Teachers, editors and recruiters – and all of us who read online content – are struggling with the problem of telling AI-generated text from the “real thing,” something that was actually written by a human. Now, finally, detection seems to have caught up. In independent testing by Chicago Booth, AI detector Pangram was able to tell AI from human writing with an accuracy of more than 99.8%, and with almost no false positives. It was the only tool that remained reliable under the strict limits an academic institution would demand before accusing anyone of cheating with AI. And in early August 2026, Anthropic announced the arrival of a “made by AI” marker in content delivered by Claude models. All models launched on or after August 2, 2026, weave an invisible watermark into every text they generate – a mechanism Anthropic has since confirmed to be a version of Google DeepMind’s SynthID-Text – with older models to follow during the EU AI Act’s transition period. (Claude also signs supported file formats – for example images – with C2PA metadata; this article deals with text only.) There is no opting out, and, as the other big AI companies signed the same Code of Practice driven by the EU AI Act, the rest of the industry will likely follow soon. Google already did.<\/p>\n
It seems that the arms race is over. Teachers can grade again faithfully, editors can trust their inbox, and honest writers finally have proof on their side. Sounds great, doesn’t it?<\/p>\n
It isn’t great at all: take two students who hand in the same assignment; the first wrote every paragraph herself, then asked AI to improve the language, find typos and fix the grammar, while the second pasted the assignment straight into an AI chatbot, took the output and ran it through a “humanizer” tool, a service that exists abundantly online, and made some additional manual edits. In this new reality of watermarks and detectors, the first – honestly self-authored – essay carries an AI signature. The second comes back clean. The first one will likely have to defend herself; the second one goes unnoticed.<\/p>\n
Both AI detector and AI watermark measure only one thing: that a text was in contact with AI. The problem is that “contact with AI” is not fraud in itself. These two mechanisms tell honest from dishonest the same way a security scanner tells weapons from a few coins you forgot in your pocket. In the latter case, the error translates into a quick body search or another round trip through the detector; with written output, the result may be a misconduct hearing or – if it’s not a graded assessment but instead a job application or other writing – outright rejection without an opportunity to remedy the situation.<\/p>\n
And then there is the third person. Maybe a non-native speaker, maybe someone not so good with words, but someone with really original ideas worth sharing. Previously, this individual would have needed a lot of money to pay for a competent ghostwriter; nowadays, by feeding AI with their highly relevant thoughts and using the result, they can also be heard. And the question is: why shouldn’t they? Nothing they present is “AI slop,” as many people call AI-produced output without much value nowadays.<\/p>\n