\u201cThese detectors are notoriously, wildly inaccurate,\u201d Alice Lemee, a ghostwriter and digital writing coach, said in a video<\/a> she made about Substack\u2019s new AI detection features. \u201cAll it takes is one false accusation for a writer to have their reputation almost irreversibly tarnished.\u201d<\/p>\n Substack announced the AI detection features on Tuesday in a post from CEO Christ Best<\/a>, who said that \u201cIt\u2019s getting harder to tell what\u2019s real on the internet\u201d and that \u201cwhen content made by no one takes over parts of the internet that are supposed to be human, it pollutes the commons and makes it hard to discover and hear human voices.\u201d<\/p>\n Best explained that Substack has partnered with the AI detection tool Pangram, which is now built in to Substack and allows any user to scan a piece of writing. Pangram then produces a percentage-based score determining how much of the writing was AI or human generated. (Disclosure: Pangram previously bought an ad on 404 Media). We\u2019ve covered research<\/a> from Pangram, or research that relies on its AI detector<\/a> before, showing how AI generated writing is flooding every corner of the internet.\u00a0<\/p>\n As we\u2019ve previously reported, AI detectors are not perfect, and Pangram itself is not immune to labeling human content as being AI, and labeling AI as human. Max Spero, the CEO of Pangram, recently told us that<\/a> the company is constantly working on minimizing errors, and that it estimates its false positive rate at roughly one in 10,000.\u00a0\u00a0<\/p>\n \u201cWe have now built the mirror image. Pangram is a real machine, trained on vast amounts of text to make a probabilistic guess, and we have pointed it at your writing to work out whether a person is hidden inside,\u201d Sam Illingworth, a professor and the author of Slow AI, said on his Substack in a post titled \u201cSubstack\u2019s AI Detector and the Return of the Witch Hunt.\u201d \u201cTo decide if there is a human on the other end, Substack asks a machine.\u201d<\/p>\n Substack also added a way for users to add a \u201cHow I make this\u201d statement, where they can explain their writing process and disclose if they use AI.\u00a0<\/p>\n \u201cWe\u2019re not against people using AI to assist their work, and we think people should be free to choose which tools they use to express themselves,\u201d Best said. \u201cSome on the platform are human-writing maximalists<\/a>, others are publicly exploring the frontier of using AI tools while sweating the details<\/a> to make work they stand behind. We use AI all the time at Substack to write software, do research, and build product features like clipping<\/a>, translations<\/a>, and more. But people should know what they\u2019re getting.\u201d<\/p>\n \u201cI think the best way for Substack to address this is to invite further dialogue,\u201d Illingworth told me. \u201cI actually really like the new feature that lets authors tell their readers how they construct their newsletters using AI, and this is the kind of dialogue they should encourage rather than ascribing a score that we know to be incorrect. My worry is that not only do these AI detectors generate false flags for non-native English speakers and neurodiverse writers, but they also remove any opportunity for dialogue because they start from a position of suspicion. Whereas what we need to be doing is developing opportunities for trust between readers and authors.\u201d<\/p>\n “The tools we’ve introduced today are intended to increase transparency and give readers more context, not to prohibit or penalize AI-assisted writing. They do not impact discovery on the platform,\u201d a Substack spokesperson told me. \u201cWe’re also encouraging Substack publishers to add a \u2018How I make this\u2019 statement, where they can explain their process directly, including how they use (or don’t use) AI, and set expectations for readers. Creators can disable detection on their posts pre- and post-publication, as well as report and remove scans on their own work that they believe are mistaken. Find more information on how the features work here<\/a>.\u201d<\/p>\n Substack\u2019s attempt to detect and label AI generated content was also celebrated by many readers who are exhausted by AI slop and other internet platforms that can\u2019t or don\u2019t want to label it. I just published story about how there is so much unlabeled AI slop on Spotify<\/a> that people are now creating their own sites to detect and label it without the company\u2019s help.\u00a0\u00a0<\/p>\n But the backlash from some Substack users shows that companies can\u2019t just flip a switch if they want to ban or give users the ability to filter out AI content from their feeds. As the flood of AI writing and images we see online and the real world every day makes clear, generating slop is easy. What we\u2019re going to do about it is still an unsolved problem<\/a>. <\/p>\n About the author<\/p>\n Emanuel Maiberg is interested in little known communities and processes that shape technology, troublemakers, and petty beefs. Email him at emanuel@404media.co\n<\/p>\n
![\"Emanuel](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/04\/headshot-1.jpg\")
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