How AI detection works — and how far to trust it
How do AI detectors work?
AI detectors measure statistical properties of text — how predictable each word is to a language model, how uniform the rhythm and phrasing are — and compare those patterns against large samples of human and AI writing. No detector “knows” who wrote a text; every detector estimates a probability. Cobalynx reports that probability as calibrated confidence with published error rates — the full pipeline is documented on our methodology page.
Can AI detectors be trusted?
Only as a probabilistic signal — never as proof. Peer-reviewed testing has found that none of 14 popular detectors reached 80% accuracy across conditions, and famous false positives include the U.S. Constitution. Cobalynx publishes its own measured error rates on the evidence page so you know exactly how much to trust each verdict — our longer answer is in Can AI detectors be trusted?
What is the most accurate AI detector?
There is no single honest answer: accuracy depends on text length, language, the generating model, and how much editing happened afterwards — and most “most accurate” rankings are published by vendors grading themselves. The useful question is whether a detector publishes reproducible error rates at a stated decision threshold. Ours are public, with raw counts and confidence intervals, on the evidence page.
Why do different AI detectors give different results?
Because they use different training data, different decision thresholds, and no shared calibration standard. A text scoring “83% AI” on one tool and “human” on another is not a paradox — it is what uncalibrated probabilities look like. Calibration means a stated confidence has a measured real-world error rate; that is the property we build for, as explained on the methodology page.
Can AI writing be detected after paraphrasing or “humanizers”?
Detection genuinely degrades — adversarial rewriting is the hardest case for every detector, ours included. Rather than claiming immunity, we measure it: our published results include a paraphrase-attacked test split, reported both flatteringly and unflatteringly on the evidence page.
Short text and the limits nobody advertises
Do AI detectors work on short text?
Not reliably — anyone’s. Below roughly 150 words there is not enough statistical signal for a meaningful verdict, which is why Cobalynx requires a minimum length instead of guessing. For chat messages, single reviews, or short emails, paste a batch or a full transcript as one text.
Can Cobalynx prove a text is human?
No — and neither can anyone else, which is why we never output “human-verified.” No technology available today can prove human authorship; a tool that claims to certify it is overclaiming. We report the probability that text is AI-generated, calibrated against a public benchmark documented on the evidence page.
What is a false positive, and what is your rate?
A false positive is human writing wrongly flagged as AI — the most damaging error a detector can make. Our measured false-positive rates, with raw counts and confidence intervals per confidence band, are published on the evidence page; we tune our thresholds so that high-confidence false accusations stay as close to zero as the measurement supports.
Students, teachers, and false accusations
Why was my essay flagged as AI when I wrote it myself?
False positives are real: formal structure, grammar-tool polish, and non-native English patterns all mimic the statistics detectors look for. A flag is a signal, not a verdict. Save your version history and drafts — they are stronger evidence than any score — and see our step-by-step guide for the falsely accused.
How can I prove I didn’t use AI?
Process evidence beats any detector: Google Docs or Word version history, outlines, notes, and prior writing samples show the messy human timeline of real work. Detector scores — including ours — should never be the sole basis for an accusation, and the published error-rate research supports saying exactly that to your institution. The full response playbook is at falsely accused of using AI?
Can Turnitin detect ChatGPT in 2026?
It flags most unedited AI output, but independent studies report much lower real-world accuracy than its marketing — one 2026 peer-reviewed test measured 0.61 overall accuracy — and a higher false-positive rate than the advertised figure. Treat any detector score, Turnitin’s or ours, as one input to a human process, never as the verdict itself.
Another detector flagged my writing — is it accurate?
Independent studies report meaningful false-positive rates for all mainstream detectors, concentrated on non-native speakers and polished formal prose. If you have been flagged elsewhere, the right response is a calibrated second opinion plus your version history — not panic. See the honest numbers on detector accuracy and what to do about an accusation.
Do AI detectors discriminate against non-native English speakers?
Mainstream detectors have shown dramatically elevated false-positive rates on non-native writing — a Stanford study found more than 60% of essays by non-native speakers flagged. This failure mode is real, so we test for it and publish our own non-native (ESL) error rate on the evidence page instead of ignoring it.
Should schools use AI detectors at all?
As one signal inside a process with human judgment and process evidence, they can help; as an automatic verdict they cause documented harm — which is why several universities disabled them. Our design goal is a detector that cannot be misused as an oracle: calibrated confidence, published error rates, and no certainty stamp. Our guide for teachers lays out the fair-process checklist.
Watermarks and file provenance
Does ChatGPT watermark its text?
No. OpenAI prototyped text watermarking but never shipped it; occasional invisible Unicode characters in outputs are artifacts, not a watermark. Statistical detection is the only option for unwatermarked text today — how watermarks actually work is explained in What is an AI text watermark?
Which AI companies watermark their text?
Very few — and for text, mostly via keyed schemes like SynthID that only the provider itself can verify. We track the live per-provider status, generated from the same configuration as our detection backend, on Who watermarks AI text today?
How do I check if an image is AI-generated?
There are two different questions: what the pixels look like (guesswork) and what the file says about itself (provenance). Cobalynx checks PNG and JPEG files for C2PA Content Credentials and generator metadata — deterministic evidence when present — using the file check on the home page. We do not guess from pixels, and we say so; the full walkthrough is in the Content Credentials guide.
What are C2PA Content Credentials?
A cryptographically signed manifest inside a file that records who or what created and edited it — the standard the EU AI Act points to for machine-readable marking. Drop a file on our checker to read any credentials it carries — the Content Credentials guide explains each possible outcome.
If an image has no Content Credentials, is it real?
Unknown — absence proves nothing, because most authentic photos carry no credentials and credentials can be stripped by screenshots and re-saves. The asymmetry matters: the presence of an AI-generator manifest is near-conclusive evidence of AI origin, but an empty result is not evidence of anything.
What the law requires
Is AI content required to be labeled by law?
In the EU, yes for many cases: Article 50 of the AI Act became enforceable on August 2, 2026, requiring machine-readable marking of synthetic media and disclosure for chatbots and deepfakes. Rules differ in China, California, and elsewhere — the full picture, with deadlines and fines, is on our AI content law page.
Do companies have to tell me when I’m talking to a bot?
In the EU, yes: Article 50(1) requires disclosure at first contact unless it is already obvious, with penalties up to €15 million or 3% of worldwide turnover. Elsewhere it varies by jurisdiction. What that right means in practice — and how to check for yourself — is covered in Am I talking to a bot?
Everyday situations
How can I tell if I’m chatting with a bot?
Behavioral tells beat text analysis on short messages: instant lengthy replies, no memory of what you said, and uniform tone are stronger signals than any statistical scan of a two-line reply. For a full transcript of 150+ words, statistical detection becomes meaningful — paste the whole conversation, not one message. Our bot-spotting guide has the complete checklist.
How can I tell if an email was written by AI?
Single short emails are usually below the reliable detection floor, so look for context signals first: does it reference anything only your real correspondent would know? Longer emails and threads can be checked with calibrated confidence. The practical walkthrough is at Was this email written by AI?
Can recruiters tell if a cover letter is AI-written?
Human intuition performs poorly: in published testing, recruiters who believed they could spot AI text (67% claimed to) measured at roughly 52% accuracy — coin-flip territory. Detectors do better on full-length letters but carry false-positive risk, so no candidate should be rejected on a score alone. Both sides of the hiring table are covered in the cover-letter guide.
How can I spot AI-written reviews?
One short review is below any detector’s floor — the signal lives in batches. Paste several reviews from the same product or reviewer together as one text: repeated phrasing across reviews, timing bursts, and empty reviewer profiles are the patterns that matter. An AI-flagged review is not automatically fake, either — real buyers polish with AI. The batch method is walked through in the fake-review guide.
How can I tell if a dating match is using AI?
Ask specific personal questions, insist on a video call early, reverse-search photos, and check photo files for generator credentials with the file check. For messages, paste a long chat history — single messages are too short to classify honestly. And whatever any tool says: never send money to someone you have not met. The safety-first walkthrough is the AI dating scam check.
How can I tell if an article was written by AI?
Long-form text is where detection works best — articles of 500+ words are our strongest published accuracy band, with the numbers on the evidence page. Remember what the verdict means: it estimates statistical origin, not truthfulness or quality. An AI-drafted article can be accurate and human-edited; a fully human one can be wrong. The full reader’s guide is Did a human write this?
About Cobalynx
Is Cobalynx really free with no signup?
Yes — paste text or drop a file, with no account and no word-count paywall. We also tell you plainly what we cannot do — short text, pixel guessing, proving humanity — instead of making you pay to find out.