Cinemorphosis

Category: technology

  • When College Students Fall Into the Trap of Adaptive Fragility

    When College Students Fall Into the Trap of Adaptive Fragility

    Adaptive Fragility
    noun

    This is the condition in which students trained inside fast-moving technical ecosystems emerge looking competent but built on brittle frames. Their skills gleam on the surface—current languages, platforms, workflows—but beneath that polish lies structural unpreparedness for volatility. Adaptive fragility sets in when education prizes tool mastery over judgment, interpretation, and learning agility. Graduates function smoothly until the environment shifts, at which point their expertise depreciates overnight. Liberal education expands adaptive range. Narrow technical training breeds specialists who shatter when conditions mutate.

    Authority figures often shepherd students straight into this trap. They preach the gospel of STEM as if it were both economic salvation and moral virtue, a modern scientific priesthood with guaranteed rewards. What they rarely mention is the cost of over-specialization. The STEM acolyte can emerge so tightly focused that they resemble a racehorse in blinders—powerful, disciplined, and nearly helpless when the track disappears. When the labor market swerves and demands a pivot, these graduates discover they were never trained to see the larger terrain, much less navigate it.

  • A College Instructor’s Biggest Challenge Is Closing the Abstraction Resistance Gap

    A College Instructor’s Biggest Challenge Is Closing the Abstraction Resistance Gap

    Abstraction Resistance Gap
    noun

    There is a widening cultural mismatch between the need for abstract intellectual capacities—critical thinking, judgment, conceptual flexibility—and a population trained to expect concrete, instant, screen-mediated results. The abstraction resistance gap opens when societies raised on prompts and outputs lose the ability to value thought that cannot be immediately displayed, optimized, or monetized. Ideas that require time, silence, and struggle arrive speaking a language the audience no longer understands. Teaching fails not because the ideas are wrong, but because they require translation into a cognitive dialect that has gone extinct.

    If you are a college writing instructor facing students who spent four years of high school outsourcing their homework to AI, you are standing on the front lines of this gap. Your task is not merely to assign essays. It is to supply a framework for critical thinking, a vocabulary for understanding that framework, and—hardest of all—a reason to choose it over frictionless delegation. You are asking students to resist the gravitational pull of machines and to decline the comfortable role of Non Player Character.

    Your enemy is not ignorance. It is time. No one becomes a critical thinker overnight. It takes years of sustained reading and what Cal Newport calls deep work: long stretches of attention without dopamine rewards. When you pause long enough to consider the difficulty of this task—and the odds stacked against it—it can drain the optimism from even the most committed instructor. You are not teaching a skill. You are trying to resurrect a way of thinking in a culture that has already moved on.

  • The Doomed Defiance of the Promethean Delusion

    The Doomed Defiance of the Promethean Delusion

    Promethean Delusion
    noun

    The Promethean impulse—named for the mythic thief who stole fire from the gods—now animates the fantasy that technological optimization can transform humans into frictionless, quasi-divine beings without cost or consequence. In this delusion, machines are no longer tools that extend human capacity; they are ladders to transcendence. Power is mistaken for wisdom. Speed for meaning. Anything that resists optimization is treated as a design flaw waiting to be patched.

    Limits become intolerable. Slowness is framed as inefficiency. Mortality is treated as a bug. Kairos—the lived, sacred time through which meaning actually forms—is dismissed as waste, an obstacle to throughput. What emerges is not liberation but derangement: expanding capability paired with a shrinking sense of what a human life is for.

    So what does it mean to be human? The answer depends on which story you choose to inhabit. The Promethean tech evangelist sees the human being as an unfinished machine—upgradeable, indefinitely extendable, and perhaps immortal if the right knobs are turned. All problems reduce to engineering: tighten this screw, loosen that one, eliminate friction, repeat.

    The Christian story is harsher and more honest. It begins with brokenness, not optimization—with mortal creatures who cannot save themselves and who long for reconciliation with their Maker. To reject this account is to rebel, to attempt demigodhood by force of will and code. As John Moriarty observed, “The story of Christianity is the story of humanity’s rebellion against God.” The dream of becoming frictionless and divine is not progress; it is a doomed defiance. It does not end in transcendence but in collapse—moral, spiritual, and eventually civilizational.

  • Kairos vs. Chronos: The Battle for Human Time

    Kairos vs. Chronos: The Battle for Human Time

    Kairos names a rare kind of time—the moment when life thickens and becomes meaningful. It is the time of attention and presence, when learning actually happens, when a sentence suddenly makes sense, when an idea lands with the force of revelation. Kairos is not counted; it is entered. You don’t measure it. You feel it. It is the time of epiphany, imagination, and inward transformation.

    Chronos, by contrast, is time broken into units and put to work. It is the time of clocks, calendars, deadlines, and dashboards. Chronos asks how long something took, how efficiently it was completed, and whether it can be done faster next time. It governs offices, classrooms, and productivity apps. Chronos is indispensable—but it is also merciless.

    Kairos belongs to myth, enchantment, and meaning. Chronos belongs to business, logistics, and quarterly reports. We need both. But when life tilts too far toward chronos, we find ourselves strapped to the Hamster Wheel of Optimization, mistaking motion for progress. The cost is steep. We don’t just lose kairos—the sacred time of depth and presence. We lose vitality, interiority, and eventually our sense of being fully alive.

    This tension animates the work of Paul Kingsnorth, particularly in Against the Machine: On the Unmaking of Humanity. Kingsnorth’s project is not nostalgia but boundary-setting. He argues that preserving our humanity requires limits—lines we refuse to cross. The dream of using machines to become demigods is not liberation; it is derangement. The fantasy of the uberhuman, endlessly optimized and frictionless, is a story told by technologists whose ambition for profit and control is vast, but whose understanding of human nature is alarmingly thin.

    Machines can extend our reach. They cannot supply meaning. That still requires kairos—time that cannot be optimized without being destroyed.

  • A Human Lexicon for Education in the Machine Age

    A Human Lexicon for Education in the Machine Age

    Abstraction Resistance Gap
    noun

    The cultural mismatch between the necessity of abstract intellectual capacities—critical thinking, judgment, conceptual flexibility—and a population habituated to concrete, immediate, screen-mediated results. The abstraction resistance gap emerges when societies trained on prompts, outputs, and instant utility struggle to grasp or value modes of thought that cannot be quickly demonstrated or monetized. In this gap, teaching fails not because ideas are wrong, but because they require translation into a cognitive language the audience no longer speaks.

    Adaptive Fragility
    noun

    The condition in which individuals trained narrowly within fast-changing technical ecosystems emerge superficially skilled but structurally unprepared for volatility. Adaptive fragility arises when education prioritizes tool-specific competence—coding languages, platforms, workflows—over transferable capacities such as judgment, interpretation, and learning agility. In this state, graduates function efficiently until conditions shift, at which point their skills depreciate rapidly. Liberal education builds adaptive range; purely technical training produces specialists who break when the environment mutates.

    Anchored Cognition
    noun

    The cultivated condition of using powerful tools without becoming absorbed by them. Anchored cognition is not innate; it is achieved through long exposure to demanding texts, sustained attention, and the slow accumulation of intellectual reference points—history, philosophy, literature, and religion—that give thought depth and perspective. It develops by reading widely, thinking without prompts, and learning to name one’s inner states with precision, so emotion and impulse can be examined rather than obeyed.

    A person with anchored cognition can zoom in and out—trees and forest—without panic. AI becomes a partner for testing ideas, sharpening curiosity, and exploring possibilities, not a replacement for judgment or imagination. The anchor is the self: a mind trained to stand on its own before it delegates, grounded enough to use machines without surrendering to them.

    Algovorous
    adjective

    Characterized by habitual consumption of algorithmically curated stimuli that prioritize engagement over nourishment. An algovorous person feeds continuously on feeds, prompts, and recommendations, mistaking stimulation for insight. Attention erodes, resilience weakens, and depth is displaced by endless, low-friction intake.

    AI Paradox of Elevation and Erosion
    noun

    The simultaneous condition in which AI raises the technical floor of student performance while hollowing out intellectual depth. In this paradox, syntax improves, structure stabilizes, and access widens for students previously denied basic instruction, even as effort, voice, and conceptual engagement fade. The same tool that equalizes opportunity also anesthetizes thinking, producing work that is formally competent yet spiritually vacant. Progress and decline occur at once, inseparably linked.

    Algorithmic Applebeeism
    noun

    The cultural condition in which ideas are mass-produced for ease of consumption rather than nourishment. The term borrows from Applebee’s, a ubiquitous American casual-dining chain that promises abundance and comfort through glossy menu photos but delivers food engineered for consistency rather than flavor—technically edible, reliably bland, and designed to offend no one. Algorithmic Applebeeism describes thinking that works the same way: arguments that look satisfying at first glance but are interchangeable, frictionless, and spiritually vacant. AI does not invent this mediocrity; it simply industrializes it, giving prepackaged thought scale, speed, and a megaphone.

    Algorithmic Technical Debt
    noun

    The condition in which institutions normalize widespread AI reliance for short-term convenience while deferring the long-term costs to learning, judgment, and institutional capacity. Algorithmic technical debt accumulates when systems choose ease over reform—patching workflows instead of rebuilding them—until dependency hardens and the eventual reckoning becomes unavoidable. Like a diet of indulgence paired with perpetual promises of discipline, the damage is gradual, invisible at first, and catastrophic when it finally comes due.

    Aspirational Hardship Economy
    noun

    The cultural marketplace in which discipline, austerity, and voluntary suffering are packaged as sources of identity, meaning, and belonging. In the aspirational hardship economy, difficulty is no longer avoided but branded, monetized, and broadcast—sold through fitness, Stoicism, and self-mastery influencers who translate pain into purpose. The paradox is that while physical hardship is successfully marketed as aspirational, intellectual hardship remains poorly defended by educators, revealing a failure of persuasion rather than a lack of appetite for difficulty itself.

    Austerity Automation
    noun

    The institutional practice of deploying AI as a cost-saving substitute for underfunded human services—tutoring, counseling, and support—under the guise of innovation. Austerity automation is not reform but triage: technology fills gaps created by scarcity, then quietly normalizes the absence of people. Once savings are realized, the logic expands, placing instructional roles on the same chopping block. What begins as emergency coverage becomes a permanent downgrade, as fiscal efficiency crosses ethical boundaries and keeps going.

    Cathedral-of-Tools Fallacy
    noun

    The mistaken belief that access to powerful, sophisticated tools guarantees competence, growth, or mastery. The cathedral-of-tools fallacy occurs when individuals enter a richly equipped system—AI platforms, automation suites, or advanced technologies—without the foundational knowledge, discipline, or long-term framework required to use them meaningfully. Surrounded by capability but lacking orientation, most users drift, mimic surface actions, and quickly burn out. What remains is task-level operation without understanding: button-pushing mistaken for skill, and humans reduced to reactive functionaries rather than developing agents.

    Cognitive Atrophy Drift (CAD)
     noun

    The slow erosion of intellectual engagement that occurs when thinking becomes optional and consequences are algorithmically padded. Characterized by procrastination without penalty, task completion without understanding, and a gradual slide into performative cognition. Subjects appear functional—submitting work, mimicking insight—but operate in a state of mental autopilot, resembling NPCs executing scripts rather than agents exercising judgment. Cognitive Atrophy Drift is not a sudden collapse but a fade-out: intensity dulls, curiosity evaporates, and effort is replaced by delegation until the mind becomes ornamental.

    Cognitively Outsourced
    adjective

    Describes a mental condition in which core cognitive tasks—analysis, judgment, synthesis, and problem-solving—are routinely delegated to machines. A cognitively outsourced individual treats external systems as the primary site of thinking rather than as tools, normalizing dependence while losing confidence in unaided mental effort. Thought becomes something requested, not generated.

    Constraint-Driven Capitulation
    noun

    The reluctant surrender of intellectual rigor by capable individuals whose circumstances leave them little choice. Constraint-driven capitulation occurs when students with genuine intelligence, strong authenticity instincts, and sharp critical sensibilities submit to optimization culture not out of laziness or shallowness, but under pressure from limited time, money, and security. In this condition, the pursuit of depth becomes a luxury, and calls for sustained rigor—however noble—feel quixotic, theatrical, and misaligned with the realities of survival.

    Countercultural Difficulty Principle
    noun

    The conviction that the humanities derive their value precisely from resisting a culture of speed, efficiency, and frictionless convenience. Under the countercultural difficulty principle, struggle is not an obstacle to learning but its central mechanism; rigor is a feature, not a flaw. The humanities do not exist to accommodate prevailing norms but to challenge them, insisting that sustained effort, patience, and intellectual resistance are essential to human formation rather than inefficiencies to be engineered away.

    Digitally Obligate
    adjective

    Unable to function meaningfully without digital mediation. Like an obligate species bound to a single habitat, the digitally obligate individual cannot navigate learning, communication, or decision-making outside screen-based systems. Digital tools are not aids but prerequisites; unmediated reality feels inaccessible, inefficient, or unintelligible.

    Epistemic Humility in the Dark
    noun

    The deliberate stance of acknowledging uncertainty amid technological upheaval, marked by an acceptance that roles, identities, and outcomes are unsettled. Epistemic humility in the dark rejects false mastery and premature certainty, favoring cautious exploration, intellectual curiosity, and moral restraint. It is the discipline of proceeding without a map—aware that clarity may come later, or not at all—and remaining open to unanticipated benefits without surrendering judgment.

    Frictionless Knowledge Fallacy
    noun

    The belief that learning should be effortless, instantaneous, and cheaply acquired, treating knowledge as a consumable product rather than a discipline earned through struggle. Under the frictionless knowledge fallacy, difficulty is misdiagnosed as bad design, rigor is reframed as exclusion, and depth is sacrificed in favor of speed and convenience—leaving education technically accessible but intellectually hollow.

    Intellectual Misfit Class
    noun

    A small, self-selecting minority devoted to the life of the mind in a culture organized around speed, efficiency, and optimization. Members of the intellectual misfit class read demanding texts—poetry, novels, plays, polemics—with obsessive care, deriving meaning, irony, moral language, and civic orientation from sustained attention rather than output metrics. Their identity is shaped by interiority and reflection, not productivity dashboards. Often underemployed relative to their intellectual commitments—teaching, writing, or working service jobs while pursuing serious thought—they exist in quiet opposition to the dominant culture’s hamster wheel, misaligned by temperament rather than by choice.

    Irreversibility Lock-In
    noun

    The condition in which a technology becomes so thoroughly embedded across institutions, habits, and economic systems that meaningful rollback is no longer possible, regardless of uncertainty about which specific platforms will prevail. In irreversibility lock-in, debate shifts from prevention to adaptation; resistance becomes symbolic, and policy arguments concern mitigation rather than reversal. The toothpaste is already out, and the tube has been discarded.

    Optimization Consolation
    noun

    The habit of seeking emotional and intellectual relief through systems that promise improvement without discomfort. Optimization consolation thrives in environments saturated with AI tutors, productivity hacks, dashboards, streaks, and accelerated learning tools, offering reassurance in place of understanding. In this condition, efficiency becomes a coping mechanism for loneliness, precarity, and overload, while slowness and struggle are treated as failures. The result is a mindset fundamentally incompatible with the humanities, which require patience, attention, and the willingness to endure difficulty without immediate payoff.

    Osmotic Mastery Fallacy
    noun

    The belief that pervasive exposure to advanced technology will automatically produce competence, judgment, and understanding. Under the osmotic mastery fallacy, institutions embed AI everywhere and mistake ubiquity for learning, while neglecting the cognitive capacities—critical thinking, adaptability, and analytical flexibility—that make such tools effective. The result is a widening asymmetry: increasingly powerful tools paired with increasingly thin users, trained to operate interfaces rather than to think.

    Pedagogical Deskilling
    noun

    The gradual erosion of teaching as a craft caused by routine reliance on AI to design assignments, generate rubrics, produce feedback, and manage bureaucratic obligations. In pedagogical deskilling, educators move from authorship to oversight, from judgment to approval, and from intellectual labor to editorial triage. The teacher remains present but increasingly operates as a curator of machine output rather than a maker of learning experiences. What is gained in efficiency is lost in tacit knowledge, professional confidence, and pedagogical depth.

    Policy Whiplash
    noun

    The condition in which institutions respond to disruptive technology with erratic, contradictory, and poorly informed rules—swinging between zealotry, prohibition, and confusion. In policy whiplash, governance is reactive rather than principled, driven by fear, hype, or ignorance rather than understanding. The result is a regulatory landscape with no shared map, where enforcement is inconsistent, credibility erodes, and participants learn to navigate around rules instead of learning from them.

    Relevance Panic
    noun

    The institutional reflex to dilute rigor and rebrand substance in response to cultural, political, and economic pressure. Relevance panic occurs when declining enrollments and hostile funding environments drive humanities departments to accommodate shortened attention spans, collapse disciplines into vague bureaucratic umbrellas, and adopt euphemistic titles that promise accessibility while masking austerity. In this state, technology—especially AI—serves as a convenient scapegoat, allowing institutions to avoid confronting a longer, self-inflicted accommodation to mediocrity.

    Rigor Aestheticism
    noun

    The desire to be associated with intellectual seriousness without submitting to the labor it requires. Rigor aestheticism appears when students are energized by the idea of difficult texts, demanding thinkers, and serious inquiry, but retreat once close reading, patience, and discomfort are required. The identity of rigor is embraced; its discipline is outsourced. AI becomes the mechanism by which intellectual aspiration is preserved cosmetically while effort is quietly removed.

    Sacred Time Collapse
    noun

    The erosion of sustained, meaningful attention under a culture that prizes speed, efficiency, and output above all else. Sacred time collapse occurs when learning, labor, and life are reorganized around deadlines, metrics, and perpetual acceleration, leaving no space for presence, patience, or intrinsic value. In this condition, AI does not free human beings from drudgery; it accelerates the hamster wheel, reinforcing cynicism by teaching that how work is done no longer matters—only that it is done quickly. Meaning loses every time it competes with throughput.

    Survival Optimization Mindset
    noun

    The belief that all aspects of life—including education—must be streamlined for efficiency because time, money, and security feel perpetually scarce. Under the survival optimization mindset, learning is evaluated not by depth or transformation but by cost-benefit calculus: minimal effort, maximal payoff. Demanding courses are dismissed as indulgent or irresponsible, while simplified, media-based substitutes are praised as practical and “with the times.” Education becomes another resource to ration rather than an experience to endure.

    Workflow Laundering
    noun

    The strategic use of multiple AI systems to generate, blend, and cosmetically degrade output so that machine-produced work passes as human effort. Workflow laundering replaces crude plagiarism with process-level deception: ideas are assembled, “roughed up,” and normalized until authorship becomes plausibly deniable. The goal is not learning or mastery but frictionless completion—cheating reframed as efficiency, and education reduced to project management.

  • AI Is a Gym, But the Students Need Muscles

    AI Is a Gym, But the Students Need Muscles

    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 game: “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 sitting on their hands. Now they are overcorrecting in a frenzy, embedding AI everywhere as if saturation were the same thing as competence. It isn’t. It’s panic dressed up as innovation.

    The prevailing assumption seems to be that if AI is everywhere, mastery will somehow emerge by osmosis. But what’s actually happening is the opposite. Colleges are training students to rely on frictionless services while neglecting the very capacities that make AI usable in any meaningful way: critical thinking, the ability to learn new things, and flexible modes of analysis. The tools are getting smarter; the users are getting thinner.

    Clune faces a genuine rhetorical problem. He keeps insisting that we need abstractions—critical thinking, intellectual flexibility, judgment—but we live in a culture that has been 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, then, is not instruction but translation: What is critical thinking, and how do you sell it to people addicted to immediate, AI-generated results?

    The fish analogy holds. A fish is aquatic; water is not a preference but a prison. A young person raised entirely on screens, prompts, and optimization tools treats that ecosystem as reality itself. Like the fish, they are confined to a single environment. We can name this condition precisely. They are cognitively outsourced, trained to delegate thinking to machines as if this were normal or healthy. They are algovorous, endlessly stimulated by algorithms that quietly erode attention and resilience. They are digitally obligate, unable to function without mediation. By definition, these orientations exclude critical thinking. They produce people who are functional inside digital systems and dysfunctional 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 send them into the job market as a fragile, narrow organism. In some fields, they will be unemployable. Clune points to a telling statistic: history majors currently have an unemployment rate roughly half that of recent computer science graduates. The implication is brutal. Liberal arts training produces adaptability. Coding alone does not. As the New York Times put it in a headline Clune cites, “Goodbye, $165,000 Tech Jobs. Student Coders Seek Work at Chipotle.” AI is replacing coders. A life spent inside a tiny digital ecosystem does not prepare you for a world that mutates.

    Is AI the cause of this dysfunction? No. The damage was done long before ChatGPT arrived. I use AI constantly, and I 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 have 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 diagnose myself when I become maudlin, entropic, dissolute, misanthropic, lugubrious, or vainglorious. I have history, philosophy, and religion as reference points. All of this adds up to what we lazily call “critical thinking,” but what it really means is being fully human.

    Someone who has outsourced thought and imagination from childhood cannot suddenly use AI well. They are neither liberated nor empowered. They are brittle, dependent, and easily replaced.

    Because I am a lifelong weightlifter, I’ll offer a more concrete analogy. AI is a massive, state-of-the-art gym: barbells, dumbbells, Smith machines, hack squats, leg presses, lat pulldowns, pec decks, cable rows, preacher curls—the works. Now imagine you’ve never trained before. You’re twenty-eight, inspired by Instagram physiques, and vaguely determined to “get in shape.” You walk into this cathedral of iron with no plan, no understanding of hypertrophy, recovery, protein intake, progressive overload, or long-term discipline. You are surrounded by equipment, but you are lost. Within a month, you will quit. You’ll join the annual migration of January optimists who vanish by February, leaving the gym once again to the regulars.

    AI is that gym. It will eject most users. Not because it is hostile, but because it demands capacities they never developed. Some people will learn isolated tasks—prompting here, automating there—but only in the way someone learns to push a toaster lever. These tasks should not define a human being. When they do, the result is a Non Player Character: reactive, scripted, interchangeable.

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

    If colleges recklessly embed AI into every corner of the curriculum, they are not educating thinkers. They are manufacturing NPCs. And for that, they deserve public shame.

  • Stupidification Didn’t Start with AI—It Just Got Faster

    Stupidification Didn’t Start with AI—It Just Got Faster

    What if AI is just the most convenient scapegoat for America’s long-running crisis of stupidification? What if blaming chatbots is simply easier than admitting that we have been steadily accommodating our own intellectual decline? In “Stop Trying to Make the Humanities ‘Relevant,’” Thomas Chatterton Williams argues that weakness, cowardice, and a willing surrender to mediocrity—not technology alone—are the forces hollowing out higher education.

    Williams opens with a bleak inventory of the damage. Humanities departments are in permanent crisis. Enrollment is collapsing. Political hostility is draining funding. Smartphones and social media are pulverizing attention spans, even at elite schools. Students and parents increasingly question the economic value of any four-year degree, especially one rooted in comparative literature or philosophy. Into this already dire landscape enters AI, a ready-made proxy for writing instructors, discussion leaders, and tutors. Faced with this pressure, colleges grow desperate to make the humanities “relevant.”

    Desperation, however, produces bad decisions. Departments respond by accommodating shortened attention spans with excerpts instead of books, by renaming themselves with bloated, euphemistic titles like “The School of Human Expression” or “Human Narratives and Creative Expression,” as if Orwellian rebranding might conjure legitimacy out of thin air. These maneuvers are not innovations. They are cost-cutting measures in disguise. Writing, speech, film, philosophy, psychology, and communications are lumped together under a single bureaucratic umbrella—not because they belong together, but because consolidation is cheaper. It is the administrative equivalent of hospice care.

    Williams, himself a humanities professor, argues that such compromises worsen what he sees as the most dangerous threat of all: the growing belief that knowledge should be cheap, easy, and frictionless. In this worldview, learning is a commodity, not a discipline. Difficulty is treated as a design flaw.

    And of course this belief feels natural. We live in a world saturated with AI tutors, YouTube lectures, accelerated online courses, and productivity hacks promising optimization without pain. It is a brutal era—lonely, polarized, economically unforgiving—and frictionless education offers quick solace. We soothe ourselves with dashboards, streaks, shortcuts, and algorithmic reassurance. But this mindset is fundamentally at odds with the humanities, which demand slowness, struggle, and attention.

    There exists a tiny minority of people who love this struggle. They read poetry, novels, plays, and polemics with the obsessive intensity of a scientist peering into a microscope. For them, the intellectual life supplies meaning, irony, moral vocabulary, civic orientation, and a deep sense of interiority. It defines who they are. These people often teach at colleges or work on novels while pulling espresso shots at Starbucks. They are misfits. They do not align with the 95 percent of the world running on what I call the Hamster Wheel of Optimization.

    Most people are busy optimizing everything—work, school, relationships, nutrition, exercise, entertainment—because optimization feels like survival. So why wouldn’t education submit to the same logic? Why take a Shakespeare class that assigns ten plays in a language you barely understand when you can take one that assigns a single movie adaptation? One professor is labeled “out of touch,” the other “with the times.” The movie-based course leaves more time to work, to earn, to survive. The reading-heavy course feels indulgent, even irresponsible.

    This is the terrain Williams refuses to romanticize. The humanities, he argues, will always clash with a culture devoted to speed, efficiency, and frictionless existence. The task of the humanities is not to accommodate this culture but to oppose it. Their most valuable lesson is profoundly countercultural: difficulty is not a bug; it is the point.

    Interestingly, this message thrives elsewhere. Fitness and Stoic influencers preach discipline, austerity, and voluntary hardship to millions on YouTube. They have made difficulty aspirational. They sell suffering as meaning. Humanities instructors, despite possessing language and ideas, have largely failed at persuasion. Perhaps they need to sell the life of the mind with the same ferocity that fitness influencers sell cold plunges and deadlifts.

    Williams, however, offers a sobering reality check. At the start of the semester, his students are electrified by the syllabus—exploring the American Dream through Frederick Douglass and James Baldwin. The idea thrills them. The practice does not. Close reading demands effort, patience, and discomfort. Within weeks, enthusiasm fades, and students quietly outsource the labor to AI. They want the identity of intellectual rigor without submitting to its discipline.

    After forty years of teaching college writing, this pattern is painfully familiar to me. Students begin buoyant and curious. Then comes the reading. Then comes the checkout.

    Early in my career, I sustained myself on the illusion that I could shape students in my own image—cultivated irony, wit, ruthless critical thinking. I wanted them to desire those qualities and mistake my charisma as proof of their power. That fantasy lasted about a decade. Eventually, realism took over. I stopped needing them to become like me. I just wanted them to pass, transfer, get a job, and survive.

    Over time, I learned something paradoxical. Most of my students are as intelligent as I am in raw terms. They possess sharp BS detectors and despise being talked down to. They crave authenticity. And yet most of them submit to the Hamster Wheel of Optimization—not out of shallowness, but necessity. Limited time, money, and security force them onto the wheel. For me to demand a life of intellectual rigor from them often feels like Don Quixote charging a windmill: noble, theatrical, and disconnected from reality.

    Writers like Thomas Chatterton Williams are right to insist that AI is not the root cause of stupidification. The wheel would exist with or without chatbots. AI merely makes it easier to climb aboard—and makes it spin faster than ever before.

  • AI Normalization and the Death of Sacred Time

    AI Normalization and the Death of Sacred Time

    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 smooths the road toward a harder crash. Things may improve eventually, but only after we stop pretending that faster is the same as better, and convenience is the same as progress.

  • AI High School Graduates Are Here—and They’re Better Cheaters Than We Are Teachers

    AI High School Graduates Are Here—and They’re Better Cheaters Than We Are Teachers

    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 tired. Our incoming college students are not stumbling innocents. They are veterans of four full years of AI high school. They no longer dabble in crude copy-and-paste plagiarism. That’s antique behavior. Today’s students stitch together outputs from multiple AI models, then instruct the chatbot to scuff the prose with a few grammatical missteps so it smells faintly human and slips past detection software. This is not cheating as shortcut; it is cheating as workflow optimization.

    Meanwhile, high school teachers may be congratulating themselves for assigning Shakespeare, Keats, and Dostoevsky, but many are 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. They are inside, ordering 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 becoming the teacher as editor.

    AI’s grip tightens 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. It is not reform; it is triage. And once institutions get a taste of saving money by not hiring tutors and counselors, it is naïve to think that teaching positions will remain untouchable. Cost-saving rarely stops at the first ethical boundary it crosses.

    That is why this 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 weekly in my college classroom. I read plenty of AI slop—essays with perfect grammar and no pulse, paragraphs that gesture toward ideas they never quite touch. Some students have clearly checked out, 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 submitting essays with clean syntax and logical structure. They are using 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 is incomplete. We do not 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 I tell my students the only honest thing left to say: 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 Copy-Paste Generation and the Myth of the Fallen Classroom

    The Copy-Paste Generation and the Myth of the Fallen Classroom

    There is no ambiguity in Ashanty Rosario’s essay title: “I’m a High Schooler. AI Is Demolishing My Education.” If you somehow miss the point, the subtitle elbows you in the ribs: “The end of critical thinking in the classroom.” Rosario opens by confessing what every honest student now admits: she doesn’t want to cheat with AI, but the tools are everywhere, glowing like emergency exits in a burning building. Some temptations are structural.

    Her Exhibit A is a classmate who used ChatGPT to annotate Narrative of the Life of Frederick Douglass. These annotations—supposed evidence of engaged reading—were nothing more than “copy-paste edu-lard,” a caloric substitute for comprehension. Rosario’s frustration reminds me of a conversation with one of my brightest students. On the last day of class, he 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. He is not a slacker. He is a time-management pragmatist surviving the 21st century. He reads the AI summaries, synthesizes them, and writes excellent essays. Of course I’d love for him to spend slow hours with books, but he is not living in 1954. He is living in a culture where time is a scarce resource, and AI is his oxygen mask.

    My daughters and their classmates face the same problem with Macbeth. Shakespeare’s language might as well be Martian for a generation raised on TikTok compression and dopamine trickle-feeds. They watch film versions of the play and use AI to decode plot points so they can answer the teacher’s study questions without sounding like they slept through the Renaissance. Some purists will howl that this is intellectual cheating. But as a writing instructor, I suspect the teacher benefits from students who at least know what’s happening—even if their knowledge comes from a chatbot. Expecting a 15-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 a classroom years overdue. To blame AI for the degradation of education 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 almost certainly lean on AI just to keep my head above water. The difference is that 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, frankly, everyone else is doing it. It’s an arms race of survival, not a moral collapse.

    Still, Rosario is right about the aftermath. She writes: “AI has softened the consequences of procrastination and led many students to avoid doing any work at all. There is little intensity anymore.” When thinking becomes optional, students drift into a kind of algorithmic sleepwalking. They outsource cognition until they resemble NPCs in a glitching video game—avatars performing human imitation rather than human thought. My colleagues and I see it, semester after semester: the fade-out, the disengagement, the slow zombification.

    Colleges are scrambling to respond. Should we police AI with plagiarism detectors? Should we ban laptops and force students to write essays in composition books under watchful eyes like parolees in a literary halfway house? Should we pretend the flood can be stopped with a beach towel?

    Reading Rosario’s lament about “cookie-cutter AI arguments,” I thought of my one 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 gravitates toward the easy, the prepackaged, the frictionless. AI didn’t invent mediocrity. It merely handed it a megaphone.

    Rosario, clearly, is not an Applebee’s soul. She’s Michelin-level in a world eager to eat microwaved Hot Pockets. Of course her heart sinks when classmates settle for fast-food literacy. I want to tell her that if she were in high school in the 1970s, she’d still witness an appetite for shortcut learning. The tools would be different, the essays less slick, but the gravitational pull toward mediocrity would be the same. The human temptation to bypass difficulty is not technological—it’s ancestral. AI simply automates the old hunger.