Modern artificial intelligence systems demonstrate extraordinary scale and task performance, yet continue to exhibit persistent structural weaknesses: brittleness under pressure, hallucination, premature certainty, shallow reasoning, and failure to recover from internal collapse. Despite extensive investment in architecture, data, and optimization techniques, these failure modes recur across model families and scales.