AI has become easier to use, easier to trust, and harder to separate from daily culture. The central problem is not only what these systems can produce. It is also the model of the mind they quietly assume.
The article challenges a familiar idea: that human thought can be understood as a kind of computation. That comparison has helped drive a century of technical progress, but it may also be too narrow to explain how people actually think, learn, move, and act in the world.
The Computer Metaphor Has Limits
Norbert Wiener, described in the source as the godfather of cybernetics, once said, “The thought of every age is reflected in its technique.” For the past century, one of the strongest techniques has been computing. It is no surprise that computers have become a dominant way to describe the mind.
That view appears in the AI world today. Google’s Demis Hassabis calls the brain “a biological approximation to a Turing machine.” Elon Musk describes it more directly: “people should just think of the brain as a biological computer.”
The source article argues that this framing is useful but incomplete. The computational model traces back to Alan Turing and treats thought as a sequence: information comes in from the world, the mind manipulates it, and behavior comes out. In simple terms, perception leads to cognition, and cognition leads to action.
That structure helped technologists imagine more advanced machines, from basic calculating devices to artificial neural networks and generative AI. But the same structure can flatten the complexity of human beings. John von Neumann doubted that this approach could capture the “exceptional complexity of the human nervous system.”
Brains Do More Than Process Inputs
The alternative view in the source comes through Paul Cisek, a neuroscientist at the University of Montreal. His work is described as a biological model of brain and nervous system development spanning millions of years. Instead of treating the brain mainly as an information processor, he frames it as a feedback-control system.
That distinction matters. A feedback-control system does not simply receive information and then produce a response. It acts in the world in ways that change the information it receives next.
John Dewey, writing at the turn of the 20th century, described a similar idea. He said the mind is “more truly termed organic than reflex, because the motor response determines the stimulus, just as truly as sensory stimulus determines movement.”
In this account, thinking is not sealed inside the head. It is tied to motion, attention, environment, and available action. A person does not only observe the world and calculate a response. A person moves through the world and uses that movement to shape what becomes visible, possible, and meaningful.
A Baseball Catch Shows the Difference
The source uses a baseball example to show why the difference is practical. In a computational model, an outfielder catching a fly ball appears to be solving a hidden physics problem. The player would need to estimate velocity, gravity, and other variables before reaching the right spot.
The feedback-control account is simpler. The player can keep the ball in the same position within the visual field and move to preserve that relation. The action changes the stimulus. The player is not only calculating where the ball will land; the player is controlling the interaction with the ball while moving.
This explanation better matches the experience of the action itself. It also avoids separating mind from body. In the source’s framing, human intelligence is dynamic, embodied, and continuous with the environment around it.
Evolution Points to Control, Not Just Computation
The article connects this argument to evolutionary history. Across ancient fish, amphibians, mammals, primates, and modern humans, new behaviors emerged as new environmental possibilities appeared. The source gives one example: when dinosaurs died off, some nocturnal creatures could move in the daytime with less risk of being eaten, which opened the way for new capacities.
Cisek describes the history of the nervous system as one of “continuous extension of control further and further into the world.” That phrase points to a different map of the brain. Instead of a left-to-right flow from input to output, the source describes a model unfolding through evolutionary time.
One example is the hippocampus. As vertebrate animals developed greater mobility, they benefited from systems for exploration, including the use of landmarks and navigation at night. The source links that development to episodic memory, or memory of past experiences.
The computational model, according to the article, does not map cleanly onto these observable neuroscientific structures. It also hides how much brains are doing to control living organisms’ interactions with their environments.
Why This Matters for AI
The source’s larger concern is that AI products are being built and adopted around a thin idea of human cognition. If the mind is imagined as a computer, then AI looks like a more powerful extension of the same basic process. But if human thought depends on feedback, bodies, action, and social systems, then the comparison becomes far less stable.
The article also stresses the social side of human intelligence. People have spent thousands of years building institutions and norms for learning from and communicating with each other. The source argues that Big Tech companies have worked over less than a decade to dismantle those systems.
That is why the article frames AI as something tempting but risky for cognition. It is easy to use in the moment, but the danger is that it can weaken the structures that make human understanding durable: direct engagement with the world, active problem-solving, shared norms, and social learning.
The point is not that the computer metaphor has produced nothing useful. It clearly has. The point is that a metaphor can guide an industry while also blinding it. If AI systems are built on an incomplete picture of the mind, their cultural effects may be harder to manage than their technical abilities alone suggest.