Norbert Wiener, the pioneering figure behind cybernetics, once observed that each era’s prevailing thoughts find their expression in its technology. Over the last hundred years, that reflection has taken the form of computers, a trend that the AI sector continues to embody.
Google’s Demis Hassabis describes the brain as ‘a biological approximation of a Turing machine’, while Elon Musk puts it bluntly: ‘Think of the brain as a biological computer.’ (I often find myself worrying about Musk’s own brain.)
But humans, however, are far more intricate than a simple computer analogy suggests—and far more complex than most AI developers seem to recognize. As AI products become increasingly woven into everyday culture, this mismatch helps explain the chaos they often create.
AI, it turns out, resembles a cognitive hot‑dog: instantly appealing but ultimately detrimental to the health and durability of the mental systems we’ve refined over millennia.
Grasping the issue requires a dash of neuroscience combined with a full view of human evolutionary history.
The notion that brains are computers originates with Alan Turing. He framed thinking as a three‑stage algorithmic process: the mind receives data from the environment (input), processes it (computation), and produces actions (output). Perception fuels cognition, which in turn drives behavior. In a slightly more detailed visual, it can be illustrated like this chart:
In many respects, this framework has spurred progress. By viewing our brains as computers, engineers have advanced computing from basic calculators to sophisticated neural networks and generative AI, constantly striving for ever more precise reflections of our own mental processes.
Yet this reflection, while not entirely inaccurate, is distorted and incomplete.