Why LLMs Don't Actually Reason

A former Google DeepMind researcher argues that current LLMs lack genuine reasoning capabilities, relying instead on fast pattern matching rather than the deliberative search mechanisms seen in AlphaGo. The core issue is that LLMs maintain no persistent, inspectable epistemic state, meaning they cannot track hypotheses or evidence systematically. This architectural flaw makes them unreliable for high-stakes fields like medicine and science where auditability is critical. True machine intelligence requires a separation between knowledge representation and manipulation, moving beyond next-token prediction to auditable inference.