 80% at 200 tokens and about 95% at 400 tokens, at a target 1% false-positive rate. The figures describe tested conditions, not universal accuracy. Shorter text gives a detector less material to assess, and constrained content such as mathematics offers fewer plausible word choices for a watermark to influence. OpenAI discusses its performance tests and limitations in its announcement and help article.
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Language also affects reported detection
In a separate OpenAI evaluation using 500 synthetic English prompts translated into the other 23 official EU languages, the company reported detection rates of 69.0% for Spanish and 42.2% for Romanian at a 1% false-positive rate. Those figures apply to that test setup and should not be generalized to every text in those languages.
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OpenAI says watermarking and detection remain early technologies with significant limitations. False positives and false negatives are possible, and the company said initial detector access would be restricted to approved researchers and expert organizations rather than made publicly available at launch. The performance figures above are OpenAI’s evaluations, not an independent audit.
- A positive result may indicate a detectable pattern associated with the supported OpenAI output, but it does not measure how much a person contributed.
- It does not establish authorship or ownership, determine legal responsibility, or verify whether the text is accurate.
- A negative result does not prove a human wrote the text. It may reflect a short passage, editing, translation, an unsupported model or export path, or text generated before watermarking was applied.
OpenAI states in its October 5, 2026 announcement: “A watermark does not measure human contribution.” It also says: “The absence of a detected watermark does not prove human authorship.” Read any result as limited evidence, not a verdict about a person or document.
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How this relates to the EU AI Act
Article 50 of the EU AI Act sets transparency obligations, including machine-readable marking and detection of certain AI-generated content, subject to technical feasibility. The European Commission says Article 50 applies from August 2, 2026. The AI Act Service Desk says providers whose systems were placed on the market before that date have until December 2, 2026, to comply with the paragraph 2 marking and detection obligation.
The Act does not name textGrain as the required method. It is OpenAI’s implementation choice; the legal requirement is framed in terms of machine-readable marking and detection. The Commission says adherence to its Code of Practice is voluntary, while the underlying Article 50 transparency obligations are legal obligations; providers using other measures must demonstrate their adequacy. See the Commission’s Code of Practice guidance, the AI Act Service Desk explanation of Article 50, and the AI Act text.
Article 50 also addresses certain deployers that publish AI-generated text to inform the public about matters of public interest. It includes an exception for text that has undergone human review or editorial control under a responsible publisher. This is not a rule that every user must publicly label every ChatGPT response.
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