Can a commercial tool reliably tell AI-written text from human writing, and why might one accuracy score mislead?

This explores how accurate Originality.ai's detector is at flagging AI-written content. To be direct: none of the papers in this collection test Originality.ai, so they can't give you an accuracy figure for it. What they can tell you is how AI-text detection behaves in general, and that tells you which questions to ask of any vendor's accuracy claims.

The first surprise is that machines can often tell AI text apart even when people can't. AI-written text differs in measurable ways from human writing, for example in how varied its vocabulary is. Yet human judges, trained linguists included, can't reliably spot those differences, and newer models are getting harder to spot even as they drift further from human patterns Can humans detect AI text if machines can measure it?. A review of 30 studies found that human guesses cluster around chance for text, images and voice Can people reliably spot content made by AI?. So a detector really can see things people miss. Research systems report very high numbers: simple, interpretable language features reached 99% accuracy on AI-written Reddit arguments Can simple linguistic features detect AI-written arguments?, and a fiction detector reached 93% using only story structure, such as who drives the plot and how events are ordered Can AI stories be detected without analyzing writing style?.

The catch is that these results come from narrow, controlled settings: one genre, known models, and text nobody has edited. Real-world accuracy depends on whether the text was rewritten, and here the evidence is thin. One paper claims heavily rewritten messages slip past detectors, but it never ran a detector to check Do rewrites that hide authorship also fool AI detectors?. The fiction work suggests a partial answer. Surface-style signals are easy to scrub, but structural choices survive light editing because changing them takes a real rewrite. A detector that leans on word-level style is likely more fragile than one that reads deeper patterns.

The most useful finding for anyone using a tool like Originality.ai is about what happens around the detector. When people accuse others of using AI, those accusations don't line up with the features that actually separate AI text from human text. A study of 25 million Hacker News and Reddit comments found that slop accusations work as social gatekeeping rather than detection Do AI slop accusations actually detect AI text?, and the people harmed are often human writers who get falsely accused Do unfounded AI accusations harm human writers instead?. A detector's false-positive rate matters as much as its headline accuracy. A separate line of work argues that the real question is often quality rather than authorship: 'slop' is a judgment about coherence and relevance that applies to human and machine text alike Can we judge text quality without knowing who wrote it?.

If you're evaluating Originality.ai or any similar tool, ask three questions. Was it tested on edited text or only raw model output? What is its false-positive rate on human writers? Was it tested on the newest models? The collection suggests detection is possible in principle but fragile in practice, and nothing here independently checks this vendor's numbers.