What Stays Human When the Machine Becomes Fluent
Notes before a salon — May 13, Chinatown.
In the early 2000s, researchers studying epilepsy patients identified a single neuron in one patient’s brain that fired only when she encountered Jennifer Aniston. Her photograph, her name in text, even a stylized cartoon drawing — the same cell, every time. That is how human cognition organizes meaning: symbolically.
Large language models work differently. They don’t hold concepts. Given everything you’ve typed so far, the model calculates which token is most probable next, then the next, and the next. There is no Jennifer Aniston neuron inside a model. There are billions of weights, spread across vast stretches of text, distributing the probabilities of words against other words.
This sounds like a technical detail. It is the architectural fact that shapes what these systems do, where they fail, and what they quietly optimize for.