However, attributing genuine understanding to these systems remains deeply contentious. The debate is not merely philosophical; it has practical implications for how these models are deployed, regulated, and trusted. Large language models generate outputs by sampling from probability distributions conditioned on input prompts. They have no persistent memory, no intrinsic motivations, and no grounding in embodied experience. Their reasoning, such as it is, can be disrupted by subtle changes to input phrasing, and they routinely produce confident-sounding falsehoods that researchers term hallucinations. These limitations expose a fundamental property: current models approximate the surface statistics of human language use without possessing the underlying cognitive structures that give language its semantic force.