Cinemorphosis

Tag: artificial-intelligence

  • A New Depression: AI Affected Disorder

    A New Depression: AI Affected Disorder

    Recursive Mimicry

    noun

    Recursive Mimicry names the moment when imitation turns pathological: first the machine parrots human language without understanding, and then the human parrots the machine, mistaking fluent noise for thought. As linguist Emily Bender’s “stochastic parrot” makes clear, large language models do not think, feel, or know—they recombine patterns with impressive confidence and zero comprehension. When we adopt their output as a substitute for our own thinking, we become the parrot of a parrot, performing intelligence several steps removed from intention or experience. Language grows slicker as meaning thins out. Voice becomes ventriloquism. The danger of Recursive Mimicry is not that machines sound human, but that humans begin to sound like machines, surrendering authorship, judgment, and ultimately a sense of self to an echo chamber that has never understood a word it has said.

    AI Affected Disorder

    noun

    A cognitive and existential malaise brought on by prolonged reliance on generative AI as a substitute for original thought, judgment, and voice. AI Affected Disorder emerges when Recursive Mimicry becomes habitual: the individual adopts fluent, machine-generated language that feels productive but lacks intention, understanding, or lived reference. The symptoms are subtle rather than catastrophic—mental fog, diminished authorship, a creeping sense of detachment from one’s own ideas—much like Seasonal Affective Disorder under artificial light. Work continues to get done, sentences behave, and conversations proceed, yet thinking feels outsourced and oddly lifeless. Over time, the afflicted person experiences an erosion of intellectual agency, mistaking smooth output for cognition and ventriloquism for voice, until the self begins to echo patterns it never chose and meanings it never fully understood.

    ***

    It is almost inevitable that, in the AI Age, people will drift toward Recursive Mimicry and mistake it for thinking. The language feels familiar, the cadence reassuring, and—most seductively—it gets things done. Memos are written, essays assembled, meetings survived. Academia and business reward the appearance of cognition, and Recursive Mimicry delivers it cheaply and on demand. But to live inside that mode for too long produces a cognitive malaise not unlike Seasonal Affective Disorder. Just as the body wilts under artificial light and truncated days, the mind grows dull when real thought is replaced by probabilistic ventriloquism. Call it AI Seasonal Disorder: a gray fog in which nothing is exactly wrong, yet nothing feels alive. The metaphors work, the sentences behave, but the inner weather never changes.

    Imagine Disneyland in 1963. You’re seated in the Enchanted Tiki Room, surrounded by animatronic birds chirping about the wonders of modern Audio-Animatronics. The parrots speak flawlessly. They are cheerful, synchronized, and dead behind the eyes. Instead of wonder, you feel a low-grade unease, the urge to escape daylight-starved into the sun. Recursive Mimicry works the same way. At first it amuses. Then it unsettles. Eventually, you realize that a voice has been speaking for you—and it has never known what it was saying.

  • The New Role of the College Instructor: Disruption Interpreter

    The New Role of the College Instructor: Disruption Interpreter

    Disruption Interpreter

    noun

    A Disruption Interpreter is a teacher who does not pretend the AI storm will pass quickly, nor claim to possess a laminated map out of the wreckage. Instead, this instructor helps students read the weather. A Disruption Interpreter names what is happening, explains why it feels destabilizing, and teaches students how to think inside systems that no longer reward certainty or obedience. In the age of AI, this role replaces the old fantasy of professorial authority with something more durable: interpretive judgment under pressure. The Disruption Interpreter does not sell reassurance. He sells literacy in chaos.

    ***

    In his essay “The World Still Hasn’t Made Sense of ChatGPT,” Charlie Warzel describes OpenAI as a “chaos machine,” and the phrase lands because it captures the feeling precisely. These systems are still young, still mutating, constantly retraining themselves to score higher on benchmarks, sound more fluent, and edge out competitors like Gemini. They are not stabilizing forces; they are accelerants. The result is not progress so much as disruption.

    That disruption is palpable on college campuses. Faculty and administrators are not merely unsure about policy; they are unsure about identity. What is a teacher now? What is an exam? What is learning when language itself can be summoned instantly, convincingly, and without understanding? Lurking beneath those questions is a darker one: is the institution itself becoming an endangered species, headed quietly toward white-rhino status?

    Warzel has written that one of AI’s enduring impacts is to make people feel as if they are losing their grip, confronted with what he calls a “paradigm-shifting, society-remaking superintelligence.” That feeling of disorientation is not a side effect; it is the main event. We now live in the Age of Precariousness—a world perpetually waiting for a shoe to drop. Students have no clear sense of what to study when career paths evaporate mid-degree. Older generations watch familiar structures dissolve and struggle to recognize the world they helped build. Even the economy feels suspended between extremes. Will the AI bubble burst and drag markets down with it? Or will it continue inflating the NASDAQ while hollowing out everything beneath it?

    Amid this turbulence, Warzel reminds us of something both obvious and unsettling: technology has never really been about usefulness. It has been about selling transformation. A toothbrush is useful, but it will never dominate markets or colonize minds. Build something, however, that makes professors wonder if they will still have jobs, persuades millions to confide in chatbots instead of therapists, hijacks attention, rearranges spreadsheets, and rewires expectations—and you are no longer making a tool. You are remaking reality.

    In a moment where disruption matters more than solutions, college instructors cannot credibly wear the old costume of authority and claim to know where this all ends. We do not have a clean exit strategy or a proven syllabus that leads safely out of the jungle. We are more like Special Ops units cut off from command, scavenging parts, building and dismantling experimental aircraft while under fire, hoping the thing flies before it catches fire. Students are not passengers on this flight; they are co-builders. This is why the role of the Disruption Interpreter matters. It names the condition honestly. It helps students translate chaos machines into intelligible frameworks without pretending the risks are smaller than they are or the answers more settled than they feel.

    In a college writing class, this shift has immediate consequences. A Disruption Interpreter redesigns the course around friction, transparency, and judgment rather than polished output. Assignments that reward surface-level fluency are replaced with ones that expose thinking: oral defenses, annotated drafts, revision histories, in-class writing. These structures make it difficult to silently outsource cognition to AI without consequence. The instructor also teaches students how AI functions rhetorically, treating large language models not as neutral helpers but as persuasive systems that generate plausible language without understanding. Students must analyze and revise AI-generated prose, learning to spot its evasions, its false confidence, and its tendency to sound authoritative while saying very little.

    Most importantly, evaluation itself is recalibrated. Correctness becomes secondary to agency. Students are graded on the quality of their decisions: what they chose to argue, what they rejected, what they revised, and why. Writing becomes less about producing clean text and more about demonstrating authorship in an age where text is cheap and judgment is scarce. One concrete example is the Decision Rationale Portfolio. Alongside an argumentative essay, students submit a short dossier documenting five deliberate choices: a claim abandoned after research, a source rejected and justified, a paragraph cut or reworked, a moment when they overruled an AI suggestion, and a risk that made the essay less safe but more honest. A mechanically polished essay paired with thin reasoning earns less credit than a rougher piece supported by clear, defensible decisions. The grade reflects discernment, not sheen.

    The Disruption Interpreter does not rescue students from uncertainty; he teaches them how to function inside it. In an era defined by chaos machines, precarious futures, and seductive shortcuts, the task of education is no longer to transmit stable knowledge but to cultivate judgment under unstable conditions. Writing classes, reimagined this way, become training grounds for intellectual agency rather than production lines for compliant prose. AI can assist with language, speed, and simulation, but it cannot supply discernment. That remains stubbornly human. The Disruption Interpreter’s job is to make that fact unavoidable, visible, and finally—inescapable.

  • Gollumification

    Gollumification

    Gollumification

    noun

    Gollumification names the slow moral and cognitive decay that occurs when a person repeatedly chooses convenience over effort and optimization over growth. It is what happens when tools designed to assist quietly replace the very capacities they were meant to strengthen. Like Tolkien’s Gollum, the subject does not collapse all at once; he withers incrementally, outsourcing judgment, agency, and struggle until what remains is a hunched creature guarding shortcuts and muttering justifications. Gollumification is not a story about evil intentions. It is a story about small evasions practiced daily until the self grows thin, brittle, and dependent.

    ***

    Washington Post writer Joanna Slater reports in “Professors Are Turning to This Old-School Method to Stop AI Use on Exams” that some instructors are abandoning written exams in favor of oral ones, forcing students to demonstrate what they actually know without the benefit of algorithmic ventriloquism. At the University of Wyoming, religious studies professor Catherine Hartmann now seats students in her office and questions them directly, Socratic-style, with no digital intermediaries to run interference. Her rationale is blunt and bracing. Using AI on exams, she tells students, is like bringing a forklift to the gym when your goal is to build muscle. “The classroom is a gymnasium,” she explains. “I am your personal trainer. I want you to lift the weights.” Hartmann is not being punitive; she is being realistic about human psychology. Given a way to cheat ourselves out of effort—or out of a meaningful life—we will take it, not because we are corrupt, but because we are wired to conserve energy. That instinct once helped us survive. Now it quietly betrays us. A cheated education becomes a squandered one, and a squandered life does not merely stagnate; it decays. This is how Gollumification begins: not with villainy, but with avoidance.

    I agree entirely with Hartmann’s impulse, even if my method would differ. I would require students to make a fifteen-minute YouTube video in which they deliver their argument as a formal speech. I know from experience that translating a written argument into an oral one exposes every hollow sentence and every borrowed idea. The mind has nowhere to hide when it must speak coherently, in sequence, under the pressure of time and presence. Oral essays force students to metabolize their thinking instead of laundering it through a machine. They are a way of banning forklifts from the gym—not out of nostalgia, but out of respect for the human organism. If education is meant to strengthen rather than simulate intelligence, then forcing students to lift their own cognitive weight is not cruelty. It is preventive medicine against the slow, tragic, and all-too-modern disease of Gollumification.

  • Hyper-Efficiency Intoxication Will Change Higher Learning Forever

    Hyper-Efficiency Intoxication Will Change Higher Learning Forever

    Hyper-Efficiency Intoxication

    noun

    The dopamine-laced rush that occurs when AI collapses hours of cognitive labor into seconds, training the brain to mistake speed for intelligence and output for understanding. Hyper-Efficiency Intoxication sets in when the immediate relief of reclaimed time—skipped readings, instant summaries, frictionless drafts—feels so rewarding that slow thinking begins to register as needless suffering. What hooks the user is not insight but velocity: the sense of winning back life from effort itself. Over time, this chemical high reshapes judgment, making sustained attention feel punitive, depth feel inefficient, and authorship feel optional. Under its influence, students do not stop working; they subtly downgrade their role—from thinker to coordinator, from writer to project manager—until thinking itself fades into oversight. Hyper-Efficiency Intoxication does not announce itself as decline; it arrives disguised as optimization, quietly hollowing out the very capacities education once existed to build.

    ***

    No sane college instructor assigns an essay anymore under the illusion that you’ll heroically wrestle with ideas while AI politely waits in the hallway. We all know what happens: a prompt goes in, a glossy corpse comes out. The charade has become so blatant that even professors who once treated AI like a passing fad are now rubbing their eyes and admitting the obvious. Hua Hsu names the moment plainly in his essay “What Happens After A.I. Destroys College Writing?”: the traditional take-home essay is circling the drain, and higher education is being forced to explain—perhaps for the first time in decades—what it’s actually for.

    The problem isn’t that students are morally bankrupt. It’s that they’re brutally rational. The real difference between “doing the assignment” and “using AI” isn’t ethics; it’s time. Time is the most honest currency in your life. Ten hours grinding through a biography means ten hours you’re not at a party, a game, a date, or a job. Ten minutes with an AI summary buys you your evening back. Faced with that math, almost everyone chooses the shortcut—not because they’re dishonest, but because they live in the real world. This isn’t cheating; it’s survival economics.

    Then there’s the arms race. Your classmates are using AI. All of them. Competing against them without AI is like entering a bodybuilding contest while everyone else is juiced to the gills and you’re proudly “all natural.” You won’t be virtuous; you’ll be humiliated. Fairness collapses the moment one side upgrades, and pretending otherwise is naïve at best.

    AI also hooks you. Hsu admits that after a few uses of ChatGPT, he felt the “intoxication of hyper-efficiency.” That’s not a metaphor—it’s a chemical event. When a machine collapses hours of effort into seconds, your brain lights up like it just won a small lottery. The rush isn’t insight; it’s velocity. And once you’ve tasted that speed, slowness starts to feel like punishment.

    Writing instructors, finally awake, are adapting. Take-home essays are being replaced by in-class writing, blue books, and passage identification exams—formats designed to drag thinking back into the room and away from the cloud. These methods reward students who’ve spent years reading and writing the hard way. But for students who entered high school in 2022 or later—students raised on AI scaffolding—this shift feels like being dropped into deep water without a life vest. Many respond rationally: they avoid instructors who demand in-class thinking.

    Over time, something subtle happens. You don’t stop working; you change roles. You become, in Hsu’s phrase, a project manager—someone who coordinates machines rather than generating ideas. You collaborate, prompt, tweak, and oversee. And at some point, no one—not you, not your professor—can say precisely when the thinking stopped being yours. There is no clean border crossing, only a gradual fade.

    Institutions are paralyzed by this reality. Do they accept the transformation and train students to be elite project managers of knowledge? Or do they try to resurrect an older model of literacy, pretending that time, incentives, and technology haven’t changed? Neither option is comfortable, and both expose how fragile the old justifications for college have become.

    From the educator’s chair, the nightmare scenario is obvious. If AI can train competent project managers for coding, nursing, physical therapy, or business, why not skip college altogether? Why not certify skills directly? Why not let employers handle training in-house? It would be faster, cheaper, and brutally efficient.

    And efficiency always wins. When speed, convenience, and cost savings line up, they don’t politely coexist with tradition—they bulldoze it. AI doesn’t argue with the old vision of education. It replaces it. The question is no longer whether college will change, but whether it can explain why learning should be slower, harder, and less efficient than the machines insist it needs to be.

  • Humanification

    Humanification

    It is not my job to indoctrinate you into a political party, a philosophical sect, or a religious creed. I am not here to recruit. But it is my job to indoctrinate you about something—namely, how to think, why thinking matters, and what happens when you decide it doesn’t. I have an obligation to give you a language for understanding critical thinking and the dangers of surrendering it, a framework for recognizing the difference between a meaningful life and a comfortable one, and the warning signs that appear when convenience, short-term gratification, and ego begin quietly eating away at the soul. Some of you believe life is a high-stakes struggle over who you become. Others suspect the stakes are lower. A few—regrettably—flirt with nihilism and conclude there are no stakes at all. But whether you dramatize it or dismiss it, the “battle of the soul” is unavoidable. I teach it because I am not a vocational trainer turning you into a product. I am a teacher in the full, unfashionable sense of the word—even if many would prefer I weren’t.

    This battle became impossible to ignore when I returned to the classroom after the pandemic and met ChatGPT. On one side stood Ozempification: the seductive shortcut. It promises results without struggle, achievement without formation, output without growth. Why wrestle with ideas when a machine can spit out something passable in seconds? It’s academic fast food—calorie-dense, spiritually empty, and aggressively marketed. Excellence becomes optional. Effort becomes suspicious. Netflix beckons. On the other side stood Humanification: the old, brutal path that Frederick Douglass knew by heart. Literacy as liberation. Difficulty as transformation. Meaning earned the hard way. Cal Newport calls it deep work. Jordan Peele gives it a name—the escape from the Sunken Place. Humanification doesn’t chase comfort; it chases depth. The reward isn’t ease. It’s becoming someone.

    Tyler Austin Harper’s essay “ChatGPT Doesn’t Have to Ruin College” captures this split perfectly. Wandering Haverford’s manicured campus, he encounters English majors who treat ChatGPT not as a convenience but as a moral hazard. They recoil from it. “I prefer not to,” Bartleby-style. Their refusal is not naïveté; it’s identity. Writing, for them, is not a means to a credential but an act of fidelity—to language, to craft, to selfhood. But Harper doesn’t let this romanticism off the hook. He reminds us, sharply, that honor and curiosity are not evenly distributed virtues. They are nurtured—or crushed—by circumstance.

    That line stopped me cold. Was I guilty of preaching Humanification without acknowledging its price tag? Douglass pursued literacy under threat of death, but he is a hero precisely because he is rare. We cannot build an educational system that assumes heroic resistance as the norm. Especially not when the very architects of our digital dystopia send their own children to screen-free Waldorf schools, where cursive handwriting and root vegetables are treated like endangered species. The tech elite protect their children from the technologies they profit from. Everyone else gets dopamine.

    I often tell students this uncomfortable truth: it is easier to be an intellectual if you are rich. Wealth buys time, safety, and the freedom to fail beautifully. You can disappear to a cabin, read Dostoevsky, learn Schubert, and return enlightened. Most students don’t have that option. Harper is right—institutions like Haverford make Humanification easier. Small classes. Ample support. Unhurried faculty. But most students live elsewhere. My wife teaches in public schools where buildings leak, teachers sleep in cars, and safety is not guaranteed. Asking students in survival mode to honor an abstract code of intellectual purity borders on insult.

    Maslow understood this long ago. Self-actualization comes after food, shelter, and security. It’s hard to care about literary integrity when you’re exhausted, underpaid, and anxious. Which is why the Ozempic analogy matters. Just as expensive GLP-1 drugs make discipline easier for some bodies, elite educational environments make intellectual virtue easier for some minds. Character still matters—but it is never the whole story.

    Harper complicates things further by comparing Haverford to Stanford. At Stanford, honor codes collapse under scale; proctoring becomes necessary. Intimacy, not virtue alone, sustains integrity. Haverford begins to look less like a model and more like a museum—beautiful, instructive, and increasingly inaccessible. The humanities survive there behind velvet ropes.

    I teach at a community college. My students are training for nursing, engineering, business. They work multiple jobs. They sleep six hours if they’re lucky. They don’t have the luxury to marinate in ideas. Humanification gets respectful nods in class discussions, but Ozempification pays the rent. And pretending otherwise helps no one.

    This is the reckoning. We cannot shame students for using AI when AI is triage, not indulgence. But we also cannot pretend that a life optimized for convenience leads anywhere worth going. The challenge ahead is not to canonize the Humanified or condemn the Ozempified. It is to build an educational culture where aspiration is not a luxury good—where depth is possible without privilege, and where using AI does not require selling your soul for efficiency.

    That is the real battle. And it’s one we can’t afford to fight dishonestly.

  • Good-Enoughers

    Good-Enoughers

    In the fall of 2023, I was standing in front of thirty bleary-eyed college students, halfway through a lesson on how to spot a ChatGPT essay—mainly by its fondness for lifeless phrases that sound like they were scraped from a malfunctioning inspirational calendar. That’s when a business major raised his hand with the calm confidence of someone revealing a trade secret and said, “I can guarantee you everyone on this campus uses ChatGPT. We don’t submit it raw. We tweak a few sentences, paraphrase a little, and boom—no one can tell.”

    Before I could respond, a computer science student piled on. “It’s not just for essays,” he said. “It’s my life coach. I ask it about everything—career moves, crypto, even dating.” Dating advice. From ChatGPT. Somewhere, right now, a romance is unfolding on AI-generated pillow talk and a bullet-pointed list of conversation starters.

    That was the moment I realized I was staring at the biggest educational rupture of my thirty-year career. Tools like ChatGPT have three superpowers: obscene convenience, instant availability, and blistering speed. In a world where time is money and most writing does not need to summon the ghost of James Baldwin, AI is already good enough for about 95 percent of professional communication. And there it is—the phrase that should make educators break out in hives: good enough.

    “Good enough” is convenience’s love language. Imagine waking up groggy and choosing between two breakfasts. Option one is a premade smoothie: beige, foamy, nutritionally ambiguous, and available immediately. Option two is a transcendent, handcrafted masterpiece—organic fruit, thick Greek yogurt, chia seeds, almond milk—but to get it you must battle orb spiders in your backyard, dodge your neighbor’s possessed Belgian dachshund, and then spend quality time scrubbing a Vitamix before fighting traffic. Which one do most people choose?

    Exactly. The premade sludge. Because who has time for spider diplomacy and blender maintenance before a commute? Convenience wins, quality loses, and you console yourself with the time you saved. Eventually, you stop missing the better option altogether. That slow adjustment—lowering your standards until mediocrity feels normal—is attenuation.

    Now swap smoothies for writing. Writing is far harder than breakfast, and millions of people are quietly recalibrating their expectations. Why labor over sentences when the world will happily accept algorithmic mush? Polished prose is becoming the artisanal smoothie of communication: admirable, expensive, and increasingly optional. AI delivers something passable in seconds, and passable is the new benchmark.

    For educators, this is not a quirky inconvenience. It’s a five-alarm fire. I did not enter this profession to train students to become connoisseurs of adequacy. I wanted to cultivate thinkers, stylists, arguers—people whose sentences had backbone and intent. Instead, I find myself in a dystopia where “good enough” is the new gospel and I’m preaching craft like a monk selling calligraphy at a tech startup demo day.

    In medicine, the Hippocratic Oath is “Do no harm.” In teaching, the unspoken oath is blunter and less forgiving: never train your students to become Good-Enoughers—those half-awake intellectual zombies who mistake adequacy for achievement and turn mediocrity into a permanent way of life.

    Whatever role AI plays in my classroom, one line is nonnegotiable. The moment I use it to help students settle for less—to speed them toward adequacy instead of depth—I’m no longer teaching. I’m committing educational malpractice.

  • Cognitive Thinning and Cognitve Load-Bearing Capacity

    Cognitive Thinning and Cognitve Load-Bearing Capacity

    In his bracing essay “Colleges Are Preparing to Self-Lobotomize,” Michael Clune accuses higher education of handling AI with the institutional equivalent of a drunk chainsaw. The subtitle gives away the indictment: “The skills that students will need in an age of automation are precisely those that are eroding by inserting AI into the educational process.” Colleges, Clune argues, spent the first three years of generative AI staring at the floor. Now they’re overcorrecting—embedding AI everywhere as if saturation were the same thing as competence. It isn’t. It’s panic dressed up as innovation.

    The prevailing fantasy is that if AI is everywhere, mastery will seep into students by osmosis. But the opposite is happening. Colleges are training students to rely on frictionless services while quietly abandoning the capacities that make AI usable in any serious way: judgment, learning agility, and flexible analysis. The tools are getting smarter. The users are getting thinner.

    That thinning has a name. Cognitive Thinning is the gradual erosion of critical thinking that occurs when sustained mental effort is replaced by convenience. It sets in when institutions assume that constant exposure to powerful tools will produce competence, even as they dismantle the practices that build it. As AI grows more capable, students are asked to do less thinking, tolerate less uncertainty, and carry less intellectual weight. The result is a widening imbalance: smarter systems paired with slimmer minds—efficient, polished, and increasingly unable to move beyond the surface of what machines provide.

    Clune wants students to avoid this fate, but he faces a rhetorical problem. He keeps insisting on abstractions—critical thinking, intellectual flexibility, judgment—in a culture trained to distrust anything abstract. Telling a screen-saturated society to imagine thinking outside screens is like telling a fish to imagine life outside water. The first task isn’t instruction. It’s translation.

    The fish analogy holds. A fish is aquatic; water isn’t a preference—it’s a prison. A young person raised entirely on screens, prompts, and optimization tools treats that ecosystem as reality itself. Like the fish, they know only one environment. We can name this condition precisely. They are cognitively outsourced, trained to delegate thought as if it were healthy. They are algovorous, endlessly stimulated by systems that quietly erode attention and resilience. They are digitally obligate, unable to function without mediation. By definition, these orientations crowd out critical thinking. They produce people who function smoothly inside digital systems and falter everywhere else.

    Drop such a person into a college that recklessly embeds AI into every course in the name of being “future-proof,” and you don’t produce adaptability—you produce fragility. In some fields, this fragility is fatal. Clune cites a telling statistic: history majors now have roughly half the unemployment rate of recent computer science graduates. The implication is blunt. Liberal education builds range. Narrow technical training builds specialists who snap when the environment shifts. As the New York Times put it in a headline Clune references: “Goodbye, $165,000 Tech Jobs. Student Coders Seek Work at Chipotle.” AI is replacing coders. Life inside a tiny digital ecosystem does not prepare you for a world that mutates.

    Is AI the cause of this dysfunction? No. The damage predates ChatGPT. I use AI constantly—and enjoy it. It sharpens my curiosity. It helps me test ideas. It makes me smarter because I am not trapped inside it. I have a life beyond screens. I’ve read thousands of books. I can zoom in and out—trees and forest—without panic. I have language for my inner life, which means I can catch myself when I become maudlin, entropic, dissolute, misanthropic, lugubrious, or vainglorious. I have history, philosophy, and religion as reference points. We call this bundle “critical thinking,” but what it really amounts to is being fully human.

    Someone who has outsourced thought and imagination since childhood cannot suddenly use AI well. They aren’t liberated. They’re brittle—dependent, narrow, and easily replaced.

    Because I’m a lifelong weightlifter, let me be concrete. AI is a massive, state-of-the-art gym: barbells, dumbbells, Smith machines, hack squats, leg presses, lat pulldowns, pec decks, cable rows—the works. Now imagine you’ve never trained. You’re twenty-eight, inspired by Instagram physiques, vaguely determined to “get in shape.” You walk into this cathedral of iron with no plan, no understanding of recovery, nutrition, progressive overload, or discipline. You’re surrounded by equipment—and completely lost. Within a month, you quit. You join the annual migration of January optimists who vanish by February, leaving the gym to the regulars.

    AI is that gym. It doesn’t eject users out of malice. It ejects them because it demands capacities they never built. Some people learn isolated tricks—prompting here, automating there—but only the way someone learns to push a toaster lever. When these tasks define a person, the result is a Non Player Character: reactive, scripted, interchangeable.

    Students already understand what an NPC is. That’s why they fear becoming one.

    If colleges embed AI everywhere without building the human capacities required to use it, they aren’t educating thinkers. They’re manufacturing NPCs—and they deserve to be called out for it.

    Don’t wait for your institution to save you. Approach education the way you’d approach a gym. Learn how bodies actually grow before touching the weights. Know the muscle groups. Respect recovery. Understand volume, exhaustion, and nutrition. Do the homework so the gym doesn’t spit you out.

    The same rule applies to AI. To use it well, you need a specific kind of mental strength: Cognitive Load-Bearing Capacity. This is the ability to use AI without surrendering your thinking. You can see it in ordinary behaviors: reading before summarizing, drafting before prompting, distrusting answers that sound too smooth, and revising because an idea is weak—not because a machine suggested a synonym. It’s the capacity to sit with confusion, compare sources, and arrive at judgment rather than outsource it.

    This capacity isn’t innate, and it isn’t fast. It’s built through resistance: sustained reading, outlining by hand, struggling with unfamiliar ideas, revising after failure. Students with cognitive load-bearing capacity use AI to pressure-test their thinking. Students without it use AI to replace thinking. One group grows stronger and more adaptable. The other becomes dependent—and replaceable.

    Think of AI like a piano. You can sit down and bang out notes immediately, but you won’t produce music. Beautiful playing requires trained fingers, disciplined ears, and years of wrong notes. AI works the same way. Without cognitive load-bearing capacity, you get noise—technically correct, emotionally dead. With it, the tool becomes expressive. The difference isn’t the instrument. It’s the musician.

    If you want to build this capacity, forget grand reforms. Choose consistent resistance. Read an hour a day with no tabs open. Write before prompting. Ask AI to attack your argument instead of finishing it. Keep a notebook where you explain ideas in your own words, badly at first. Sit with difficulty instead of dodging it. These habits feel inefficient—and that’s the point. They’re the mental equivalent of scales and drills. Over time, they give you the strength to use powerful tools without being used by them.

  • The Hamster Wheel of Optimization

    The Hamster Wheel of Optimization

    In “AI Has Broken High School and College,” Damon Beres stages a conversation between Ian Bogost and Lila Shroff that lands like a diagnosis no one wants but everyone recognizes. Beres opens with a blunt observation: today’s high school seniors are being told—implicitly and explicitly—that their future success rests on their fluency with chatbots. School is no longer primarily about learning. It has become a free-for-all, and teachers are watching from the sidelines with a whistle that no longer commands attention.

    Bogost argues that educators have responded by sprinting toward one of two unhelpful extremes: panic or complacency. Neither posture grapples with reality. There is no universal AI policy, and students are not using ChatGPT only to finish essays. They are using it for everything. We have already entered a state of AI normalization, where reliance is no longer an exception but a default. To explain the danger, Bogost borrows a concept from software engineering: technical debt—the seductive habit of choosing short-term convenience while quietly accruing long-term catastrophe. You don’t fix the system; you keep postponing the reckoning. It’s like living on steak, martinis, and banana splits while assuring yourself you’ll start jogging next year.

    Higher education, Bogost suggests, has compounded the problem by accumulating what might be called pedagogical debt. Colleges never solved the hard problems: smaller class sizes, meaningful writing assignments, sustained feedback, practical skill-building, or genuine pipelines between students and employers. Instead, they slapped bandages over these failures and labeled them “innovation.” AI didn’t create these weaknesses; it simply makes it easier to ignore them. The debt keeps compounding, and the interest is brutal.

    Bogost introduces a third and more existential liability: the erosion of sacred time. Some schools still teach this—places where students paint all day, rebuild neighborhoods, or rescue animals, learning that a meaningful life requires attention, patience, and presence. Sacred time resists the modern impulse to finish everything as fast as possible so you can move on to the next task. AI dependence belongs to a broader pathology: the hamster wheel of deadlines, productivity metrics, and permanent distraction. In that world, AI is not liberation. It is a turbocharger for a life without meaning.

    AI also accelerates another corrosive force: cynicism. Students tell Bogost that in the real world, their bosses don’t care how work gets done—only that it gets done quickly and efficiently. Bogost admits they are not wrong. They are accurately describing a society that prizes output over meaning and speed over reflection. Sacred time loses every time it competes with the rat race.

    The argument, then, is not a moral panic about whether to use AI. The real question is what kind of culture is doing the using. In a system already bloated with technical and pedagogical debt, AI does not correct course—it traps us in what I call the Hamster Wheel of Optimization: A cultural condition in which speed, efficiency, and constant output are mistaken for progress, trapping individuals and institutions in endless motion without direction or meaning. On the Hamster Wheel of Optimization, short-term convenience is endlessly prioritized while long-term costs—intellectual, moral, and human—quietly accumulate. Learning becomes task completion, education becomes workflow management, and sacred time is crowded out by deadlines, metrics, and permanent distraction. AI does not create this condition; it accelerates it, serving as a turbocharger for a system already addicted to doing more, faster, and cheaper, even as depth, reflection, and purpose steadily erode.

  • Pedagogical Liminality

    Pedagogical Liminality

    Lila Shroff argues that education has entered its Wild West phase in her essay “The AI Takeover of Education Is Just Getting Started,” and she’s right in the way that makes administrators nervous and instructors quietly exhausted. Most of you are not stumbling innocents. You are veterans of four full years of AI high school. You no longer engage in crude copy-and-paste plagiarism. That’s antique behavior. You’ve learned to stitch together outputs from multiple models, then instruct the chatbot to scuff the prose with a few grammatical imperfections so it smells faintly human and slips past detection software. This is not cheating as shortcut; it is cheating as workflow optimization.

    Meanwhile, many high school teachers congratulate themselves for assigning Shakespeare, Keats, and Dostoevsky while willfully ignoring the obvious. Students are using AI constantly—for summaries, study guides, feedback, and comprehension scaffolding. AI is CliffsNotes on growth hormones, and pretending otherwise is an exercise in institutional denial.

    Educators, of course, are not standing outside the saloon wagging a finger. We are inside, ordering fizzy drinks. Shroff notes that teachers now use AI to design assignments, align curriculum to standards, grade against rubrics, and complete the paperwork that keeps schools legally hydrated. Nearly a third of K–12 teachers reported weekly AI use last year, and that number has only climbed as profession-specific tools like MagicSchool AI churn out rubrics, worksheets, and report-card comments on demand. The teacher as craftsman is quietly mutating into the teacher as editor.

    AI tightens its grip most aggressively where schools are already bleeding resources. In districts short on tutors and counselors, AI steps in as a substitute for services that were never funded in the first place. This is not reform; it is triage. And once institutions develop a taste for saving money by not hiring tutors and counselors, it is naïve to think teaching positions will remain sacred. Cost-cutting rarely stops at the first ethical boundary it crosses.

    That is why this moment feels like the Wild West. There is no shared map. Some schools welcome AI like a messiah. Others quarantine it like a contagious disease. Many simply shrug and admit they are baffled. Policy is reactive, inconsistent, and often written by people who do not understand the technology well enough to regulate it intelligently.

    I see the consequences every week in my college classroom. I read plenty of AI slop—essays with flawless grammar and no pulse, paragraphs that gesture toward ideas they never quite touch. Some students have checked out entirely, outsourcing not just sentences but thinking itself. And yet AI is also an undeniable equalizer. Students emerging from underfunded schools with sixth-grade literacy levels are now submitting essays with clean syntax and logical structure. They use AI to outline arguments, test thesis ideas, and stabilize skills they were never taught. The tool giveth, and the tool holloweth out.

    People like to invoke “too big to fail,” but the analogy doesn’t hold. We don’t know which AI—ChatGPT, Gemini, Claude, or some yet-unseen contender—will dominate. What we do know is that AI is already embedded in education, culture, and the economy. There is no reversing this process. The toothpaste is not going back in the tube, no matter how sternly we lecture it.

    So understand this about me and my fellow instructors: we don’t know what we’re doing. Our roles are unsettled. Our identities are unstable. We are feeling our way through a dark cave without a map and without guarantees. There may be light ahead, or there may not.

    The only sane posture is humility—paired with curiosity, caution, and a sober gratitude that even a force this disruptive may yield benefits we are not yet wise enough to recognize. The name for this condition is Pedagogical Liminality: the in-between state educators now inhabit as teaching crosses from the pre-AI world into an uncharted machine age. Old rules no longer hold. New ones have not yet solidified. The ground keeps shifting under our feet.

    In this state, arrogance is dangerous. Despair is paralyzing. Certainty is counterfeit. Pedagogical Liminality is not failure; it is the honest middle passage—awkward, uncertain, and unavoidable—before a new educational order can be named.

  • Mediocrity Amplification Effect

    Mediocrity Amplification Effect

    As you watch your classmates use AI for every corner of their lives—summarizing, annotating, drafting, thinking—you may feel a specific kind of demoralization set in. A sinking question forms: What does anything matter anymore? Is life now just a game of cheating the system efficiently? Is this where all the breathless hype about “the future” has landed us—an economy of shortcuts and plausible fraud?

    High school student Ashanty Rosario feels this acutely. She gives voice to the heartbreak in her essay “I’m a High Schooler. AI Is Demolishing My Education,” a lament not about laziness but about loss. She doesn’t want to cheat. But the tools are everywhere, glowing like emergency exit signs in a burning building. Some temptations, she understands, are structural.

    Her Exhibit A is devastating. A classmate uses ChatGPT to annotate Narrative of the Life of Frederick Douglass. These annotations—supposed proof of engaged reading—are nothing more than copy-paste edu-lard: high in calories, low in nutrition, and utterly empty of struggle. The form is there. The thinking is not.

    Rosario’s frustration echoes a moment from my own classroom. On the last day of the semester, one of my brightest students sat in my office and casually admitted that he uses ChatGPT to summarize all his reading. His father is a professor. He wakes up at five for soccer practice. He takes business calculus for fun. This is not a slacker. This is a time-management pragmatist surviving the twenty-first century. He reads the summaries, synthesizes the ideas, and writes excellent essays. Of course I wish he spent slow hours wrestling with books—but he is not living in 1954. He is living in a culture where time is scarce and AI functions as an oxygen mask.

    My daughters and their classmates face the same dilemma with Macbeth. Shakespeare’s language might as well be Martian for a generation raised on TikTok compression and dopamine drip-feeds. They watch film adaptations. They use AI to decode plot points so they can answer study questions without sounding like they slept through the Renaissance. Purists howl that this is cheating. But as a writing instructor, I suspect teachers benefit from students who at least know what’s happening—even if the knowledge arrives via chatbot. Expecting a fifteen-year-old to read Macbeth cold is like assigning tensor calculus to a preschooler. They haven’t done their priors. So AI becomes a prosthetic. A flotation device. A translation machine dropped into classrooms years overdue.

    Blaming AI for educational decline is tempting—but it’s also lazy. We live in a society where reading is a luxury good and the leisure class quietly guards the gates.

    In the 1970s, I graduated from a public high school with literacy skills so thin you could read the room through them. I took remedial English my freshman year of college. If I were a student today, dropped into 2025 with those same deficits, I would absolutely lean on AI just to keep my head above water. The difference now is scale. Today’s students aren’t just supplementing—they’re optimizing. They tell me this openly. Over ninety percent of my students use AI because their skills don’t match the workload and because everyone else is doing it. This isn’t a moral collapse. It’s an arms race of survival.

    Still, Rosario is right about the aftermath. “AI has softened the consequences of procrastination,” she writes, “and led many students to avoid doing any work at all. There is little intensity anymore.” When thinking becomes optional, students drift into algorithmic sleepwalking. They outsource cognition until they resemble NPCs in a glitching video game—avatars performing the motions of thought without the effort. My colleagues and I see it every semester: the fade-out, the disengagement, the slow zombification.

    Colleges are scrambling. Should we police AI with plagiarism detectors? Ban laptops? Force students to write essays in blue books under watchful eyes like parolees in a literary halfway house? Should we pretend the flood can be held back with a beach towel?

    Reading Rosario’s complaint about “cookie-cutter AI arguments,” I thought of my lone visit to Applebee’s in the early 2000s. The menu photos promised ambrosia. The food tasted like something engineered in a lab to be technically edible yet spiritually vacant. Applebee’s was AI before AI—an assembly line of flavorless simulacra. Humanity has always gravitated toward the easy, the prepackaged, the frictionless. AI didn’t invent mediocrity. It just handed it a megaphone.

    Rosario is no Applebee’s soul. She’s Michelin-level in a world eager to microwave Hot Pockets. Of course her heart sinks when classmates settle for fast-food literacy. I want to tell her this: had she been in high school in the 1970s, she would have witnessed the same hunger for shortcuts. The tools would be clumsier. The prose less polished. But the gravitational pull would be identical. The urge to bypass difficulty is not technological—it’s ancestral.

    What’s new is scale and speed. In the AI age, that ancient hunger is supercharged by what I call the Mediocrity Amplification Effect: the phenomenon by which AI accelerates and magnifies our long-standing temptation to dilute effort and settle for the minimally sufficient. Under this effect, tools meant to assist learning become accelerants of shortcut culture. Procrastination carries fewer consequences. Intensity drains away. Thinking becomes optional.

    This is not a new moral failure. It is an old one, industrialized—private compromise transformed into public default, mediocrity polished, normalized, and broadcast at scale. AI doesn’t make us lazy. It makes laziness louder.