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The hallucination problem in large language models sits at the base of every responsible AI deployment decision.
A model trained on next-token prediction will sometimes produce confident, well-structured text that has no factual grounding, with no internal mechanism to distinguish that output from accurate information.
That failure mode has now surfaced inside PwC, one of the Big Four professional services firms, which published reports on artificial intelligence that contained errors introduced by the same technology those reports were meant to assess.
Where the failure sits in the stack Hallucination is not a fringe case. It is a known property of how transformer-based language models generate text: the model optimizes for coherent output, not verified truth.
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