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OpenAI announced on October 5, 2026, that it will roll out text watermarks for eligible ChatGPT and Codex outputs in the European Union over the coming weeks. The watermark, called textGrain, is a statistical pattern in the words a model chooses—not hidden characters or invisible spaces. OpenAI’s own tests found that synonym edits can sharply lower detection, but that does not mean every watermark can be reliably erased or that a particular text has been “cleaned.”
What the text watermark does
OpenAI says textGrain subtly changes the distribution of word and word-piece choices during generation. A detector can then look for the resulting statistical pattern across a passage. The signal is embedded in the wording rather than added as a separate object.
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That means copying and pasting text does not add, preserve, or remove hidden watermark-only characters: OpenAI says there are none. The system is different from a scheme that inserts invisible spaces, special characters, or metadata into a file. OpenAI’s Help Center explains the text watermark mechanism and transformations.
Where and when OpenAI says it will apply textGrain
In its October 5, 2026 announcement, OpenAI said the phased EU rollout would cover eligible ChatGPT and Codex outputs across plans. It did not announce watermarking as a global default for ChatGPT at launch. For API customers globally, watermarking was described as an opt-in for select models and off by default. These are announcement-date plans; the announcement’s “over the coming weeks” wording does not establish that rollout is complete in every account or region. OpenAI’s announcement describes the planned rollout.
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What EU AI Act Article 50 requires
OpenAI frames the rollout as a response to the EU AI Act’s transparency rules. Article 50 requires providers of certain AI systems to mark generated or manipulated outputs in a machine-readable format and to make them detectable as artificially generated or manipulated, subject to technical feasibility. It does not name textGrain or require OpenAI’s particular method. The text of the EU AI Act sets out the obligation.
The European Commission says Article 50 applies from August 2, 2026. The Commission’s 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 Commission’s transparency guidance and the AI Act Service Desk’s Article 50 guidance explain the timing and obligations.
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Article 50 also covers certain deployers publishing AI-generated text to inform the public about matters of public interest. That disclosure duty has exceptions, including text that has undergone human review or editorial control under a responsible publisher. It is therefore not a blanket rule that every ChatGPT user must label every response publicly. The Commission says its Code of Practice is voluntary; the underlying transparency obligations in the Act are legal requirements, and providers using other compliance measures must demonstrate their adequacy. The Commission’s Code of Practice page describes its status.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHow easily edits can weaken detection
OpenAI’s 2026 evaluation shows why the headline claim needs a qualification: changing some words can reduce the chance that its detector recognizes the signal. In 400-token passages, OpenAI reported about 92% detection before synonym replacement, about 66% after replacing 10% of words, and 17% after replacing 25%. These are company-reported results under the stated test conditions, not proof that every watermark can be removed or that any given rewrite will evade detection. OpenAI’s announcement reports the figures.
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Detection also depends on the text itself. In OpenAI’s 2026 evaluation of psychology content at a target 1% false-positive rate, it reported detection of about 80% for 200-token passages and about 95% for 400-token passages. The longer passages were more reliably detected in that evaluation; those rates should not be treated as universal accuracy figures for other subjects, languages, or text sources.
Short or highly constrained outputs create particular limits. OpenAI says math is harder to watermark because it allows fewer plausible word choices. Its Help Center says the Code of Practice does not require watermarking for outputs shorter than 200 tokens—about 150 English words—or for code snippets. OpenAI’s Help Center describes these limits.
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Detection varies by language and content
OpenAI’s reported results are not uniform across languages. In an 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, each at a target 1% false-positive rate. Those results are tied to that evaluation’s prompts and conditions, not a guarantee for all Spanish or Romanian writing. OpenAI’s Help Center provides the language figures.
OpenAI warns that its watermarking and detection technology is early and can produce both false positives and false negatives. It is restricting initial detector access to approved researchers and expert organizations, rather than releasing a public detector at launch. A reader should not assume they can submit arbitrary text to an official public checker. OpenAI’s announcement describes the detector access policy and limitations.
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What a detector result can—and cannot—show
A detected signal is limited evidence relevant to whether OpenAI model output may have contributed to a passage. OpenAI says a watermark does not measure human contribution and does not establish authorship or ownership, legal responsibility, or factual accuracy. Conversely, no detected watermark does not prove that a human wrote the text. A miss may reflect passage length, editing, translation, an unsupported model or export path, or generation before watermarking was applied. OpenAI’s explanation of detector interpretation sets out these limits.
In practice, textGrain is best understood as a machine-readable signal that may help identify eligible OpenAI-generated text, not as a definitive authorship test. Editing can weaken that signal, while length, language, and subject matter affect how detectable it is.
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