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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches“Our minds aren’t equipped to handle AI” is an argument, not an established scientific finding. In his October 5, 2026 opinion essay for The Verge, Benjamin Riley warns that routine reliance on generative AI could displace the effort, feedback and shared practices through which people build knowledge and judgment. The essay offers a useful question for classrooms and everyday AI use: does a tool help people think, or make it easier to skip the thinking?
What does Riley mean by “our minds aren’t equipped”?
Riley is not claiming that people cannot use AI or that every encounter with it causes harm. His title expresses a concern about how generative AI may fit with human learning. He argues that treating the brain as a computer that receives input, processes it and produces output leaves out important parts of cognition: acting in the world, responding to feedback and learning with other people.
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The piece is an opinion essay drawing on neuroscience, evolutionary history and education examples, not a report of a new experiment. Its claims about the general effects of AI use should therefore be read as Riley’s argument, not as a settled scientific conclusion.
Why question the “mind as computer” metaphor?
Input, processing and output
A computational account describes thought in three broad stages: information comes in, the brain processes it, and a response comes out. Riley says this model has been productive for computing, but argues it is incomplete as a full account of human nervous systems and behavior.
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Action, feedback and environment
In the alternative framing Riley draws on, cognition belongs to an organism acting in a physical and social environment. Perception and action affect one another: people try things, encounter consequences, adjust and learn. The essay invokes neuroscientist Peter Cisek in support of this action-oriented view, but the underlying scholarly source is not available here to assess independently.
This distinction matters to Riley’s argument because an AI system can produce an answer without reproducing the human process of working through a problem, testing an idea or learning from its consequences. A fluent output is not, by itself, evidence that a learner has developed the understanding needed to judge or use it.
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How could AI change learning?
Riley’s concern is cognitive delegation: using a tool to take over some of the thinking a person might otherwise do. In education, he argues, a student who asks AI for a solution may avoid the effortful practice involved in understanding how to reach it. He also warns that if people rely on delegation more often, the social conditions that support independent reasoning could weaken, making further delegation more attractive.
The essay refers to studies about students and AI, but the available text does not provide enough bibliographic or methodological detail to assess their samples, methods or findings. It therefore does not establish that AI use generally causes cognitive harm, or that every kind of assistance has the same effect. The more limited, practical question is whether a particular use replaces a learning activity the student needs to practise.
Why does Riley focus on schools and social learning?
Learning is not only an individual process. Riley emphasizes imitation, language, shared practices and institutions as ways people pass knowledge between generations. Schools, on this account, do more than deliver information: they create settings for explanation, practice, discussion and correction with teachers and peers.
That is why the essay treats AI in education as more than a question of whether a chatbot can answer a question. The relevant trade-off is whether a tool supports learning alongside human instruction and interaction, or displaces the activities through which learners develop knowledge and judgment.
Riley names ChatGPT and Anthropic’s Claude for Teachers as examples of AI promoted in education. He also cites school and district restrictions, including a claim about Norway and students under 13. The available account does not independently verify the wording, scope or current status of those policies, so they should be understood as examples Riley raises rather than a current guide to school rules.
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What does Riley recommend doing?
Riley presents “cognitive immunization” as a way to keep thinking active rather than surrendering every difficult task to automation. In practice, the essay’s recommendations point to these habits:
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- Try first without AI. Work through a problem or draft an initial answer yourself when the goal is to build the underlying skill.
- Verify AI output. Check claims against reliable material instead of treating a confident answer as proof.
- Discuss and explain. Use critical discussion with teachers or peers to test reasoning and surface misunderstandings.
- Set deliberate limits. Make room for periods or tasks where AI is not used, especially when independent practice is the point.
These are recommendations in Riley’s essay, not a validated intervention with a measured effect size. They can still serve as a practical way to distinguish AI use that assists learning from use that bypasses it.
How should readers interpret the “junk food” comparison?
Riley uses phrases such as “junk food” and “cognitive hot dog” to make his warning vivid. They are metaphors, not clinical categories or quantified comparisons. They convey the author’s view that convenient AI-generated answers may be appealing while offering less of the effort and learning he values; they do not prove that AI has a universal or inevitable effect on cognition.
Riley also discloses that he provided informal advice to Schools Beyond Screens, an organization he says pushed for school restrictions. That context is relevant when weighing the essay’s policy advocacy, alongside its distinction between argument and independently demonstrated evidence.
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The central idea readers can take from the essay is a useful distinction: getting an answer and learning how to reason toward one are not necessarily the same thing. Riley makes a case for preserving practice, feedback, verification and shared learning when AI is available.
The essay, as available, does not independently establish how large or widespread any cognitive effects are, which students or tasks may be most affected, or the current status of the policy examples it mentions. Its title is best read as a caution about unreflective reliance, not a scientific verdict that human minds are categorically unable to handle AI.
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