Something I’ve been noticing while using language models for research and general questions is how good they’ve become at producing answers that feel complete and authoritative. Not necessarily correct. Just convincing. A structured explanation with confident wording and clear reasoning naturally reduces the urge to double check it. Not because people are careless, but because verification still takes time and the answer already feels finished. What seems interesting is the imbalance this creates. AI has drastically lowered the cost of generating plausible explanations, but the cost of verifying information hasn’t really changed. So we may be entering a situation where producing convincing knowledge scales much faster than confirming whether it’s actually true. Sometimes I test this by asking a model something I already know the answer to. Even when it’s wrong, the explanation can sound polished enough that you almost want to accept it anyway. Curious if anyone here has seen research specifically focused on this problem. Not alignment in the usual sense, but systems designed to verify or audit model outputs before people treat them as knowledge. submitted by /u/GalacticEmperor10
Originally posted by u/GalacticEmperor10 on r/ArtificialInteligence
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