Art in the Age of Algorithms: Patronage, Practice, and the Persistence of Human Creativity

A montage of decades of culture, fictiona, real, and meme in the artistic stylings of Hayao Miyazaki, posted to Reddit


The Eternal Return of Innovation

No human invention, once birthed into material or conceptual existence, ever wholly disappears. The afterlives of tools and techniques often exceed their origin epochs, resurrected in forms both reverent and reimagined. Flint knapping, for example—a Paleolithic survival craft—persists not as necessity but as cultural curiosity and artisanal revival. Similarly, oil painting endures not because it is efficient, but because it offers a sensual and symbolic resistance to the high-speed reproducibility of digital media (Benjamin, 2008). The persistence of horseback riding, bow hunting, silver gelatin photography, and hand-loomed textiles testifies to a peculiar anthropological principle: innovation does not erase what came before—it recontextualizes it (Lowenthal, 1985).

This phenomenon is particularly salient in the domain of artistic practice, where every major technological rupture has historically been accompanied by both a moment of anxiety and a long arc of adaptation. The printing press did not annihilate the scribal class; it restructured it. Illuminators became fine artists, marginalia became visual wit, and the book evolved into a new site of visual-literary symbiosis (Eisenstein, 1980). Photography, for all its supposed threat to painting, ultimately catalyzed the Impressionists, Post-Impressionists, and Cubists—movements that would redefine not only aesthetic values but the very ontology of image-making (Benjamin, 2008). In short, artistic forms evolve less through extinction than through exaptation.

And yet, the arrival of generative artificial intelligence—trained to mimic, distort, or recompose visual and textual styles at unprecedented speed and scale—has struck a uniquely dissonant chord. Artists, illustrators, authors, and musicians report not only economic precarity, but a deeper philosophical displacement. If AI can convincingly replicate the style of a human artist, does the value of artistic labor collapse into a simulation economy where authenticity is no longer tethered to authorship? Is this a technological watershed of the same kind as the camera or the press, or does it portend a rupture of a different magnitude—one that challenges the very basis upon which art and artistry have rested since the Paleolithic?

To answer this, one must first parse a distinction often blurred in the current discourse: the difference between art as a practice of expression and art as a means of economic survival. Historically, the latter has always been contingent on some form of patronage—be it tribal, ecclesiastical, aristocratic, bourgeois, or commercial. In the Upper Paleolithic, cave artists likely operated within a social economy of ritual significance, compensated in food, status, or protection rather than currency (Lewis-Williams, 2002). By the Renaissance, artistic production was tightly bound to elite patronage systems that provided not only material support but ideological scaffolding—art was made to signify, reinforce, or challenge the power of those who could afford to underwrite it (Goldthwaite, 1993). In modernity, this patronage became increasingly democratized through the market, though it never escaped its dependence on broader economic structures.

Importantly, throughout these transformations, the core of artistic production persisted—driven by imagination, formal exploration, and personal or collective expression—even when its mode of survival changed. The same individual who once painted an altarpiece for a Medici chapel might, in another century, have designed editorial illustrations, commercial logos, or NFTs. The tools changed. The audience changed. The economic platform changed. But the impulse did not.

This long historical view is crucial for contextualizing GenAI’s emergence. Unlike prior technologies, which extended the range or fidelity of human expression (the camera lens, the printing press, the synthesizer), GenAI operates via simulation: it learns from human art to produce work that feels human, even when no human hand touched the canvas or composition. It is not a tool used by the artist—it is, increasingly, seen as a substitute for the artist (McCormack, Gifford, & Hutchings, 2019). This ontological shift is what many creators instinctively resist, and for good reason. While previous innovations enabled artists to reimagine their media, GenAI threatens to abstract the artist from the act—to sever the intimate, agonistic, and often uncertain relationship between the hand, the mind, and the medium.

Yet to view GenAI as a harbinger of artistic death is to mistake one form of disruption for finality. Innovation has always been a double-edged sword: it destabilizes existing economies, but it also catalyzes new forms, new practices, and new configurations of value. The camera displaced portrait painters but gave birth to visual modernism. The synthesizer drew ire from classical purists but birthed entire genres of music. The printing press rendered scribes obsolete as a profession, but in doing so created an expanded literary culture that eventually allowed more people to become writers, poets, or pamphleteers (Eisenstein, 1980; Benjamin, 2008).

The question, then, is not whether GenAI will eliminate artists—it won’t—but rather what it demands artists to become. Just as the post-Gutenberg illuminator did not vanish but reoriented their skills toward new forms of artistic production, contemporary creators must consider how their roles, media, and markets will shift in this new ecology. Will we see a return to slower, hand-made processes as a form of cultural distinction, akin to the artisanal turn in food, fashion, and photography? Will authenticity be revalorized precisely because it cannot be algorithmically reproduced? Or will we enter a hybridized era, where artists collaborate with generative systems to achieve effects otherwise impossible (Elgammal et al., 2017)?

Such questions cannot be answered purely through philosophical rumination; they require an engagement with political economy. Who owns the models? Who profits from the outputs? How are training datasets assembled, and do they respect the intellectual and emotional labor of the artists whose work forms the substrate of these machines? In the face of such systemic complexity, it is insufficient to say that art will survive—we must ask what kind of art, under what conditions, and for whom.

To this end, the remainder of this essay will explore the dialectic between technological acceleration and artistic practice. It will trace historical analogs to situate GenAI within a broader lineage of disruption and adaptation. It will analyze the impact of GenAI on artistic labor, authorship, and the shifting terrain of patronage. Most importantly, it will argue that while art as a commercial pathway is in flux, art as a human compulsion is not. The challenge, as ever, is not to preserve old forms for their own sake, but to forge new ones that respond to our changing tools, economies, and sensibilities without forfeiting the very thing that makes art matter.


Innovation and Artistic Persistence

A photograph of a sketch I made turned into an oil painting by Midjourney.

The arc of artistic history is not one of uninterrupted progress, but of recursive disruptions—moments when a new medium or technology arrives, destabilizes established practices, and ultimately becomes the platform for a new equilibrium. Yet across these shifts, a pattern persists: the core impulse to create, to signify, and to communicate remains intact. Artistic practices are rarely extinguished; instead, they are displaced, redefined, or repositioned. To understand the contemporary discourse surrounding generative artificial intelligence (GenAI) and its perceived threat to artistic labor, one must recognize the deeper historical currents in which it participates.

Perhaps the most canonical example of such a transformation is the invention of the printing press in the mid-fifteenth century. The advent of movable type dramatically altered the ecology of written communication. Scribes and illuminators—once indispensable—found their roles either diminished or entirely obviated in the face of mechanical reproduction. The richly illustrated codices of monastic scriptoria gave way to the uniform pages of printed books, severing the artisanal from the textual in favor of efficiency and scale. And yet, this moment of loss birthed an aesthetic reaction: the elevation of book arts, the refinement of typographic design, and the flourishing of engraving as an independent form (Eisenstein, 1980). The visual ornamentation that once served scripture or state found a new life in allegorical prints and secular decoration. Some illuminators became painters; others migrated into more commercial domains. The printing press did not end visual art—it relocated its function, altered its audience, and transformed its economic conditions.

The invention of photography in the 19th century introduced a similar crisis, particularly for painters and portraitists. With the daguerreotype and later developments in photographic emulsions, image-making was no longer solely the domain of the trained hand. Machines could now “capture” reality with uncanny fidelity and in a fraction of the time. For academic painters who built careers on the verisimilitude of oil and brush, the camera appeared to invalidate centuries of skill. But here, too, disruption bred innovation. In response to photography, painters abandoned mimetic representation and explored new visual vocabularies. Impressionism, with its luminous spontaneity, arose not as a rejection of realism per se, but as a reassertion of vision over optics. Later movements—Cubism, Fauvism, Abstract Expressionism—would push this logic further, asserting that art was not about replication but about mediation, transformation, and presence (Benjamin, 2008).

Photography itself evolved from a threat to a medium of profound expressive depth. What began as a mechanical technique became a cultural and artistic language in its own right. The initial anxiety that it would displace painting gave way to the recognition that it could do what painting could not—reveal, document, compress time, and democratize the image. The same trajectory can be seen in the history of music. The rise of digital sampling, synthesizers, and audio software in the late 20th century caused panic in classical and analog traditions, which feared that human nuance would be drowned in digital homogeny. But rather than replace musicianship, these tools expanded it. Entire genres—hip hop, electronic ambient, glitch, vaporwave—owe their existence to the radical accessibility of digital composition.

In each of these cases, the initial disruption appeared to imperil the artist, only for new configurations of creativity and audience to emerge. What survived was not a particular technique, but the capacity for artists to reorient themselves in response to technological constraints and affordances. Crucially, however, these adaptations often occurred alongside shifts in patronage structures. With the printing press, the consolidation of a literate public enabled the rise of commercial publishing. With photography, the art market redefined value through scarcity and intentionality. With digital music, new forms of distribution and monetization (e.g., streaming, licensing, live performance) emerged to supplement or replace record sales.

What ties these epochs together is the persistence of art under economic pressure, not its immunity to it. Art was never a self-sufficient domain—it has always required a substrate of support, whether in the form of aristocratic patronage, religious institutions, commercial commissions, or mass audiences. When technological shifts altered the economics of art, they also forced artists to rethink their roles, products, and publics. The disruption of the scribal class after Gutenberg is not unlike the current displacement faced by digital illustrators as GenAI models trained on open web data begin to produce aesthetically convincing images with little to no human input. The question is not whether illustration will survive—it will—but how it will survive, and under what conditions.

GenAI presents a disruption with particular characteristics. Unlike the camera or synthesizer, it is not simply a new tool that expands the expressive capacity of its user. It is a system that ingests the outputs of thousands (or millions) of artists to produce work that simulates intentionality. It operates through neural nets trained on enormous datasets—often scraped without the consent of the original creators—allowing it to mimic existing styles or invent plausible variants (Elgammal et al., 2017). In this sense, GenAI occupies a liminal space: it is neither wholly autonomous nor transparently instrumental. It challenges the notion of the artist not merely as a maker, but as a distinctive agent—someone whose labor, perspective, and training endow the work with value.

This is where the analogy to earlier disruptions becomes fragile. While previous technologies displaced forms of labor, they did not obscure authorship. The camera still required a photographer. The synthesizer needed a composer. GenAI complicates this. Its ability to produce images, music, or text in a matter of seconds based on a prompt destabilizes the boundary between conception and execution. As McCormack, Gifford, and Hutchings (2019) argue, this raises urgent questions about autonomy, authorship, and authenticity in machine-mediated art. Who is the artist—the prompter, the programmer, the model, or the dataset?

And yet, even here, history offers insights. Many pre-modern artists worked collaboratively, anonymously, or under the signature of a workshop. The notion of individual authorship is historically contingent—a Romantic and modernist ideal rather than a universal condition. In this sense, GenAI might be viewed not as a radical break, but as a return to distributed creativity. However, what makes the present moment ethically volatile is not the collaboration per se, but the asymmetry of extraction. Whereas Renaissance studios trained apprentices to imitate the master’s style, GenAI models extract and recombine human-made art without reciprocity. This is not collaboration; it is commodification at scale.

Still, not all is erosion. New genres are emerging: prompt engineering, AI-assisted collage, hybrid performance, and meta-commentary on machinic creativity. Artists are already responding—not just by resisting GenAI, but by absorbing it, shaping it, embedding it within their existing practices. The result may be the emergence of a new aesthetic paradigm, one in which the “hand” becomes conceptual rather than physical, and the role of the artist shifts from production to curation of generative processes.

To return to the historical analogy: photography displaced certain painters, but it also produced Cartier-Bresson, Diane Arbus, and Cindy Sherman. The synthesizer did not end orchestration; it expanded the sonic palette and democratized sound. Each disruption generated friction, yes—but also new forms of excellence, new modes of training, and new aesthetics. What we are witnessing now is the early turbulence of integration, not a terminal rupture.

What remains to be seen is how society will recalibrate the structures of recognition and reward. If we no longer rely on traditional indicators—manual labor, scarcity, and mastery—to define artistic value, then new metrics must emerge. These might include the clarity of conceptual framing, the intentionality of prompt design, or the ability to subvert the machine’s aesthetic expectations. Alternatively, a cultural backlash may elevate the handmade, the imperfect, the idiosyncratic as markers of human authenticity. Either way, art will persist—not as a static identity but as a shapeshifting response to historical conditions.

The GenAI moment, then, is not a singularity but a continuation. It fits within a lineage of artistic crises and metamorphoses. The tools change; the drive does not. Artists will adapt, as they always have—not by retreating into nostalgia or purity, but by discovering new freedoms within new constraints. Innovation may obscure the path, but it does not extinguish the journey.


Patronage, Time, and the Economies of Art

The Relationship between Time, Art, and Money, by BMcC, Dali-E, and Midjourney

The advent of generative AI in the creative sphere has not just sparked philosophical debates about authorship and authenticity—it has struck directly at the economic core of artistic labor. While earlier sections have traced the persistence of art as a human impulse across epochs of disruption, the current moment forces a sharper reckoning: what is threatened by GenAI is not creativity, but commercial viability. It is not that artists will cease to create; it is that many may no longer be able to make a living doing so. The line between inspiration and exploitation, between creative process and content replication, has never been thinner. The result is not only a shift in what can be made, but in who is allowed to survive within the creative economy.

To understand this tension, we must first address a bifurcation that often goes unspoken in discussions of art and technology: the distinction between art as vocation and art as profession. The former gestures toward a calling—something existentially driven, personally fulfilling, spiritually or intellectually necessary. The latter, by contrast, is shaped by labor structures, compensation, deadlines, client expectations, and platforms for dissemination. These identities frequently overlap, but they are not interchangeable. One can be a devoted artist without being economically successful; one can be commercially prolific while creatively disengaged. The GenAI rupture collapses this nuance by flooding the market with fast, cheap, and stylistically convincing alternatives, thereby collapsing price points and squeezing those whose labor once held recognized value within specific production contexts.

Consider the freelance illustrator who relies on contract work for book covers, advertising campaigns, game concept art, or editorial design. In an earlier moment, such work was protected—if not by law, then by the implicit social contract that skill took time and training, and was therefore worth paying for. With GenAI systems capable of producing publishable imagery in seconds, the calculus changes. Why commission an artist for two weeks when a prompt can yield dozens of iterations in two minutes? Clients, particularly in underfunded sectors, increasingly ask for “touch-ups” on AI-generated images or hybrid workflows that significantly reduce human input. This is not speculation—it is already happening across publishing, design, and entertainment industries (McCormack, Gifford, & Hutchings, 2019).

What makes this disruption particularly insidious is its mimetic nature. GenAI doesn’t just replace creative output—it replaces it in the language of the artist’s own style. The issue here is not simply competition; it is aesthetic appropriation at computational scale. Trained on datasets scraped from the open internet—often without consent or attribution—these systems absorb the formal, gestural, and compositional choices of working artists and repackage them under a veneer of originality. The artist’s hand is flattened into an aesthetic template, dissociated from labor, intention, or authorship. As such, the speed of replication is not neutral; it is extractive. It laps the original creator while drawing legitimacy from their practice.

Some defenders of GenAI invoke historical parallels: have not artists always borrowed, referenced, or evolved from one another? Is not the history of art one long chain of stylistic iteration—from Raphael’s nods to classical statuary to Warhol’s recontextualization of mass media? Yes—and no. There is a profound ethical difference between homage and untraceable, anonymized assimilation. Artists working within traditions of appropriation often did so transparently, dialogically, or critically. GenAI, by contrast, effaces the source while replicating its style, rendering attribution obsolete and legal redress difficult. The result is not a generative commons, but a privatized marketplace where the collective output of thousands of creators becomes fuel for corporate-scale automation (Elgammal et al., 2017).

Herein lies the second axis of disruption: not just speed, but scale. A single artist can produce only so much in a given time. GenAI, by contrast, can flood digital marketplaces with hundreds of variations on a theme, optimized for virality, search engine performance, and mass customization. The value of scarcity, already under pressure in digital media, collapses further. Where once a singular work might command attention through craft or concept, now it must compete with algorithmically tuned visual saturation. This oversupply doesn’t just make discovery harder—it devalues the labor that precedes discovery. The platforms that host these images (e.g., DeviantArt, ArtStation, Instagram) are themselves complicit, tweaking visibility algorithms in ways that favor “engagement” over process or provenance.

But perhaps the most emotionally charged dimension of this shift is the erosion of professional pride. For many artists, especially those who spent years honing a practice, the knowledge that someone with no artistic training can produce a comparable visual product via prompt commands strikes at the dignity of mastery itself. This is not mere gatekeeping—it is a legitimate critique of a system that now celebrates results detached from process. What is threatened here is not art, but artistic identity. The labor that once conferred meaning through discipline, repetition, and refinement now appears optional, even anachronistic.

And yet, it must be asked: how new is this anxiety? The Romantic myth of the solitary genius was always intertwined with broader structures of class and labor. What we call “authorship” was historically a rare privilege, conferred through patronage, education, and access to cultural capital. Most working artists throughout history—painters, weavers, engravers, decorators—were artisans first, and only artists by retrospective elevation. The threat posed by GenAI is thus not just technological, but sociological: it democratizes style while obliterating the social scaffolding that once protected the artisan’s economic niche.

Still, the question lingers: if art is expressive, why does this matter? Shouldn’t those who create for fulfillment rather than profit be unbothered by these shifts? The answer is both simple and damning: because labor must eat. The notion that artists should be content with expression while their markets collapse is both romantic and cruel. In a society where time, materials, housing, and health are commodified, the ability to make art at all is inextricably bound to economic survival. When GenAI undercuts the pathways by which artists convert skill into sustenance, it does not merely challenge aesthetics—it imposes new conditions of precarity. To tell a working artist to “just make art anyway” is akin to telling a laid-off teacher to “just keep teaching in your living room.”

There is also an asymmetry of empowerment at play. The tools of GenAI are not evenly distributed. The computational infrastructure required to train and deploy these models is controlled by a handful of powerful companies—OpenAI, Adobe, Meta, Stability AI—whose monetization strategies revolve around user engagement, licensing, and scalable deployment. Artists are invited to “leverage” these tools, but rarely to participate in their governance or compensation structures. The result is a digital feudalism in which creative labor is extracted from below and monetized from above. This differs radically from earlier techno-artistic shifts, where at least the tools (cameras, brushes, synthesizers) were owned and operated by the artists themselves.

And yet, despite all this, the creative impulse persists. Some artists are embracing GenAI—not as a threat, but as a medium. Prompt poetry, AI-assisted collage, adversarial aesthetics, and hybrid installations are emerging as new forms of expression that neither reject nor surrender to the machine. These works interrogate, subvert, or co-opt the very conditions of their production. They ask what it means to curate, to seed, to collaborate with a system whose internal logic is both alien and deeply indebted to human culture. If this moment is marked by loss, it is also marked by experimentation—a liminal phase in which artists are reshaping the discourse of value, authorship, and creativity itself.

In sum, GenAI does not disrupt art as a human phenomenon; it disrupts art as an economic structure and a social contract. It transforms what is valued, how value is assigned, and who is allowed to claim authorship. It flattens difference, accelerates production, and scrambles attribution. But it also catalyzes reflection, critique, and new modalities of making. The work ahead is not to resist GenAI in total, but to build frameworks—legal, ethical, aesthetic—that preserve space for the human amid the algorithmic. If art is to remain a vocation and a profession, then the terms of labor must be renegotiated, not just philosophically, but materially.


Co-option, Emulation, and Artistic Innovation

A Hall of Mirrors by BMcC, Dali-E, and Midjourney

Art, far from being a closed system of original expressions, has always thrived on emulation, citation, and transformation. The Renaissance was built upon the intentional revival of classical forms; the Baroque borrowed from the Renaissance and exaggerated it into theatrical excess. Every artistic epoch engages in a complex dance with its predecessors, borrowing gestures, subverting conventions, and recontextualizing symbols. The notion that true art emerges only from originality is a relatively modern conceit—one sharpened in the Romantic era and codified in modernist aesthetics. Even then, originality was never total; it was always relational, defined by difference within a known system.

This genealogical nature of art is what makes claims of GenAI’s stylistic mimicry both familiar and unsettling. Yes, artists have always borrowed from one another. Manet borrowed from Velázquez. Warhol borrowed from advertising. Barbara Kruger borrowed from political propaganda. But in each of these cases, the borrowing was positional—it was part of a conscious intervention, a commentary, a rupture. The artist stood in relation to their source, either revering it, challenging it, or reframing it. There was dialogue, tension, and—crucially—intent.

GenAI does not enter into such a relationship. It does not cite, it does not critique, and it does not feel the weight of the referent. Its outputs, while often visually striking, are non-positional: they float in an aesthetic void, untethered to context or intent. When a human artist emulates, the act is embedded within a network of influences, communities, and ideological choices. When GenAI emulates, it collapses that network into surface resemblance. The result may be compelling at the level of form, but it is often vacuous at the level of content.

This distinction is not semantic—it is structural. The difference between artistic emulation and algorithmic synthesis lies in how meaning is produced and received. A contemporary artist painting in the style of Van Gogh is immediately legible as participating in a historical dialogue; their choice is readable as homage, parody, critique, or personal exploration. A GenAI image “in the style of Van Gogh,” by contrast, has no such context. It does not mean because it was not made to mean. It is an imitation without position—technically accurate, yet spiritually inert.

And yet, even this critique requires nuance. There are domains—particularly in design, fashion, and applied arts—where surface matters more than substance. A logo, a background, a quick illustration—these may not require deep positionality to fulfill their function. In these cases, GenAI is not displacing profundity; it is displacing low-stakes creative labor that has long been commodified. One could argue that in these realms, its mimicry is not a rupture but an evolution—another shift in the tools available for solving aesthetic problems.

But when we move from utility to artistic identity, the stakes change. Artistic innovation, as historically understood, arises not just from combining elements, but from navigating the social and conceptual terrain of why they are combined, how they resist or reinforce norms, and what they risk in doing so. Picasso did not invent Cubism by splicing visual inputs; he invented it by confronting the politics of representation, perspective, and fragmentation. Hannah Höch did not create photomontage by compiling images at random, but by weaponizing collage as a feminist and Dadaist act. Innovation is not the rearrangement of pixels—it is the repositioning of meaning within form.

This is where GenAI’s limitations are most apparent. Its outputs, while often innovative in appearance, lack intentional rupture. They recombine styles, but they do not critique or repurpose them. They surprise aesthetically, but rarely intellectually. They reflect a deep learning of patterns, but not of stakes. As McCormack et al. (2019) note, “without the capacity for autonomous intention, computer-generated art risks becoming a form of cultural echo rather than cultural intervention.”

That said, GenAI’s outputs are not wholly devoid of innovation. When embedded within human workflows, these tools can spark new forms of creativity—prompt-based storytelling, algorithmic collaboration, adversarial aesthetics. Artists have already begun experimenting with GenAI as a partner rather than a replacement, using its capacity for rapid recombination to break out of their own stylistic habits. In this mode, GenAI becomes not the generator of meaning, but a provocation—an estranged mirror through which the artist sees differently.

Indeed, this capacity for defamiliarization may be GenAI’s most fruitful contribution. When its outputs are treated not as final products but as raw material, they can push artists into unfamiliar terrain. Much as the Surrealists used chance procedures (e.g., exquisite corpse, automatic writing) to circumvent the conscious mind, contemporary artists can use GenAI to escape cliché or habituated form. But this only works when the human artist remains central—when the tool is curated, critiqued, redirected.

The challenge, then, is not to reject GenAI outright, but to recognize its limits and reassert the value of positionality in creative labor. This requires a cultural shift: to move beyond the fetish of output and toward a renewed emphasis on process, context, and interpretation. It means asking not just what is made, but why, for whom, and with what awareness of lineage and implication.

It also demands a reckoning with the power asymmetries encoded in aesthetic production. If GenAI systems are trained on global databases of art but governed by a handful of private firms, then the dialogue between past and present is no longer a mutual inheritance—it is a form of top-down appropriation. This is not artistic evolution; it is industrial simulation. As such, we must be vigilant: not just about style, but about structure—not just about what is created, but about who controls the terms of creation.

In the end, GenAI does not herald the death of innovation, but its redistribution. If it allows anyone to make something that looks like art, then the cultural bar for what counts as innovative may rise. Artists will need to articulate not just aesthetic difference, but conceptual stakes. Innovation will not be defined by novelty of form alone, but by novelty of position—by how one speaks through and against the machine, rather than merely with it.

In this sense, the tradition of artistic co-option remains alive—but altered. Where once artists borrowed from the canon or their contemporaries, they now borrow from a machinic archive that reflects, distorts, and flattens human expression. The task ahead is to re-inject intentionality into this archive—to wield its outputs not as ends, but as provocations. Only then can imitation become transformation, and style become meaning once more.


Creation vs. Commercialization: Reclaiming the Purpose of Art

Inspirational Friction by BMcC, Dali-E, and Midjourney

What is art, really, when no one is watching? This question, long deferred in favor of market logics, now returns with force in the age of generative artificial intelligence. The friction between art as an inner necessity and art as a commercial product has always existed, but GenAI heightens its urgency. For the first time, machines not only assist in the making of art—they appear to replace it. And yet, this perceived replacement only holds true if art is viewed as a transaction: a thing bought, sold, licensed, or optimized. If, instead, we return to a deeper view—art as a generative act of presence, meaning-making, and human compulsion—then the grounds for crisis begin to shift.

Historically, art has oscillated between these two poles: the sacred and the saleable. In prehistoric societies, image-making was embedded in ritual and myth, not monetized as a commodity. In the Paleolithic caves of Lascaux or Chauvet, we see artistic expression inseparable from spiritual function—a record of the inner and outer world not meant to circulate, but to resonate (Lewis-Williams, 2002). As societies evolved into agricultural and stratified forms, artistic labor became increasingly tethered to structures of patronage. The artist was not only a conduit for the sacred, but a servant of wealth and power. Cathedrals, palaces, and portraits were all sites of both expression and commission.

Yet even here, the act of creation retained its dual logic—to make meaning, and to make a living. The Renaissance master was both metaphysician and tradesman, innovator and employee. The tension was not eliminated, but held in balance. It was only in the modern period—with the rise of capitalist art markets and romantic individualism—that these poles began to fracture. The “true artist” became an outsider to commerce, while the “commercial artist” was seen as lesser: a technician, a sellout, a stylist without soul. This dichotomy, though seductive, has always been artificial. Art has always required materials, time, labor, and support—even when it aspires to transcend them.

GenAI throws this division into crisis because it appears to resolve the contradiction: it produces the product of art without requiring the process. The commercial artifact is generated instantly, cheaply, and without fatigue. There is no hunger, no doubt, no revision. There is only the output—a visual, a lyric, a voiceover—delivered on demand. In this frame, art becomes pure commodity: surface without struggle. And for those whose livelihood depends on artmaking, the threat is existential—not because their passion is displaced, but because their labor is.

This brings us to the critical question: What, if anything, remains sacred in art once it is decoupled from its economic function? Can artistic practice survive without commercial validation? Should it? Or must we reimagine the structures of support, patronage, and recognition that allow creation to flourish under non-extractive conditions?

In many ways, this moment echoes earlier phases of artistic deprofessionalization. When photography democratized image-making, many painters lost commissions but found freedom. When punk rock exploded, musicians with no formal training created a seismic cultural shift by rejecting virtuosity. When YouTube opened the gates to video creators, new genres emerged that didn’t need film schools or studios to exist. In all these cases, the loss of gatekeeping led to both chaos and innovation—a destabilization that allowed more people to create, even as others found themselves displaced.

But GenAI differs in one essential way: it collapses process entirely. It allows those with no artistic experience to generate outputs indistinguishable—at first glance—from those created through long, rigorous engagement with form. For some, this feels like liberation; for others, theft. It raises a profound question: if the emotional or philosophical weight of art is in its making, and that making is removed, what value remains? This is not simply a matter of aesthetics; it is a matter of ethics.

Artists know this intimately. The process—the false starts, the long nights, the revisions, the slow unfolding of intuition—is not a bug; it is the point. It is in the struggle that meaning is formed. A GenAI model, in contrast, operates without memory, without embodiment, without risk. Its “creativity” is probabilistic—pattern matching across latent space. It has no interiority. It is a vessel for recombination, not reflection. Its speed, while seductive, obscures its absence of stakes.

And yet, not all artistic practice is about suffering. There is joy, too, in spontaneity. GenAI, when integrated thoughtfully, can be a tool of play—a sketch partner, a brainstorm assistant, a refractor of habits. The line between assistance and automation is not fixed; it is determined by how the tool is used. Some artists already engage GenAI as a medium rather than a competitor, treating it as one would treat collage, chance operations, or found material. In these contexts, the act of creation remains central—the machine is shaped by human desire, not the other way around.

This distinction is crucial: Are you using the tool to express something unique, or are you using it to avoid the act of expression altogether? The former is extension; the latter, abdication. Creation without intentionality becomes production. And production without reflection becomes noise.

What we are confronting, then, is not merely the automation of aesthetics, but the erosion of intentional space. In a culture already shaped by acceleration, GenAI threatens to sever the last tether between mindful making and mass consumption. It replaces hours of focus with moments of input. And while this may thrill advertisers, it impoverishes the artistic psyche. The result is a cultural flattening—works that dazzle but do not linger, that mimic depth but offer no interior.

But if we accept that art is not just a product, but a practice, then the future is not bleak—it is demanding. It asks us to recenter process as the site of meaning. To resist the market’s fetish for immediacy. To rebuild systems that support creation even when it is slow, difficult, or unmonetizable. This may mean new forms of patronage: public arts funding, cooperative collectives, alternative economies of attention. It may mean smaller audiences, slower circuits of exchange, and deeper engagements with fewer works. But it may also mean freedom—to create outside the tyranny of engagement metrics, trends, and instant gratification.

In this light, GenAI becomes not a verdict, but a catalyst. It forces the question: What do we value in art? The answer will differ for each artist, each audience, each tradition. But one truth remains: when the world accelerates, art can slow it down. When culture flattens, art can deepen it. When technology overwhelms, art can rehumanize. But only if we choose to protect the spaces where art is made for its own sake, not just for the feed.

In the end, GenAI may redefine the marketplace. But it cannot define the maker—unless we let it.


Conclusion: Toward a New Cultural Synthesis

A New Weave by BMcC, Dali-E, and Midjourney

If there is one constant across the long arc of artistic history, it is this: disruption births adaptation. Technologies change, economies collapse, new tools unsettle old hierarchies—but the impulse to create remains. Generative artificial intelligence now stands as the latest of these disruptions, uncanny in its capacity to mimic and proliferate, yet fundamentally tethered to the aesthetic histories it absorbs. It threatens the livelihoods of artists, yes—but it also challenges us to reconsider what it is we value in the act of making.

What GenAI exposes, perhaps more than any previous technological shift, is the fragility of the social contract surrounding art. We are reminded that the ability to make a living from art is not a natural right, but a constructed privilege—one that depends on social, institutional, and infrastructural support. In its absence, the line between vocation and survival becomes strained. This is not to say that GenAI should be rejected wholesale, but that its rise demands a recalibration of our systems of value, authorship, labor, and cultural stewardship.

This recalibration begins with clarity of categories. If art is understood merely as output—an image, a soundbite, a paragraph—then GenAI is a formidable competitor. It is fast, prolific, and increasingly refined. But if art is a practice—one that involves intentionality, experimentation, risk, and relation—then GenAI is not a replacement but a provocation. It invites us to distinguish between the look of art and the experience of making it, between aesthetic surface and creative depth.

The most hopeful vision of the GenAI era is not one of resistance, but of synthesis. Just as earlier technologies were assimilated into artistic traditions—photography into visual modernism, digital tools into conceptual performance—GenAI, too, can become a medium rather than a threat. This will require artists to shift from defense to direction: to assert their agency not only in what they make, but in how they engage the machine. Prompting, curation, refusal, remixing—these are not abdications of authorship; they are its evolution.

Still, synthesis alone is not enough. There must also be protection—legal, ethical, infrastructural. Artists whose work is used to train generative models should have recourse to attribution, compensation, and opt-out mechanisms. Transparent data governance, participatory oversight, and public accountability are not luxuries; they are necessities if we are to preserve the integrity of creative labor in a machine-mediated world. Without these, GenAI will not be a democratizing force—it will be a tool of cultural extraction, where the labor of the many fuels the profit of the few.

Parallel to protection must come reimagination. We must rethink what patronage looks like in the 21st century. If the old models—nobility, church, industrial capital—are insufficient or compromised, then new ones must emerge. Cooperative ownership, platform co-ops, guaranteed basic income for artists, decentralized funding models—these are not utopian dreams but pragmatic responses to a changing creative economy. Art has always needed its ecosystems. We are overdue to design better ones.

In this context, the concept of “originality” itself may evolve. No longer the solitary genius, the artist of the near future may be seen as a synthesist, a navigator of informational abundance, a contextualizer. Their power will lie not in what they alone can make, but in what they can gather, frame, and interpret. In a world flooded with content, the most valuable currency may be curation, not creation.

At the same time, we may witness a counter-movement—a resurgence of slow, manual, embodied forms of art that assert presence against simulation. Calligraphy, analog film, performance, sculpture—these may not be scalable, but they are irreplaceable in their tactile resistance to digitization. As GenAI becomes ubiquitous, the handmade may take on new symbolic power: not as nostalgic retreat, but as an aesthetic of refusal, a reclaiming of labor and time.

Ultimately, what GenAI compels is not a decision between old and new, human and machine, but a reaffirmation of purpose. What is art for? Who is it for? How do we want to live with it? These are not technological questions, but cultural ones. And they demand answers rooted in ethics, imagination, and solidarity.

The future of art will not be decided by algorithms alone. It will be shaped by the frameworks we build—intellectual, legal, and emotional—for holding meaning amid abundance, for finding voice amid noise, for creating not just images, but forms of life.

Art, after all, has never been merely what we make. It is how we live with what we make. And that, even in the age of generative machines, remains a profoundly human act.


Sources

The Work of Art in the Age of Mechanical Reproduction

Benjamin, W. (2008). The work of art in the age of mechanical reproduction (J. A. Underwood, Trans.). Penguin UK. (Original work published 1936)

The Printing Press as an Agent of Change

Eisenstein, E. L. (1980). The printing press as an agent of change: Communications and cultural transformations in early-modern Europe. Cambridge University Press.

Creative Adversarial Networks (CAN)

Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068.

The Past is a Foreign Country

Lowenthal, D. (1985). The past is a foreign country. Cambridge University Press.

Wealth and the Demand for Art in Italy, 1300–1600

Goldthwaite, R. A. (1993). Wealth and the demand for art in Italy, 1300–1600. Johns Hopkins University Press.

The consumption of Italian paintings in Avignon during the 14th century

Anheim, É. (2020). The consumption of Italian paintings in Avignon during the 14th century. Études De Lettres, 314, 23–42.

The Mind in the Cave: Consciousness and the Origins of Art

Lewis-Williams, D. (2002). The mind in the cave: Consciousness and the origins of art. Thames & Hudson.

  • Cambridge University Press
    https://www.cambridge.org/core/journals/antiquity/article/abs/david-lewiswilliams-2002-the-mind-in-the-cave-consciousness-and-the-origins-of-art-london-thames-and-hudson-320pp-index-figures-illustrations-in-black-and-white-and-colour/C1D83B5F228DD3F356AE822B15690F46
  • OpenLibrary
    https://openlibrary.org/works/OL459806W/The_mind_in_the_cave
  • Purchase

Autonomy, Authenticity, and Authorship in Computer-Generated Art

McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. In Proceedings of the 10th International Conference on Computational Creativity (ICCC 2019).

Posted in Reactions | Leave a comment

From Horses to High-Tech: Parallels in Societal Transitions

“Digital Mustangs” by B McC and Dali-E3

Introduction

At the turn of the twentieth century, the United States experienced a radical transformation in its economic and social fabric. The nation had once relied on horses not only for transportation and agricultural labor but also as the foundation of entire industries—from livery services and feed production to racing and related entertainment. Estimates suggest that, in its heyday, the horse industry contributed substantially to national economic output (Oklahoma Historical Society, n.d.). However, as the automobile emerged as a cleaner, faster, and more efficient mode of transport, horse‐based industries rapidly declined. This transition, marked by extensive urban and rural economic disruption, spurred the construction of paved roads, highways, and an entirely new regulatory and infrastructural framework that reshaped American cities and lifestyles (Smiley, n.d.).

Fast forward to today, the United States finds itself amid another transformational period—this time shifting from traditional analog business models to digital‐first systems. Digital technologies are now the engines of economic activity, a role that parallels the centrality once held by the horse. Recent statistics from the U.S. Bureau of Economic Analysis show that in 2021 the digital economy contributed approximately 10.3% of U.S. gross domestic product (GDP) (Highfill & Surfield, 2022), and industry analyses indicate that digital goods and services now account for roughly 19% of GDP (O’Grady, 2024). Just as the replacement of horses with automobiles led to profound secondary and tertiary effects—such as suburbanization, shifts in labor markets, and environmental challenges—the digital revolution is already altering business operations, labor relations, and regulatory frameworks.

This essay argues that the challenges and societal impacts observed during America’s transition from a horse‐based to an automobile‐based economy provide valuable insights for understanding the current shift toward digitalization. The historical transition involved not only the disappearance of established industries but also secondary changes in urban planning, consumer behavior, and even environmental policy. Today, the digital-first model similarly disrupts traditional sectors, from brick-and-mortar retail to conventional media and manufacturing, while also generating new issues such as data privacy, cybersecurity risks, and labor displacement due to automation.

By examining historical sources on the automobile revolution (Smiley, n.d.; Oklahoma Historical Society, n.d.) alongside modern research on digital economic metrics (Highfill & Surfield, 2022; O’Grady, 2024) and studies on intangible capital (Corrado, Hulten, & Sichel, 2009), this essay explores both the immediate and ripple effects of these two distinct yet conceptually analogous transitions. In doing so, it will highlight how past policies—such as massive federal investments in road infrastructure and regulatory reforms in traffic and public health—facilitated a smoother adjustment and long-term benefits that reshaped society. The discussion will then turn to the modern era, where the challenges of digital transformation demand similar proactive policies and investments in human and digital capital. The goal is to demonstrate that while the technologies differ, the underlying dynamics of disruption, adaptation, and eventual resolution are remarkably consistent over time.

In the following sections, the essay first details the multifaceted challenges of the transitions. It then examines the broader societal impacts and the mechanisms that have been (or might be) used to resolve them. Ultimately, the historical experience of replacing horse‐based industries with automobiles offers a roadmap for mitigating today’s digital disruptions through coordinated public policy, strategic investments, and thoughtful regulation.


The Challenges of Transition

Historical Challenges: From Horses to Automobiles

The early twentieth century was marked by a dramatic reallocation of resources and labor as America transitioned away from a horse-based economy. Horses were once ubiquitous in urban and rural life, supporting industries that ranged from feed production and stable management to transportation and racing. This infrastructure—built around the needs of equine labor—was deeply embedded in the social and economic life of the country (Oklahoma Historical Society, n.d.). However, as the automobile emerged as a superior alternative, many established horse-based industries began to collapse. Livery services that once catered to horse-drawn carriages were rendered obsolete, and the supply chains for horse feed and maintenance experienced sudden disruption. This economic displacement caused significant job losses and forced communities, especially in rural and semi-urban areas, to adapt or perish (Smiley, n.d.).

Urban centers, too, faced unique challenges. Prior to the automobile, the by-products of horse-based transportation—such as manure and related waste—created severe public health hazards and contributed to unsanitary living conditions. The automobile, while initially posing its own set of challenges (including safety and pollution concerns), eventually catalyzed sweeping infrastructural changes. Federal programs, most notably the Interstate Highway System, were launched to accommodate and regulate motor vehicle traffic. These changes required enormous public investment and a complete overhaul of existing municipal regulations (Smiley, n.d.). Secondary effects included dramatic shifts in urban planning: as cities restructured their layouts to favor automobiles, suburbanization accelerated, and new patterns of consumption and labor emerged.

Modern Challenges: The Shift to Digital-First Models

Today, the digital revolution is disrupting traditional analog business models in a manner reminiscent of the horse-to-automobile transition. As firms adopt digital technologies, core sectors such as retail, media, and financial services are being redefined. Digital platforms now account for an ever-growing share of economic output; for example, the U.S. digital economy contributed 10.3% of GDP in 2021 (Highfill & Surfield, 2022), and projections suggest that this figure may rise to nearly 19% in the near future (O’Grady, 2024). Unlike the tangible disruptions seen a century ago, digital transformation creates challenges that are often abstract yet far-reaching.

One of the primary modern challenges is economic displacement. Traditional analog processes—reliant on physical interactions and paper-based transactions—are being replaced by digital systems that require entirely new skill sets. The resulting job displacement is significant; workers in sectors such as conventional retail, print media, and traditional banking may find their roles diminished or obsolete if they do not acquire digital competencies. Similar to the early 1900s, this technological disruption necessitates a comprehensive re-skilling and retraining effort. Government and industry must collaborate to provide vocational training programs that prepare the workforce for digital tasks (Corrado, Hulten, & Sichel, 2009).

Another pressing issue is the regulatory vacuum in which the digital economy currently operates. Digital transactions, data privacy, cybersecurity, and intellectual property rights present challenges that have no direct historical analogue. The rapid pace of digital innovation often outstrips the development of appropriate regulatory frameworks. As companies collect vast amounts of data and rely on intangible assets such as software and digital content, questions arise about fair compensation and market competition. For instance, debates over the distribution of economic value between tech giants and their users have become increasingly prominent (Tett, 2024). Moreover, the risk of data breaches and cyberattacks demands that regulators create policies to protect consumers while fostering innovation—a balance that proved challenging in the early automotive era as well, when new safety standards and environmental regulations had to be rapidly developed (Smiley, n.d.).

The digital shift also brings significant labor market implications. Unlike the physical displacement caused by the disappearance of horse-based industries, digital automation affects the production process in less visible but equally profound ways. Artificial intelligence, machine learning, and robotics are automating routine tasks, potentially displacing millions of workers. However, unlike the clear-cut replacement of horses by automobiles—which eventually generated new industries such as auto manufacturing—the net effect of digital automation on employment is less certain. The “digital divide” exacerbates these issues, as workers without access to digital resources or training are left behind (Highfill & Surfield, 2022).

Lastly, the secondary and tertiary impacts of digital transformation are beginning to reshape physical and social spaces. Traditional retail centers are closing as e-commerce grows, and many office spaces are being repurposed for mixed-use developments. Environmental concerns also arise; while digital processes may reduce some forms of pollution, the rapid expansion of data centers and the associated energy demands introduce new challenges. Recent forecasts by Reuters (2025) suggest that increased investments in data centers will contribute modestly to GDP growth while simultaneously raising energy consumption—a modern echo of the infrastructural challenges faced during the automobile revolution.

Together, these challenges illustrate that while the technologies are different, the underlying dynamics of disruptive change remain consistent. Both transitions require extensive adaptation in economic structures, labor markets, regulatory frameworks, and urban planning.


Impacts and Their Resolution

Historical Impacts and Their Resolutions

The transition from a horse-based society to an automobile-centric one generated immediate, far-reaching impacts. The obsolescence of horse-based industries resulted in significant job losses and forced a rapid reallocation of resources. However, the transformation also opened up new economic opportunities. Public investments in infrastructure—most notably the Interstate Highway System—provided the backbone for economic integration, boosted productivity, and reshaped urban and rural landscapes (Smiley, n.d.). Safety regulations and environmental measures, though initially lagging, eventually evolved to mitigate negative externalities such as traffic accidents and air pollution. In addition, the automobile revolution spurred secondary changes such as suburbanization, which in turn redefined consumer behavior and created entirely new industries (Oklahoma Historical Society, n.d.).

Government intervention played a critical role in smoothing the disruptive effects. Federal funding and tax policies supported the construction of new roads and highways, while regulatory reforms established standards for vehicle safety and emissions. These measures not only compensated for the economic displacement experienced during the transition but also set the stage for long-term economic growth and improved quality of life.

Modern Impacts and Proposed Resolutions

The digital-first transition is already producing impacts that mirror many of the disruptions observed a century ago. The digital economy’s growing contribution to GDP is accompanied by the displacement of traditional jobs, particularly in sectors that have long relied on analog processes. As digital platforms capture increasing market share, legacy industries must either innovate or face decline. This disruption necessitates massive retraining and education initiatives aimed at equipping workers with the skills required for digital tasks. Studies have shown that investments in intangible capital—including digital skills and intellectual property—are critical for economic growth (Corrado, Hulten, & Sichel, 2009).

To address these challenges, policymakers must take a multifaceted approach. First, public–private partnerships can facilitate the rapid expansion of digital infrastructure and retraining programs. For example, government incentives for broadband expansion and federal funding for vocational education can help bridge the digital divide. Second, regulators must develop robust frameworks to safeguard data privacy, ensure cybersecurity, and promote fair competition in digital markets. The European Union’s General Data Protection Regulation (GDPR) provides one model for protecting consumers while allowing innovation to flourish (Tett, 2024).

Additionally, the environmental impacts of digital expansion require urgent attention. The surge in data center construction—projected to boost GDP by up to 20 basis points in 2025–2026 (Reuters, 2025)—also poses challenges in terms of energy consumption and carbon emissions. Investing in renewable energy sources and improving energy efficiency within digital infrastructures are essential steps to counterbalance these negative externalities.

Finally, as digital platforms reshape social interactions and urban landscapes, planners must consider the broader social impacts. Just as the repurposing of urban spaces followed the decline of horse-drawn carriage facilities, the decline of physical retail and the rise of remote work will require creative urban planning solutions. Policymakers might incentivize mixed-use developments that integrate digital and physical amenities, thereby promoting community cohesion and economic resilience.

In summary, both historical and modern transitions share similar trajectories of economic displacement followed by innovation-driven recovery. The key to managing these transitions lies in proactive public policy, strategic investments in human and digital capital, and the development of comprehensive regulatory frameworks that balance innovation with social and environmental safeguards.


Conclusion

Reflecting on the sweeping transformations that reshaped America a century ago—from a society dependent on horses to one driven by automobiles—it is clear that disruptive technological change has always carried both significant challenges and immense opportunities. The automobile revolution brought about economic displacement, urban restructuring, and profound social changes; yet, coordinated government action and strategic investments ultimately transformed these challenges into long-term benefits (Smiley, n.d.; Oklahoma Historical Society, n.d.).

Today, as the digital economy expands—now contributing between 10% and 19% of U.S. GDP (Highfill & Surfield, 2022; O’Grady, 2024)—similar dynamics are unfolding. Traditional analog industries are being upended by digital technologies, and while this shift promises gains in efficiency, productivity, and global connectivity, it also poses serious questions regarding job displacement, regulatory adequacy, and social equity. Modern challenges such as data privacy and cybersecurity, along with concerns about corporate concentration and unequal value capture, echo the disruptive forces of the past. Proactive policies—including workforce retraining, robust digital regulations, and investments in sustainable digital infrastructure—are essential to ensure that the benefits of the digital transformation are widely shared.

Moreover, research on intangible capital (Corrado, Hulten, & Sichel, 2009) underscores that the measurement of economic output must evolve to reflect these new realities. Just as the automobile era necessitated new measures of infrastructure investment and urban planning, the digital age demands a rethinking of economic metrics and policy frameworks. The lessons from history thus provide a valuable guide: with careful planning, strategic public investment, and adaptable regulatory frameworks, the disruptive energy of technological change can be harnessed to foster inclusive and sustainable growth.

In conclusion, although the digital revolution is technologically distinct from the transition that replaced horse power with automobiles, the underlying process of disruption, adaptation, and eventual recovery remains similar. By learning from historical transitions and integrating those lessons into modern policymaking and business strategy, society can navigate the turbulent waters of change and create a future where innovation serves as a catalyst for economic prosperity and social well-being.


Citations

Introduction

The Challenges of Transition

Impacts and Their Resolution

Conclusion

Posted in Musings and Observations | Leave a comment

Punch Cards, AI, and the Future of Work

Adapting to Rapid Change

I. Introduction

In every era, the intersection of human labor and technology undergoes a fundamental recalibration, often marked by cycles of displacement, adaptation, and redefinition of skills. The current wave of artificial intelligence (AI) replacing low-level coders is no exception. It is tempting to view this transition as unprecedented in scale or consequence, but history offers a striking parallel. The automation of routine coding tasks in the mid-20th century, catalyzed by the advent of compilers and precompilers, displaced a generation of punch-card programmers and initiated a profound transformation of the computing workforce. Then, as now, technological innovation promised efficiency and progress while simultaneously forcing workers to confront the specter of obsolescence. By examining this historical precedent, we gain not only a sense of continuity but also an opportunity to anticipate and mitigate the dislocations AI will undoubtedly bring.

In the 1950s and 1960s, punch cards were the lifeblood of computation. These simple, perforated pieces of cardstock served as the bridge between human thought and machine logic. Programming in this era required a kind of mechanical literacy, a mastery of the physical processes by which code was translated into rows of punched holes. Yet this labor-intensive method of programming would soon be disrupted. High-level programming languages like FORTRAN and COBOL, introduced in 1957 and 1959 respectively, rendered much of this expertise obsolete. These languages allowed programmers to write instructions in syntax that more closely resembled natural language, which was then translated into machine-readable code by compilers. The result was a profound abstraction of the programming process—one that empowered some but displaced others. Historian Martin Campbell-Kelly notes that “compilers removed the need for programming at the machine level, simultaneously democratizing and destabilizing the profession” (Computer: A History of the Information Machine). This democratization of programming widened access to computing, but it also accelerated the erosion of punch-card expertise as a sought-after skill.

For many punch-card programmers, the shift was abrupt and unforgiving. Companies were eager to adopt the efficiency of new tools, and workers who lacked the flexibility or resources to adapt were left behind. While precise statistics on workforce displacement during this period remain elusive, anecdotal accounts from industry veterans and organizational records suggest a significant contraction in roles directly tied to punch-card operation. Some programmers transitioned into emerging fields such as systems analysis or application design, roles made possible by the very languages that had rendered punch cards obsolete. Others exited the field entirely, finding themselves unprepared to navigate the demands of a rapidly evolving labor market.

The historical resonance with today’s AI revolution is striking. Just as compilers automated the translation of human thought into machine instructions, AI systems such as OpenAI’s Codex and tools like GitHub Copilot are automating large portions of routine coding, generating entire functions and algorithms with a few well-crafted prompts. According to a report by McKinsey & Company, these tools are accelerating a profound shift in the skills needed for software development: “AI automation is reducing the demand for traditional coding tasks while increasing the need for roles requiring problem-solving, adaptability, and cross-functional collaboration” (McKinsey). As these low-level tasks are delegated to AI, the skills required to remain competitive in the field are shifting upward, favoring roles that emphasize abstract thinking, architecture, and the integration of AI-generated outputs into larger systems.

However, the pace of this transition is vastly accelerated compared to the punch-card era. While it took nearly two decades for compilers to become ubiquitous across the computing industry, the adoption of AI tools in software development has been meteoric, driven by the near-instantaneous dissemination of technologies via the internet and global networks. This acceleration is emblematic of a broader trend: the cycles of technological paradigm shift are growing shorter and more intense. Consider, for instance, the evolution of entertainment media. It took more than three decades for radio to give way to television as the dominant medium in the United States. By contrast, the transition from cable television to internet video platforms like YouTube unfolded within a single decade, and the rise of streaming services such as Netflix disrupted the latter paradigm in even less time. As these cycles continue to shorten, workers are increasingly challenged to adapt to multiple revolutions within a single career span—a prospect that raises urgent questions about lifelong learning, professional development, and economic resilience.

To navigate this accelerating turbulence, we must look beyond the specifics of any single technological shift and instead consider the broader patterns of displacement and reinvention that characterize industrial history. The displacement of punch-card programmers in the 1960s offers both cautionary lessons and reasons for optimism. It reminds us that adaptation is possible but requires intentional effort: industries must prioritize retraining initiatives, individuals must embrace a mindset of continuous learning, and policymakers must act to ensure equitable access to opportunities in emerging fields. At the same time, this history underscores the risks of inaction. Just as workers who clung to punch-card expertise were left behind, so too will those who fail to engage with AI’s transformative potential find themselves sidelined in tomorrow’s economy.

This essay explores the parallels between the punch-card era and the present, arguing that the lessons of the past can inform a more equitable and forward-thinking approach to the challenges of the AI age. Section II provides a detailed historical analysis of the punch-card era, examining how compilers redefined programming and reshaped the workforce. Section III draws on this historical context to identify similarities and differences between the 1960s and today, with a particular focus on the social and economic implications of technological displacement. Section IV shifts to a broader perspective, exploring how the acceleration of paradigm shifts is challenging traditional notions of career development and professional stability. Finally, the conclusion offers practical recommendations for fostering resilience in an era of constant change, from policy-level interventions to strategies for lifelong learning. By linking the past to the present, this essay aims to illuminate a path forward—one that recognizes both the risks and the opportunities of our current technological revolution.

Punch Card Programming – B McC / DALI-E 3

II.A. Overview of Punch-Card Programming

In the mid-20th century, punch cards were the backbone of digital computation. These simple pieces of perforated cardstock—approximately 7 inches by 3.25 inches—stored information as a series of holes, encoding data and instructions that could be processed by machines. First popularized by Herman Hollerith for tabulating the 1890 U.S. Census, punch cards had, by the 1950s, evolved into the primary medium for interacting with digital computers like the IBM 1401 and UNIVAC I (Ceruzzi 52). For over two decades, the labor of programming was inseparably linked to the manual operation of punch cards, requiring a workforce trained in the precise, methodical art of keypunching and card organization.

Programming in this era was a fundamentally physical process. A single punched card could encode just 80 columns of information, typically representing one line of code. This meant that a small program could span hundreds—or even thousands—of cards, which had to be manually arranged in the correct sequence. A dropped deck of cards was, famously, a disaster that could set back hours of work (Ceruzzi 55). Moreover, punch cards were not just storage media but served as tangible artifacts of intellectual labor. Handling a stack of cards was akin to holding the “blueprint” of a computation, a physical representation of abstract logic.

The process of creating these cards required a workforce of operators, often women, who worked on keypunch machines to translate written instructions into the punched holes that machines could read. In fact, by the 1950s, the vast majority of keypunch operators were women, reflecting broader gendered labor patterns in early computing (Abbate 6). These operators were critical intermediaries in the programming process, yet their contributions were often overshadowed by the more visible work of computer engineers and scientists.

The Shift to High-Level Languages

By the late 1950s, the introduction of high-level programming languages like FORTRAN (1957) and COBOL (1959) began to fundamentally alter the punch-card paradigm. These languages allowed programmers to write code in syntax resembling natural language, which could then be translated into machine-readable instructions by a compiler. This shift marked a profound abstraction of the programming process, automating many of the labor-intensive tasks that had defined the punch-card era.

The transition was transformative but also disruptive. Historian Martin Campbell-Kelly describes compilers as a “double-edged sword” that simultaneously broadened access to programming while undermining the expertise of manual programmers (Campbell-Kelly 119). No longer tethered to the limitations of physical media, programmers were free to focus on higher-order tasks, such as algorithm design and system optimization. However, this newfound efficiency came at the cost of displacing an entire generation of keypunch operators and manual coders.

Cultural and Practical Significance

Beyond their technical functionality, punch cards carried cultural significance as symbols of the early computer age. They represented both the promise of automation and the rigid discipline required to harness it. Each hole punched into a card was a testament to human effort—a product of deliberate thought and manual labor. Even as compilers rendered punch cards obsolete, their legacy endured in the metaphors and practices of modern computing. Terms like “debugging” originated in the punch-card era, when literal bugs—such as moths trapped in relay switches—had to be removed to keep machines running smoothly (Grier 52).

The punch-card era is a reminder of the intimate, often tactile relationship between humans and technology during the formative years of computing. It was a time when programming demanded not only intellectual acumen but also manual dexterity, patience, and the ability to work within severe constraints. These constraints spurred creativity, but they also imposed limits that would ultimately give way to a new paradigm—one that valued abstraction, efficiency, and automation over physicality and routine.


Section II.B: Workforce Attrition During the 1960s–70s Transition

Economic Shifts and Workforce Dynamics

In computing, the shift away from punch-card operations epitomized this transformation. High-level languages such as FORTRAN and COBOL reduced the need for human intermediaries who translated instructions into machine code. Instead, compilers took over, enabling a single programmer to accomplish in hours what might have taken a team of keypunch operators days to complete.

The displacement of punch-card programmers is emblematic of the challenges posed by automation. Although the overall demand for computing professionals grew during the 1960s and 1970s, the skills required to thrive in the evolving labor market changed dramatically. The U.S. Department of Labor reported that while positions such as “computer systems analysts” and “program designers” experienced rapid growth during this period, roles tied to data entry and manual computation declined sharply (Occupational Outlook Handbook 1975). For many displaced workers—particularly women, who formed the majority of the punch-card workforce—opportunities for advancement were limited. Access to retraining programs or higher education was uneven, reflecting systemic inequities in who benefited from the computing revolution (Martin & Hall).

Cultural Perceptions of Automation

The cultural response to the rise of compilers and cognitive automation was shaped by both optimism and apprehension. On one hand, the narrative of technological progress celebrated the triumph of abstraction and efficiency. The ability to write code in high-level languages like COBOL, which was famously marketed as being “English-like,” was heralded as a democratizing force that would open the field of programming to a wider range of participants (Sammet 37). On the other hand, fears of deskilling and unemployment loomed large. Public discourse on automation during the 1960s often fixated on the broader societal implications of machine intelligence, reflecting anxieties that machines were encroaching on domains traditionally reserved for human expertise (Haigh).

A 1966 cover story in Time Magazine, titled “The Automation Jobless,” warned of a future in which white-collar jobs were increasingly vulnerable to mechanization—a fear that resonates with today’s concerns about AI.

The displacement of punch-card programmers also contributed to a growing awareness of the gendered dynamics of automation. Women, who had been integral to the punch-card workforce, were disproportionately impacted by the shift toward compilers and higher-level programming. While some women successfully transitioned into new roles as systems analysts or software developers, many others were excluded from the emerging professional class of computer scientists, which was increasingly dominated by men (Abbate 9). These gendered disparities in career progression reinforced broader societal patterns of exclusion, ensuring that the rewards of technological progress were distributed unevenly.

Legacy and Lessons for Today

The economic and cultural disruptions of the punch-card era offer enduring lessons for contemporary debates about AI-driven automation. Just as compilers displaced punch-card operators, today’s AI systems are automating routine coding tasks, raising fears of widespread job displacement among low-level programmers. The parallels are striking: then, as now, automation forces workers to contend with shifting skill requirements, while industries face the challenge of ensuring that the benefits of technology are equitably distributed.

However, the history of the punch-card era also highlights the opportunities inherent in technological change. As lower-level tasks became automated, new fields such as software engineering, systems architecture, and database management emerged, providing avenues for career growth and innovation. The challenge lies in ensuring that these opportunities are accessible to all, rather than concentrated in the hands of those with existing privilege. Policymakers, educational institutions, and industry leaders must collaborate to create pathways for retraining and upskilling, just as they must confront the systemic inequities that limit access to these resources.


II.C. Broader Economic and Cultural Impact

The transition from punch cards to compilers in the mid-20th century did not occur in isolation; it reflected and reinforced broader economic and cultural shifts taking place in postwar America and beyond. At its core, this technological transition epitomized the growing trend toward automation—an innovation that promised efficiency and productivity while simultaneously exacerbating economic inequalities and cultural anxieties about job security. Just as industrial automation had transformed manufacturing during the preceding decades, the rise of high-level programming languages signaled the dawn of cognitive automation, challenging established labor patterns and forcing societies to confront the implications of a technology-driven future.

Economic Shifts: From Labor-Intensive to Abstract Work

The automation of low-level programming tasks mirrored broader trends in postwar industrial economies, where labor-saving technologies increasingly supplanted repetitive, manual jobs. Between 1947 and 1973, U.S. labor productivity rose at an unprecedented rate of 3% annually, largely due to technological advances across sectors (Gordon 95). In computing, the shift away from punch-card operations epitomized this transformation. High-level languages such as FORTRAN and COBOL reduced the need for human intermediaries who translated instructions into machine code. Instead, compilers took over, enabling a single programmer to accomplish in hours what might have taken a team of keypunch operators days to complete.

The displacement of punch-card programmers is emblematic of the challenges posed by automation. Although the overall demand for computing professionals grew during the 1960s and 1970s, the skills required to thrive in the evolving labor market changed dramatically. The U.S. Department of Labor reported that while positions such as “computer systems analysts” and “program designers” experienced rapid growth during this period, roles tied to data entry and manual computation declined sharply (Occupational Outlook Handbook 1975). For many displaced workers—particularly women, who formed the majority of the punch-card workforce—opportunities for advancement were limited. Access to retraining programs or higher education was uneven, reflecting systemic inequities in who benefited from the computing revolution (Greenbaum 131).

Cultural Perceptions of Automation

The cultural response to the rise of compilers and cognitive automation was shaped by both optimism and apprehension. On one hand, the narrative of technological progress celebrated the triumph of abstraction and efficiency. The ability to write code in high-level languages like COBOL, which was famously marketed as being “English-like,” was heralded as a democratizing force that would open the field of programming to a wider range of participants (Sammet 37). On the other hand, fears of deskilling and unemployment loomed large. Public discourse on automation during the 1960s often fixated on the broader societal implications of machine intelligence, reflecting anxieties that machines were encroaching on domains traditionally reserved for human expertise (Hannah 12). A 1966 cover story in Time Magazine, titled “The Automation Jobless,” warned of a future in which white-collar jobs were increasingly vulnerable to mechanization, a fear that resonates with today’s concerns about AI.

The displacement of punch-card programmers also contributed to a growing awareness of the gendered dynamics of automation. Women, who had been integral to the punch-card workforce, were disproportionately impacted by the shift toward compilers and higher-level programming. While some women successfully transitioned into new roles as systems analysts or software developers, many others were excluded from the emerging professional class of computer scientists, which was increasingly dominated by men (Abbate 9). These gendered disparities in career progression reinforced broader societal patterns of exclusion, ensuring that the rewards of technological progress were distributed unevenly.

Legacy and Lessons for Today

The economic and cultural disruptions of the punch-card era offer enduring lessons for contemporary debates about AI-driven automation. Just as compilers displaced punch-card operators, today’s AI systems are automating routine coding tasks, raising fears of widespread job displacement among low-level programmers. The parallels are striking: then, as now, automation forces workers to contend with shifting skill requirements, while industries face the challenge of ensuring that the benefits of technology are equitably distributed.

However, the history of the punch-card era also highlights the opportunities inherent in technological change. As lower-level tasks became automated, new fields such as software engineering, systems architecture, and database management emerged, providing avenues for career growth and innovation. The challenge lies in ensuring that these opportunities are accessible to all, rather than concentrated in the hands of those with existing privilege. Policymakers, educational institutions, and industry leaders must collaborate to create pathways for retraining and upskilling, just as they must confront the systemic inequities that limit access to these resources.

In many ways, the cultural tensions of the 1960s mirror those of today. While the technologies differ, the underlying questions remain the same: How can societies navigate the disruptions of automation without leaving vulnerable populations behind? How can technological progress be harnessed to create a more equitable and inclusive future? By reflecting on the punch-card era, we gain a clearer understanding of how to address these questions in the age of AI.

Workforce Transition – B McC/DALI-E 3

III.A. Key Similarities Between the Two Transitions

The disruption of the workforce caused by today’s rise of artificial intelligence (AI) bears striking similarities to the transition from punch-card programming to compilers in the 1960s and 1970s. Both transitions were marked by the displacement of routine, task-specific roles, the rapid obsolescence of technical skills, and the emergence of new, higher-level professions. Although separated by half a century, these transitions reflect recurring patterns in the relationship between automation and labor: periods of displacement followed by restructuring and opportunities for reinvention.

1. Automation of Routine Tasks

In both eras, automation began by targeting the most repetitive and labor-intensive aspects of work, a trend common to nearly all technological revolutions. Punch-card programming in the mid-20th century was an arduous and repetitive process. Keypunch operators physically encoded instructions onto cards, which had to be manually sorted and fed into early computers. This laborious process was rendered obsolete by high-level languages such as FORTRAN and COBOL, which allowed programmers to bypass the mechanical labor of card punching entirely. As Martin Campbell-Kelly notes, “The punch card, once central to the computing process, was swept aside by the efficiency of compilers” (Computer: A History of the Information Machine). The result was a sharp reduction in demand for roles tied to manual data entry and low-level programming.

Today, AI systems such as OpenAI’s Codex and GitHub Copilot are similarly automating routine coding tasks. These tools can write boilerplate code, debug programs, and even generate entire functions based on natural language prompts. According to a report by McKinsey & Company, these advancements are rapidly reducing the demand for entry-level programming jobs, as AI tools handle much of the foundational work previously performed by junior developers (Hussin et al.). This mirrors the displacement experienced by punch-card programmers, whose specialized skills became redundant as higher-level abstraction tools emerged.

2. Displacement and Job Insecurity

Both transitions also resulted in significant workforce displacement, as workers whose skills were tied to the older paradigm struggled to adapt to the new one. During the punch-card era, many operators and low-level programmers found themselves unable to transition to roles requiring familiarity with high-level languages or abstract systems thinking. This was particularly true for women, who comprised the majority of keypunch operators. Historian Janet Abbate notes that while the rise of high-level programming languages opened new opportunities for some, these opportunities were often restricted to men who had access to advanced education and training programs (Recoding Gender).

A similar pattern is emerging in today’s AI-driven automation. Entry-level programmers—often those without advanced degrees or extensive professional networks—are the most vulnerable to displacement as AI tools eliminate the need for basic coding skills. The disparity in access to retraining and upskilling programs is exacerbating existing inequalities, much as it did in the 1960s. A recent study by the World Economic Forum predicts that while AI will create new roles in fields like AI ethics, machine learning optimization, and prompt engineering, these roles will require specialized knowledge that many displaced workers lack (World Economic Forum).

3. Emergence of New Roles

Despite the disruptions, both transitions led to the creation of entirely new professions that capitalized on the capabilities of the emerging technology. During the 1960s, the rise of compilers gave birth to roles such as systems analysts, software architects, and database managers—positions that required higher-level conceptual thinking and a deeper understanding of how to design, implement, and optimize complex systems. For example, the U.S. Department of Labor reported that the demand for “computer systems analysts” grew by 75% between 1965 and 1970 (Occupational Outlook Quarterly).

Similarly, the ongoing AI revolution is creating new career opportunities in fields like AI auditing, algorithm explainability, and human-AI collaboration design. As AI takes over routine tasks, workers are increasingly tasked with managing AI outputs, integrating AI models into larger systems, and ensuring that automated processes align with ethical and regulatory standards. This shift mirrors the 1960s in that the automation of low-level work has expanded the scope of what is possible in computing, creating opportunities for those equipped to adapt to the changing landscape.

4. The Imperative for Lifelong Learning

A key similarity between the two transitions is the heightened need for lifelong learning and professional development. During the punch-card era, workers who adapted to the rise of high-level languages often did so by pursuing additional training or self-education. Companies like IBM played a critical role in offering in-house training programs to help workers transition into roles involving systems analysis and application development (Ceruzzi 134). However, access to these programs was uneven, and many workers were left behind.

Today, the imperative for continuous learning is even more pronounced, as the pace of technological change accelerates. AI-driven automation is not only displacing workers but also compressing the timeline within which new skills must be acquired. Workers are now required to engage in ongoing professional development, often outside the formal structures of higher education, to remain competitive in the labor market. Online platforms like Coursera and government-funded programs in digital literacy and AI skills offer potential solutions, but participation remains unequal, reflecting persistent barriers to access (Hussin et al.).


III.B. Key Differences Between the Two Transitions

While the similarities between the punch-card era and today’s AI-driven revolution are compelling, there are also significant differences that set the two transitions apart. The current wave of technological disruption is broader in scope, faster in pace, and more complex in its demands on workers and industries. Furthermore, the societal and global context in which these changes are unfolding is fundamentally different, shaped by the internet, globalization, and rapid advances in artificial intelligence itself. Understanding these differences is crucial for contextualizing the unique challenges and opportunities of the AI age.

1. Scale and Scope of Automation

The transition from punch cards to high-level programming languages in the 1960s and 1970s primarily affected a niche workforce of programmers, operators, and data processors. While significant within the nascent computing industry, its direct impact on the broader economy was relatively contained. For example, even as compilers displaced keypunch operators, the computing workforce represented only a small fraction of the total U.S. labor force at the time (Ceruzzi 125). The rise of automation in manufacturing and agriculture had a much greater macroeconomic impact during the same period.

In contrast, today’s AI-driven automation is poised to disrupt industries far beyond computing. AI systems are automating not only routine coding tasks but also activities in finance, healthcare, logistics, education, and creative industries. According to the Future of Jobs Report 2023 by the World Economic Forum, over 23% of jobs globally are expected to experience significant transformation due to AI technologies by 2030, affecting millions of workers across sectors (World Economic Forum). This broad scope of automation creates ripple effects that extend far beyond the traditional boundaries of tech and engineering, making this transition unprecedented in scale.

2. Speed of Change

The pace of technological adoption has accelerated dramatically since the 20th century. The transition from punch cards to compilers unfolded over nearly two decades, allowing workers and organizations time to adapt incrementally. FORTRAN, introduced in 1957, and COBOL, introduced in 1959, only became widespread in the mid-1960s, providing a gradual learning curve for programmers and operators to transition to higher-level programming roles (Campbell-Kelly 118). During this time, companies like IBM invested in training programs and on-the-job education, offering workers a relatively smooth pathway to retraining (Ceruzzi 133).

In contrast, the adoption of AI tools is occurring at breakneck speed. Within just a few years, tools like GitHub Copilot and OpenAI’s Codex have gained widespread adoption, radically transforming how software is developed. McKinsey & Company notes that this compressed timeline is leaving many workers without the opportunity to upskill or transition to new roles, as the pace of change outstrips traditional education and training systems (Hussin et al.). The rapidity of this shift has heightened concerns about mass displacement and the ability of workers to adapt within such a short timeframe.

3. Technological Complexity

Another key difference lies in the complexity of the technologies driving automation. The compilers of the 1960s, while revolutionary, operated within relatively defined parameters. They translated human-written code into machine-readable instructions but relied entirely on human programmers to define the logic, solve problems, and ensure the accuracy of the final program. In other words, compilers were powerful tools, but their functionality was ultimately bounded by human input (Campbell-Kelly 120).

Today’s AI systems, by contrast, are far more complex and autonomous. Machine learning models like GPT-4, which power AI tools like Codex, are capable of independently generating, debugging, and optimizing code with minimal human intervention. These systems rely on vast datasets, probabilistic algorithms, and neural network architectures that enable them to “learn” and improve over time, introducing a level of unpredictability and opacity that was absent from earlier automation technologies. This “black box” nature of AI presents unique challenges for workers, who must not only use these systems effectively but also develop the skills to audit, interpret, and align their outputs with broader organizational goals (World Economic Forum).

4. Global and Societal Context

The societal context in which these transitions are unfolding is another key difference. The punch-card era occurred during a period of economic optimism in the post-World War II decades, when industrialized nations were experiencing rapid growth, expanding middle classes, and significant public investments in education and infrastructure. Workers displaced by automation often had access to union protections, government retraining programs, and a growing number of alternative job opportunities (Hannah 43). For example, the G.I. Bill in the United States provided returning veterans with educational benefits that enabled many to transition into emerging technical fields (Greenbaum 140).

Today, the societal context is far more fragmented. The rise of AI is unfolding in an era of increasing income inequality, job polarization, and declining investments in public education and workforce development. As noted in a 2024 report by McKinsey, the gap between workers who can access upskilling opportunities and those who cannot is widening, exacerbating socioeconomic inequalities (Hussin et al.). Furthermore, globalization has created a highly interconnected labor market, meaning that the effects of AI automation are distributed unevenly across regions, with workers in developing economies often facing the greatest risks.

How Things Change – B McC/DALI-E 3

IV.A. Historical Cycles of Technological Change

The history of technological innovation is characterized by recurring cycles of disruption and adaptation, each reshaping industries, labor markets, and consumer behavior. These cycles—driven by paradigm-shifting advancements—have not only compressed over time but have also had significant economic and cultural implications. The media and entertainment industry, in particular, offers a compelling case study of how technological change transforms value chains and profitability. From radio to streaming, each new medium has not only displaced its predecessor but has also introduced new challenges, including shifting profit pipelines and diminishing margins.

1. The Radio to Television Transition

The first major technological transition in 20th-century media was the shift from radio to television. In the 1920s and 1930s, radio dominated as the primary medium for news, music, and entertainment. By 1947, over 90% of U.S. households owned a radio (Sterling and Kittross 227). However, the advent of television in the late 1940s and its widespread adoption in the 1950s marked a significant paradigm shift. By the early 1960s, television had replaced radio as the dominant entertainment medium, with 90% of households owning a TV (Sterling and Kittross 302). This transition required significant industry adaptation, as production companies retooled their content for the visual medium and advertisers shifted their budgets to television’s broader and more engaging reach.

While television’s growth opened up new revenue streams, it also displaced older models of profitability. Radio broadcasters, for instance, saw a marked decline in advertising revenue and had to pivot toward niche markets, such as talk radio and local news, to survive. This transition highlights the economic realignment that often accompanies paradigm shifts: one medium’s rise often comes at the expense of another’s profitability.

2. The VHS and DVD Eras: Hollywood’s Profit Peak

The rise of cable television in the 1970s fragmented audiences, offering consumers more channels and specialized programming. Yet cable’s impact was soon amplified—and partially disrupted—by the introduction of home video technology in the late 1970s. The VHS rental era, which began in earnest in the early 1980s, transformed Hollywood’s profit pipelines. For the first time, studios could earn substantial revenue from home audiences, bypassing traditional box office models. By the mid-1980s, video rentals accounted for a significant share of Hollywood’s revenue, with major titles often generating more income from VHS rentals than from theatrical releases (Tryon 48).

The transition to DVDs in the late 1990s further expanded Hollywood’s profit margins. DVDs were cheaper to produce than VHS tapes, offered higher quality, and became a cultural phenomenon. By the mid-2000s, the DVD era represented the peak of Hollywood’s media-to-profit pipeline. Studios enjoyed robust margins, as DVD sales and rentals often outperformed box office revenue, and catalog titles could be re-released to eager consumers (Epstein 24). The overlapping eras of VHS and DVD created a golden age for studios, characterized by consistent profitability and predictable consumer demand.

3. The Post-DVD Decline and the Streaming Revolution

Despite the introduction of Blu-ray in the mid-2000s, the post-DVD era has been marked by diminishing returns for Hollywood studios. Blu-ray never achieved the cultural ubiquity of DVD, and its adoption was undercut by the rapid rise of digital streaming platforms. Netflix, initially launched as a DVD-by-mail service in 1997, transitioned to streaming in 2007, ushering in a new paradigm for content consumption. By 2010, streaming services had begun to disrupt the traditional Hollywood profit model, as consumers shifted away from physical media and toward subscription-based access.

This transition created significant economic pressures for studios. The streaming model, while widely adopted by consumers, operates on thinner profit margins than the physical media era. Studios accustomed to robust revenue from DVD sales now face mounting production and employment costs with limited opportunities to recoup their investments. As a result, Hollywood has seen declining profitability, even as global demand for content continues to grow. Chris Anderson’s The Long Tail describes this phenomenon, noting that while digital platforms increase access to niche content, they also dilute the profitability of blockbusters, which once sustained studio operations (Anderson 90).

4. Compression of Technological Cycles

What distinguishes the media industry’s technological transitions is the accelerating pace at which they occur. The transition from VHS to DVD unfolded over two decades, allowing Hollywood time to adapt its production, marketing, and distribution strategies. By contrast, the shift from physical media to streaming occurred within a single decade, leaving studios struggling to recalibrate their business models. This compression of technological cycles reflects broader trends across industries, driven by the internet, globalization, and exponential increases in computing power (Gordon 142).

In today’s streaming-first ecosystem, studios must compete not only with traditional rivals but also with technology companies like Netflix, Amazon, and Apple. These firms operate with entirely different cost structures and profit expectations, often prioritizing subscriber growth over immediate profitability. As a result, legacy studios face mounting pressure to adapt, even as they grapple with legacy costs and production overheads inherited from the DVD era.

5. Implications for Workforce and Industry Stability

The acceleration of paradigm shifts has profound implications for workers and industries. In the media industry, the rapid transition from physical media to digital streaming has reshaped labor dynamics, with traditional roles in DVD production, distribution, and retail largely disappearing. Meanwhile, new roles in data analytics, algorithmic content curation, and platform management have emerged, requiring workers to develop entirely new skillsets.

More broadly, the compression of technological cycles reduces the window for industries and workers to adapt. While the transition from radio to television allowed for decades of gradual adjustment, today’s shifts demand near-instantaneous responses. According to the Future of Jobs Report 2023 by the World Economic Forum, over 44% of workers globally will need to reskill within the next five years to keep pace with technological changes (World Economic Forum). This acceleration heightens the need for lifelong learning and proactive workforce development, as industries navigate increasingly volatile technological landscapes.


IV.B. Drivers of Acceleration in Technological Cycles

The accelerating pace of technological change in recent decades is not coincidental but the result of a confluence of factors that reinforce one another, creating an environment where paradigm shifts occur more frequently and disruptively. Key drivers such as exponential improvements in computing power, globalization, the internet, and shifting consumer expectations have compressed the time between technological transitions, creating new challenges for industries, policymakers, and workers. Understanding these forces is essential to navigating the rapid shifts we see today, including the AI revolution.

1. Exponential Growth in Computing Power: Moore’s Law

At the heart of accelerating technological change lies Moore’s Law, the observation made by Intel co-founder Gordon Moore in 1965 that the number of transistors on a chip doubles approximately every two years, leading to exponential increases in computing power (Moore 114). This principle has not only held true for decades but has also been a key enabler of rapid innovation across industries. From personal computers to smartphones and AI systems, the relentless improvement in hardware has drastically reduced the cost and time required to develop and deploy new technologies.

In the context of the entertainment industry, Moore’s Law made it possible to transition from bulky VHS tapes to compact DVDs and later to high-definition Blu-ray discs within a relatively short period. The same exponential improvements in computing power facilitated the rise of digital streaming platforms like Netflix, which rely on powerful servers, high-speed internet, and complex algorithms to deliver vast libraries of content to millions of users simultaneously. The compression of technological cycles in media, therefore, owes much to the hardware advances predicted by Moore’s Law.

Today, Moore’s Law is also fueling the rapid development of artificial intelligence. The computational requirements for training large language models like OpenAI’s GPT-4 or Google’s PaLM are immense, but advances in chip design and parallel processing have made it possible to train these systems in months rather than years. As AI capabilities improve exponentially, the speed of adoption and innovation in industries ranging from coding to logistics is accelerating, leaving little time for adaptation.

2. The Internet as a Catalyst for Instantaneous Dissemination

The internet has fundamentally transformed the speed at which new technologies are adopted and diffused across industries and geographies. In earlier eras, technological transitions often unfolded over decades because physical infrastructure and distribution networks limited the spread of innovations. For example, it took over 30 years for television to reach 90% of U.S. households after its commercial debut in the 1940s (Sterling and Kittross 302). By contrast, platforms like YouTube, which launched in 2005, reached 1 billion monthly users within just eight years (Lotz 91).

The internet not only accelerates the dissemination of new technologies but also enables entirely new business models. Streaming platforms, for example, depend on the internet to deliver on-demand content to consumers globally, bypassing the physical distribution networks that defined the VHS and DVD eras. Similarly, AI tools like GitHub Copilot can be instantly deployed to developers worldwide via cloud services, allowing for rapid adoption without the need for localized infrastructure or physical installations.

3. Globalization and the Feedback Loop of Innovation

Globalization has amplified the acceleration of technological cycles by creating a feedback loop in which innovations developed in one region are quickly adopted, modified, and scaled in others. In the media industry, for example, the rise of global streaming platforms like Netflix and Amazon Prime has transformed content production into a borderless enterprise. Shows like Squid Game (South Korea) and Money Heist (Spain) illustrate how globalization has collapsed cultural and geographic barriers, allowing for near-instantaneous adoption of media content on a global scale (Epstein 153).

This feedback loop is not limited to media. In AI development, advancements made by tech companies in one country are quickly disseminated and built upon by others. OpenAI’s Codex, for instance, has inspired similar initiatives from tech giants like Google and Microsoft, creating a competitive dynamic that accelerates innovation. The result is a continuous cycle of advancement that leaves little time for industries to stabilize before the next wave of disruption begins.

4. Shifting Consumer Expectations

Consumer behavior has also played a pivotal role in accelerating technological cycles. In the past, consumers were often willing to adopt new technologies gradually, as infrastructure and affordability improved. However, the digital era has fostered a culture of immediacy, where consumers expect instant access to the latest innovations. This shift in expectations has placed immense pressure on companies to innovate quickly or risk obsolescence.

In the media industry, this is evident in the transition from physical media to streaming. Consumers have come to expect seamless, on-demand access to content across devices, driving platforms like Netflix, Disney+, and Amazon Prime to compete fiercely in delivering both quantity and quality. The pressure to meet these expectations has led to unsustainable production costs for studios, as they scramble to create content at a pace that matches consumer demand. A similar dynamic is unfolding in AI development, where companies are racing to release new tools and features to satisfy growing consumer and enterprise interest.

5. Challenges of Compressed Technological Cycles

While the drivers of acceleration—exponential computing power, the internet, globalization, and shifting consumer behavior—have enabled unprecedented innovation, they have also introduced significant challenges. The compression of technological cycles leaves less time for workers and industries to adapt, exacerbating inequalities between those who can keep pace with change and those who cannot. According to the Future of Jobs Report 2023 by the World Economic Forum, 44% of workers globally will need to reskill within the next five years to remain competitive in their industries (World Economic Forum).

For policymakers, the accelerating pace of change complicates efforts to regulate emerging technologies and address their societal impacts. As AI systems become increasingly complex and autonomous, ensuring their ethical use, mitigating biases, and preventing job displacement will require coordinated efforts across governments, industries, and educational institutions.


IV.C. Implications of Shortening Cycles on Workforce Adaptation

The accelerating pace of technological change has profound implications for the workforce. As cycles of innovation shorten, workers face increasing pressure to adapt to new tools, technologies, and paradigms multiple times within a single career span. The shift from VHS to DVD, for example, unfolded over two decades, giving industries and workers time to recalibrate. In contrast, the transition from DVD to streaming services occurred within a single decade, with AI-driven tools now driving even faster disruptions. This rapid pace of change presents unique challenges for workforce adaptation, magnifies existing inequalities, and underscores the urgent need for lifelong learning and systemic reform.

1. The Rise of Lifelong Learning as a Necessity

The compression of technological cycles has rendered lifelong learning essential rather than optional. In the past, workers could often rely on a single skillset for the entirety of their careers. For instance, keypunch operators in the punch-card era spent years mastering a specialized skill without needing to significantly update their expertise. Today, however, workers must repeatedly acquire new skills to remain competitive in a labor market defined by constant disruption.

This need is particularly acute in industries like software development, where AI tools such as OpenAI’s Codex and GitHub Copilot are automating routine coding tasks. According to McKinsey & Company, workers in such industries are increasingly required to “focus on higher-order problem-solving and system integration tasks,” which demand not only technical expertise but also soft skills like adaptability and collaboration (Hussin et al.). Governments and companies are recognizing the need for upskilling programs, but the pace of technological change often outstrips the availability of effective retraining opportunities.

Online education platforms such as Coursera, Udemy, and Khan Academy have emerged as accessible tools for lifelong learning, offering courses in areas like data science, AI, and digital literacy. However, participation in these programs remains uneven, with workers in lower-income brackets or those in rural areas often facing barriers to access. This disparity raises critical questions about how to ensure equitable opportunities for reskilling in the face of accelerating change.

2. Risks of Inequality and Job Polarization

As technological cycles shorten, the risk of economic inequality intensifies. Workers with access to advanced education, professional networks, and financial resources are better positioned to adapt to new technologies, while those in low-skill or routine roles are disproportionately vulnerable to displacement. The Future of Jobs Report 2023 by the World Economic Forum predicts that 44% of workers globally will need to reskill by 2028, but notes that the burden of adaptation will fall most heavily on workers in lower-income and developing countries (World Economic Forum).

This phenomenon mirrors the dynamics of earlier technological shifts. During the transition from punch cards to compilers in the 1960s and 1970s, workers with advanced degrees were able to transition into emerging roles like systems analysis, while many keypunch operators—predominantly women—were excluded from these opportunities (Abbate 134). Similarly, in today’s AI revolution, roles in AI ethics, machine learning optimization, and prompt engineering are creating lucrative opportunities, but these positions often require highly specialized training that is inaccessible to many displaced workers.

The polarization of the labor market is also evident in the media industry. As studios transitioned from DVDs to streaming, many traditional roles in physical production and distribution disappeared, while demand for roles in data analytics, algorithmic content curation, and platform management surged. However, these new roles often require technical expertise and advanced degrees, creating barriers for workers whose previous roles required neither.

3. Challenges for Industries and Policymakers

The shortening of technological cycles creates significant challenges for industries and policymakers, as they must manage workforce disruptions on increasingly compressed timelines. In the past, industries had decades to adjust to paradigm shifts. For example, the shift from radio to television unfolded over 30 years, allowing broadcasting companies to gradually retool their business models and retrain their employees. In contrast, the transition from physical media to streaming has left many studios struggling to maintain profitability while navigating massive production and employment costs (Epstein 45).

Policymakers face an even greater challenge: designing systems that balance the benefits of technological innovation with the risks of inequality and displacement. Governments around the world have begun experimenting with new models to address these challenges. For instance:

  • Singapore’s SkillsFuture initiative offers government-funded training credits to workers, encouraging lifelong learning and skill acquisition in response to industry demands (SkillsFuture).
  • Denmark has implemented a “flexicurity” model, combining robust unemployment benefits with aggressive retraining programs to help displaced workers transition into new roles (OECD).

However, these programs are not universal, and many countries lack the resources or political will to implement large-scale retraining initiatives. The risk is that without systemic reform, the benefits of technological progress will remain concentrated among those who already have access to education and opportunity, further widening economic divides.

4. Psychological and Social Impacts

Beyond the economic challenges, the rapid pace of technological change also has psychological and social consequences. Workers forced to repeatedly adapt to new paradigms often experience stress, anxiety, and job insecurity, contributing to a phenomenon known as “technostress.” A 2021 study in the Journal of Organizational Behavior found that workers in rapidly changing industries report higher levels of burnout and decreased job satisfaction, particularly when they lack access to adequate training and support systems (Tarafdar et al.).

The cultural perception of work is also shifting. In earlier generations, workers could reasonably expect to build long-term careers within stable industries, accumulating expertise and seniority over time. Today, the notion of career stability has eroded, replaced by a growing emphasis on flexibility and adaptability. While this shift has created opportunities for those willing and able to reinvent themselves, it has also left many workers feeling unmoored in a labor market characterized by constant change.

The Future of People Power – B McC/DALI-E 3

VI. Conclusion

Throughout history, the interaction between labor and technology has been defined by cycles of disruption, adaptation, and reinvention. The parallels between the mid-20th century transition from punch-card programming to high-level compilers and today’s AI-driven automation of low-level coding reveal enduring patterns of displacement and opportunity. These patterns underscore the complexity of technological progress: while innovation drives efficiency and opens new avenues for growth, it also exacerbates inequalities and demands proactive efforts to mitigate its disruptive impacts.

The current wave of artificial intelligence, with its unprecedented speed and scope, introduces unique challenges that amplify these historical dynamics. Unlike the relatively gradual transitions of the past—such as the decades-long shift from VHS to DVD or from radio to television—today’s technological cycles are compressed into just a few years, leaving industries, workers, and policymakers scrambling to keep up. The rise of AI tools like OpenAI’s Codex and GitHub Copilot, which automate large portions of routine coding, represents a fundamental redefinition of what it means to work in the digital age. As this revolution unfolds, its implications are rippling far beyond the tech sector, affecting industries as diverse as finance, healthcare, logistics, and media.

A key lesson from history is that adaptation is possible but requires intentional effort. The punch-card era demonstrated the importance of creating pathways for workers to transition into new roles, whether through retraining programs, educational opportunities, or in-house corporate initiatives. However, it also revealed the risks of neglecting equity: many workers—particularly women and those in routine roles—were left behind, excluded from the emerging opportunities of the compiler era. Similarly, today’s AI revolution poses the risk of exacerbating existing inequalities unless systemic interventions ensure that displaced workers have access to the resources they need to adapt.

To prepare for the future, societies must prioritize lifelong learning, invest in equitable reskilling programs, and create inclusive opportunities for all workers to thrive in a rapidly changing labor market. Policymakers, industries, and individuals each have a role to play:

  • Policymakers must build robust social safety nets and support publicly funded reskilling programs, drawing on successful models like Singapore’s SkillsFuture initiative and Denmark’s “flexicurity” framework.
  • Industries must take greater responsibility for workforce development, offering on-the-job training and collaborating with educational institutions to align curricula with evolving industry needs.
  • Individuals must embrace a mindset of continuous learning, leveraging online education platforms and seeking opportunities to develop hybrid skillsets that combine technical and interpersonal competencies.

Beyond these practical measures, it is critical to foster a cultural shift that views technological change not as a threat but as an opportunity for reinvention. History has shown that technological disruptions often give rise to entirely new industries, roles, and possibilities. Just as the rise of compilers in the 1960s paved the way for careers in systems analysis, software architecture, and database management, today’s AI revolution is creating new opportunities in fields like AI ethics, algorithm explainability, and human-AI collaboration design. By cultivating resilience, adaptability, and an inclusive approach to innovation, societies can harness the transformative potential of AI to create a more equitable and prosperous future.

The cycles of technological change are shortening, but history offers both cautionary tales and reasons for optimism. The lessons of the past remind us that while innovation inevitably disrupts, it also holds the potential to uplift—if we are prepared to meet its challenges with foresight, inclusivity, and determination. The future of work will not be defined by the technologies themselves but by how we choose to navigate their impact, shaping a world where progress serves as a force for shared prosperity.


References for the Introduction

  1. Campbell-Kelly, Martin. Computer: A History of the Information Machine. 3rd ed., Westview Press, 2013. https://www.amazon.com/Computer-History-Information-Machine-Technology/dp/0813345901/.
  2. Hussin, Alharith, et al. “The Gen AI Skills Revolution: Rethinking Your Talent Strategy.” McKinsey & Company, 29 Aug. 2024.
    https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy.
  3. Lotz, Amanda D. The Television Will Be Revolutionized. 2nd ed., New York University Press, 2014. https://www.amazon.com/Television-Will-Be-Revolutionized-Second/dp/1479865257.

References for II.A

  1. Abbate, Janet. Recoding Gender: Women’s Changing Participation in Computing. MIT Press, 2012. https://www.amazon.com/dp/0262518854.
  2. Campbell-Kelly, Martin. Computer: A History of the Information Machine. 3rd ed., Westview Press, 2013. https://www.amazon.com/Computer-Martin-Campbell-Kelly/dp/1032203439.
  3. Ceruzzi, Paul E. A History of Modern Computing. 2nd ed., MIT Press, 2003. https://www.amazon.com/History-Modern-Computing/dp/0262532034
  4. Grier, David Alan. When Computers Were Human. Princeton University Press, 2005. https://www.amazon.com/When-Computers-Human-David-Grier/dp/0691091579

References for II.B

  1. Akera, Atsushi. Calculating a Natural World: Scientists, Engineers, and Computers during the Rise of U.S. Cold War Research. MIT Press, 2006.
    https://www.amazon.com/Calculating-Natural-World-Scientists-Technology/dp/0262012316
  2. Greenbaum, Joan M. In the Name of Efficiency: Management Theory and Shopfloor Practice in Data-Processing Work. Temple University Press, 1979. https://www.amazon.com/Name-Efficiency-Management-Shopfloor-Data-Processing/dp/0877221510
  3. Martin, E. W., & Hall, D. J. (1960). Chapter VIII: Data Processing: Automation in Calculation. Review of Educational Research, 30(5), 522-535. https://doi.org/10.3102/00346543030005522
  4. Haigh T. Inventing Information Systems: The Systems Men and the Computer, 1950–1968. Business History Review. 2001;75(1):15-61. https://doi.org/10.2307/3116556
  5. United States. Bureau of Labor Statistics. “Handbook of Labor Statistics 1971 : Bulletin of the United States Bureau of Labor Statistics, No. 1705,” Handbook of Labor Statistics (1971). https://fraser.stlouisfed.org/title/4025/item/498768.
  6. “Technology and the American Economy. Volume I.” Ed.gov, Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402 (GPO Y3.T22-2T22/I, $.75), 2025, eric.ed.gov/?id=ED023803.

References for II.C

  1. Abbate, Janet. Recoding Gender: Women’s Changing Participation in Computing. MIT Press, 2012. https://www.amazon.com/dp/0262518854.
  2. Gordon, Robert J. The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War. Princeton University Press, 2016. https://www.amazon.com/dp/0691175802/.
  3. Greenbaum, Joan M. In the Name of Efficiency: Management Theory and Shopfloor Practice in Data-Processing Work. Temple University Press, 1979. https://www.amazon.com/Name-Efficiency-Management-Shopfloor-Data-Processing/dp/0877221510/
  4. Hannah, Leslie. The Rise of the Corporate Economy. Methuen & Co., 1976. https://www.amazon.com/dp/0416726705/.
  5. Sammet, Jean E. Programming Languages: History and Fundamentals. Prentice Hall, 1969. https://www.amazon.com/Programming-Languages-Fundamentals-Automatic-Computation/dp/0137299885
  6. United States. Bureau of Labor Statistics. “Occupational Outlook Handbook, 1974-75 Edition : Bulletin of the United States Bureau of Labor Statistics, No. 1785,” Occupational Outlook Handbook (1974). https://fraser.stlouisfed.org/title/3964/item/499178, accessed on January 19, 2025.

References for III.A

  1. Abbate, Janet. Recoding Gender: Women’s Changing Participation in Computing. MIT Press, 2012. https://www.amazon.com/dp/0262518854.
  2. Campbell-Kelly, Martin. Computer: A History of the Information Machine. 3rd ed., Westview Press, 2013. https://www.amazon.com/Computer-Martin-Campbell-Kelly/dp/1032203439.
  3. Ceruzzi, Paul E. A History of Modern Computing. 2nd ed., MIT Press, 2003. https://www.amazon.com/History-Modern-Computing/dp/0262532034
  4. Hussin, Alharith, et al. “The Gen AI Skills Revolution: Rethinking Your Talent Strategy.” McKinsey & Company, 29 Aug. 2024.
    https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy.
  5. United States. Bureau of Labor Statistics. Area Wage Surveys. [Washington]: [U.S. Govt. Print. Off.], 1971.
  6. World Economic Forum. The Future of Jobs Report 2023. World Economic Forum, 2023. https://www.weforum.org/reports/the-future-of-jobs-report-2023/.

References for III.B

  1. Campbell-Kelly, Martin. Computer: A History of the Information Machine. 3rd ed., Westview Press, 2013. https://www.amazon.com/Computer-Martin-Campbell-Kelly/dp/1032203439.
  2. Ceruzzi, Paul E. A History of Modern Computing. 2nd ed., MIT Press, 2003. https://www.amazon.com/History-Modern-Computing/dp/0262532034
  3. Greenbaum, Joan M. In the Name of Efficiency: Management Theory and Shopfloor Practice in Data-Processing Work. Temple University Press, 1979. https://www.amazon.com/Name-Efficiency-Management-Shopfloor-Data-Processing/dp/0877221510/
  4. Hannah, Leslie. The Rise of the Corporate Economy. Methuen & Co., 1976. https://www.amazon.com/Rise-Corporate-Economy-British-Experience/dp/080181894X.
  5. Hussin, Alharith, et al. “The Gen AI Skills Revolution: Rethinking Your Talent Strategy.” McKinsey & Company, 29 Aug. 2024.
    https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy.
  6. World Economic Forum. The Future of Jobs Report 2023. World Economic Forum, 2023. https://www.weforum.org/reports/the-future-of-jobs-report-2023/.

References for IV.A

  1. Anderson, Chris. The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion, 2006. https://www.amazon.com/dp/1401309666.
  2. Epstein, Edward Jay. The Hollywood Economist: The Hidden Financial Reality Behind the Movies. Melville House, 2010. https://www.amazon.com/Hollywood-Economist-Hidden-Financial-Reality/dp/1933633840
  3. Gordon, Robert J. The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War. Princeton University Press, 2016. https://www.amazon.com/Rise-Fall-American-Growth-Princeton/dp/0691147728.
  4. Lotz, Amanda D. The Television Will Be Revolutionized. 2nd ed., New York University Press, 2014. https://www.amazon.com/Television-Will-Be-Revolutionized-Second/dp/1479865257.
  5. Sterling, Christopher H., and John Michael Kittross. Stay Tuned: A History of American Broadcasting. 3rd ed., Lawrence Erlbaum Associates, 2001. https://www.amazon.com/Stay-Tuned-American-Broadcasting-Communication/dp/0805826246
  6. Tryon, Chuck. On-Demand Culture: Digital Delivery and the Future of Movies. Rutgers University Press, 2013. https://www.amazon.com/Demand-Culture-Digital-Delivery-Future/dp/0813561094
  7. World Economic Forum. The Future of Jobs Report 2023. World Economic Forum, 2023.
    https://www.weforum.org/reports/the-future-of-jobs-report-2023/.

References for IV.B

  1. Epstein, Edward Jay. The Hollywood Economist: The Hidden Financial Reality Behind the Movies. Melville House, 2010. https://www.amazon.com/Hollywood-Economist-Hidden-Financial-Reality/dp/1933633840
  2. Lotz, Amanda D. The Television Will Be Revolutionized. 2nd ed., New York University Press, 2014. https://www.amazon.com/Television-Will-Be-Revolutionized-Second/dp/1479865257.
  3. G. E. Moore, “Cramming more components onto integrated circuits, Reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff.,” in IEEE Solid-State Circuits Society Newsletter, vol. 11, no. 3, pp. 33-35, Sept. 2006, doi: 10.1109/N-SSC.2006.4785860.
  4. Sterling, Christopher H., and John Michael Kittross. Stay Tuned: A History of American Broadcasting. 3rd ed., Lawrence Erlbaum Associates, 2001. https://www.amazon.com/Stay-Tuned-American-Broadcasting-Communication/dp/0805826246
  5. World Economic Forum. The Future of Jobs Report 2023. World Economic Forum, 2023.
    https://www.weforum.org/reports/the-future-of-jobs-report-2023/.

References for IV.C

  1. Abbate, Janet. Recoding Gender: Women’s Changing Participation in Computing. MIT Press, 2012. https://www.amazon.com/dp/0262518854.
  2. Epstein, Edward Jay. The Hollywood Economist: The Hidden Financial Reality Behind the Movies. Melville House, 2010. https://www.amazon.com/Hollywood-Economist-Hidden-Financial-Reality/dp/1933633840
  3. Hussin, Alharith, et al. “The Gen AI Skills Revolution: Rethinking Your Talent Strategy.” McKinsey & Company, 29 Aug. 2024.
    https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy.
  4. Tarafdar, Monideepa & D’Arcy, John & Turel, Ofir & Gupta, Ashish. (2015). The Dark Side of Information Technology. MIT Sloan Management Review. 56. 61-70. https://sloanreview.mit.edu/article/the-dark-side-of-information-technology/.
  5. World Economic Forum. The Future of Jobs Report 2023. World Economic Forum, 2023.
    https://www.weforum.org/reports/the-future-of-jobs-report-2023/.
  6. SkillsFuture Singapore. “Empowering Learning for Life.” SkillsFuture, Government of Singapore, 2024. https://www.skillsfuture.gov.sg/.

References for the Conclusion

  1. Abbate, Janet. Recoding Gender: Women’s Changing Participation in Computing. MIT Press, 2012. https://www.amazon.com/dp/0262518854.
  2. European Commission. “Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence.” European Union, 2021. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206.
  3. Hussin, Alharith, et al. “The Gen AI Skills Revolution: Rethinking Your Talent Strategy.” McKinsey & Company, 29 Aug. 2024.
    https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy.
  4. SkillsFuture Singapore. “Empowering Learning for Life.” SkillsFuture, Government of Singapore, 2024. https://www.skillsfuture.gov.sg/.
  5. World Economic Forum. The Future of Jobs Report 2023. World Economic Forum, 2023.
    https://www.weforum.org/reports/the-future-of-jobs-report-2023/.
  6. OECD. “Denmark: Flexicurity and Welfare Reform.” OECD Employment Outlook 2023, Organisation for Economic Co-operation and Development, 2023.
    https://www.oecd.org/employment/outlook/.
Posted in Musings and Observations | Leave a comment

The Erosion of Educational Value and Economic Mobility in Post-Pandemic America

Literacy, Inequality, and the Fight for Systemic Reform


Introduction

The COVID-19 pandemic was a catalyst for profound societal shifts, exposing deep-seated inequities in education, economic opportunity, and public services. Among these changes is a growing disillusionment among parents regarding the value of education, particularly as a pathway to upward mobility and economic stability for their children. Historically, education has been championed as the cornerstone of the “American Dream”—a means for each generation to rise above the challenges of the previous one. However, for many young families, this belief is no longer a certainty but an aspiration clouded by systemic barriers, economic stagnation, and a shrinking public trust in institutions.

This shift in perception did not emerge in isolation but reflects a confluence of trends that have gained momentum over the past two decades. Chief among these is the stark reality that many young Americans are now worse off economically than their parents—a phenomenon tied to the growing consolidation of wealth in fewer households and the corresponding erosion of public resources. For young parents, this economic reality has transformed education from a near-guaranteed investment in future prosperity to a gamble with increasingly uncertain returns.

At the same time, the decline in literacy rates and critical thinking skills across the country compounds these challenges, further diminishing the ability of individuals to advocate for systemic reform. Reading proficiency beyond a ninth-grade level, a prerequisite for critical engagement with complex issues, has been steadily declining. This has far-reaching consequences, as the ability to critically evaluate information underpins not only personal growth but also informed citizenship and resistance to political and economic manipulation.

This paper argues that the post-pandemic shift in how parents value education is inseparable from these broader socioeconomic forces. The generational decline in economic mobility, driven by wealth inequality and political manipulation, has undermined public services and the societal contract. These trends, in turn, exacerbate the decline in literacy and critical thinking, creating a self-perpetuating cycle of disempowerment. Understanding this interconnected web of challenges is essential for envisioning a path forward.

The Decline of Education as a Value-Based Investment

For much of the 20th century, education was regarded as a nearly infallible investment. The post-World War II era saw the rise of robust public education systems and the GI Bill, which opened doors to higher education for millions of Americans. As a result, generations of Americans, particularly Baby Boomers, experienced unprecedented economic mobility. However, by the early 21st century, the promise of education as a means of advancement began to falter. Rising tuition costs, coupled with stagnant wages and skyrocketing student debt, have cast doubt on whether higher education offers a sufficient return on investment.

This skepticism was further amplified during the pandemic, which disrupted traditional schooling models and laid bare systemic inequities. Families with resources could afford private tutors, micro-schools, or the technology necessary for remote learning, while less privileged households struggled to keep children engaged. These disparities, coupled with the financial strain many families faced during the pandemic, led parents to question whether the education system could deliver on its promise of upward mobility.

Economic Realities for Young Families

The generational economic decline facing young parents today is a direct consequence of systemic wealth consolidation and policy decisions that favor the affluent at the expense of the majority. According to a study by Stanford University, only 50% of Americans born in the 1980s earn more than their parents, compared to 90% of those born in the 1940s (Stanford, 2016). This stark decline in absolute income mobility reflects a broader trend of economic stagnation, exacerbated by policies that have prioritized corporate profits and tax breaks for the wealthy.

The consolidation of wealth has also had profound effects on public services, particularly education. Public schools in underfunded districts struggle to provide even basic resources, let alone the kind of enrichment programs that foster critical thinking and higher-order literacy. Meanwhile, private institutions, accessible only to a wealthy minority, continue to thrive, further entrenching the divide between economic classes. For young parents caught in this divide, the dream of a better future for their children is increasingly out of reach.

The Role of Literacy and Critical Thinking

Compounding these challenges is the decline in literacy and critical thinking skills, which are essential for navigating complex societal issues. According to the National Assessment of Educational Progress (NAEP), only about one-third of American students perform at or above proficiency levels in reading, with scores declining steadily over the past decade (NAEP, 2022). This trend is especially concerning given that reading proficiency at or above a ninth-grade level is often considered the minimum threshold for developing critical thinking skills.

Critical thinking, defined as the ability to evaluate arguments, synthesize information, and form independent judgments, is a cornerstone of civic engagement and resistance to manipulation. Without it, individuals are more susceptible to political propaganda, misinformation, and the allure of simple solutions to complex problems. This decline in literacy and critical thinking is not merely an educational issue but a societal one, with implications for democracy, social mobility, and economic equity.

The Political Manipulation of Public Services

The erosion of public trust in education and other societal institutions is further exacerbated by political manipulation. Gerrymandering, voter suppression, and policy gridlock have contributed to a political system that often prioritizes the interests of the wealthy over the needs of the majority. Education, once a bipartisan priority, has become a casualty of this manipulation, with funding cuts disproportionately affecting underserved communities.

The result is a vicious cycle: economic inequality undermines access to quality education, which in turn diminishes literacy and critical thinking skills. This disempowerment leaves individuals less equipped to advocate for systemic change, allowing wealth consolidation and political manipulation to continue unchecked.

A Path Forward

This paper seeks to explore these interconnected challenges in detail, beginning with an analysis of the post-pandemic shift in parents’ valuation of education. It will then examine the economic realities facing young families, the decline in literacy and critical thinking, and the systemic forces that perpetuate these trends. Finally, it will propose a path forward, advocating for systemic reforms to address wealth inequality, revitalize public education, and restore the promise of upward mobility for future generations.

Students working in a futuristic learning space that incorporates the old and the new.
“Tomorrow’s Classroom” by B McC / Dali-E 3

Section 1: Post-Pandemic Shift in the Valuation of Education

The COVID-19 pandemic was a transformative event that reshaped many facets of society, including how parents perceive the value of education. Long seen as a cornerstone of upward mobility and societal success, education is increasingly viewed through a more skeptical lens, particularly among younger parents. The pandemic’s unprecedented disruption to traditional schooling exposed deep inequalities in access, quality, and outcomes, forcing families to reevaluate whether education remains the reliable investment it was once believed to be.

This section explores the factors driving this post-pandemic shift in attitudes, including the historical framing of education as a value-based investment, the immediate impacts of the pandemic on educational systems, and the ways in which economic realities have reframed the relationship between parents and the education system.


1.1 The Historical Context of Education as a Value-Based Investment

In the mid-20th century, education was almost universally regarded as a vital tool for upward mobility. Initiatives like the GI Bill and the expansion of public education systems fueled economic growth and allowed millions of Americans to achieve a standard of living that exceeded that of their parents. The post-war era cemented the idea that education was not only a personal investment but also a public good, essential for national progress.

However, the rising cost of higher education and the decline in real wages since the 1970s have begun to unravel this narrative. Between 1980 and 2020, the average cost of tuition and fees for public four-year colleges increased by over 200% after adjusting for inflation, while median household income rose by only 15% in the same period (National Center for Education Statistics, 2021). This economic mismatch has led many parents to question whether the traditional education system can still deliver on its promise of economic stability and mobility.


1.2 The Pandemic’s Impact on Education

The pandemic accelerated these doubts by laying bare the systemic inequities that define American education. Schools nationwide transitioned to remote learning, a shift that disproportionately affected families without access to reliable technology or internet connections. According to a 2021 Pew Research Center study, nearly one-quarter of low-income families reported that their children were unable to complete assignments due to a lack of internet access, compared to just 6% of higher-income families (Pew Research Center, 2021).

These disparities underscored the extent to which socioeconomic status determines educational outcomes. While wealthier families could afford private tutors, micro-schools, or other supplementary resources, lower-income families often struggled to keep their children engaged in remote learning. Consequently, many young parents began to question whether the public education system, as currently structured, was capable of meeting the needs of their children.

Furthermore, the shift to remote learning disrupted traditional support systems for children, such as in-person interactions with teachers, extracurricular activities, and peer socialization. The absence of these elements led to significant learning losses, particularly among vulnerable populations. A 2022 report by McKinsey & Company estimated that students in low-income schools experienced an average of seven months of learning loss during the pandemic, compared to four months for students in high-income schools (McKinsey & Company, 2022). These learning gaps further eroded confidence in the ability of education to serve as a great equalizer.


1.3 Rising Skepticism About Education as a Financial Investment

The financial pressures facing young parents today have also contributed to a reassessment of education’s value. For families already struggling with stagnating wages and rising living costs, the idea of taking on student debt for an uncertain return is increasingly unappealing. According to data from the Federal Reserve, outstanding student loan debt in the United States surpassed $1.7 trillion in 2022, with the average borrower carrying a balance of over $30,000 (Federal Reserve, 2022).

This financial burden is compounded by the fact that many college graduates struggle to find jobs that justify the cost of their degrees. A 2023 study by Georgetown University’s Center on Education and the Workforce found that nearly 40% of college graduates were employed in jobs that did not require a degree (Georgetown University, 2023). These economic realities have led many parents to reevaluate whether higher education is still a viable path to prosperity for their children.


1.4 The Moral Reframing of Education

In addition to its financial aspects, the pandemic has prompted a moral reevaluation of education’s purpose. Many parents, particularly those from younger generations, are increasingly viewing education not solely as a means to economic advancement but as a tool for personal growth and civic engagement. This shift reflects broader societal changes, including growing disillusionment with traditional career paths and the prioritization of mental health and well-being over material success.

However, this moral reframing of education is not without its challenges. Public schools, long focused on standardized testing and academic benchmarks, are often ill-equipped to foster the holistic development that parents now value. As a result, many families are turning to alternative education models, such as homeschooling or private and charter schools, which promise greater flexibility and personalization. According to the U.S. Census Bureau, the percentage of American households homeschooling their children doubled from 5.4% in spring 2020 to 11.1% by the fall of that year (U.S. Census Bureau, 2021).


1.5 The Emergence of Alternative Schooling Models

The rise of alternative schooling models is both a symptom and a driver of the broader shift in how parents value education. Micro-schools, homeschooling, and other nontraditional options have gained popularity among parents seeking greater control over their children’s education. These models often emphasize experiential learning, critical thinking, and community engagement—qualities that many parents feel are lacking in traditional public schools.

However, the growing reliance on alternative education also raises concerns about equity. While wealthier families can afford to invest in these customized approaches, lower-income families are often left behind, further exacerbating existing inequalities. Without systemic reforms, the proliferation of alternative schooling risks deepening the divide between the “haves” and the “have-nots,” both economically and educationally.


Conclusion

The post-pandemic shift in how parents value education reflects a broader reckoning with the systemic inequities and economic pressures that define modern American life. For young parents, education is no longer a guaranteed investment in upward mobility but a complex gamble influenced by financial, moral, and practical considerations. The pandemic’s exposure of educational disparities, coupled with the financial challenges facing young families, has transformed the way education is perceived and prioritized.

Understanding this shift is essential for addressing the broader societal challenges explored in subsequent sections. From the economic realities facing young families to the decline in literacy and critical thinking, these interconnected issues demand a comprehensive response that goes beyond surface-level reforms.

A great divide seperates the walled off sections of society, represented by ramshackle housing in the foreground, a palace in the mid backed by skyscrapers, and dense urban areas to the west and east of the palace.
“The Divides of Society” by B McC / Dali-E 3

Section 2: Economic Challenges and Wealth Inequality for Young Parents

The economic realities facing young parents today are shaped by decades of growing wealth inequality and systemic barriers to upward mobility. Once seen as a cornerstone of the American Dream, the ability to improve one’s financial standing relative to previous generations has become increasingly out of reach for many. This shift has been driven by structural changes in the economy, the consolidation of wealth among a small elite, and public policies that disproportionately benefit the affluent while neglecting the broader population. For young parents, these economic pressures create a sense of financial insecurity that directly influences their perceptions of education, public institutions, and societal obligations.

This section explores the interconnected forces driving economic inequality, the specific challenges faced by young parents, and the broader implications for American society.


2.1 Generational Decline in Economic Mobility

For much of the 20th century, economic mobility was a defining feature of the American Dream. Post-war policies, including the GI Bill and widespread investment in infrastructure and education, created opportunities for millions to achieve a higher standard of living than their parents. However, this trend has reversed in recent decades, with younger generations facing stagnating wages, rising costs, and limited opportunities for advancement.

A landmark study from Stanford University found that only 50% of Americans born in the 1980s earn more than their parents, compared to 90% of those born in the 1940s (Stanford, 2016). This decline in absolute income mobility reflects broader economic trends, including the outsourcing of manufacturing jobs, the decline of union power, and the rise of the gig economy. For young parents, these challenges translate into financial instability, limited savings, and a growing sense that the economic system is rigged against them.


2.2 Wealth Consolidation and Economic Inequality

The consolidation of wealth among a small elite has further exacerbated economic inequality. According to the Federal Reserve’s Distributional Financial Accounts (DFA), the top 1% of U.S. households controlled approximately 32% of the nation’s wealth in 2022, while the bottom 50% collectively held just 2.5% (Federal Reserve, 2024). This concentration of wealth has profound implications for economic mobility, as it limits access to opportunities and resources for the majority of Americans.

One of the most significant effects of wealth consolidation is the erosion of public goods and services. Wealthy individuals and corporations often use their influence to shape policies that prioritize tax cuts and deregulation, leaving public institutions underfunded. For young parents, this means reduced access to affordable housing, quality education, and reliable healthcare—all of which are critical for achieving economic stability and upward mobility.


2.3 The Financial Realities of Young Parenthood

The economic challenges faced by young parents are compounded by the rising costs of raising children. According to the U.S. Department of Agriculture, the average cost of raising a child from birth to age 18 in the United States is now over $310,000, not including higher education (USDA, 2022). This financial burden is particularly acute for families in lower and middle-income brackets, who often struggle to balance the costs of childcare, housing, and healthcare.

Housing costs, in particular, have become a major source of financial strain. Between 2000 and 2023, the median price of a home in the United States increased by nearly 150%, while median household income grew by just 27% (National Association of Realtors, 2023). For many young families, the dream of homeownership—a key marker of financial stability—remains out of reach.

In addition to these challenges, many young parents are burdened by student loan debt, which limits their ability to save for the future or invest in their children’s education. According to the Federal Reserve, approximately 44 million Americans collectively owe more than $1.7 trillion in student loan debt, with younger borrowers disproportionately affected (Federal Reserve, 2022).


2.4 Political Manipulation and the Undermining of Public Services

Economic inequality is not merely the result of market forces but is also shaped by deliberate policy decisions. Gerrymandering, lobbying, and campaign financing laws have allowed wealthy individuals and corporations to exert outsized influence over the political system. This has led to policies that prioritize the interests of the few over the needs of the many, such as tax cuts for the wealthy and reductions in funding for public services.

Education is a particularly stark example of this dynamic. Public schools in low-income areas often receive significantly less funding than schools in wealthier districts, perpetuating cycles of poverty and limited opportunity. According to a 2023 report by the Education Law Center, the most underfunded schools in the United States face funding gaps of over $150 billion annually, leaving millions of students without access to the resources they need to succeed (Education Law Center, 2023).

The defunding of public services also extends to healthcare, transportation, and social safety nets, all of which disproportionately impact young families. For parents already struggling to make ends meet, the lack of reliable public infrastructure further erodes trust in the social contract and fuels disillusionment with institutions.


2.5 Sociopolitical Impacts on Young Families

The economic pressures faced by young parents have broader implications for society as a whole. Financial instability and limited access to resources contribute to declining birth rates, as many couples delay or forgo having children due to economic uncertainty. According to the CDC, the U.S. birth rate fell to a record low in 2022, driven in part by the economic challenges facing millennials and Generation Z (CDC, 2022).

In addition, economic inequality and political manipulation have fueled growing distrust in institutions, with many young families feeling that the system is stacked against them. This disillusionment undermines civic engagement and weakens the social fabric, making it more difficult to address the root causes of inequality.


Conclusion

The economic realities facing young parents today are the result of decades of growing wealth inequality, policy decisions that favor the elite, and systemic barriers to upward mobility. For many families, these challenges create a sense of financial insecurity that undermines trust in education, public services, and the broader social contract. Addressing these issues requires not only economic reforms to reduce inequality but also a renewed commitment to investing in public goods and ensuring equal access to opportunities for all.

Flying books and butterflies explode out og a book in the foreground, while students read with lights burning through their texts in the background.
“Reading leads to Explosive Thought” by B McC / Dali-E 3

Section 3: Decline in Literacy and Critical Thinking

Literacy and critical thinking are cornerstones of an informed and engaged citizenry. Advanced literacy—the ability to comprehend, analyze, and critique complex materials—forms the foundation for critical thinking, enabling individuals to evaluate information, consider opposing viewpoints, and make reasoned decisions. However, in the United States, literacy rates have stagnated, with proficiency levels showing alarming declines over the last two decades. This trend has far-reaching implications, not only for individual success but also for societal cohesion, democratic participation, and economic equity.

This section explores the decline in literacy and critical thinking, the systemic issues driving these trends, and the broader consequences for American society.


3.1 Literacy Rates and Educational Trends

The National Assessment of Educational Progress (NAEP), often referred to as the “Nation’s Report Card,” reveals a troubling picture of literacy in the United States. According to the 2022 NAEP report, only 33% of American students in grades 4 and 8 scored at or above proficiency in reading—a decrease from previous years (NAEP, 2022). High school students fare no better, with many graduating without the ability to read at a level necessary for critical engagement with complex materials.

Further, the United States ranks behind many developed nations in reading proficiency. A 2019 report by the Programme for International Student Assessment (PISA) ranked the U.S. 13th in reading performance among 79 participating countries, highlighting significant gaps in reading comprehension and critical analysis skills (OECD, 2019). These deficiencies have persisted despite increases in education spending, raising questions about the effectiveness of current pedagogical approaches.


3.2 The Role of Standardized Testing and Curriculum Narrowing

One major driver of declining literacy and critical thinking skills is the overemphasis on standardized testing. Since the passage of the No Child Left Behind Act (NCLB) in 2001, schools have faced mounting pressure to improve test scores, often at the expense of deeper learning. Curricula have been narrowed to focus on test preparation, leaving little room for the development of critical thinking, creativity, and analytical skills.

Studies show that excessive test preparation often leads to superficial learning, where students are trained to memorize facts rather than understand concepts. A 2020 report by the Brookings Institution found that while standardized tests can measure basic proficiency, they fail to capture students’ ability to think critically or solve complex problems (Brookings Institution, 2020).

The narrowing of curricula also disproportionately affects low-income schools, which often lack the resources to provide enrichment programs or advanced coursework. This disparity contributes to a widening gap in literacy and critical thinking skills between affluent and disadvantaged students.


3.3 Literacy Beyond the Ninth-Grade Level: A Prerequisite for Critical Thinking

Reading beyond a ninth-grade level is widely recognized as a benchmark for higher-order literacy and critical thinking. At this level, readers can engage with complex texts, identify biases, synthesize information from multiple sources, and form independent judgments. However, the decline in literacy rates means that fewer Americans are reaching this threshold.

For example, a 2021 study by the National Center for Education Statistics (NCES) found that nearly half of American adults read at or below an eighth-grade level, limiting their ability to critically evaluate news, literature, or academic material (NCES, 2021). This lack of advanced literacy has profound implications, particularly in an era of information overload and widespread misinformation.

Without the ability to critically analyze content, individuals are more susceptible to manipulation by political propaganda, advertising, and social media algorithms. This vulnerability undermines democratic participation, as citizens struggle to navigate complex policy debates or discern credible sources from false information.


3.4 The Connection Between Literacy and Civic Engagement

Critical thinking is not merely an academic skill but a civic one. A literate and informed populace is better equipped to engage in democratic processes, advocate for systemic change, and hold leaders accountable. However, the decline in literacy and critical thinking has weakened the foundation of civic engagement in the United States.

Research shows a direct correlation between literacy levels and voter participation. According to a 2020 report by the Pew Research Center, individuals with higher levels of education and literacy are significantly more likely to vote, contact elected officials, and participate in community activities (Pew Research Center, 2020). Conversely, those with lower literacy levels are less likely to engage in these activities, perpetuating cycles of disempowerment and inequality.

The decline in critical thinking also affects public discourse, as citizens increasingly rely on sound bites and partisan talking points rather than engaging in nuanced debate. This polarization is exacerbated by the echo chambers of social media, where algorithms prioritize content that reinforces existing beliefs rather than challenging them.


3.5 Educational Inequities and Systemic Barriers

The decline in literacy and critical thinking is not evenly distributed across the population. Students from low-income families, racial and ethnic minorities, and rural communities face systemic barriers that limit their access to quality education. These disparities are driven by factors such as underfunded schools, lack of access to technology, and fewer opportunities for enrichment.

For example, a 2022 report by the Education Trust found that schools serving predominantly Black and Hispanic students receive $23 billion less in funding annually compared to schools serving predominantly white students (Education Trust, 2022). This funding gap translates into fewer resources for literacy programs, smaller libraries, and larger class sizes, all of which negatively impact reading outcomes.

These inequities not only perpetuate the cycle of poverty but also contribute to broader societal divides. Without equal access to education, marginalized communities are left at a significant disadvantage, limiting their ability to participate fully in economic and civic life.


3.6 Social and Political Consequences of Declining Literacy

The consequences of declining literacy and critical thinking extend beyond the individual, affecting society as a whole. A less literate populace is less capable of resisting authoritarianism, misinformation, and exploitation. As literacy and critical thinking decline, so too does the public’s ability to challenge systemic inequality, advocate for change, or hold leaders accountable.

This dynamic creates a vicious cycle, where economic inequality and political manipulation further erode education systems, leading to even greater declines in literacy and civic engagement. Breaking this cycle requires systemic reforms that prioritize literacy and critical thinking as essential components of education.


Conclusion

The decline in literacy and critical thinking in the United States is a multifaceted problem with far-reaching implications. From the narrowing of curricula to systemic inequities in funding, the factors driving this trend are deeply entrenched in the broader social and economic fabric of the nation. Addressing these issues requires a renewed commitment to literacy as a public good and an essential foundation for civic engagement, critical thinking, and social progress.

The many chains and challenges tied to educational success.
“The Chains that link Education and Society” by B McC / Dali-E 3

Section 4: Inextricable Links Between These Factors

The interconnectedness of economic inequality, declining literacy rates, and the erosion of faith in education forms a self-reinforcing cycle that threatens the foundations of American society. Wealth consolidation limits access to quality education, while underfunded schools contribute to declining literacy and critical thinking skills. These trends, in turn, leave individuals less equipped to challenge systemic inequalities, perpetuating a cycle of disempowerment and mistrust. Understanding the ways in which these factors influence and amplify one another is critical for envisioning solutions that address the root causes of these challenges.


4.1 Economic Inequality and Educational Disparities

Economic inequality is both a cause and a consequence of disparities in education. Wealth consolidation among the top 1% has left public schools in underserved areas starved of resources, creating significant gaps in educational quality. Schools in low-income neighborhoods often lack adequate funding for textbooks, technology, and extracurricular programs, while wealthier districts benefit from local tax bases that fund state-of-the-art facilities and advanced coursework.

According to the National Center for Education Statistics (NCES), per-pupil spending in the wealthiest school districts is more than double that in the poorest districts (NCES, 2022). This disparity leads to vastly different outcomes in literacy and critical thinking, as students in underfunded schools are less likely to receive individualized attention, enrichment opportunities, or exposure to diverse perspectives. These deficits not only limit their immediate academic success but also reduce their long-term economic and civic engagement prospects.

The lack of access to quality education perpetuates economic inequality by creating barriers to higher education and well-paying jobs. In this way, the economic conditions that disadvantage young parents also hinder their children, creating a multigenerational cycle of poverty and limited opportunity.


4.2 Declining Literacy and Its Role in Perpetuating Inequality

The decline in literacy and critical thinking skills exacerbates economic and political inequality by limiting individuals’ ability to advocate for change. Literacy is more than a skill—it is a form of power that enables individuals to access information, understand complex systems, and engage in meaningful dialogue. When literacy declines, so does the capacity for resistance against systemic exploitation.

Low literacy rates are particularly problematic in a society where wealth and power are concentrated in the hands of a few. Without the ability to critically analyze policies, evaluate sources of information, or challenge dominant narratives, individuals are more susceptible to manipulation. This vulnerability creates fertile ground for political and corporate interests to exploit public ignorance, further entrenching their power.

For example, the rise of misinformation and “fake news” has been amplified by declining critical thinking skills. A 2020 study by the Stanford History Education Group found that 52% of Americans struggle to distinguish between credible news sources and sponsored content (Stanford, 2020). This inability to critically evaluate information undermines public discourse and allows harmful ideologies to proliferate unchecked.


4.3 The Political Manipulation of Public Services

Economic inequality and declining literacy do not occur in a vacuum; they are often the result of deliberate policy decisions. Gerrymandering, voter suppression, and the influence of money in politics have enabled a small elite to shape policies that prioritize their interests at the expense of the majority. These policies include tax cuts for the wealthy, deregulation of industries, and reductions in funding for public services like education and healthcare.

The defunding of public schools, in particular, has profound implications for literacy and critical thinking. Without adequate resources, schools are unable to provide the kind of rigorous, comprehensive education needed to prepare students for the challenges of modern life. This creates a feedback loop in which underfunded schools produce graduates who are less equipped to participate in democracy, making it easier for those in power to maintain the status quo.

For example, a 2023 analysis by the Education Law Center found that states with the most gerrymandered legislatures were also those with the largest funding gaps between wealthy and poor school districts (Education Law Center, 2023). This correlation suggests that political manipulation not only perpetuates economic inequality but also undermines the very institutions designed to mitigate it.


4.4 The Role of Literacy in Civic and Political Engagement

Literacy is a foundational skill for civic engagement, enabling individuals to understand complex issues, evaluate candidates and policies, and participate in meaningful dialogue. However, declining literacy rates have weakened the public’s ability to hold leaders accountable or demand systemic change.

Research shows a strong correlation between literacy and voter participation. According to a 2019 study by the Pew Research Center, individuals with higher literacy levels are significantly more likely to vote, attend town hall meetings, and contact their elected officials (Pew Research Center, 2019). Conversely, those with lower literacy levels are less likely to engage in these activities, perpetuating cycles of political disengagement and disenfranchisement.

This dynamic disproportionately affects marginalized communities, where lower literacy rates often coincide with higher levels of economic hardship. Without access to the tools needed to advocate for change, these communities remain trapped in cycles of poverty and political exclusion.


4.5 Systemic Barriers to Breaking the Cycle

Breaking the cycle of economic inequality, declining literacy, and political manipulation requires addressing the systemic barriers that perpetuate these trends. These barriers include:

  • Unequal Funding for Public Schools: The reliance on local property taxes to fund education creates stark disparities in resources and opportunities.
  • Underinvestment in Adult Education: Programs that promote adult literacy and lifelong learning are often underfunded, leaving many without the skills needed to compete in a changing economy.
  • Political Barriers to Reform: Gerrymandering and voter suppression limit the ability of disenfranchised communities to elect leaders who represent their interests.

Addressing these barriers requires a holistic approach that prioritizes equity in education, reforms campaign finance laws, and ensures fair representation in government. Without these systemic changes, the cycle of inequality and disempowerment will continue to perpetuate itself.


Conclusion

The interconnectedness of economic inequality, declining literacy, and political manipulation forms a self-reinforcing cycle that undermines both individual opportunity and societal progress. Addressing these issues requires more than surface-level reforms; it demands systemic change that prioritizes equity, literacy, and civic engagement as foundational principles of a just society. Understanding the links between these factors is the first step toward breaking the cycle and creating a future where education, opportunity, and democracy are accessible to all.


Call to Action: Breaking the Cycle of Inequality and Disempowerment

Addressing the intertwined crises of economic inequality, declining literacy, and political manipulation requires a comprehensive, multi-pronged approach rooted in systemic reform. Strengthening civic engagement and rebuilding trust in democratic institutions are critical for creating a society where education, opportunity, and equity are accessible to all. This call to action expands on previous recommendations, emphasizing reforms in education, economic policy, and political systems while targeting corruption as a central barrier to progress.

This revised call to action outlines key strategies: restoring education as a public good, addressing economic inequality, revitalizing civic engagement, and ending political corruption.


Investing in Education as a Public Good

Education is fundamental to individual and societal progress. Reestablishing it as a public good requires addressing funding inequities, fostering critical thinking, and providing robust support for educators.

Steps to Take:

  1. Equitable School Funding:
    • Reform the reliance on local property taxes, ensuring fair distribution of resources across all school districts.
    • Increase Title I funding for schools serving low-income communities.
    • Institute a broad tax on corporations specifically to address workforce education, with no loopholes to obfuscate taxable obligations.
  2. Literacy and Critical Thinking Initiatives:
    • Develop nationwide literacy programs targeting underserved populations, starting at a grassroots level, then expanding regionally, then statewide.
    • Integrate media literacy and critical thinking skills into school curricula to combat misinformation.
    • Set a national standard for literacy that must be upheld in every state education department, preventing individual states not aligned with furthering education from bypassing the need.
  3. Support for Educators and Infrastructure:
    • Offer income-outcome based cash incentives for educators to work in underserved areas and expand professional development programs.
    • Modernize school facilities to create safe, enriching environments for students – this means embracing newer literacy technology, not abandoning fundamentals.
    • Provide a national fund for offsetting teacher salaries to a national average, allowing underserved states to employ teachers as easily as affluent and highly populated areas.

Addressing Economic Inequality

Economic reforms are essential for breaking the cycle of poverty and creating upward mobility for all Americans. Targeted measures can reduce wealth disparities, support young families, and alleviate financial burdens tied to education.

Steps to Take:

  1. Progressive Taxation and Wealth Redistribution:
    • Raise taxes on high-income earners and implement higher rates for capital gains.
    • Close corporate tax loopholes to increase funding for public services.
  2. Support for Families:
    • Expand access to affordable childcare and preschool programs.
    • Strengthen paid parental leave policies to support working families.
  3. Student Loan Reform:
    • Expand income-driven repayment options and increase funding for grants and scholarships.
    • Provide loan forgiveness programs for graduates working in public service roles.

Strengthening Civic Engagement and Political Representation

Revitalizing democracy requires bold reforms to reduce the influence of money in politics, increase access to voting, and ensure fair representation. Strengthening civic engagement also involves directly addressing political corruption.

Steps to Take:

  1. Campaign Finance Reform:
    • Repeal Citizens United v. Federal Election Commission to limit corporate influence in elections.
    • Pass legislation that establishes public financing for campaigns to level the playing field.
    • Require full transparency for political donations and lobbying activities (Princeton University, 2014).
  2. Combat Gerrymandering and Voter Suppression:
    • Establish independent redistricting commissions to prevent partisan gerrymandering.
    • Expand access to voting through measures such as universal mail-in voting, same-day voter registration, and early voting options (Pew Research Center, 2020).
  3. End Political Corruption:
    • Repeal Citizens United to restore the integrity of campaign finance laws.
    • Ban stock trading and private sector positions for legislators while in office to eliminate conflicts of interest.
    • Break the revolving door between government and lobbying by imposing a four-year waiting period for legislators and senior officials before joining lobbying firms.
    • Create an independent ethics commission with authority to investigate and enforce these measures.

Building a Culture of Lifelong Learning and Critical Thinking

Promoting literacy and critical thinking across all age groups is vital for empowering individuals to navigate complex societal challenges. Lifelong learning initiatives can reduce disparities and equip citizens to engage meaningfully in civic life.

Steps to Take:

  1. Community-Based Literacy Programs:
    • Partner with libraries, non-profits, and community centers to offer free literacy workshops and book distribution programs.
    • Develop outreach initiatives targeting marginalized communities and adults.
  2. Media Literacy and Misinformation Resistance:
    • Integrate media literacy into school curricula to teach students how to evaluate sources, identify biases, and combat misinformation (Stanford, 2020).
    • Launch public awareness campaigns to educate citizens about recognizing and countering fake news.
  3. Support for Lifelong Learning:
    • Offer tax incentives for employers providing continuing education programs.
    • Offer tax benefits to employees who engage in adult education, either in the form of direct compensation or tax credits.
    • Expand access to free or low-cost online courses and certification programs for adults, and tie certification programs to employment eligibility in both the public and private sector.
Marching towards an equitable, just future, with liberty and temerance
“A March towards Liberty, Temperance, and Justice, despite the current regieme” by B McC / Dali-E 3

A Vision for the Future

The challenges of economic inequality, political manipulation, and declining literacy threaten the very fabric of American society, but they are not insurmountable. By addressing systemic corruption, restoring education as a public good, and empowering citizens through literacy and engagement, we can create a future that values equity, opportunity, and democracy.

This vision requires collective action. Policymakers must prioritize reforms that dismantle barriers to civic participation and economic mobility. Communities must champion initiatives that promote lifelong learning and critical thinking. And citizens must demand accountability, transparency, and representation that reflect their values and aspirations.

The time to act is now.


Citations

Here is the revised MLA-style works cited page with direct links to the specific resources rather than their publishing authorities:


Brookings Institution. Standardized Testing and Student Learning: Understanding the Relationship. 2020, www.brookings.edu/research/standardized-testing-learning/.

CDC. “Birth Rates in the United States Hit Record Lows in 2022.” Centers for Disease Control and Prevention, 2022, www.cdc.gov/nchs/data/databriefs/db377.pdf.

Education Law Center. Funding Gaps: An Analysis of Disparities in U.S. Public School Funding. 2023, edlawcenter.org/research/funding-gaps.html.

Education Trust. Funding Inequities in America’s Public Schools. 2022, edtrust.org/resource/funding-gaps-2022/.

Federal Reserve. Distributional Financial Accounts (DFA). 2024, www.federalreserve.gov/releases/z1/dataviz/dfa/index.html.

Georgetown University Center on Education and the Workforce. The Overeducation of America’s Workforce. 2023, cew.georgetown.edu/cew-reports/overeducation/.

McKinsey & Company. COVID-19 Learning Loss and Its Long-Term Impact on Students. 2022, www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-education-an-emerging-k-shaped-recovery.

National Association of Realtors. Median Housing Prices Over the Last Two Decades. 2023, www.nar.realtor/research-and-statistics/housing-statistics.

National Center for Education Statistics. The Nation’s Report Card: Reading Proficiency Levels. 2022, nces.ed.gov/nationsreportcard/.

National Center for Education Statistics. Adult Literacy in the United States. 2021, nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2019179.

OECD. PISA 2019 Results: What Students Know and Can Do. 2019, www.oecd.org/pisa/publications/pisa-2019-results-volume-i-5f07c754-en.htm.

Pew Research Center. Generational Views on Voter Participation and Literacy. 2020, www.pewresearch.org/fact-tank/2020/06/16/young-americans-less-likely-to-vote/.

Princeton University. Gerrymandering and Public Services: The Connection to Education. American Political Science Review, 2014, journals.sagepub.com/doi/10.1177/1532673X14522150.

Stanford History Education Group. Evaluating Information: The Cornerstone of Civic Online Reasoning. 2020, sheg.stanford.edu/civic-online-reasoning.

Stanford University. The Decline of Absolute Income Mobility in the United States Since 1940. 2016, news.stanford.edu/features/2016/mobility/.

USDA. Expenditures on Children by Families: The Cost of Raising a Child. 2022, www.usda.gov/media/blog/2017/01/13/cost-raising-child.


Posted in Musings and Observations | Leave a comment

The Divergent Paths of Vehicle Electrification: A Comparative Analysis of the U.S. and China

The transition from gas-powered vehicles to electric vehicles (EVs) represents a crucial element in global efforts to reduce carbon emissions and combat climate change. However, the pace and direction of this transition have varied significantly across the world, shaped by differing political, economic, and industrial contexts. In the United States, industrial lobbies have historically influenced the continued dominance of hybrid and gas-powered cars, reflecting entrenched corporate interests that often resist rapid change. In contrast, China has made swift progress in the adoption of EVs, driven by strategic state interventions, including the conversion of gas stations to electric battery swap locations. This divergence underscores the broader challenge of overcoming corporate meddling and traditional ideologies that hinder progress, particularly in the context of global energy production and consumption.

The American Dilemma: Industrial Lobbies and the Slow Adoption of Electric Vehicles

In the United States, the automobile industry’s transition to electric vehicles has been significantly impeded by powerful industrial lobbies representing both car manufacturers and the fossil fuel industry. These entities have a vested interest in maintaining the status quo, where gas-powered and hybrid vehicles continue to dominate the market. The American automobile industry has long been characterized by a reluctance to fully commit to electrification, often promoting hybrid vehicles as a compromise solution that allows the continued use of internal combustion engines .

This resistance is not merely a matter of technological preference but is deeply rooted in the political economy of the United States. The influence of industrial lobbies in shaping transportation policy is well-documented, with significant lobbying efforts aimed at slowing the transition to EVs. The Alliance for Automotive Innovation, representing major car manufacturers, has been a vocal opponent of stringent fuel efficiency standards and has lobbied against policies that would accelerate the adoption of EVs . This corporate meddling reflects a broader pattern of resistance to change, driven by the fear of losing market share and the significant investments already made in traditional automotive technologies.

The persistence of fossil fuel interests further complicates this picture. The oil and gas industry, a major player in the U.S. economy, has also lobbied against the rapid adoption of EVs, fearing a decline in demand for gasoline . This industry’s influence extends beyond transportation, affecting energy policy more broadly, including the continued reliance on coal and natural gas for power production. The U.S. energy sector remains heavily reliant on these carbon-intensive sources, despite the growing availability of renewable alternatives .

China’s Electrification Strategy: State Intervention and Infrastructure Development

In contrast to the American experience, China has taken a proactive approach to vehicle electrification, driven by a combination of state intervention and strategic infrastructure development. The Chinese government’s commitment to reducing carbon emissions has led to a series of policies aimed at promoting EVs, including subsidies for manufacturers and consumers, stringent emissions standards, and significant investments in charging infrastructure .

One of the most innovative aspects of China’s EV strategy is the conversion of traditional gas stations into electric battery swap locations, as highlighted in recent developments . This approach addresses one of the main challenges of EV adoption: the need for convenient and fast charging solutions. By leveraging existing infrastructure and adapting it to the needs of electric vehicles, China has been able to rapidly scale up its EV deployment, making it a global leader in the field.

China’s success in this area is not merely a result of technological innovation but also reflects the country’s ability to mobilize state resources in pursuit of strategic goals. Unlike in the United States, where corporate interests often dictate policy, the Chinese government has taken a more centralized approach, prioritizing long-term environmental and economic objectives over short-term profits . This has allowed for a more coherent and coordinated effort to transition away from fossil fuels, both in transportation and in energy production more broadly.

The Broader Context: Energy Production and the Role of Corporate Interests

The divergence between the U.S. and China in the transition to electric vehicles is emblematic of a broader global challenge: the continued dominance of carbon-intensive energy sources. Despite significant advances in renewable energy technologies, the world remains heavily reliant on coal and natural gas for power generation. This reliance is driven not by technological necessity but by entrenched corporate interests that benefit from the status quo .

The recent findings regarding Germany’s energy policy underscore this point. As noted in recent research, Germany could have eliminated its reliance on carbon-producing power sources if it had not abandoned its nuclear power program . This decision, influenced by public opinion and political considerations in the wake of the Fukushima disaster, has left Germany more dependent on coal and natural gas than it might have been otherwise. This situation highlights the difficulty of overcoming traditional ideologies and the powerful economic interests that sustain them.

Conclusion: Toward a New Paradigm for Progress

The contrasting experiences of the U.S. and China in the transition to electric vehicles illustrate the broader challenge of achieving forward momentum as a species. Progress in addressing climate change and transitioning to sustainable energy sources requires a willingness to abandon traditional ideologies and the economic structures that support them. This includes not only the fossil fuel industry but also the political and corporate interests that have a vested interest in maintaining the status quo.

As the world grapples with the urgent need to reduce carbon emissions, the lessons from China’s rapid deployment of EV infrastructure and Germany’s energy policy missteps are clear. To achieve meaningful progress, it is essential to prioritize long-term environmental and societal goals over short-term profits and to challenge the entrenched interests that stand in the way of change. Only by embracing a new paradigm—one that values sustainability over immediate economic gains—can humanity hope to overcome the challenges of climate change and secure a viable future for generations to come.

References

  1. Alliance for Automotive Innovation. (2021). “About Us.” https://www.autosinnovate.org/about.
  2. Lutsey, N., & Nicholas, M. (2019). Update on electric vehicle costs in the United States through 2030. The International Council on Clean Transportation (ICCT). https://theicct.org/sites/default/files/publications/EV_cost_2020_2030_20190401.pdf.
  3. Oil Change International. (2020). Big Oil’s Real Agenda on Climate Change. http://priceofoil.org/2020/10/14/big-oils-real-agenda-on-climate-change/.
  4. U.S. Energy Information Administration. (2022). Annual Energy Outlook 2022. https://www.eia.gov/outlooks/aeo/.
  5. BloombergNEF. (2023). China EV Market Outlook 2023. https://about.bnef.com/blog/china-ev-market-outlook-2023/.
  6. Youtube Video: Electric Vehicle Battery Swapping Stations in China (2024). https://www.youtube.com/watch?v=anXQfRuAkZw.
  7. The China Government’s Five-Year Plan for New Energy Vehicles (2020). https://english.www.gov.cn/news/topnews/202012/11/content_WS5fd3078dc6d0f72576942d40.html.
  8. Carbon Tracker. (2022). The Future’s not in Gas: Why gas is a risk for investors and the planet. https://carbontracker.org/reports/the-futures-not-in-gas/.
  9. Meinel, J., & Sterner, M. (2024). The Role of Nuclear Energy in Germany’s Energy Transition: What Might Have Been. Energy Policy, 174, 113221. https://www.tandfonline.com/doi/full/10.1080/14786451.2024.2355642.
Posted in Musings and Observations | Leave a comment

Liminal Spaces: The Evolution of Communication and Education in the Age of AI and the Telegram

The concept of a “liminal space” refers to the intermediate phase or condition that exists between two distinct states, often characterized by ambiguity, disorientation, and the potential for transformation. Liminality can be applied to various contexts, from cultural rites of passage to transitional periods in history. This essay explores two significant liminal spaces: the liminal space of education in the age of artificial intelligence (AI) and the liminal space of communication at the advent of the telegram. These two periods, though separated by over a century, share profound similarities in how they represent transitional phases in societal evolution, raising concerns about the effects of technological change on literacy and communication practices.

As AI increasingly permeates education, altering how knowledge is imparted, retained, and assessed, concerns echo those voiced during the advent of the telegram in the 19th century. The telegram, a revolutionary communication tool of its time, was met with both awe and skepticism. Critics argued that the brevity enforced by telegrams would degrade literacy and lead to the demise of the art of letter writing. Similar fears emerged with the introduction of email, SMS, and other digital communication methods. Despite these concerns, written communication has not only persisted but proliferated, demonstrating a remarkable adaptability to new forms and formats.

This essay will delve into the parallels between these two liminal spaces, drawing on historical and contemporary sources to explore how concerns about literacy and communication have persisted and evolved with technological advancements. It will also consider the implications of these changes for the future of education and communication in the AI age.

The Advent of the Telegram: A Liminal Space in Communication

The invention of the telegraph and the subsequent popularization of the telegram in the 19th century marked a significant turning point in human communication. Before the telegraph, long-distance communication was limited to letters, which could take days, weeks, or even months to reach their destination. The telegraph, by contrast, enabled almost instantaneous communication across vast distances, transforming business, politics, and personal relationships.

However, this transformation did not occur without resistance. Critics of the time expressed concerns that the telegram’s emphasis on brevity would erode literacy and degrade the quality of written communication. As James W. Carey notes in Communication as Culture, the telegraph “reduced complex statements to mere fragments, destroying context and nuance” (Carey, 1989). The telegram’s limitations in word count and cost led to the truncation of language, favoring short, terse messages over elaborate prose. This shift prompted fears that the art of letter writing, with its emphasis on detailed expression and narrative, would become obsolete.

These concerns were not unfounded. The shift from letters to telegrams did alter communication practices, but not in the ways critics anticipated. Instead of destroying literacy, the telegraph introduced new forms of writing that emphasized efficiency and clarity. While the long-form letter became less common, new forms of written communication emerged, adapted to the constraints and possibilities of the telegraph. This adaptation process exemplifies the concept of liminality: a transitional phase in which old forms are not entirely discarded but are transformed and integrated into new practices.

The Echoes of the Telegram: Email, SMS, and the Persistence of Written Communication

The fears expressed during the advent of the telegram found echoes in the reactions to subsequent communication technologies, particularly email and SMS. When email became widespread in the late 20th century, critics voiced concerns that it would lead to a decline in formal writing skills. Email, like the telegram, encouraged brevity and informality, often dispensing with the conventions of traditional letter writing, such as salutations, closings, and attention to grammar and spelling.

In a 1993 article in The New York Times, communication theorist Neil Postman expressed concerns that email would “debase the art of letter writing” by promoting speed over substance (Postman, 1993). He argued that the instantaneous nature of email encouraged hasty, poorly considered communication, leading to a decline in the quality of written expression. Similar concerns were raised with the advent of SMS, which, with its 160-character limit, required even greater conciseness than email or telegrams.

However, as with the telegram, these fears did not fully materialize. While email and SMS did lead to changes in writing practices, they did not eradicate formal writing. Instead, they contributed to the diversification of written communication. Formal writing continued to exist alongside these new forms, often within the same platforms. For example, while emails could be brief and informal, they could also be long and detailed when the situation required it. Similarly, while SMS encouraged shorthand and abbreviations, it also led to the development of a new digital literacy, with its own conventions and norms.

Education in the Age of AI: A New Liminal Space

As we move further into the 21st century, education is entering its own liminal space, driven by the rapid development of AI technologies. AI has the potential to revolutionize education, offering personalized learning experiences, automating administrative tasks, and providing new tools for assessment and feedback. However, as with the advent of the telegram, email, and SMS, these changes have sparked concerns about the impact on literacy and the quality of education.

One of the primary concerns is that AI could lead to a decline in critical thinking and writing skills. With AI-powered tools like automated essay scoring, there is a fear that students may become overly reliant on machines to evaluate their work, leading to a reduction in their ability to self-assess and improve their writing. Moreover, the use of AI-generated content, such as summaries and paraphrases, could undermine the development of deep reading and analytical skills, as students may rely on these tools instead of engaging with texts directly.

Critics argue that AI’s emphasis on efficiency and automation could lead to a more standardized and superficial approach to education. In a 2019 article in The Chronicle of Higher Education, educational theorist Cathy N. Davidson warned that “AI threatens to reduce complex thinking to a set of algorithms” and that “the richness of human thought cannot be captured by machines” (Davidson, 2019). She expressed concern that AI could lead to a homogenization of education, where creativity and individuality are sacrificed in favor of conformity and standardization.

However, as with previous technological shifts, the impact of AI on education is likely to be more complex and multifaceted than these concerns suggest. While AI does present challenges, it also offers opportunities for enhancing education in ways that were previously unimaginable. For example, AI can provide personalized learning experiences tailored to each student’s needs, helping to close achievement gaps and support diverse learning styles. Additionally, AI can assist teachers in identifying areas where students are struggling, enabling more targeted interventions and support.

The Persistence of the Written Word

Despite the proliferation of new communication technologies, from telegrams to AI, the written word has shown remarkable resilience. Each technological shift has been accompanied by fears that literacy and the quality of written communication would suffer, yet these fears have often been overstated. Instead of leading to the decline of writing, new technologies have contributed to its evolution, introducing new forms and conventions that coexist with older practices.

One of the most striking examples of this persistence is the continued importance of written communication in the digital age. While video conferencing and social media platforms like TikTok have become increasingly popular, particularly during the COVID-19 pandemic, they have not replaced written communication. In fact, the volume of written content produced and consumed online has grown exponentially, encompassing everything from emails and text messages to social media posts, blogs, and online articles.

Moreover, the digital age has seen the emergence of new forms of writing, such as microblogging on platforms like Twitter, which challenges traditional notions of length and format. These new forms have expanded the possibilities for written expression, allowing for greater flexibility and creativity. At the same time, they have also led to the development of new literacies, as users learn to navigate and produce content within these formats.

The resilience of written communication in the face of technological change suggests that writing is deeply embedded in human culture and cognition. While the forms and practices of writing may change, the fundamental need for written expression remains. This resilience also points to the adaptability of literacy, as new technologies create opportunities for innovation and experimentation in writing.

Conclusion

The liminal spaces of education in the age of AI and communication at the advent of the telegram reveal striking parallels in how technological change prompts concerns about literacy and the quality of communication. In both cases, critics feared that new technologies would erode the richness of written expression, leading to a decline in literacy and the art of writing. However, these fears have often been overstated, as new technologies have contributed to the evolution and diversification of writing practices.

The advent of the telegram, email, and SMS each introduced new forms of communication that emphasized brevity and efficiency, but they did not lead to the demise of formal writing. Instead, they coexisted with older forms, contributing to the development of new literacies and conventions. Similarly, while AI presents challenges for education, it also offers opportunities for enhancing learning and supporting diverse learners.

As we navigate the liminal space of education in the age of AI, it is important to recognize that technological change is not inherently detrimental to literacy or the quality of communication. Rather, it offers new possibilities for innovation and creativity in writing and learning. By embracing these possibilities, we can ensure that literacy continues to thrive in the digital age, just as it has in previous periods of technological change.

References

Carey, J. W. (1989). Communication as Culture: Essays on Media and Society. Routledge.

Davidson, C. N. (2019). The New Education: How to Revolutionize the University to Prepare Students for a World in Flux. The Chronicle of Higher Education.

Postman, N. (1993). Technopoly: The Surrender of Culture to Technology. *The New York

Posted in Uncategorized | Leave a comment

Eleven Years in Percolation

A Philosopher Giving that Lecture on the Orrery, in which a Lamp is put in place of the Sun

On the occasion I have been asked “What do you do for a living?”  – for the last twenty years or so, my answer has been “I’m a technologist” (a phrase I first crossed in the writings of Cory Doctorow).

That answer may seem vague, perhaps even fanciful,  but I see it as honest – it is the answer a painter, a sculptor, or a 3d environment rendering specialist might provide to the same question: “I’m an artist.”  The answer I provide owes its genesis largely to my bread-earning – over the course of the last decade and a half, my paycheck has been tied to technology, specifically information systems and internet technologies. Despite the mundane aspect of this answer, the reason I reply with something less specific than the title tied to my paycheck is my deep-rooted passion for technology, and how it can be applied to make life better.

When this blog was in its pre-infancy, really just a series of conversations between J and I, it was this passion which fueled the topics we bounced between Those conversations gradually led us to the central aim of this blog – an explanation of the question:

In what ways has technology changed the world we live in, and in what ways will it continue to change it?

That inquiry is about as broad as my interpretation of the answer to “What do you do for a living?” It has also been well covered, by columnists and authors of greater experience or prestige than either of us possess. The exploration is boundless – it affects labor, ideology, theology, social norms, entertainment, language, education, psychology, science, humor – everything! In order to try and tackle a subject so immense, we decided we needed two things: a perspective of narrative, and a method of analysis.

When attempting to crystallize the many perspectives on technology into fixed pathways, and categorize the forms those perspectives tend to evolve towards, we took a page from Alexander Berzin (though neither of us are Buddhists), whose writings stipulate that all phenomena fall under three categories – destructive, constructive, or unspecified. The analysis of the constructive or destructive powers and repercussion will be the core of our conversation, with plenty of musing on the more ethereal nature of the unspecified. To ensure the thoroughness of our exploration, J and I will rebut each other regularly on our chosen perspectives.  Additionally, it is our intent to develop and publish  a lexicon of terminology related to key movements in technology, as well as the perspectives our pre-Industrial and Industrial revolution minds might have applied to our concepts.

As a lifelong pessimist, I chose the rockier of the two perspectives to focus on – I am going to generally concern myself with the constructive power and potential of technology.   The unspecified phenomena, certainly, will be useful in the exploration of concepts and prognostication, but. lacking hard discernible measures of impact, will likely be ancillary, at best, in defending my position.  The motivation behind my selection of perspective, I hope, will become clearer the more I write about the subject, but, for now, we’ll leave the modus operandi cloaked in the humorously glib phrase:  “Hoping for Star Trek”.

As to analysis methodology, we hit upon what we both think is a fairly novel approach to looking at all these changes everyone has experienced or read about to some degree in the modern world we live in. The proposed method of our analysis led us to explore this potential, despite the aforementioned depth of writing on the subject. We are going to discuss these perspectives on technology using the springboard of writings, writers, philosophies and philosophers of the epoch of industrial revolution, in Europe, then later the United States, then, even later, Japan, Russia, India, and China.

It is our initial research into this wealth of perspectives and writings which led us to the name of this blog, tritely abbreviated in a most modern method. The phrase “Contemporary Reactions to the Machine” is from an essay by Thomas Carlyle in the 1873 Edinburgh Review entitled “Signs of the Times”. Though Carlyle’s views are largely contrary to the view I mean to defend in looking at the breadth of modern technological phenomena (he was quite concerned about the negative and visible side-effects of the industrial revolution), even in my opening research, I found evidence supporting my perspective in his work:

Know’st thou Yesterday, its aim and reason?
Work’st thou well To-Day for worthy things?
Then calmly wait the Morrow’s hidden season,
And fear not thou what hap soe’er it brings.

Carlyle’s (and others) attribute to Goethe

This is a hopeful little rhyme, suggesting full understanding of the past and present is not necessary to the success of a better tomorrow.  When I went to look for the contextual publication of the original piece online, I came upon an article from a 1920 volume of The American Journal of Philology, which explains, in several pages, that the original author of the cited text was not the well-known German genius, but, in fact, should be attributed to a French literary figure, François de Maucroix , who pre-dated Goethe’s birth by a considerable window.

When trying to come up with an example of the constructive power of technology, a contemporary reaction to the machine emerged – one of Wonder. This Wonder comes, in part, as I consider the contextual search leading me to refute the popular citation of Carlyle’s times to a similar search I might have done thirty years ago. Aside from being specifically tied to geographical proximity to a collection that might have the original article, in addition to the Journal clarifying the origin of the verse (or the patience to write and receive a number of letters to determine the matter), the simple retrieval of the pertinent details, even by a well-versed research librarian of thirty years ago would have taken far longer than a few typed words and mouse clicks it took me earlier today. Beyond that initial information retrieval, in a few more clicks, I had a fair approximation of a general (Wikipedia) knowledge base on the key players, as well as a stable of additional resources and footnotes to follow up on.

Take this same search back twenty years, before the establishment of clear-cut online information resources, and I might have been able to garner some leads myself from a CD-ROM-based encyclopedia, or some basic reference material available through a BBS or Usenet node. It is possible that I may have been able to find the text of Carlyle’s article on Project Guttenberg, or a similar repository of pre-Creative Commons digital publications, but that is fairly unlikely, given the esoteric nature of the subject matter.

In all, this illustration has not proven that technology and progress are positive forces more so than negative ones, nor has it done anything to lessen or deflect the easily quantifiable destructive aspects of these items (as I am sure any printed-on-dead-tree Encyclopedia publisher of yesteryear would be fast to point out).  What my example has done is provide a simple and direct example highlighting the speed with which information can be shared, ingested, analyzed, regurgitated, and acted on, with a fair level of sophistication.  Indeed, the platform on which these thoughts are being published and shared is a byproduct of this age, and you, dear reader, are a participant in it!

“Must go faster, Must go faster!”

Ian Malcolm, Jurassic Park

None of this exposition is taking into account the myriad of other research options I had related to self-published website sources, or multimedia sources I could have accessed through free or paid sources. Similar acts of research and learning are taking place at a nearly incomprehensible rate, simultaneous to my own, on subjects both weightier and more meaningful than my meager example. To add further amazement to my pyre of Wonder, I am not even citing or referencing the “best” tools available with which to augment my store of knowledge – simply the free and readily available ones I can get to with minimal interaction.

Though his observations are a bit hyperbolic, and tied far more to the physical experiences of technological advances, as opposed to the informational research experiences, I’m forced to agree with comedian Louis C.K. on the popular perspectives and lack of Wonder at the technology all around us, which, contextually, we tend to take for granted:

Western society, particularly it’s historians, have been quite fond of establishing decades, centuries, ages and epochs to quick-reference blocks of time for generalized discussion of progress. The era preceding the Industrial Revolution in England (the 18th Century) was named by critic Donald Greene “The Age of Exuberance” – it was a time of turmoil between classical genres of writing, and new forms, meant to entertain and inform, sometimes doing both at once.  The artwork, above, is an echo of this time, where an artist felt that an oil painting, normally reserved for veneration of religious or political figures, would be an excellent medium through which to express their awe and appreciation for the advances in learning and science.

The challenges in form and method throughout the Age of Exuberance were mirrored by the content those forms dealt with – satire and novels challenged deeply ingrained societal constructs and traditions. These charges against literature and society in the Age of Exuberance promoted paved the way for the sweeping technological and social changes of the Industrial Revolution.  I see many parallels between that time, and the neo-technological society which led to the birth and rampant outgrowth of the Internet – and I see those parallels extending into the echoes that follow that era – times of great societal upheaval and technological advance.

I hope, someday, people will look back at the near-miraculous accomplishments achieved within a mere two decades (to say nothing of what potentially lies ahead) and refer to it as “The Age of Wonder” – for we are truly in a place and time like none other before us, which, hopefully, will pale before the times technology will bring us to beyond tomorrow’s sunrise.

Posted in Reactions | Leave a comment