How to Detect AI-Generated Content Using Perplexity and Burstiness

A friend of mine who runs a content agency told me over coffee last week: "we've tried every AI detector out there and they're all snake oil." I told him I thought I could build a better one. He laughed. Fair enough. The AI Content Detector I built runs entirely in the browser. No uploads, no subscriptions, no cloud API charging you per scan. It uses ten statistical metrics and eighteen sentence-level signals to figure out whether text was written by a human or generated by ChatGPT, Claude, Gemini, or whatever LLM people are using this week. I want to explain how it actually works, because most "AI detector" marketing pages are deliberately vague about their methodology. Why perplexity and burstiness alone don't cut it Every blog post about AI detection mentions perplexity and burstiness. They're real metrics, they do measure something useful, but here's the uncomfortable truth I discovered after weeks of testing: modern AI models like GPT-4 and Claude produce text with high perplexity