Lacewing Journal
Category: AI detection
Everything we have published in this topic. Sources cited, numbers checked, and our own products criticised where they deserve it.
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Building an AI content QA pipeline that actually catches the right things
Five stages, the thresholds, and what to build versus buy. Detection belongs at exactly one of them. For five of the six risks a content team actually faces, it is the wrong instrument entirely.
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Where AI detection actually pays for itself, and where it will bury you
Detector accuracy is not what decides whether your flags are trustworthy. Prevalence is. The ten-minute calculation to run before you buy, and six questions no marketing page can answer.
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Watermarking and provenance: the case for marking AI content at source
Detection is forensics on a scene nobody photographed. SynthID, C2PA and the EU AI Act point at a different answer, and each has a specific failure the marketing does not mention.
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The false positive problem: who AI detectors wrongly accuse, and why
Detector errors are not random. They concentrate on second-language writers, neurodivergent writers and anyone writing in a constrained register. The evidence, the arithmetic, the court record, and a procedure that holds up.
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How AI content detectors actually work, and where they break
Perplexity, probability curvature, supervised classifiers, watermarking. What each generation of detector actually measures, what independent testing found, and the theoretical ceiling nobody can engineer around.
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The 10 best AI detectors in 2026, ranked honestly
Ten detectors ranked on independent test results, real pricing, and where each one falls down. Two of them are ours, and we have said so plainly along with what they get wrong.
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AI content detectors in 2026: what actually works, and what does not
A short field report on what AI content detection can and cannot do in 2026, written by a team that sells it.