By Dr. Tawhid CHTIOUI, Founding President of aivancity, the leading school for AI and data
\n\n\n\nA student turns in a brilliant paper. The ideas flow smoothly, are well-structured, and are original without being confusing. The reasoning is coherent, the writing is polished, and the references are relevant. There’s nothing to criticize. Yet the professor hesitates. Something doesn’t sit right. It’s not a mistake or a slip-up. It’s something else. A vague feeling: this paper could have been produced by artificial intelligence.
\n\n\n\nThis scene is no longer unusual. It plays out in schools, agencies, creative firms, consulting firms, and newsrooms. It does not signal the end of hard work or intelligence. Rather, it marks a more subtle, more profound shift: “proper” creativity—the kind we have long valued—is now reproducible.
\n\n\n\nThis observation is no longer a hunch or a technological fantasy. It is now backed by science. A study of unprecedented scope, published in early 2026 in *Scientific Reports* (Nature group), compared the creative performance of more than 100,000 human participants with that of the best generative artificial intelligence models. The result is both clear and unsettling: AI has now reached the level of average human creativity, while still being unable to compete with the most creative individuals.
\n\n\n\nThis threshold changes everything. It doesn’t mean that machines are becoming brilliant. It means that average performance can now be automated… Yet for decades, our educational systems and labor markets have been based on an implicit promise: creativity offers protection. Creativity sets us apart. Being creative guarantees a form of irreplaceability. That promise is faltering. Not because AI is superior to humans, but because it has become good enough in areas where we thought we were still unique.
\n\n\n\nThis raises an unavoidable question: What is the value of a school that teaches students to do what a machine can already do perfectly well? What becomes of employability when creativity is no longer rare, but standardized?
\n\n\n\nThe question is no longer whether AI is creative. The question is what we should teach from now on, what we should stop valuing, and what “creating” still means in a world where average creativity has become a basic technical skill.
\n\n\n\nIt is this quiet, educational, professional, and deeply human shift that this opinion piece sets out to explore.
\n\n\n\nFor a long time, creativity has held a unique place in our conception of human intelligence. It was seen as a rare faculty, difficult to measure, deeply rooted in lived experience, sensitivity, intuition, and that distinctly human ability to connect the unexpected and break away from established frameworks. In a world marked by the growing automation of technical and analytical tasks, it seemed like a refuge. A last bastion. A clear dividing line between humans and machines.
\n\n\n\nThis vision quietly shaped both our educational systems and our labor markets. Calculations, processes, and logical reasoning could be automated, but not imagination. Not invention. Not creativity. To be creative was to be safe.
\n\n\n\nThis narrative is now under threat.
\n\n\n\nThe study published in 2026 in Scientific Reports (see the study overview on the aivancity blog here) does not claim that artificial intelligence is creative in the human sense of the term. It makes a more unsettling—and arguably more far-reaching—point: average human creativity is no longer beyond the reach of machines. In other words, what our institutions have long valued as a distinctive trait—generating original, varied, and relevant ideas within a given framework—can now be replicated by large-scale artificial systems.
\n\n\n\nThis point is crucial. It is not a spectacular breakthrough, nor is it a symbolic victory of machines over humans. It is a matter of alignment. AI does not excel. It achieves. It stabilizes at a “sufficient” level. Yet, in the worlds of business and education, “sufficient” is often more decisive than exceptional. It is scalable. Industrializable. Optimizable.
\n\n\n\nCreativity thus takes on a new status. It ceases to be an exception and becomes a function—an operational capacity that can be activated on demand, adjusted through parameters, responsive to context, yet detached from lived experience. This transformation is profound, as it touches on the very definition of what we have hitherto called “intelligence.”
Edgar Morin reminded us that human intelligence is inseparable from complexity, uncertainty, and meaning. Yet what contemporary creative AI demonstrates is not complex intelligence, but average intelligence. A statistical intelligence, extraordinarily effective at exploring the space of already known possibilities, at producing plausible associations, at generating acceptable variations. It does not create against the world. It creates within what is already there.
\n\n\n\nThis is precisely why it performs so well within the frameworks we ourselves have built. Our school assessments, our professional performance criteria, our competitive exams, and our creative deliverables were designed to reward intelligent conformity, measured originality, and controlled transgression. AI doesn’t need to disrupt these frameworks. It slips into them with formidable ease.
\n\n\n\nTherefore, the question is no longer whether creativity is human or artificial. It is about understanding what kind of creativity we have institutionalized. By valuing creativity that is average, reproducible, and contextualized but lacks genuine risk-taking, we have paved the way for its automation. AI is merely revealing a preexisting vulnerability.
\n\n\n\nThis shift has a major consequence: creativity no longer serves as a protective barrier. It no longer, on its own, ensures lasting differentiation. It has become a minimum threshold of competence, an implicit prerequisite—almost a new form of intellectual literacy. Not being creative has become a disadvantage. Being creative is no longer enough.
And so a chapter in history comes to a close—one in which creativity could be invoked as a guarantee, one in which it constituted a relatively stable form of symbolic capital. What AI reveals is not the end of human creativity, but the end of its comfort.
\n\n\n\nThe question now is not “how to stay creative,” but how to go beyond creativity that has become commonplace. And this question directly concerns schools, far more so than technology.
\n\n\n\nOne of the most unsettling lessons to be drawn from the rise of generative AI does not concern the technology itself, but rather the mirror it holds up to our educational institutions. For if artificial intelligence is now capable of matching average human creativity, it is not only because it is advancing rapidly. It is also because our educational systems have, for a long time, made “average” an implicit goal.
\n\n\n\nAI is not a genius. On the contrary, it is remarkably “correct.” It produces clean, structured, and coherent responses. It knows how to connect ideas, rephrase, and suggest acceptable variations. It follows instructions, understands expectations, and optimizes its output based on explicit or implicit criteria. In other words, it embodies with uncanny precision the kind of student that schools know best how to recognize, evaluate, and reward.
\n\n\n\nThis observation deserves to be taken seriously. For several decades now, modern schools have gradually organized themselves around standardized assessment systems, seeking to objectify intellectual performance—to make it measurable, comparable, and reproducible. Even when they claim to value creativity, they do so through rubrics, expectations, and formats. Acceptable creativity is defined as that which remains legible, explainable, and justifiable—creativity without excessive disruption, creativity that does not disturb the established framework.
\n\n\n\nYet it is precisely in this area that AI excels.
\n\n\n\nGenerative artificial intelligence does not need to understand the deeper meaning of what it produces to succeed in such a system. It requires no intention, no lived experience, and no subjectivity. It simply needs to master the statistical patterns of outputs deemed “good” or “creative” in a given context. It then becomes the ideal average student: never off-topic, rarely daring, always conforming to implicit expectations.
\n\n\n\nThis point marks a major conceptual shift. If a machine can meet the criteria for academic success in creative tasks, it is not merely because it is making progress. It is because those criteria were designed to reward moderate, controlled, and predictable creativity. AI does not subvert the school system; it reveals its underlying logic.
\n\n\n\nEdgar Morin had already highlighted the limitations of an educational system that is too fragmented and overly focused on assessment at the expense of complex thinking. In the age of generative AI, this critique takes on new relevance.
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And by making it average, we’ve made it automatable.
The issue is therefore no longer merely educational. It is fundamentally political and civilizational. What is the value of an educational system whose implicit ideal can be embodied by a machine? What does “educating” mean when the expected standard of excellence corresponds to a level that AI can achieve consistently, quickly, and inexpensively?
\n\n\n\nGiven this reality, two paths are emerging. The first involves making only minor adjustments to existing practices, integrating AI as an additional tool without questioning the fundamental goals of education. This approach is reassuring, but it is insufficient. It risks turning schools into mere training grounds for skills that can already be automated.
\n\n\n\nThe second path is more demanding. It requires a radical shift in what schools choose to prioritize. No longer is the focus on the ability to produce a “creative” response that meets implicit expectations, but rather on the ability to think outside the box, to explore uncertainty, to take intellectual risks, to make context-specific judgments, and to accept choices that are not optimal in an algorithmic sense.
\n\n\n\nIn a world where AI has reached an average level of creativity, education is no longer about learning to give the right answer. It is about learning to resist the obvious answer. It is about cultivating cognitive discomfort, reflective deliberation, and the ability to question one’s own work. In a sense, it is about reviving a form of intelligence that is unruly, difficult to assess, yet profoundly human.
\n\n\n\nThus, AI does not threaten schools because of what it can do. It challenges them because of what it reveals: a long-standing confusion between creativity and intelligent conformity. As long as this confusion persists, schools will continue to produce graduates whom machines can already imitate. True educational transformation begins when this confusion is finally resolved.
When creativity ceases to be rare, it also ceases to be innocent. As long as it was perceived as an exceptional, almost mysterious faculty, it could be celebrated as a gift, a talent, a free expression of the individual. But once it becomes reproducible, industrializable, and accessible on a large scale through artificial systems, its nature changes. It is no longer merely a capacity. It becomes a binding act.
\n\n\n\nGenerative artificial intelligence can now produce thousands of ideas, concepts, narratives, visuals, or strategies in a matter of seconds. It rapidly explores the space of possibilities, suggests variations, simulates styles, and rephrases intentions. This capacity for mass generation creates a dangerous illusion: one that confuses creation with production. Yet production has never been enough to create.
\n\n\n\nTo create, in the truest sense, always involves making a choice. And to choose is to give something up.
\n\n\n\nThis is precisely where the fundamental divide between human creativity and artificial creativity lies. AI does not give up. It does not take ownership. It bears no symbolic, social, or ethical responsibility for what it generates. It is not accountable for the impact of its outputs on the world. It does not know why one idea deserves to exist, nor why another should be discarded.
\n\n\n\nIn an environment saturated with plausible creations, true rarity no longer lies in the idea itself, but in the intention behind it. It is no longer about formal originality, but about the ability to situate a creation within a context, a story, and a purpose. In other words, human creativity is shifting from the realm of performance to that of responsibility.
This shift is significant. It means that creativity can no longer be taught as a mere expressive or technical skill. It has become inseparable from reflection on meaning, consequences, and applications. To create, from now on, is to accept that one’s work shapes the imagination, guides behavior, and influences decisions. It is to accept a form of intellectual and moral responsibility.
\n\n\n\nHuman creativity cannot be separated from the systems within which it operates. It influences the world, and the world, in turn, influences it. While AI optimizes locally relevant solutions, creative humans are capable—at least in theory—of considering systemic effects, tensions, and contradictions. They may choose to create less, but to create better. To create against the odds. To create despite apparent inefficiency.
\n\n\n\nIn this new landscape, creativity ceases to be a measurable performance metric and becomes an intellectual stance. It requires the ability to say no to certain proposals that are appealing but meaningless. To slow down when everything pushes us to speed up. To prioritize long-term coherence over immediate impact. To embrace the discomfort of choices that do not maximize traditional indicators of success.
\n\n\n\nThis shift has profound implications for education. Teaching creativity can no longer be limited to stimulating the imagination or increasing the number of ideation exercises. The goal is now to cultivate minds capable of judgment, discernment, and prioritization. Minds capable of understanding that all creation is also a stance—explicit or implicit—on the world as it is and as it could become.
\n\n\n\nUnderstood in this way, human creativity does not disappear in the face of AI. It is being redefined. It is leaving the comfortable realm of expression to enter the more demanding realm of responsibility. This shift is uncomfortable, as it breaks with the reassuring idea of spontaneous creativity, free from all constraints. But it is undoubtedly the condition for human creation to retain its distinctive value in a world where machines already know how to create effectively.
\n\n\n\nThe question is therefore no longer simply: Who is creative? It has become: Who is willing to take responsibility for what they create? It is this question that now quietly shapes the future of education… and that of the job market.
When creativity ceases to be a distinctive advantage and becomes merely a functional minimum, the labor market does not adapt gradually. It undergoes a sudden restructuring. Not around a simple dichotomy between humans and machines, but around a deeper, more subtle divide: the one that separates exceptional creativity from average creativity.
\n\n\n\nWhat the fact that AI has reached the average human creative level reveals is not the disappearance of creative professions, but their extreme polarization. The market is not shrinking. It is becoming more concentrated. It is reorganizing itself around two asymmetrical poles.
\n\n\n\nOn one hand, there is a minority of creative professionals described as “outside the box.” They are defined not only by their ability to generate original ideas, but also by their capacity to guide the creative process, set its direction, and weigh multiple possibilities. These profiles are becoming strategic. They are sought after not for their productivity, but for their judgment, vision, and ability to create coherence. They are increasingly taking on roles as architects of meaning, creative directors, and framework designers—where the challenge is not to produce more, but to produce differently.
\n\n\n\nOn the other hand, there is a much larger group of average creative professionals who have long been at the heart of the cultural, communications, design, marketing, and consulting industries—and even certain skilled intellectual roles. These professionals are neither incompetent nor useless. But they are becoming replaceable. Whereas their creativity was once enough to justify their economic value, it is now becoming comparable, reproducible, and scalable. Competition no longer comes solely from other humans, but from artificial systems capable of producing at an equivalent level, faster and at a lower cost.
\n\n\n\nThis polarization has a major consequence: creativity no longer guarantees employability. It no longer serves as the safety net that many imagined it to be. It has become a necessary but insufficient condition—a minimum requirement, rather than a lasting differentiator.
In this new landscape, creativity is undergoing a shift in social status. It is no longer a privilege conferred by a degree or by belonging to a profession considered creative. It is becoming a requirement for constant self-improvement. It is no longer enough simply to be creative. One must be creative in a different way—more radically, more intentionally, more contextually. One must create in areas where AI cannot yet decide, arbitrate, give up, or take responsibility.
\n\n\n\nThis shift explains a phenomenon that is already evident: the simultaneous rise in value of certain highly specialized roles and the erosion of a large number of mid-level careers. Whereas traditional industrialization primarily threatened low-skilled jobs, the industrialization of creativity now affects the very heart of intellectual and symbolic professions. It does not eliminate the human element; it simply selects differently.
\n\n\n\nThis shift poses a major challenge for both individuals and organizations. For professionals, it requires a fundamental reevaluation of career paths. Simply learning to use creative tools is no longer enough. One must develop the ability to contextualize one’s work, to make sense of it, and to understand the systems within which one operates. For companies, it means rethinking how they recruit, evaluate, and value creativity. Performance can no longer be measured solely by visible output. It must incorporate the quality of choices, the consistency of direction, and accountability for creative decisions.
\n\n\n\nThus, the future of creative work does not hinge on direct competition with AI, but rather on a reconfiguration of roles. Machines handle rapid generation, massive exploration, and infinite variation. Humans handle decision-making, direction, and engagement. This division is neither automatic nor guaranteed. It requires a profound transformation of education, professional culture, and the criteria for social recognition.
\n\n\n\nThe central question is therefore no longer: Will creative professions disappear? It has become: How many creative professionals are we willing to train who are capable of going beyond what machines already do well?
\n\n\n\nIt is this question that now determines the future of creative employment and, more broadly, the role of human labor in an economy driven by automated creativity.
\n\n\n\nThe emergence of generative artificial intelligence at a level comparable to average human creativity does not signal the end of creativity or the triumph of machines. It marks a much more profound shift—a silent yet transformative shift in what we have hitherto called intelligence, talent, and value.
\n\n\n\nCreativity hasn’t disappeared. It has simply changed in nature. What once set us apart is now merely a technical baseline. What once protected us is now vulnerable. This shift isn’t the result of AI’s superiority, but of a more uncomfortable reality: we had confused creativity with intelligent conformity, originality with measurable performance, and expression with responsibility.
\n\n\n\nSchools now face a historic choice. Either they continue to teach a form of creativity that can be modeled, interpreted, and assessed—and in doing so, they will effectively be preparing generations for a competition they cannot win. Or it dares to undertake a more radical transformation: teaching judgment, discernment, complex thinking, the ability to create beyond the norm, to resist the obvious, and to embrace choices that are not optimal but are meaningful.
\n\n\n\nThe world of work, too, is entering an era of clarification. Creative professions are not disappearing; they are simply being organized differently. A minority of professionals capable of providing direction, making decisions, and giving meaning will see their value increase. A majority of average creative professionals, long reassured by their expressive skills, will have to reinvent themselves or face long-term vulnerability. Creativity is no longer a given. It is becoming a requirement for excellence.
\n\n\n\nUltimately, what AI reveals is not our obsolescence. It is our responsibility. The responsibility to choose what we create, why we create it, and for what kind of world. The responsibility to train not producers of ideas, but architects of meaning. The responsibility, finally, to accept that human intelligence is no longer measured by what it can generate, but by what it can reject, prioritize, and embrace.
\n\n\n\nThe question, then, is not whether AI is creative. The question is whether we are ready to hold ourselves to higher standards again.
\n\n\n\nFor in an age when machines are capable of creating effectively, being human no longer means producing more, but taking responsibility for what we bring into the world.
\n\n\n\n\n\n\n\nThis article draws primarily on research in cognitive psychology, artificial intelligence, and the philosophy of complexity. It does not aim to establish an ontological equivalence between human and artificial creativity, but rather to analyze the systemic effects of their functional convergence on education and the workplace.
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