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People have real qualms with AI detectors, and so do I, but the evidence is mounting that Pangram in particular very rarely gets it wrong (especially on longer chunks of text, but increasingly even on short ones). @brianx.bsky.social tested it and it didn't miss: www.nytimes.com/2026/08/13/t...

nytimes.comI Tested a Popular A.I. Slop Detector. It Felt Empowering.Pangram excels at distinguishing chatbot-generated words from human writing. But it’s not reliable for spotting artificial images.

AI detectors rightly got a bad rap because they got it wrong often enough to be worse than useless — actively harmful. Pangram has a different algorithm that by all accounts simply works much better. I'd still be wary of basing an accusation on Pangram alone, but... www.nytimes.com/2026/07/29/b...

The case for using Pangram as at least an indicator of likely AI writing is much stronger than it was for Turnitin/GPTZero. Its widespread use would also deter a lot of folks from even trying to pass off AI slop as authentic. Is that enough to justify it, or is any nonzero error rate intolerable?

It might sound like a rhetorical question but I'm genuinely interested in how folks answer it. Obviously purveyors of AI writing don't want this. But what about people who hate AI slop but also disapprove of AI use in general, or worry deeply about false accusations? How do you weigh the tradeoff?

This is a great question. I'd love to see research on Pangram's accuracy for non-native speakers specifically. Logically its error rate couldn't be *that* high or else its overall error rate couldn't be so miniscule. But any amount of bias makes the tool's use problematic from a fairness standpoint.

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I have not found it to be shockingly high but it definitely misses some AI writing. I will say I find it reassuring overall that they intentionally tuned it to err on the side of false negatives over false positives, which are far more harmful.

Jason Wilson@jasonaw.bsky.social

I can only counter this with my own anecdotes but I have found Pangram delivers a shockingly high proportion of false negatives

That said, even false negatives can cause problems, especially if there are non-negligible systematic biases. To the extent that people rely heavily on Pangram to flag potential AI writing, it would mean some folks will be a bit more likely to get away with using AI than others.

A surprising thing a Pangram engineer told me is that, at least so far, AI writing getting better over time has *not* made it any harder for Pangram to accurately detect. One theory is that making AI writing more humanlike is not the big labs' top priority right now; they're more focused on agents.

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My impression is that, if anything, the big labs *want* AI writing to be detectable. It's broadly assumed that they integrate watermarking techniques into their models, and their models develop identifiable mannerisms that they could train out but deliberately don't.

Honestly I think if Pangram ever did falsely flag one of my articles as being something other than 100% human-written, I would probably become militantly opposed to it (even knowing how rare that is). I don't blame anyone who has experienced that for never trusting it, or anything like it, again.

(Pangram accurately rates the whole book as 100% human). Not only that, if I changed 4 em dashes to commas in the 1,200-word chapter, Originality's "likely AI" rating fell by 40%. I brought this to them a year ago and repeatedly since. They have not fixed it.

What are your thoughts on all this in relation to the new implementing of watermarking (from Anthropic but likely soon across the board), which should (for 500+-word texts) be essentially actually impossible to false positive?

Litbowl@litbowl.bsky.social

This feels like it should be way bigger news in publishing and in tech media. Plenty of questions, but Anthropic is actually fundamentally CHANGING outputs now to encode proof that they are AI-generated. If others follow suit for EU regulation, this potentially resolves a ton of detection stuff.

I've complained about this before, but it drives me crazy that articles like this do not mention GANs. If you can train a model to discriminate human/AI, you can use that data to train a model to generate things on the human side. THAT IS HOW THESE SYSTEMS WORK.