Ever wonder why a detector can flag AI‑generated text but can’t tell if anyone actually thought about it?
The Simple Version
Detectors look for patterns that usually come from AI, not for the spark of genuine ideas.
How It Actually Works
Pattern Spotting
The model scans sequences of words for statistical fingerprints that differ from human‑written text.
Threshold Decision
If the fingerprint crosses a set threshold the system flags the piece as AI‑generated.
Technical Deep Dive
Substack’s Pangram scanner uses a fine‑tuned transformer to produce an embedding score.
It compares the score against a calibrated cutoff that balances false positives and false negatives.
Developers can adjust the cutoff via an API to suit their own risk tolerance.
Why This Matters
AI slop floods feeds, drowning out nuanced commentary and making it harder to find quality voices.
When detectors only signal “likely AI” without confirming intent, brands and publishers risk mislabeling authentic work.
That noise can erode trust in platforms that rely on human attention as a scarce resource.
Key Takeaways
- Detect patterns, not meaning.
- Human intent still can’t be measured automatically.
- Adjustable thresholds let developers trade precision for recall.
- AI slop threatens the value of genuine attention.
- Platforms must balance detection with fairness.