Teachers run essays through them before grading. Editors run drafts through them before publishing. Job platforms run cover letters through them before forwarding a candidate. In every case, the tool spits out something like "78% likely AI-generated" and everyone treats it as a verdict. It isn't one -- it's a statistical guess, and understanding how that guess is made tells you exactly when to trust it and when to ignore it.
How these tools actually guess
AI detectors don't check text against a database of known AI output -- there's no such database, and even if there were, it would be useless the moment a new model shipped. Instead, they look at patterns in word choice and sentence structure that large language models tend to produce more often than humans do: unusually even sentence length, a narrow range of predictable word choices (low "perplexity"), and transitions that follow a very regular rhythm. Humans write this way sometimes too. Models increasingly write in messier, more varied ways on purpose, because they were trained or prompted to. The detector is pattern-matching against a moving target.
Why false positives keep happening
The most consistent criticism of AI detectors -- backed by actual research, not just anecdote -- is that they misfire more often on text written by non-native English speakers. Simpler vocabulary, more formulaic sentence construction, and fewer idioms are stylistic traits that overlap heavily with what detectors flag as "AI-like." A detector trained mostly on native-English AI and human samples doesn't have a clean way to tell the difference between "this sounds like a model" and "this sounds like someone writing carefully in their second language." The same goes for very formal writing, like legal or technical documentation, which was already stiff and repetitive long before AI tools existed.
Paraphrasing breaks detection almost completely
Run AI-generated text through a paraphrasing tool, or just manually reword a few sentences, and detection scores tend to drop sharply. This isn't a loophole specific to one detector -- it's a structural weakness in how all of them work. They're measuring surface-level statistical patterns, not meaning or origin. Change the surface enough and the signal they're looking for disappears, even though the underlying content and ideas are unchanged. This is also why combining AI drafting with manual editing -- which is an increasingly normal way people actually write now -- produces text that sits in a genuinely ambiguous middle ground no detector can resolve.
What a score of 80% actually means
It does not mean "80% of this text was written by AI" or "we are 80% certain a human didn't write this." It means the text's statistical fingerprint resembles the detector's AI-sample training data more than it resembles its human-sample training data, by a margin the tool expresses as a percentage. That's a meaningfully different claim, and it's one almost no one reading the output actually understands when they see a single bold number.
Where these tools genuinely earn their place
None of this makes AI detectors useless. They're reasonable as a first-pass signal when you're skimming a large batch of submissions and need to flag items for a closer human read, rather than as the sole basis for an accusation. They're also useful as a sanity check on your own writing -- if a detector flags something you wrote yourself as AI-generated, that's a decent sign your prose has drifted toward generic, over-smoothed phrasing worth tightening up regardless of who wrote it.
Use it as a signal, not a verdict
Treat any AI detection score as a prompt to look closer, never as proof on its own -- especially before making a decision that affects someone's grade, job, or reputation. Plexto's AI Content Detector gives you that same kind of informal read directly in your browser, with no sign-up required, for exactly the use case it's good for: a quick first look, not a final ruling.