I Trust Smoke Detectors, But I Do Not Trust AI Detectors
If a smoke detector goes off, I check the kitchen; if an AI detector goes off, I suspect the detector.
While physical sensors measure an independent signal, AI text detectors have nothing other than the very probability space derived from human text. Attempting to judge the output of a model optimized to mimic humans based on statistical similarity represents a methodological impasse rather than a measurable physical reality.
Independent Physical Signals and the Smoke Detector Analogy
I trust smoke detectors. They sample the air, measure particle density, and trigger an alarm immediately when an optical threshold is crossed. Behind it lies a measurable, independent physical signal.
However, I do not trust “AI detectors.” Because there is a fundamental problem at the core: Artificial intelligence learns from billions of human-written texts. Then another software program comes along, looks at the writing produced by a system trained on those texts, and attempts to decide: “Did a human write this, or did AI?”
Based on what? Word selection? Sentence length? The text being unusually well-structured? Predictable words appearing in succession?
None of these traits are unique to artificial intelligence. A human can write in a remarkably structured, concise, and predictable manner. An AI can also write chaotically, personally, idiosyncratically, and with errors. The sole objective of the model is already to learn the distribution of human text and emulate it.
| Evaluation Metric | Smoke Detector | AI Text Detector |
|---|---|---|
| Measured Signal | Particle density, optical scattering (Physical reality) | Word sequence probability, statistical prediction |
| Reference Independence | Signal and sensor are strictly independent | Model is trained entirely on human text distribution |
| Threshold Clarity | Definitive binary physical threshold | Subjective similarity score within the same text space |
| Doubt Upon Trigger | Investigate the smoke source in the room | Scrutinize the detector itself first |
Two Authors in the Same Text Space: Human and Machine
Between smoke and a smoke detector, there is an independent physical signal. In an AI detector, no such signal exists.
The tool holds nothing other than raw text. And within that exact same text space, both humans and machines write. Consequently, these tools merely produce a “similarity” score: such as “73% resembles AI.”
Yet what is it resembling, and compared to what? The model was already trained specifically to resemble human text. Therefore, what you call “AI-like text” is nothing other than “text optimized to resemble humans.” There are no two distinct, segregated categories between which an objective boundary can be drawn.
The Dilemma of Resembling One’s Own Distribution
In essence, the tool is asking how much a distribution resembles itself. That is why the issue is not merely that “these tools provide similarity scores rather than definitive verdicts.”
The core problem is whether that similarity is a measurable, discriminatory parameter at all. If you cannot measure it objectively, the resulting numerical score is little more than decoration.
Despite this, devaluing human effort—accusing students, interrogating employees, or rejecting academic papers based on an unmeasurable similarity metric—is an unforgivable mistake.
If a smoke detector goes off, I check the kitchen first. But if an AI detector goes off, I suspect the detector first.
📋 Related Links
🌐 LinkedIn Post: I Trust Smoke Detectors, But I Do Not Trust AI Detectors
🧠 LLM Dynamics: Short Circuit in LLM Models: Why Does AI “Lie” to Us?
📄 Economics & Reliability: The Cheap LLM Illusion: Why Cost Per Successful Run Matters More Than Token Price
🔗 AI Architecture: Do Not Explain Everything to the Agent, Teach It Where to Look (Harness Architecture)
Last updated: August 2026 | Version: 1.0