AI Text Detection: Arms Dealers in a War on Truth

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Writings<br>AI Text Detection: Arms Dealers in a War on Truth<br>Technical audit on Pangram, commentary on alignment of businesses selling an authority on truth, and how it may throw fire on the epidemic of AI-generated content rather than quelling it.

Ethan Smith<br>Jul 02, 2026

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Pangram, at face value, comes off as doing a morally good duty cleaning up the internet, rebelling against slop precipitating from models made by big companies and detecting deceit. And I agree with this mission for the most part.<br>However, doing this effectively and fairly hinges on both the reliability of the detector and also an accurate understanding of where it falls short

Ultimately a revenue-creating startup and if leveraging a “for the people” aura means growth and marketing, they’d be incentivized to lean into that.

If it was a perfect detector, I’d have no beef. I don’t have qualms with pointing out AI writing, particularly if the author is dishonest about their usage.

Where it is a problem is they’ve garnered a sense of authority on a final verdict of the authorship of a piece of text. The posts and marketing from their employees go around deeming content as AI generated. Again, if this could be certainly factual, fine, but otherwise it’s witch-hunting on noisy and fallible metrics that are repeatedly reported as ground truth.

I believe you can be right most of the time, but if we’re publicly shaming people, punishing students/academics on something that could be wrong, it’s a risk we should be taking seriously. This is a similar reason polygraph lie-detectors have become a controversial raise in courts and often inadmissible as evidence in many jurisdictions.<br>Lately, my X feed has been filled with claims of various writings to be authored by AI to the point where its become an epidemic. Maybe they’re true. Maybe they’re not.<br>This article aims to not to decipher the validity of these accusations directly, but highlight:<br>Points of fragility with AI text detection, and what the research shows.

An alignment and incentives issue with an AI-text-detection as service.

An epidemic around a lack of falsability, and that providing hard proof is often hopeless.

Note: All text here is human-written, though Claude was used in help for research, interpreting results, stress-testing claims and ideas in this piece, playing devil’s advocate against my arguments. Additionally, there are a few direct quotes from Claude where I felt the statement did a good job at summarizing an idea; these are explicitly labeled.<br>This in itself, transparency in how AI was used, is something I hope catches on as a norm. In best case, I think it can be an effective thought-sparring partner and help keep authors from overclaiming by providing opposing perspectives or searching for evidence.

The point to be made here is not that AI-text-detection is useless or even that the product is “broken,” I have good reason to believe Pangram is one of the top options, but claims are overstated in a way that creates risk, some of the use cases have introduced, potentially, new problems and drama, and the company may have incentives to double down on these fiascos rather than pull back.

I’d like to give an overview of the main points in case there isn’t time to read the whole thing. If anything, and not too deep into the technicals,<br>Section 1 in understanding classifiers and what an output tells us.

Section 8 in understanding company positioning

On Efficacy

False positive rate varies enormously based on how the text was created<br>Pangram shines the most when dealing with purely human or purely LLM articles. This would be the closest spot where 1-in-10,000 FPR holds (caveats)

The realm of hybrid authorship, a common workflow for many of mixing one’s own writing with AI edits etc., based on the EditLens report contributions of estimating different levels of AI involvement (the only available reference here), shows substantial drops in classification accuracy both in the “home-court” lab-created examples validated on and in testing on external datasets.

Prose polishing, rewording etc. can trigger AI detection far more commonly, a single AI-edit or even asking for paraphrasing through the Grammarly app lands a substantial portion of examples in still the fully-human area and even more past the threshold of fully AI (box plots unfortunately do not show us frequency but assuming a loose gaussian-like spread, I’d estimate as much as ~20% could be classified as fully AI<br>The number of edits chart, based on standard deviation (which can be distorted for bounded asymmetric data), suggests after 1 edit ~15% of examples can remain in fully human space, meanwhile 1.5 standard deviations above mean reveal 1 edit can cause about 6.5% examples to become classified as fully AI

But ultimately, nothing in the UI tells you that this text you put in is of this category and you should recalibrate...