On that point - it also becomes dangerous. AI can be confidently and spectacularly wrong, yet still present its answers with the polish of authority. The real problem is that it increasingly feeds on its own output. If people use AI to write articles, reports, or marketing copy that contain inaccuracies, those pieces enter the digital ecosystem. Later, AI systems may reference that material as if it were legitimate source content. The result is a bizarre echo chamber - AI citing AI citing AI - where errors are recycled, reinforced, and gradually mistaken for fact. Over time, this creates a subtle but serious risk: misinformation doesn’t just spread - it hardens into something that begins to resemble consensus. It is bad enough when humans circulate misinformation through ignorance, poor education, misunderstanding, or outright malicious intent. But the problem takes on a different scale when machines begin to amplify it. When enough machine-generated text repeats the same mistake, the repetition itself starts to masquerade as evidence. At that point, the distinction between fact and frequency becomes dangerously blurred. What is repeated most often can start to look like what is most true - even when it is simply the product of the same original error being echoed again and again by the machines that were meant to inform us.
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