The arrival of artificial intelligence probably marks one of the most striking developments humanity has experienced. Not so much because of its technological power as because of what it disrupts: our age-old relationship with knowledge and learning. Faced with this shift, one question stands out: what must we still learn, and how?
When the printing press already disrupted learning
History offers us a fascinating mirror. In 1455, when Gutenberg invented the printing press, he did not merely revolutionize the reproduction of books. He triggered a radical transformation of learning itself. Before the printing press, knowing meant memorizing. Copyist monks held power through their ability to retain and reproduce texts. Memory was the cardinal skill.
With the printing press, this skill suddenly became… obsolete. Why memorize when you can look things up?
The printed book democratized knowledge, but it also radically transformed what was expected of an educated mind. The challenge was no longer to remember everything, but to know where to look, how to cross-reference sources, how to think critically.
This transition was anything but natural. The medieval university, based on disputatio and recitation, took centuries to adapt. Some intellectuals of the time even criticized the printing press as a threat to true knowledge, the kind that had to be embodied in the human mind.
Today we are living through an upheaval of comparable scale with artificial intelligence.
AI: the end of knowledge as capital?
For us, humans of the 21st century, the challenge is no longer so much to know, but to use. This transition is dizzying and destabilizing. It calls into question centuries of educational practices built on the accumulation of knowledge.
When an AI can, in a few seconds, generate a summary of any subject, explain a mathematical theorem, translate a text into 50 languages or code a functional application, what remains of the traditional value of the “fount of knowledge”?
Those who adapt will not be the ones who know the most, but those who know how to:
Make connections between seemingly distinct fields of knowledge
Ask the right questions to extract the best from artificial intelligences
Critically evaluate generated answers
Mobilize intelligence – human and artificial – to solve complex problems
Create value from the raw material that is knowledge
As a result, learning will move toward acquiring the ability to use AIs and to “manage” within an extended team – made up of humans and machines – to mobilize intelligence more than knowledge.
Does school prepare us for these demands?
Here is the uncomfortable question: is our education system ready for this shift?
For now, the mission of education remains strongly focused on the acquisition of knowledge, and the evaluation system values the restitution of that knowledge far more than its use in solving a problem.
Certainly, group work and project-based approaches are encouraged. Certainly, critical thinking and creativity are timidly being introduced into curricula. But adaptation is so slow while AIs are evolving at a dizzying speed.
It’s a bit as if, in the 16th century, we had continued training copyist monks by teaching them increasingly sophisticated calligraphy techniques, while ignoring the printing press rumbling at the door.
A shift that is too slow
The paradox is striking: we forbid the use of ChatGPT in exams at the very moment when companies are hiring people capable of working effectively with these tools.
We continue to value memorizing mathematical formulas when the real skill lies in the ability to model a complex problem and orchestrate different approaches to solving it.
We evaluate students individually while the professional world increasingly demands the ability to collaborate in distributed, multidisciplinary teams, where humans and AI work together.
Dreaming of an adaptable education system
I dream of an education system that integrates AI from the earliest age, not as a threat to fight or a trendy gadget, but as a permanent learning partner.
A system that would train problem-solvers rather than founts of knowledge. Architects of solutions rather than reciters of lessons.
This system would develop from childhood what I call the Adaptation Coefficient: the ability to effectively modify one’s behaviors, cognitions and emotions in response to environmental changes.
The four pillars of adaptive learning
To develop this capacity for adaptation in the face of AI, learning should be structured around four fundamental dimensions:
🧠 Cognitive Dimension: Learning to learn with AI
Developing mental flexibility to navigate between different sources of information
Cultivating critical thinking to evaluate AI-generated answers
Mastering the art of questioning (prompt engineering as a new literacy)
Knowing how to unlearn knowledge that has become obsolete
🎯 Behavioral Dimension: Experimental agility
Quickly testing different approaches with AI
Iterating without fear of error
Adjusting one’s method based on the results obtained
Developing a culture of prototyping and continuous improvement
💗 Emotional Dimension: Managing uncertainty
Accepting that AI may surpass us in certain areas
Tolerating the ambiguity of sometimes imperfect answers
Maintaining motivation in the face of the rapid obsolescence of technical skills
Cultivating intellectual humility while preserving confidence in one’s own worth
🤝 Social Dimension: Orchestrating hybrid intelligence
Collaborating effectively in human-AI teams
Communicating one’s needs to intelligent systems
Knowing when to delegate to AI and when to mobilize human intelligence
Building collectives where human-machine complementarity creates value
Ingenuity and creativity as new frontiers
Today’s and tomorrow’s challenges will call on ingenuity and creativity far more than the mere possession of knowledge.
Faced with climate change, we don’t need more experts able to recite IPCC reports. We need creative minds capable of imagining systemic solutions that don’t yet exist.
Faced with geopolitical tensions, we don’t need specialists who know the history of conflicts by heart. We need thinkers capable of weaving unprecedented bridges between cultures and worldviews.
Faced with technological acceleration, we don’t need technicians who master the latest trendy framework. We need architects capable of designing systems that will remain relevant despite the obsolescence of tools.
Yet ingenuity and creativity are not transmitted through the lecture. They are cultivated through experimentation, embraced error, detours, play, interdisciplinary collaboration.
A story of adaptation: the example of the Renaissance
Once again, history enlightens us. The Renaissance was not simply a period of rediscovering ancient knowledge. It was a moment of radical adaptation to a new availability of knowledge.
The humanists of the Renaissance were not necessarily the most erudite of their time. They were the ones who knew how to make connections between separate disciplines: art and mathematics (Leonardo da Vinci), astronomy and theology (Copernicus), anatomy and sculpture (Michelangelo).
What made this era great was not the amount of knowledge available – even though the printing press considerably increased it – but the ability of the brightest minds to orchestrate that knowledge to create new syntheses, new visions of the world.
AI places us in a comparable situation. It does not replace human intelligence. It augments it, multiplies it, and above all, it frees it from tasks of pure memorization and calculation to deploy it where it excels: systemic vision, creative intuition, ethical judgment, contextual understanding.
Toward learning and adaptable organizations
This transformation of learning does not concern only school. It challenges all our organizations.
The values carried by the 1Clusif movement – transmission, responsibility, entrepreneurial spirit, creativity – take on their full meaning in this context. Building adaptable organizations means creating environments where continuous learning becomes natural, where experimentation is encouraged, where collective intelligence – human and artificial – can fully express itself.
It also means recognizing that adaptation is neither rigid resistance (“we won’t change”) nor passive submission (“we’ll delegate everything to the machines”). Adaptation is a step to the side, a third way that preserves what makes us human while embracing new possibilities.
Conclusion: adaptation as a vital skill
So, in the age of AI, do we still need to learn?
Yes, more than ever. But what we must learn has radically changed.
We must learn to question rather than to recite. We must learn to connect rather than to compartmentalize. We must learn to create rather than to reproduce. We must learn to collaborate – with humans and machines – rather than to perform individually. We must learn to adapt – again and always – rather than to freeze in our certainties.
This capacity for adaptation, this Adaptation Coefficient we talk about, is not a luxury reserved for a few. It is the vital skill of the 21st century. For everyone. In every field.
It is time for our education system to recognize this and adapt itself. It is time for our organizations to make it a strategic focus of development. It is time for us, individually, to accept becoming permanent learners in a constantly changing world.
Because as Darwin said – and this quote takes on its full meaning in the age of AI – “It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.”
This article is part of a broader reflection on organizational and individual adaptability. If you would like to explore these questions further or contribute to collective research on the Adaptation Coefficient, feel free to discover our ongoing study.