Our minds aren’t equipped to handle AI
AI is junk food for the mind: easy, tempting and ultimately very bad for you.
Norbert Wiener, godfather of cybernetics, once said, “The thought of every age is reflected in its technique.” For the past century, our thought has been reflected in our computers, including by those in the AI industry. Google’s Demis Hassabis calls the brain “a biological approximation to a Turing machine.” Elon Musk puts it more bluntly, declaring that “people should just think of the brain as a biological computer.”
But humans are more complex than a straightforward comparison to computers gives us credit for — and far more than most of the AI industry seems to appreciate. And as their products push ever deeper into culture, this helps explain why they’re making such a mess. AI, it turns out, is something like a cognitive version of a hot dog: It appeals to us in the moment, but undermines the health and sustainability of cognitive systems we’ve evolved over several millennia.
To understand the problem, we’ll need a little neuroscience and the entirety of human evolutionary history.
The idea that brains are computers traces back to Alan Turing. It posits thought as a three-step process, functionally an algorithm: Our minds take in information from the world (input), manipulate it in some fashion (computation), and then enact behavior (output). Perception leads to cognition, which in turn leads to action.
In many ways, this model has been productive. Imagining our brains as computers, technologists have pushed actual computing from simple adding machines to artificial neural networks and generative AI, seeking ever more detailed mirrors of our own minds.
But this mirror’s image, while not exactly wrong, is warped and limited. John von Neumann doubted that the computational model could possibly capture the exceptional complexity of the human nervous system. That is, our nervous systems evolved to help us navigate the large ecological system that we call the world, and we act upon the world to exercise control over it.
The computational approach looks at the end product of our minds and tries to reverse engineer how they function. But instead of working backward, we might instead examine the long arc of evolutionary history to build our model from the ground up. This is the approach favored by Paul Cisek, a neuroscientist at the University of Montreal, who has developed a biological model of brain and nervous system development spanning millions of years.
Cisek contends that rather than information processors, our brains are better understood as feedback-control systems. Our bodies do not just receive input, they take action to adjust what that input is, contingent on what options are available. Writing at the turn of the 20th century, philosopher John Dewey described the mind as a circuit where the motor response determines the stimulus just as truly as sensory stimulus determines movement.
What exactly is the difference between the two approaches?
Here is a classic example from baseball: catching a fly ball in the outfield. According to the computational model, solving this problem must involve some complicated and subconscious mental calculus wherein the outfielder estimates the ball’s velocity, calculates the effect of gravity, and undertakes untold mathematical procedures to compute where the ball will go.
In contrast, the feedback-control approach suggests a simple heuristic: keep the ball in the same position within your visual field, and then move to maintain that situation. We take action to adjust the stimulus we receive.
This is not only more true to the experiences of anyone who has played center field, it also avoids separating out the mental process from physical movement, and avoids invoking the use of complex calculations that the computational model requires. Humans are more dynamic than that.
This approach also neatly maps to the biological architecture of brains as they have evolved over time. From ancient fish to amphibians, mammals, primates, and modern humans, new behaviors emerge in response to new environmental possibilities. The history of our nervous system is one of continuous extension of control further and further into the world.
We can both lay out a map of behaviors and abilities as they emerged over time and overlay the specific physical components of the brain to these capabilities.
Instead of moving from left to right as in the computational model, this model unfolds from top to bottom over evolutionary time. As vertebrate animals developed mobility, specialized systems for exploration led to the development of the hippocampus and episodic memory of past experiences.
We cannot do this with the computational model of the mind because it does not sync up to observable neuroscientific structures. Cisek suggests we need to dramatically shift the paradigm we use to understand the relationship between our brains and behavior, moving away from algorithmic inputs and outputs and toward dynamic feedback systems.
The Social Dimension of Human Evolution
Human development has largely centered on feedback from the physical world, but one of our most important feedback loops arises from our profoundly social dispositions. The computational model does not account for this well, and neither does the AI industry, which has worked to dismantle institutions and norms built over thousands of years.
Hundreds of thousands of years ago, our ancestors learned to imitate each other. Mimicking gestures and body movements allowed them to pass along successful practices like stone tool-making and coordinate complex activities through shared rituals. It was the dawn of human culture.
We have spent thousands of years building institutions and norms for learning from and communicating with each other, and Big Tech companies are systematically working to dismantle them.
Humans soon progressed to imitating sounds and oral language. As wandering hunter-gatherer ancestors settled into non-transient communities, they developed complex agricultural practices that allowed them to cultivate food rather than migrating to find it.
Eventually, written marks were used to represent spoken languages and abstract ideas. Formal education emerged to ensure knowledge was shared among groups, followed by institutions that collectivized human decision-making such as markets, law, and democracy.
Each of these processes is continuous with our biological evolution, fostering an ever broader range of control over the world around us.
And sometimes this cultural change creates unintended consequences that hurt instead of help.
Human diets are perhaps the most obvious example. In our hunter-gatherer days, fatty foods were rare and precious for survival, leading us to seek them out. Agricultural techniques eventually made fatty foods plentiful, creating modern health challenges.
AI clogs up our capacity to develop the knowledge we need to navigate the world.
Just as excessive dietary fat harms physical bodies, AI poses a similar danger to our cognitive capabilities by clogging our capacity to develop the knowledge we need in our heads to navigate the world. It is a cognitive hot dog.
An occasional hot dog will not harm anyone, but it causes problems if it becomes a regular meal. The same is true for artificial intelligence; the harm comes from making it a staple of our mental diet.
Yet that is exactly what Big Tech hyperscalers are trying to do.
The Educational Impact of Artificial Intelligence
These efforts will not benefit students or teachers. Instead, they represent a clear example of how AI weakens systems relied upon to develop human cognition in exchange for convenience.
Former Google software engineer François Chollet described AI as a tool of cognitive automation, defined as encoding human abstractions in software to automate tasks normally performed by humans. Scholars argue generative AI functions as a cultural and social technology allowing humans to access accumulated information.
AI enthusiasts have made numerous predictions about these advantages, dismissing disadvantages as comparable to previous automation tools like calculators or the written word.
Never before have we developed and broadly deployed something so explicitly intended to supplant human thinking.
Whatever the effects of earlier technologies, AI is explicitly intended by its evangelists to supplant human thinking.
Students use tools like ChatGPT en masse to avoid the effortful thinking necessary to build durable knowledge. Evidence highlights negative impacts in educational settings, with studies showing students stopping homework usage when relying on AI, which harms long-term learning and critical thinking.
No other species' brain develops over such an extended period, and human cultural institutions have historically transmitted knowledge across generations. Rather than protecting this endeavor, many school administrators and tech hyperscalers are pushing AI rapidly into education ecosystems.
Cognitive Delegation and the Pushback Against AI
If we view the mind solely as a computer, AI appears advantageous to learning. However, this model fails because human minds exist within a broader cognitive ecosystem where making choices about expending cognitive energy extends our agency and control.
Because effortful thinking is difficult, technological off-ramps encourage cognitive delegation. Researchers warn that as delegation spreads, the social environment supporting autonomous reasoning weakens, making further delegation even more attractive.
Researchers recommend cognitive immunization to preserve institutions keeping human cognition active, including unaided problem solving, verification, critical discussion, and deliberate disengagement from AI usage.
Society calls such institutions schools.
Policy levers are also shifting. Norway recently banned most AI uses in schools for children under 13, and teachers' unions and school districts in cities like New York and Los Angeles have implemented similar restrictions.
Public resistance is growing, evident in graduation ceremonies and student-led letters criticizing institutional embraces of AI in favor of human inquiry and imperfection.
The control systems in our brains are products of millions of years of biological evolution and millennia of cultural evolution. Moving beyond the computational model of the mind helps reveal the full picture of human nature.
Eat healthy, and think healthy.
