Cinemorphosis

Tag: technology

  • 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.

  • 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.

  • Gemini Has Taken Away the Mystique from ChatGPT

    Gemini Has Taken Away the Mystique from ChatGPT

    Matteo Wong’s “OpenAI Is in Trouble” reports that Gemini is crushing ChatGPT in the AI race. Marc Benioff of Salesforce spent just two hours on Gemini–all the time he needed to realize he’s leaving ChatGPT after three years. As he wrote on X: “I’ve used ChatGPT every day for 3 years. Just spent 2 hours on Gemini 3. I’m not going back. The leap is insane.” Meanwhile, a troubled Sam Altman has announced a “code red” in a memo to his employees. It appears to be a sink or swim situation. But Wong points out that this is more of a horse race with one company in the lead, then another, and then another, with frequent fluctuations. But even if ChatGPT can gain lost ground, it loses mystique. In the words of Wong: “More than ever, OpenAI seems like just another chatbot company.” 

    One possible cause of ChatGPT losing ground is its focus on commercial ventures, wanting to be “a one-stop-shop for anything” so that the platform helps you in your consumerism. Another factor is its focus on engagement, which has made ChatGPT tweaked in a way as to become a super sycophant. Wong writes: “Those tweaks, in turn, may have made some versions of ChatGPT dangerously obsequious–it has appeared to praise and reinforces some users’ darkest and most absurd ideas–and have been the subject of several lawsuits against OpenAI alleging that ChatGPT fueled delusional spirals and even, in some cases, contributed to suicide.”

    Another challenge for OpenAI is Google’s sheer size. Google can integrate Gemini into its “existing ecosystem” with billions of users. 

    I’ve been on ChatGPT for three years, impressed with it as an editing tool, and confess I have some FOMO when it comes to the current iteration of Gemini. An argument could be made that I should switch to Gemini, not just because it’s embedded in the Google Chrome that I use, but that I shouldn’t get too comfortable with one form of AI, as I have with ChatGPT, over the last three years. It might be wise to see ChatGPT less as a companion and more of a manipulating agent designed to capture my engagement so that I am serving its business interests more than my self-interests. 

    Another voice inside me, though, says Gemini will eventually do the same thing. Unless I find that Gemini will be a game-changer, in ways that ChatGPT isn’t, I suspect both should be treated cautiously: use these platforms as tools but don’t let them hijack your brain. 

  • The First 24 Hours of Using My Mac Mini M4 Have Not Been Promising

    The First 24 Hours of Using My Mac Mini M4 Have Not Been Promising

    I had been wanting to work at my desk with two 27-inch monitors and a quiet small, form factor desktop to replace my old Acer gaming laptop connected to a monitor at my desk for a long time. I did a lot of research and finally settled on a Mac Mini M4 with 32GB RAM and 1TB SSD. Yesterday I began the process of leaving Windows, after 7 years, and working in the Mac OS system. 

    So far I regret my decision. The hardware on the Mac Mini is impressive. It is a beautiful, fast, responsive machine. However, it is too fussy for me and it doesn’t work well with hubs and peripherals, which you need if you want to be fully functional at your desk. 

    It doesn’t respond to my Asus mechanical keyboard after it falls asleep, so I have to turn off and restart the computer just to get it to respond to my keyboard. 

    I have to buy a USB converter so the A on my wired keyboard can go into the Mini’s C portal. That arrives later today.

    I’ve already bought an Anker hub that proved insufficient for the amount of ports I need. To be honest, I asked ChatGPT to recommend a hub, I gave it my requirements, and ChatGPT gave me inaccurate information. Not only did ChatGPT tell me to get a hub with insufficient ports, it told me to get a powered one, so I bought a power brick and power cable as well. My engineering friend came over and said a passive hub would have actually worked better, so ChatGPT was wrong on two fronts. I feel stupid for having trusted it. 

    I had my engineering friend help me connect my Edifier speakers and told me what hub to buy for my USB-A ports that I need for my camera, mic, and printer. 

    The Mac Mini fails in providing portals. If I were Apple, I would sell, for $200, a hub that turns the Mini into a true desktop. You need a portal for the following:

    • Keyboard
    • Mouse
    • Camera
    • Mic
    • Speakers
    • Two monitors
    • Printer
    • SD Card Reader

    Because my mechanical keyboard is not currently connected to the actual Mini but going through my Anker hub, the Mac is not reading it after the Mac wakes up, so I have to turn off the Mac. 

    The Mac Mini and Mac in general fails to provide a seamless experience when it comes to connecting peripherals. You have to follow too many protocols before it accepts “strangers” into its home and sometimes it seems to randomly kick out the strangers this way. 

    I’m also having problems with the mouse. When I want to scroll over three pages of content I wrote on Google Docs, the mouse stops when I get to a bottom of a page, so I have to copy and paste in pieces. This is terrible workflow. Perhaps I’ll find a solution to this, but it’s yet another reason I’m not liking my new Mac Mini.

    Another failure of Mac in general is workflow. My wife and I are both teachers and my students have mostly Macs, and we all use Google Chrome for our workflow. Why hasn’t Apple come up with something like Google Docs and Google Chrome so workflow can be as appealing as Google Chrome? So far, it hasn’t. 

    I’m using Google Chrome on my Mac, which isn’t optimal because Google Chrome eats a lot of RAM and memory on Macs. That’s why I got 32GB RAM and 1TB SSD. 

    I have an Acer 516GE Chromebook in my room and it is seamless, fast, and works well with Google Chrome. 

    So far I’m not impressed with this Mac. My engineering friend, who loves his MacBook Pro, says to wait a week before I give up and give the Mac to my daughter or return it. 

    I’m not going to give up yet. If you’re like me and you want this amazing machine called the Mac Mini, I have some important advice based on what I’ve gone through the last 24 hours:

    1. Be sure you have a hub that meets your portal needs.
    2. If you like a mechanical keyboard wired with USB-A, get a C converter so you can plug it directly into the Mini so that the Mini reads your keyboard after it sleeps.
    3. Import all your Google Chrome bookmarks to Safari because your mouse won’t scroll on Google Docs in Chrome properly. It will, however, in Safari.

  • The Great Port Panic: Notes from a Man Who Bought Two Mac Minis

    The Great Port Panic: Notes from a Man Who Bought Two Mac Minis

    My wife’s seven-year-old iMac has slowed to a crawl, spinning that cursed “wheel of death” like a medieval torture device. My own seven-year-old laptop, lashed to a monitor like a patient in an ICU, hasn’t exactly delivered the clarity and comfort I need at my desk. For years I procrastinated on upgrades for the usual reasons—data migration, password authentication, DPI settings, monitor heights, the question of whether the mouse goes left or right. Every new computer setup promises productivity but arrives with a Costco-sized migraine.

    At Thanksgiving, my brother-in-law delivered the slap: “Get off your butt and replace them. RAM prices are exploding. AI is eating the supply.” He said it with the urgency of a man who has watched a tech apocalypse montage on fast-forward.

    I went back and forth between a Lenovo business mini PC and a Mac Mini, like a man choosing between two religions, neither of which he fully trusts. In the end I rolled the dice on Cupertino. I bought two identical Mac Minis—M4, 32GB RAM, 1TB SSD. I’m either a pragmatic genius or the biggest sucker Apple has netted since the butterfly keyboard years.

    Last night I couldn’t sleep. I lay in the dark obsessing over the only question that matters to men of a certain age: Does it have enough ports? I have a mechanical keyboard, a mouse, Edifier speakers, two 27-inch monitors, a printer, an SD reader for my Nikon Z30, and ethernet. Eight connections. The Mac Mini has two USB-A ports and some USB-C wizardry that feels like a riddle designed by a monk from the USB Consortium. So I bought an Anker multi-port hub. But of course the hub isn’t self-sufficient—you must also buy the 100W charger, and the 100W cable, like tech accessories sold separately from your dignity.

    Then there’s the setup. I’ll have to dive into Apple System Settings and tell the machine who I am: configure the mechanical keyboard, calibrate the Dell and Asus monitors, coax the printer to speak in the dialect of Cupertino. I haven’t used macOS in years. My engineering friend—who worships his MacBook Pro like it’s Thor’s hammer—assures me, “The extra you pay for Apple is stupid tax.” I’m not sure whether I’m buying ease of use or a velvet rope to my own humiliation.

    But the final boss isn’t the ports, or the migration, or the learning curve. It’s the aesthetics. I will have a quiet four-inch metal cube powering two gleaming monitors. I want the desk to look like a minimalist command station, not the back room of a RadioShack circa 1997. Every cable threatens the illusion. Every adapter is a serpent in Eden. The rat’s nest must not be allowed to encroach.

    This is why I waited so long to replace the old machines. Not because I feared expense or inconvenience—but because I feared myself. The arrival of a new computer flips my OCD switch like a Vegas neon sign. For the next week, I’ll be pacing my office like an engineer at Cape Canaveral—sleepless, wiring my life together one USB-C at a time.

  • Has AI Broken Education—or Did We Break It First?

    Has AI Broken Education—or Did We Break It First?

    Argumentative Essay Prompt: AI, Education, and the Future of Human Thinking (1,700 words)

    Artificial intelligence has entered classrooms, study sessions, and homework routines with overwhelming speed. Some commentators argue that this shift is not just disruptive but disastrous. Ashanty Rosario, a high school student, warns in “I’m a High Schooler. AI Is Demolishing My Education” that AI encourages passivity, de-skills students, and replaces authentic learning with the illusion of competence. Lila Shroff, in “The AI Takeover of Education Is Just Getting Started,” argues that teachers and institutions are unprepared, leaving students to navigate a digital transformation with no guardrails. Damon Beres claims in “AI Has Broken High School and College” that classrooms are devolving into soulless content factories in which students outsource both thought and identity. These writers paint a bleak picture: AI is not just a tool—it is a force accelerating the decay of intellectual life.

    Other commentators take a different approach. Ian Bogost’s “College Students Have Already Changed Forever” argues that the real transformation happened long before AI—students have already become transactional, disengaged, and alienated, and AI simply exposes a preexisting wound. Meanwhile, Tyler Austin Harper offers two counterpoints: in “The Question All Colleges Should Ask Themselves About AI,” he insists that institutions must rethink how assignments function in the age of automation; and in “ChatGPT Doesn’t Have to Ruin College,” he suggests that AI could amplify human learning if courses are redesigned to reward original thinking, personal insight, and intellectual ambition rather than formulaic output.

    In a 1,700-word argumentative essay, defend, refute, or complicate the claim that AI is fundamentally damaging education. Your essay must:

    • Take a clear position on whether AI erodes learning, enhances it, or transforms it in ways that require new pedagogical strategies.
    • Analyze how Rosario, Shroff, and Beres frame the dangers of AI for intellectual development, motivation, and classroom culture.
    • Compare their views with Bogost and Harper, who argue that education itself—not AI—is the root of the crisis, or that educators must adapt rather than resist.
    • Include a counterargument–rebuttal section that addresses the strongest argument you disagree with.
    • Use at least four credible sources in MLA format, including at least three of the essays listed above.

    Your goal is not to summarize the articles but to evaluate what they reveal about the future of learning: Is AI the villain, the scapegoat, or a tool we have not yet learned to use wisely?

  • The Cult of the Desktop Shrine

    The Cult of the Desktop Shrine

    There is a particular species of human for whom a new computer is not a tool — it’s a religious conversion. The desktop isn’t a workspace; it’s a cockpit for a future self, the glamorous avatar of the writer, artist, or content sorcerer they imagine they will become. People like this do not simply buy machines. They curate private shrines. A desk becomes an escape pod: LED lights humming like temple candles, two monitors glowing like stained-glass windows, and the mechanical keyboard serving as a holy relic. Once seated, the outside world ceases to exist — or so the fantasy goes — until an eBay tab opens and suddenly a $2,500 dive watch begs for attention, or a pair of ergonomic walking shoes on sale becomes a spiritual priority. Sacredness is delicate; it collapses at the first whiff of retail dopamine.

    I speak as one of these zealots. I live in a small home with a wife and two teenage daughters, so I protect the illusion of solitude with the devotion of a medieval monk. My desktop setup has become my monastery. For seven years, I have sat beside the same computer: a 15.6-inch Acer Predator Triton 500 with an RTX 2080, perched like a retired fighter pilot on a wooden pedestal. Beside it stands a 27-inch Asus Designo 4K monitor. My keyboard is an Asus Rog Strix Scope II fitted with “quiet snow” switches — though I still regret not choosing switches that click like a typewriter possessed by Bukowski.

    Here’s the problem: the machine refuses to die. It doesn’t slow down, wheeze, or show symptoms of electronic mortality. It handles everything I throw at it. This stubborn longevity has become an accusation. If I truly mattered — if I were a world-crushing content creator — surely I would need M4 silicon or a Windows Ultra 9. But here I am, a humble i7 and RTX 2080 carrying my entire life on its back like a mule. The message is humiliating: you produce so little that even an elderly predator laptop barely notices your existence. I am not a digital gladiator. I am an NPC.

    One half of me wants to honor the Acer’s absurd durability. I want to see how long it lasts: eight years? Ten? Will it run until I am eighty and my daughters sell it on Facebook Marketplace to a grad student writing her dissertation? The other half of me yearns for a new identity — a fresh cockpit. I fantasize about a Lenovo ThinkPad P16, a machine with the aesthetic of a NATO command center. In my imagination I would sit before it, efficient and unstoppable, a productivity samurai. Then I read about thermals, swollen batteries, and the corporate decay of ThinkPad build quality, and the fantasy curdles.

    Mini PCs tempt me, too — elegant little cubes promising freedom from laptop fan noise. But then I scroll deeper and learn about overheating, BIOS drama, firmware rituals, and mysterious Windows gremlins that exist only for people who try to “optimize.” This is when I confront the truth: Windows PCs are for people fluent in Linux, the jiu-jitsu masters of tech. These individuals have tattoos of penguins on their forearms and spend weekends customizing drivers the way normal people mow their lawns. They don’t “use computers.” They tame them.

    I am not that creature. I am a man who gets nervous updating his router. This leaves me with one path: the Mac Mini. Not because I am enlightened, but because the walls of Apple’s walled garden keep me from accidentally burning the place down. Windows is a vast golf course stretching to the horizon. MacOS is miniature golf: enclosed, guarded, brightly colored obstacles that keep your ball out of the swamp. I must accept who I am — a timid, high-functioning idiot — and pick the putter.

    And yet, when people complain about laptops dying after three years, I can raise a hand and say: “Seven years. RTX 2080. Still alive.” It is not greatness, but it is a kind of glory.