AI Text Detectors Are Unreliable by Design, Developer Explains Why

A developer has built an AI-content detection tool after finding that most existing detectors overstate their accuracy, often claiming near-perfect results they cannot deliver. Unlike image-based tools that can embed invisible statistical watermarks, text detection relies on weaker signals such as perplexity, sentence burstiness, and token-frequency patterns. These signals break down under common conditions like paraphrasing, short inputs, translation, or deliberate obfuscation, making confident verdicts misleading. The new tool, available at detectaiwatermarks.com, is designed to report which specific signals were detected rather than issuing a single definitive score. Its verdict is also calibrated to text length, acknowledging that a short social media post and a long essay cannot be assessed by the same standard.