AI detection tools have become increasingly common in education, publishing, and content marketing. But how do they actually work? Understanding the technology behind AI detectors can help you create better content—whether you're trying to pass detection or simply want to write more naturally.
The Two Core Metrics: Perplexity and Burstiness
Most AI detectors rely on two primary measurements:
Perplexity
Perplexity measures how "surprised" a language model is by a piece of text.
- Low perplexity = Predictable text (likely AI-generated)
- High perplexity = Unexpected word choices (likely human)
AI models like ChatGPT are trained to produce the most probable next word. This makes their output highly predictable to other AI systems. Human writers, on the other hand, make unexpected word choices, use idioms incorrectly, or structure sentences in unusual ways.
Example:
AI text: "The weather today is beautiful and perfect for outdoor activities."
Human text: "Weather's gorgeous—finally dragged myself outside."
The AI version uses predictable, formal phrasing. The human version has contractions, casual tone, and unexpected structure.
Burstiness
Burstiness measures variation in sentence structure and length.
- Low burstiness = Uniform sentence patterns (likely AI)
- High burstiness = Mixed short and long sentences (likely human)
AI tends to produce consistently structured paragraphs. Humans write in bursts—sometimes a three-word sentence, sometimes a 40-word run-on.
Example of low burstiness (AI-like):
AI detection is a growing field. Many tools now exist to identify AI content.
These tools use various techniques. The technology continues to improve.
Example of high burstiness (human-like):
AI detection? It's everywhere now. Schools use it, publishers demand it, and
honestly—the technology is fascinating but deeply flawed. Here's why.
How Major AI Detectors Work
GPTZero
GPTZero, created by Princeton student Edward Tian, analyzes text at both sentence and document levels. It calculates:
- Per-sentence perplexity scores
- Overall document perplexity
- Burstiness across the text
It then highlights specific sentences it believes are AI-generated, giving a probability score for each.
Strengths: Good at catching pure ChatGPT output Weaknesses: High false positive rate on non-native English speakers and technical writing
Copyleaks
Copyleaks combines AI detection with plagiarism checking. Their detector:
- Uses multiple AI models to cross-reference predictions
- Analyzes writing patterns specific to different AI tools
- Provides character-level highlighting
Strengths: Integrated plagiarism + AI detection Weaknesses: Can flag heavily edited AI content as human
Originality.ai
Originality.ai focuses on content marketing use cases. Features include:
- Batch scanning of entire websites
- API for automated checking
- Historical tracking of content changes
Strengths: Built for content teams and agencies Weaknesses: Subscription-based, can be expensive for individuals
Turnitin
Turnitin added AI detection to their academic plagiarism tool in 2023. Their approach:
- Trained specifically on academic writing
- Segments text into 2,500-word chunks for analysis
- Provides similarity scores alongside AI probability
Strengths: Deep integration with educational institutions Weaknesses: Only available through institutional subscriptions
Why AI Detectors Get It Wrong
AI detection is not a solved problem. Here's why false positives and negatives occur:
False Positives (Human flagged as AI)
Common causes:
- Non-native English speakers - Simplified grammar patterns match AI output
- Technical/formal writing - Academic and legal writing is inherently predictable
- Common topics - Writing about well-documented subjects uses predictable phrasing
- Edited/polished text - Heavy editing removes natural human quirks
False Negatives (AI passes as human)
Common causes:
- Prompted for casual tone - AI can mimic informal writing when asked
- Post-editing - Human edits add burstiness and unpredictability
- Older AI models - Detectors trained on GPT-4 may miss GPT-3 patterns
- Non-English content - Most detectors are optimized for English
The Detection Arms Race
AI detection is fundamentally an adversarial problem:
- Detectors improve → Train on latest AI outputs
- AI models improve → Generate more human-like text
- Humanization tools emerge → Transform AI text to evade detection
- Detectors adapt → Try to catch humanized content
- Cycle repeats
This creates an ongoing arms race with no clear winner. As AI models become more sophisticated, the line between human and AI writing blurs further.
What This Means for Content Creators
If You're Using AI for Content
- Always edit AI output - Add your voice, examples, and perspective
- Don't rely solely on AI - Use it as a starting point, not final draft
- Test before publishing - Run content through multiple detectors
- Be transparent when appropriate - Some contexts require AI disclosure
If You're Evaluating Content for AI
- Don't trust any single detector - Use multiple tools
- Consider context - Technical writing naturally scores higher
- Look for substance - AI struggles with original insights and specific examples
- Ask follow-up questions - AI-generated content often lacks depth on follow-ups
The Future of AI Detection
Several trends are emerging:
Watermarking
OpenAI and Google are developing invisible watermarks embedded in AI output. These would be undetectable to humans but readable by verification tools.
Challenge: Watermarks can be removed through paraphrasing or translation.
Stylometric Analysis
Advanced detectors are moving beyond perplexity to analyze:
- Vocabulary richness
- Syntactic patterns
- Semantic consistency
- Topic coherence
Blockchain Verification
Some propose recording content creation timestamps and author identity on blockchain to establish provenance.
Challenge: Doesn't prove the content wasn't AI-generated, just when it was registered.
Key Takeaways
- AI detectors measure perplexity and burstiness - Predictability is the key signal
- No detector is 100% accurate - False positives and negatives are common
- The technology keeps evolving - Both AI and detection improve continuously
- Context matters - Technical, formal, and non-native writing triggers false positives
- Multiple tools > single tool - Cross-reference results for accuracy
Understanding how AI detection works helps you create more authentic content—whether you're starting from scratch or refining AI-assisted drafts.
Last updated: January 2026