Norbert Wiener, godfather of cybernetics, once said, \u201cThe thought of every age is reflected in its technique.\u201d For the past century, our thought has been reflected in our computers, including by those in the AI industry. Google\u2019s Demis Hassabis calls<\/a> the brain \u201ca biological approximation to a Turing machine.\u201d Elon Musk puts it<\/a> more bluntly, declaring that \u201cpeople should just think of the brain as a biological computer.\u201d (Musk\u2019s brain often worries me.)<\/p>\n<\/div>\n But humans are more complex than a straightforward comparison to computers gives us credit for \u2014 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\u2019re 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\u2019ve evolved over several millennia.<\/p>\n<\/div>\n To understand the problem, we\u2019ll need a little neuroscience and the entirety of human evolutionary history.<\/p>\n<\/div>\n 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 slightly more elaborate terms, it looks something like this chart:<\/p>\n<\/div>\n 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.<\/p>\n<\/div>\n But this mirror\u2019s image, while not exactly wrong, is warped and limited. John von Neumann, a seminal figure in developing computers and computer science, doubted<\/a> that the computational model could possibly capture the \u201cexceptional complexity of the human nervous system.\u201d That is, our nervous systems evolved to help us navigate the large ecological system that we call \u201cthe world.\u201d And we act upon the world to exercise control over it (as best we can).<\/p>\n<\/div>\n The computational approach looks at the end product of our minds and tries to \u201creverse engineer\u201d 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\u2019s patiently developed a biological model of brain and nervous system development spanning millions of years.<\/p>\n<\/div>\n Cisek contends that rather than information processors, our brains are better understood as feedback-control systems<\/em>. Our bodies don\u2019t just receive input, they take action to adjust what that input is, contingent on what options are available. As Cisek himself is quick to note, this is not a new idea \u2014 writing at the turn of the 20th century, philosopher John Dewey described the mind as a circuit, \u201cmore truly termed organic than reflex, because the motor response determines the stimulus, just as truly as sensory stimulus determines movement.\u201d<\/p>\n<\/div>\n What exactly is the difference between the two approaches?<\/p>\n<\/div>\n Here\u2019s 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\u2019s velocity, calculates the effect of gravity, and undertakes untold other mathematical procedures to \u201ccompute\u201d where the ball will go.<\/p>\n<\/div>\n In contrast, the feedback-control approach suggests a simple heuristic \u2014 essentially, \u201ckeep the ball in the same position within your visual field, and then move to maintain that situation.\u201d We take action to adjust the stimulus we receive.<\/p>\n<\/div>\n This is not only more true to the experiences of anyone who\u2019s 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. .<\/sup><\/p>\n<\/div>\n This approach also neatly maps to the biological architecture of brains as they\u2019ve evolved over time. From ancient fish to amphibians to mammals to primates and eventually modern humans, what we observe is that new behaviors emerge in response to new environmental possibilities. When dinosaurs died off, for example, this meant some nocturnal creatures could move about the world in the daytime with less risk of being eaten, giving rise to a variety of new capacities. The history of our nervous system, Cisek observes<\/a>, is one of \u201ccontinuous extension of control further and further into the world.\u201d (His forthcoming book will explore all this in greater detail, and yes, I\u2019m hoping this essay puts subtle pressure on him to finish it.)<\/p>\n<\/div>\n This leads to an alternative and very different diagram than the one above. We can both lay out a map of behaviors and abilities as they emerged over time and<\/em> we can overlay the specific physical components of the brain to these capabilities, like so:<\/p>\n<\/div>\n Instead of moving from left to right as in the computational model, this model should be seen as unfolding from top to bottom over evolutionary time. For example, a long time ago, as vertebrate animals developed more mobility, it became useful to have specialized systems for exploration \u2014 by using landmarks, say, or navigating at night. This led to the development of what we now call the hippocampus. Importantly, this also led our ancestors to remember important moments of scurrying from one spot to another, leading to the development of \u201cepisodic memory\u201d of past experiences.<\/p>\n<\/div>\n We can\u2019t do anything remotely like this with the computational model of the mind; it simply does not sync up to observable neuroscientific structures. What\u2019s more, it obscures that so much of what brains are doing involves controlling living organisms\u2019 interactions within their environments. As such, Cisek suggests we need to dramatically shift the paradigm we\u2019re using to understand the relationship between our brains and behavior, moving away from algorithmic input-outputs and toward more dynamic feedback systems.<\/p>\n<\/div>\n Thus far, our story of human development has largely centered on feedback from the physical world. But one of humanity\u2019s most important \u201cfeedback loops\u201d arises from our profoundly social <\/em>dispositions. The computational model, it turns out, doesn\u2019t account for this well either \u2014 and neither does the AI industry. We\u2019ve spent thousands of years building institutions and norms for learning from and communicating with each other, and over the course of less than a decade, Big Tech companies have systematically worked to dismantle them.<\/p>\n<\/div>\n How? Again, we\u2019ll need some historical context.<\/p>\n<\/div>\n At some point around many hundreds of thousands of years ago, our distant ancestors did something incredible: They learned to imitate each other. Mimicking gestures and body movements allowed us to pass along successful practices \u2014 like chipping away at a stone tool \u2014 and coordinate more complex activities through shared ritualistic behaviors, which in turn shaped our cognition<\/a>. It was the dawn of human culture.<\/p>\n<\/div>\n We\u2019ve 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<\/p>\n<\/div>\n<\/div>\n Humans soon progressed to imitating sounds and, in turn, to oral language. As we\u2019ve covered previously<\/a>, language is not the same as thought, but it enables us to communicate<\/em> our thoughts to each other. At some point, our wandering hunter-gatherer ancestors also started to settle down into non-transient communities, developing complex agricultural practices that allowed us to cultivate food rather than migrating to find it.<\/p>\n<\/div>\n Finally, we started to use written marks<\/a> to represent our spoken languages and other abstract ideas. We became capable of transmitting complex ideas across generations. Formal education, a process of ensuring knowledge is shared among a human group, emerged<\/a> not long after. And so too in time did practices and institutions that collectivized human decision-making, such as \u201cmarkets\u201d and \u201claw\u201d and \u201cdemocracy.\u201d<\/p>\n<\/div>\n Each claim I\u2019ve just made is contestable, and cries out for far more detail than I can provide here. But this process is continuous<\/em> with our biological evolution, in the sense that both foster an ever broader range of control over the world around us.<\/p>\n<\/div>\n And sometimes this cultural change creates unintended consequences that hurt instead of help.<\/p>\n<\/div>\n Human diets are perhaps the most obvious example. In our hunter-gatherer days, fatty foods were rare but precious to aid survival \u2014 just watch Alone <\/em>if you doubt this \u2014 and we evolved to seek them out and store them in our bodies once found. But then we culturally developed the practice of farming and other agricultural techniques that, give or take 10,000 years, have made fatty foods plentiful today \u2014 if you want a hot dog, there\u2019s plenty available.<\/p>\n<\/div>\n AI clogs up our capacity to develop the knowledge we need to navigate the world<\/p>\n<\/div>\n<\/div>\n And that\u2019s the problem. Hot dogs are a danger to our physical bodies, clogging our arteries with excessive fat we no longer need to store internally. AI poses a similar sort of danger to our cognitive capabilities, by clogging our capacity to develop the knowledge we need \u2013 in our heads \u2013 to navigate the world. It\u2019s a cognitive hot dog.<\/p>\n<\/div>\n The occasional hot dog won\u2019t harm anyone, but it sure will if it becomes a regular meal at lunch or dinner. The same is true for AI \u2014 the harm is not from its occasional use, but making it part of our mental diet.<\/p>\n<\/div>\n Yet that is exactly what Big Tech hyperscalers are trying to do.<\/p>\n<\/div>\n In a July 2025 podcast about \u201chow AI is transforming education,\u201d OpenAI VP of education Leah Belsky boasted<\/a> that \u201clearners\u201d made up more than half of the 900 million average monthly users of ChatGPT. The service was \u201cthe world\u2019s largest learning platform,\u201d she said. More recently, Anthropic announced a new initiative called \u201cClaude for Teachers<\/a>,\u201d providing free access to premium models to practicing classroom educators.<\/p>\n<\/div>\n These efforts will not benefit students, nor teachers. In fact, it\u2019s the most obvious example of how AI weakens systems that we rely on to develop our cognition, for the benefit of convenience.<\/p>\n<\/div>\n Fran\u00e7ois Chollet, formerly a software engineer at Google, has memorably described<\/a> AI as a tool of \u201ccognitive automation,\u201d which he defines as \u201cencoding human abstractions in a piece of software, then using that software to automate tasks normally performed by humans.\u201d Last year, an interdisciplinary group of scholars argued<\/a> persuasively that we should view generative AI in the form of large language models not \u201cprimarily as intelligent agents, but as a new kind of cultural and social technology<\/em>, allowing humans to take advantage of information other humans have accumulated\u201d (emphasis added).<\/p>\n<\/div>\n AI enthusiasts have made no shortage of predictions about the advantages of this, and they\u2019ve largely dismissed the disadvantages as comparable to previous forms of automation, such as computers, calculators, and even the written word. (I\u2019ve had citations to Plato\u2019s alleged opposition to writing thrown at me so many times I\u2019ve addressed them in a separate piece<\/a> \u2014 he was a writer!)<\/p>\n<\/div>\n Never before have we developed and broadly deployed something so explicitly intended to supplant human thinking<\/p>\n<\/div>\n<\/div>\n Whatever the effects of these earlier technologies, never before have we developed and broadly deployed something so explicitly intended to supplant human thinking \u2014 that\u2019s what AI evangelists themselves argue.<\/p>\n<\/div>\n Look at education. ChatGPT may be the world\u2019s largest learning platform, but students are using it en masse to avoid<\/em> the effortful thinking that\u2019s necessary to build their durable knowledge. Evidence<\/a> continues<\/a> to<\/a> mount<\/a> demonstrating<\/a> the negative<\/a> impact<\/a> of AI<\/a> tools within education settings (and Gen Z generally hates it<\/a>). A recent study<\/a> from China revealed that thousands of students essentially stopped doing their homework once they started using AI (which substantially harmed their learning). There\u2019s even evidence<\/a> that when students use large language models for supposed learning purposes, they become habituated to relaxing their judgment and critical thinking in other contexts.<\/p>\n<\/div>\n No other species\u2019 brain and body develops over such an extended period as ours, and historically, we\u2019ve complemented that with unique cultural institutions that transmit knowledge from one generation to the next. We should be zealously protective of this uniquely human endeavor, yet instead many university and school administrators, to say nothing of the Big Tech hyperscalers, are engaged in full-throated efforts to push AI as deep and as fast as they can into our education ecosystems.<\/p>\n<\/div>\n If we only <\/em>see the mind as a computer, AI might seem advantageous to learning \u2014 this is why many dream of building AI tutors. But already such efforts are failing<\/a>, because our minds are more than just input-output devices. They exist, or rather we <\/em>exist, within a broader cognitive ecosystem where we must make choices about how we choose to expend our cognitive energy. We do so to extend our agency, our control \u2014 however limited it may be \u2014 over our world.<\/p>\n<\/div>\n If we only <\/em>see the mind as a computer, AI might seem advantageous to learning \u2014 but this model is wrong<\/p>\n<\/div>\n<\/div>\n But this poses a challenge. Effortful thinking is hard, and if we\u2019re offered technological off-ramps, we\u2019ll take them \u2014 what some researchers recently called cognitive delegation<\/a>. As cognitive delegation spreads, they warn, \u201cthe social environment that supports autonomous reasoning can weaken\u201d \u2014 and as it does, such delegation \u201cbecomes still easier and more attractive.\u201d<\/p>\n<\/div>\n The researchers recommend a program of cognitive immunization<\/em> from AI, one that preserves \u201cthe practices and institutions that keep human cognition active: unaided problem solving, verification, critical discussion, [and] periods of deliberate disengagement\u201d from AI usage.<\/p>\n<\/div>\n We call such institutions \u201cschools.\u201d<\/p>\n<\/div>\n Policy levers will need to be pulled as well. I find it very<\/em> interesting that Norway recently banned<\/a> essentially all uses of AI in school for students under the age of 13. In the US, a major teachers\u2019 union has likewise called<\/a> for prohibiting chatbots in elementary schools, and major school districts such as Los Angeles<\/a> and New York City<\/a> have recently implemented bans as well. (Disclosure: I\u2019ve provided informal advice to Schools Beyond Screens, an organization that pushed for these bans.) They won\u2019t be the last to do so, or so I hope.<\/p>\n<\/div>\n The good news, if it can be called that, is that resistance is growing \u2014 perhaps you\u2019ve seen the many videos<\/a> of college graduates booing commencement speakers praising AI? That\u2019s no doubt in part due to job concerns, but I suspect many recognize the harm that AI has inflicted on their own cognitive development. Certainly, this is true for the students at the Oberlin Luddite Club, who wrote an open letter<\/a> to their university president criticizing that institution\u2019s embrace of AI: \u201cAs you embark on your year of AI, we\u2019ll embark on our own year of self-actualization \u2014 of realizing the fruits of our labor, and of embracing human imperfection and raw inquiry.\u201d Right on.<\/p>\n<\/div>\n The control systems in our brains are the product of millions of years of biological evolution and several millennia of cultural evolution. We need to pull up from the computational model of the mind to see the full picture of what makes us human, and to appreciate our profoundly social nature, the driver of human culture. We have shown remarkable resiliency as a species, but gorging on cognitive hot dogs isn\u2019t good for any of us.<\/p>\n<\/div>\n Eat healthy, and think <\/em>healthy.<\/p>\n<\/div>\n![\"This](\"https:\/\/platform.theverge.com\/wp-content\/uploads\/sites\/2\/2026\/10\/268726_Cognitive_hotdogs_CVirginia_GRAPHIC_2B.png?quality=90&strip=all&crop=0%2C0%2C100%2C100&w=2400\"\/)
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