An AI-text detector does not interview the author.
It examines the text.
That distinction matters because most detectors are trying to infer origin from statistical patterns: predictability, vocabulary, sentence structure, variation, or other features associated with examples of human and machine writing. They can estimate whether a passage resembles material produced by a model. That is not the same thing as possessing evidence of who actually wrote it.
The weakness becomes obvious when human writing shares the same statistical traits.
A 2023 study published in Patterns tested seven GPT detectors against essays written by non-native English speakers. The researchers reported a high false-positive rate, with many human-written TOEFL essays classified as AI-generated. They linked part of the problem to lower linguistic variability and predictability in the writing. See GPT detectors are biased against non-native English writers.
That does not mean every detector always performs badly. A separate 2024 study using carefully sampled GRE writing data found that purpose-built detectors could achieve strong performance without the same observed bias. The disagreement is useful: detector performance depends heavily on the data, model, task, and population being tested.
Text changes after generation
Authorship also becomes harder to infer when documents are edited.
A person can heavily revise machine-generated text. A model can rewrite human text. A student can run prose through grammar software. An editor can simplify an article. A translator can alter vocabulary and sentence structure. A detector then sees the final statistical surface, not the sequence of decisions that produced it.
The models themselves also change. A detector tuned to one generation of language models may be less reliable against newer systems or against models deliberately prompted to vary their style.
This makes a percentage such as “82% AI” dangerously easy to overinterpret.
It is a classification score produced under assumptions. It is not a timestamped writing session, revision history, prompt log, document provenance record, or confession.
Authorship needs stronger evidence
If the question truly matters, stronger evidence can include version history, drafts, notes, source material, editing records, system logs, or direct testimony that can be checked against the document’s development.
A detector may contribute one clue. It should not magically turn statistical resemblance into identity evidence.
This is particularly important for Dead Internet Theory because visual and linguistic sameness can encourage people to label anything bland, repetitive, or polished as synthetic.
Sometimes they will be right.
Sometimes a boring human wrote a boring sentence.
The detector can estimate patterns. Authorship is a historical claim about how a document came into existence. Those are different jobs.