AI detectors aren't magic. They use statistical analysis and machine learning to estimate the probability that text was machine-generated. Understanding how they work is the first step to understanding why they're often wrong — and how to write text that passes them.
Perplexity: The Predictability Score
Perplexity measures how "surprised" a language model is by each word in a text. Low perplexity means the text is highly predictable — each word is the most statistically likely choice given the previous words.
"Artificial intelligence has revolutionized the way we approach modern business challenges."
Every word is the most probable next word. Perplexity score: ~15
"AI broke my spreadsheet workflow last Tuesday. Now I can't go back to doing it manually — even though the AI gets it wrong about 30% of the time."
Unexpected word choices, specific details. Perplexity score: ~85
GPTZero and similar tools flag text with consistently low perplexity across paragraphs.
Burstiness: Sentence Variation
Burstiness measures the variation in sentence complexity throughout a text. Human writing naturally "bursts" — mixing short punchy sentences with long complex ones. AI tends to produce sentences of uniform length and complexity.
Sentence length visualization:
Each bar represents a sentence. Notice how human writing varies wildly while AI stays uniform.
Classifier Models: AI That Detects AI
Tools like Originality.ai and Copyleaks train their own neural networks on millions of examples of human and AI text. These classifiers learn patterns beyond simple perplexity — including vocabulary distribution, paragraph structure, and stylistic consistency.
The arms race problem: As AI generators improve, classifiers must be retrained. There's always a lag. A classifier trained on GPT-3.5 output may not accurately detect GPT-4o or Claude 4 text. This is why detection accuracy varies so much between models.
Watermarking: Hidden Patterns
Some AI providers embed statistical watermarks in their output — subtle patterns in word choice that are invisible to readers but detectable by specialized tools. Google DeepMind's SynthID is the most prominent example.
However, watermarks are fragile. Paraphrasing, editing, or running text through a humanizer typically destroys the watermark pattern entirely.
Why AI Detectors Fail
False positives on non-native English
Simpler vocabulary and grammar patterns mimic AI output, causing 26% higher false positive rates.
Short text unreliability
Most detectors need 250+ words to make a confident prediction. Anything shorter is essentially a coin flip.
Mixed content confusion
Text that's partially human and partially AI-edited produces wildly inconsistent scores.
Training data staleness
Detectors trained on older AI models struggle with newer ones. The technology evolves faster than detectors can adapt.
Editing destroys signals
Even light human editing of AI text can drop detection scores dramatically, making the boundary between 'AI' and 'human' meaningless.
How Humaneer Beats Every Detector
Humaneer doesn't just swap words. It addresses all four detection vectors simultaneously — increasing perplexity, adding burstiness, breaking classifier patterns, and destroying watermarks.
Now you know how they work. Beat them.
See our full test results against 8 major detectors.
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