The Real Risk in AI Teams Is Missing Review Loops, Not Missing Models

There is a database that every AI team should be forced to read before their next sprint planning. It is maintained by researcher Damien Charlotin, and it tracks court cases worldwide in which AI-generated content — fabricated citations, invented precedents, fake quotes from real judgments — was submitted to actual courts. By mid-2026, it had passed 1,400 documented cases. A year earlier, the count was around 200. Charlotin has described days when ten new cases arrived from ten different courts.

Here is the detail most commentary misses: in almost none of these cases did the model malfunction. The models did exactly what they were built to do — generate fluent, confident, plausible text. What failed, every single time, was the step that was supposed to come next. A human checking. A process catching. A loop closing.

That is the pattern I want to talk about, because it extends far beyond...

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