“A.I. Isn’t Genius. We Are.” by Christopher Beha, referencing Roland Barthes and Pierre Bourdieu, appeared in the pages of The New York Times. The investigation takes its cue from today’s discussion around large language models, highlighting how human creativity and artificial intelligence have sparked both fears and hopes, examining the cultural roots that uphold concepts of individual ingenuity, and considering the possibility that technology might surpass talent. The analysis revolves around an essential question: How much does the human factor truly matter in the birth of innovative solutions, and how much are such solutions instead an expression of social, economic, and cognitive processes?
Human Creativity and the Artificial Intelligence Debate: Fears and Potential
The controversy pitting supporters of artificial intelligence against its more critical observers found one of its symbolic starting points in the release of ChatGPT two years ago. Since then, considerable debate has focused on the risk of losing the distinctive trait of creativity we usually attribute to individuals. Some foresee the end of what is called human exceptionalism, imagining a world in which computers and algorithms outdo every form of imagination and originality. The worry is that songs, paintings, novels, and design projects might end up becoming indistinguishable from those created by professionals, marking the disappearance of the human dimension in the arts and the conception of new ideas. This fear has been fueled by interpretations that overemphasize the power of computational models and, more importantly, by a cultural tendency to implicitly and gradually underestimate what humans are truly capable of doing.
To fully understand the causes of this underestimation, we need to examine how, as early as the second half of the twentieth century, we witnessed a deconstruction of the idea of the “author.” In his famous 1967 essay, “The Death of the Author,” critic Roland Barthes argued that every text was the outcome of an interweaving of preexisting writings and that no individual truly held a creative primacy. Within a post-structuralist perspective, cultural production is interpreted as the result of historical, political, and economic dynamics that influence one another, leaving little room for an authentic individual contribution. In this vision, the author appears to be a conduit through which already-structured ideas are expressed, thus diminishing the concept of “genius” understood as the source of extraordinarily original creations.
On the one hand, there was a desire to counteract an excessive mythologizing of art and its creators, but on the other, the result was a flattening of any sense of wonder, as if everything could be reduced to combinations and recombination of existing cultural materials. In his 1979 study “Distinction,” sociologist Pierre Bourdieu stressed how aesthetic tastes are intimately connected to mechanisms of social distinction and how artistic innovation is tied to mechanisms of power rather than to free individual expression. He described culture as the realm of “cultural capital,” with the codes of the elite perpetuating certain hierarchies and marginalizing all else. According to this view, the artist does not create from nothing but rather responds to specific social conventions.
A different approach, yet similarly aimed at reducing human autonomy, came from Richard Dawkins’s neo-Darwinian materialism. With his theory of “memes,” he argued that cultural ideas spread almost mechanically. If genes are transmitted through DNA, “memes” circulate in social strata, replicating and adapting in a competitive manner. In other words, every cultural elaboration is simply a passage of preexisting content, and the mind acts as a container that recycles material, not as a furnace where a creative spark is ignited. Within these theories, the last bastion of human exceptionalism seemed to crumble, because the entire sphere of aesthetic and conceptual production was seen as a result of inherited or cultural conditioning.
Although these perspectives prompted important reflections on the role of history, biological constraints, and power structures, they also led to a widespread tendency to treat creativity as an illusion. Consequently, over the past decades, we’ve grown accustomed to viewing cinema, music, and literature as macrosystems of repetition. Every new artistic product becomes a remix of genres and codes from the past. Just think of comic-book story universes, the endless reworkings of literary motifs, or the continuous crossovers between musical styles from different eras. On a daily scale, a meme culture has flourished that magnifies the phenomenon: movie scenes, political news, and pop-cultural events immediately become “reusable” material, ready to be distorted, re-edited, and relaunched in a ceaseless flow. In such a flattened environment, the notion that a machine might generate text, images, or melodies is no longer felt to be extraordinary but instead the logical consequence of an ongoing process in which creativity and repetition merge and the distinction between new and old is weakened.
With the emergence of language models trained on vast amounts of data, many people have begun to believe that the line separating algorithms and human ingenuity is very thin. Some have declared that the human spark is definitively extinguished. Others, less alarmed, have downplayed the influence of these tools, seeing artificial intelligence as a significant technological innovation rather than humanity’s point of no return. In either case, the emphasis is on underestimating human capabilities and what humans can actually achieve. If we begin with the idea that intellectual production derives solely from combining existing information, then the possibility of a computer reproducing the same mechanisms seems perfectly natural. Conversely, recognizing our capacity to create something genuinely authentic compels us to deeply reconsider the relationship between human creativity and artificial intelligence, especially in the most advanced inventions.
Artificial Intelligence and Human Culture
The reflection on artificial intelligence extends beyond merely technological aspects or questions of employment; it touches the very foundations of culture, philosophy, and our ability to conceive entirely new ideas. Some readers may wonder whether machine learning systems are merely “enhanced tools” or whether they represent something more significant. The article that informs this discussion puts forth a hypothesis: we shouldn’t be afraid that machines might appropriate our creativity; rather, we should worry about how we ourselves have undermined the idea that human beings possess an imaginative potential not reducible to probabilistic calculations. This conclusion also stems from a tradition reminding us that logic and aesthetics are two poles of the same arc of knowledge.
The history of mathematics, philosophy, and the science in general demonstrates that certain individuals have managed to combine analytical skills with contemplative aptitude. The recent development of generative AI systems fits precisely into that long trajectory rooted in formal logic (via figures like Kurt Gödel, John von Neumann, or Alan Turing) and the visionary creativity of programmers and scholars who envisioned a form of computation capable of exploring vast semantic spaces. Every line of code, every mathematical formula or neural network architecture, holds the echo of the ingenuity of those who laid the groundwork in ages past. Consider the geometry of ancient civilizations, Aristotelian logic, or the early mechanical devices designed for calculation. Nothing that is part of the AI domain today was born in a vacuum of creativity; it is all the outcome of a pipeline composed of discoveries, insights, and intellectual exchanges among centuries of researchers and philosophers.
Furthermore, one might argue that generative artificial intelligence, with its ability to synthesize texts, images, and ideas from an enormous pool of digitized knowledge, reflects a broader phenomenon: the blending of sciences and humanities. In the past, there have been individuals who personified this union. Leonardo da Vinci’s name, for instance, is emblematic: some place him in the history of engineering due to his mechanical projects, others celebrate him as a painter. Yet the most fascinating aspect is how he moved from scientific observation of the world to artistic invention, from the pure analysis of anatomy to the pictorial representation. Anyone examining AI’s phenomenology with a historical lens might see a fresh, ambitious attempt by humanity to build intellectual tools capable of exploring and unifying different domains of knowledge.
This recognition implies admitting that the fears about the eclipse of human genius are often fueled by a misunderstanding: it’s not the machine that threatens our uniqueness, but rather us who fail to properly situate AI as a product of our ingenuity while simultaneously underestimating ourselves as thinking beings. On the contrary, if we realize that every algorithm, every neural network, and every deep learning module derives from complex processes of human development and creativity, then it becomes evident that the supposed contest between humans and AI is largely meaningless. AI is a collective, choral creation that merges the passion of physicists, engineers, linguists, and philosophers, along with the humanistic dimension of those who imagine new solutions to old problems.
Some hold that in practical terms, rising automation is disrupting entire fields of knowledge and labor. To an extent, that’s true: technology changes existing balances, shifts skill sets, and creates new spaces for innovation. Yet the value of this transformation cannot be reduced to simply tallying what is lost and what is gained, because the widespread adoption of an AI system always reflects the desire to test the limits of what is possible. Between the end users and the developers exists a chain of expertise that stretches back to remote times and takes shape in today’s software—an ongoing flow of ideas that propels humanity forward.
We must also remember that AI does not operate in a regulatory or ethical vacuum. Humans set parameters, select data, write guidelines, and define objectives. The great promise of systems like ChatGPT also lies in their capacity to raise questions about how we construct knowledge, compelling us to reflect on the origins of content and how it may be used. This awareness portrays AI as a continuous interlocutor, rather than an enemy. The echoes of collective fears mingle with the allure of a work that ultimately arises from the same spirit that led to the major technological achievements of the past.
Human Genius and AI: An Evolving Relationship
For many centuries, the concept of genius played a central role in defining what makes it possible to produce extraordinary works. The genealogy of this idea dates back to antiquity, when Socrates spoke of a “daimonion” that guided his conduct, or to Christian mystics identifying a personal connection with the divine, or still further to Enlightenment thinkers who tried to secularize the notion of inspiration. In Immanuel Kant’s view, great art came from the individual capable of creating his own rules, rather than following those already established. Romanticism then promoted the image of the author drawing on profound intuition to realize masterpieces, while mathematics was viewed as a systematic, methodical discipline. Yet the twentieth century reminded us that even in rigorous scientific fields like physics or logic, there are moments when intuitive leaps overturn established certainties, yielding unexpected solutions.
This notion of genius has been challenged from multiple angles: concerns about venerating false masters, the realization that so-called “great men” of history often benefited from privileged backgrounds, or the fact that they could commit destructive acts—all of these undermined trust in the concept. More recently, public discourse has celebrated figures like Bill Gates, Steve Jobs, and Elon Musk as “geniuses” for turning technological intuitions into economic empires, an equation that ambiguously conflates financial success with authentic innovation. The collateral effect is a further dilution of the term “genius,” as it becomes associated with managerial skills or with cutthroat economic competitiveness.
However, the critical issue is not the casual use of the word but rather the loss of trust in the possibility that sometimes someone can think outside the box, going beyond the mere sum of existing notions to produce genuinely novel creations. Here the AI question fits perfectly. In fearing that neural networks might exceed human capacities, we sense a collective defeat: if we are merely systems that process and combine information, then there is no distinction between humans and machines—which might even outperform us in every domain. Yet it is essential to invert this viewpoint, noting that machine learning models do not represent an abstract or independent reality but rather the concrete result of an engineering process built through a lengthy path of human commitment and work.
When we ask whether machines will truly replicate the deepest qualities of humanity, we should recall that algorithms do not develop authentic inspiration; they execute probabilistic models. Although computational power allows for wide-ranging variations and combinations, generating surprising texts, images, and sounds, the driving force behind these combinations remains the information we provide. No neural network has ever awakened with the awareness to ponder existential questions or discover an absolute ethical principle beyond what it was programmed to do.
This perspective does not aim to diminish AI, but rather to redefine the scale of roles. Thinking of artificial intelligence as a “foe” of creativity is contradictory because technology embodies the work of generations of researchers, artists, and thinkers who dedicated their lives to designing and refining mechanisms capable of processing information. If fear emerges, it is because we perceive in these systems the possibility that they could rival us in areas we consider “sacred” or distinctly human, such as writing novels or composing music. In reality, what resonates is the ancestral fear humans have of themselves: the reflection falls on how we use our own inventions, what responsibilities we assume in programming them, and what goals we pursue.
At the same time, AI demonstrates how humans, with their capacity for abstraction and imagination, can create digital “creatures” able to traverse countless fields of knowledge. Such results were once the domain of a few extraordinary figures—like Leonardo da Vinci, who united mathematics and art, philosophical reflection, and the invention of machines. Today, that aspiration to unify knowledge is expressed in the construction of generative models that tap into every segment of available expertise. It is a prospect that should inspire fascination more than anxiety, showing how the human mind, even collectively, can conceive structures of great versatility. If history teaches us anything, it is that technological leaps forward become opportunities to reflect on our place in the universe. Thus, the real “competitor” may not be the machine at all but our own mental laziness, our reluctance to resume a conversation about inspiration and creative impetus in a way that goes beyond economic calculations or scientific oversimplifications.
Conclusions
The evidence gathered points to a more measured perspective than the most catastrophic tones might suggest. The genuine risk is not that artificial intelligence will wipe out our creativity, but that we might abandon our recognition of the mind’s generative power, delegating even our last glimmer of curiosity to sophisticated machines. In the current scenario, many technologies already exist with functions similar to those of large language models, though without the accompanying clamor: from data analysis systems in the business world to simulation software in engineering. When placed side by side, the evolutionary leap in deep learning models appears significant, yet can be situated within a long tradition of technical achievements.
The crucial factor lies in identifying the implications for businesses and society: understanding that AI is neither a facile replacement for thought, nor an autonomous monster allows managers and entrepreneurs to more soberly assess the introduction of such tools. Every innovation must be governed by clear regulations, proper staff training, and a strategic approach that takes ethical and cultural dimensions into account. The environment in which we operate already includes similar technologies assisting organizations for years, but today’s debate provides an opportunity for a more far-reaching and inclusive vision: those investing in AI gradually discover that behind a piece of software lies an unbroken chain of expertise, the result of an ancient fusion between mathematics and the creative spirit.
In an era, intent on cutting costs, from staffing to basic research, it becomes crucial to preserve that spark of inspiration which has always made the difference between a merely replicative technology and one that genuinely benefits humanity. The prods of these models do not close any doors to collaboration among ideas, nor does it invalidate art or literature; rather, it expands our potential for achievement if we begin to believe again in our capacity to ask radical questions. Looking ahead, comparisons between AI and similar systems already in use will reveal increasingly sophisticated forms of integration, while also leaving room for new dilemmas about the nature of learning and the meaning of “thinking.” Many companies will find themselves reevaluating their decision-making processes, discovering they need individuals capable of connecting data with sensitivity to the human dimension. In a certain sense, this will require reclaiming something very ancient: a careful attention to one’s own interiority and environment, so as to perceive when a hunch deserves to be fostered until it becomes a disruptive market idea.
This is not about idolizing AI, but about placing it within a broader framework of shared creativity. Indeed, the genuine added value of these systems does not lie in the perfection of their algorithms but rather in our willingness to reflect, experiment, and embrace that dimension of the mind where intuition can become a flash of novelty. It is an invitation to combine scientific and humanistic culture in a serious way, to see computers as extensions of our quest for knowledge rather than adversaries. In a marketplace where competition drives the adoption of increasingly sophisticated solutions, the strategic difference may lie precisely in the awareness that behind every line of code there flows the plural history of humanity, and that the next steps demand both technical rigor and creative courage.
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