Noam Cohen, founder of CritiqeIL, writes that the most dangerous feature of AI is not just its mistakes, but the certainty with which it makes them. He says that a few weeks ago he asked an AI model a question he already knew well, and got a fluent, confident answer that was simply wrong. The point, he argues, is that a language model is not a database of truth, but a prediction engine that generates the next most likely word based on patterns it learned from massive amounts of text.
That means AI is often useful because the statistically likely continuation is also correct. But when the model lacks a solid basis for an answer, it does not stop or admit ignorance. It keeps producing a plausible response, and its confidence does not indicate accuracy. Once users understand that, they should change how they work with it.
Cohen offers five practical questions. First, where is AI most likely to invent details, especially hard facts such as numbers, dates, names, quotes, sources, recent events, or obscure topics? Second, what actually works to verify it? He recommends external checks, opening cited sources, confirming they exist and say what was claimed, or asking another model or doing a quick search to see whether the answers match. Third, can the risk be reduced in advance by giving the model documents or data and asking it to answer only from that material.
Fourth, he says users should watch for warning signs in the output, such as overly neat round numbers, polished full quotes, or the same smooth tone on obscure topics as on familiar ones. Fifth, they should not rely on AI for decisions that cannot be easily verified and that carry real medical, legal, or financial consequences. In such cases, he says, the tool should be used for drafts and brainstorming, while the decision remains human. Cohen concludes that the key skill is knowing when to trust AI and when to stop and verify, because that is how users get its benefits without paying for its errors.
