For all the speed and breadth of modern AI, there is one place where models-as-software still fall short of the human brain: our “wetware” comes with a biological advantage of elasticity. The mind is not a fixed machine running fixed weights; it reshapes itself in the very act of being used.
AI models do not learn continuously. Once trained, their weights are frozen until someone deliberately retrains them. There is no equivalent of the mind’s “neurons that fire together, wire together” — that quiet, ongoing rewiring that happens whether or not we notice it.
We carry a complicated biochemical machine that “fine-tunes” with every inference. Each time we think, recall, or struggle with something, information patterns are encoded and abstracted into efficient chunks automatically. Consider how a phone number you have dialed a hundred times becomes a single fluid gesture rather than ten separate digits — that compression happened on its own. An LLM, by contrast, starts from scratch no matter how many hours it has spent on the same problem; nothing from one conversation settles into the next.
And the difference runs deeper than memory. Agentic LLM behavior acquires contextual connections largely through semantic similarity of wording — matching things that sound alike. The human mind connects through abstracted relations, a higher-dimensional level of linking: ideas joined by what they mean, not merely how they are phrased. (A distinction I keep returning to from a friend’s thinking.)