Technically, an LLM predicts tokens from learned statistical patterns, not explicit reasoning. But at the scale modern models are trained, those patterns encode semantic, syntactic, and reasoning-like structure that produces functional understanding — summarization, translation, code generation, instruction-following — without human-like understanding grounded in lived experience or consciousness.

Because the model is trained on huge amounts of language, code, reasoning examples, and world knowledge, it develops internal representations that behave like understanding: it can summarize, translate, reason, write code, answer questions, and follow instructions. But it doesn't understand the way a human does — it has no lived experience, consciousness, goals, or direct grounding in the physical world unless connected to tools, sensors, or external systems. The balanced, defensible interview answer is: functional understanding, yes; human-like understanding with grounding and consciousness, no.