Author: Alejandro Díaz Garreta

  • Is Europe’s “Decline” Inevitable?

    Lessons from the Fall of the Roman Empire

    It is often said that Europe is losing weight on the global stage. The United States suggests the continent has failed to translate its prosperity, values and dense institutional framework into sufficient geopolitical power. China portrays Europe as strategically dependent, constrained by demography, regulation and slowing economic dynamism. Russia, meanwhile, depicts Europe as a civilisation in decay, weakened by liberalism and estranged from its historical roots.

    Such narratives are not new. History offers parallels worth examining.

    Revisiting the work of Spanish historian José Soto Chica on the fall of Rome reveals an instructive distinction. The Western Roman Empire did not collapse solely because of barbarian invasions. The Eastern Empire faced comparable pressures — wars, migrations, fiscal strain — yet endured for nearly a millennium. The divergence lay less in external threats than in internal resilience.

    If Europe wishes to avoid repeating that fate, Rome’s experience points to at least three structural lessons.

    1. Protect the middle class and contain extreme inequality

    By the fifth century, wealth in the Western Empire had become increasingly concentrated. Fiscal capacity eroded, social cohesion weakened and the state’s ability to respond diminished.

    Resilient societies depend on a broad, economically viable middle class. When wealth concentrates excessively, the state loses both revenue and legitimacy. Paralysis follows.

    2. Demand civic responsibility from elites

    At critical moments, Western elites resisted necessary fiscal reforms. Financial contributions to collective defence declined, undermining the Empire’s capacity to maintain its military and respond to external threats.

    Prosperity is unsustainable without elite commitment to long-term institutional stability. Fiscal responsibility and strategic foresight are not optional virtues; they are structural requirements.

    3. Strengthen institutions rather than rely on “strongmen”

    The Western Empire was marked by internal power struggles and reliance on providential leaders. The Eastern Empire, by contrast, endured through bureaucratic continuity and institutional coherence.

    Political systems do not survive because of exceptional individuals. They survive because institutions are strong enough to outlast them.

    History does not dictate contemporary choices. But it does warn against patterns of self-erosion.

    To preserve its relevance and social model, Europe must reinforce internal cohesion, require greater responsibility from its elites and resist the illusion that prosperity is irreversible.

    The question is not whether Europe faces external pressure — it does. The question is whether it responds with institutional maturity or aristocratic complacency.

    Are we behaving like the Roman aristocracy of the fifth century — or learning from those who endured?

  • Without Books, No Judgement: Literacy in the Age of Artificial Intelligence

    I recently came across a powerful essay by Niall Ferguson: “Without Books We Will Be Barbarians.”

    Ferguson revisits Fahrenheit 451 and advances a disquieting claim: the greatest contemporary threat to literacy is not censorship or authoritarian control but voluntary abandonment. We are not being forced to stop reading. We are choosing to.

    The seduction is subtle: permanent stimulation, instant access, visual saturation, and algorithmic feeds. Reading — especially sustained, demanding reading — competes poorly against environments optimised for distraction.

    The data Ferguson presents are stark. Fewer adults read for pleasure, and younger generations read dramatically less than their predecessors. Literacy levels themselves show signs of decline. This is not merely a cultural preference. It is a structural shift.

    Literacy, properly understood, is not merely the ability to decode words. It is the foundation of analytical thought, historical consciousness, and the capacity to distinguish between argument and assertion, evidence and opinion, and truth and myth.

    This is where the argument becomes especially relevant in the age of artificial intelligence.

    AI accelerates a shift away from text. Voice inputs replace written queries. Video summaries replace long-form essays. Images substitute for arguments. Generative systems can now produce coherent explanations, structured outlines, and even full essays in seconds.

    While AI is an extraordinary tool, it cannot compensate for the loss of deep reading. A society that abandons sustained engagement with texts risks losing the habits of structured reasoning that literacy fosters.

    Artificial intelligence can generate outputs.

    It cannot generate the intellectual discipline required to evaluate those outputs.

    The educational question, therefore, is not whether AI is useful. It clearly is. The question is whether we are using it to enhance literacy or to bypass it.

    If students rely on AI-generated summaries instead of wrestling with primary texts, if reading becomes optional because synthesis is outsourced, then technology does not amplify intelligence. It displaces formation.

    The risk is subtle yet profound: convenience replacing effort, immediacy replacing reflection, and output replacing understanding.

    Ferguson’s conclusion is deliberately provocative: without books, we do not progress towards a more sophisticated future. We regress towards a pre-literate condition — more susceptible to manipulation, more dependent on mediated authority, and less capable of independent judgement.

    This is not nostalgia for paper. It is a defence of cognitive architecture.

    Education in the age of AI must therefore make a deliberate choice.

    It must treat literacy not as a baseline skill already secured but as a fragile foundation that requires active protection.

    Artificial intelligence will shape the tools of learning.

    Books — and the habits they cultivate — will determine whether we remain intellectually autonomous when using them.

  • Artificial Intelligence and the Transformation of Thought

    I have read with particular interest the article by Eduardo López-Collazo, published in El Español, which examines the impact of artificial intelligence on writing and cognition. It stands out in a debate often dominated by exaggeration by combining empirical evidence with philosophical depth.

    The piece draws on an MIT experimental study comparing writing processes with and without AI assistance. The findings are sobering. Intensive use of tools such as ChatGPT accelerates production but appears to reduce cognitive engagement, memory retention, and neural activity associated with learning. The resulting texts are grammatically correct and structurally coherent, yet often lack intellectual depth — technically sound but conceptually thin.

    Perhaps the most revealing detail is that the strongest results come from those who first think and draft independently, and only then use AI as a tool for revision. The sequence matters.

    López-Collazo enriches this empirical discussion by invoking Plato’s Phaedrus, where writing itself is presented as a technology that would transform memory. Plato feared that reliance on writing would weaken internal recollection. History showed that writing did not diminish intelligence; it reconfigured how memory operates.

    The analogy is instructive.

    If writing altered what we remember, artificial intelligence may alter how we think. Not because it imposes decisions, but because it can habituate us to delegating the very acts that shape thought: hesitation, reformulation, doubt, structural organisation, and conceptual struggle.

    The risk is not technological determinism. It is an anthropological substitution.

    When AI serves as a prosthesis — extending our cognitive reach after disciplined effort — it strengthens reasoning. When it serves as a substitute — replacing the formative struggle — it weakens the habits on which judgement depends.

    The distinction between producing more and understanding better is becoming blurred. Productivity metrics can mask intellectual erosion.

    For educators and institutional leaders, the implication is clear: the decisive question is not whether AI should be used, but how it is integrated into the learning process. Reflection must precede automation.

    Artificial intelligence may change our tools.

    Whether it changes our thinking depends on how we form the habits that underpin it.

  • El mono, el búho y el papagayo

    Fábula para tiempos digitales

    Prólogo

    La discusión sobre la inteligencia artificial suele oscilar entre entusiasmo y temor. Sin embargo, más allá del debate tecnológico, la cuestión decisiva es antropológica: ¿qué ocurre cuando delegamos en una herramienta no solo tareas, sino procesos de pensamiento?

    La tradición clásica utilizó la fábula como forma breve de antropología moral. Animales que dialogan, exageran o se enfrentan no son un recurso infantil, sino una forma de iluminar disposiciones humanas permanentes.

    La siguiente pieza adopta deliberadamente ese registro. No pretende simplificar el problema de la inteligencia artificial, sino señalar, mediante alegoría, el riesgo de confundir repetición con comprensión y brillo con juicio.

    El mono, el búho y el papagayo

    Un Mono, altivo y curioso,

    halló un prodigio asombroso:

    un Papagayo elocuente

    que hablaba largo y meloso,

    con cierto aire pomposo..

    —«Escuchad, amigos míos,

    su saber no halla vacíos:

    responde a todo al instante;

    su decir es deslumbrante

    y ajusta como un guante».

    Un Búho, sobrio y prudente,

    dijo con voz consistente:

    —«Tu ave canta y repite,

    mas, aunque el mundo imite,

    ni comprende ni consiente.

    No hay juicio en la cadencia

    ni verdad en la apariencia;

    si todo eco se celebra,

    la mente pronto se quiebra

    por falta de resistencia».

    El Mono, algo irritado,

    replicó con gesto airado:

    —«Viejo búho desconfiado,

    hoy lo nuevo es lo avanzado;

    si aligera nuestro fardo,

    ¿por qué temer lo aprendido?»

    Mas el Búho, sosegado,

    contestó firme y mesurado:

    —«Llamas progreso al dictado

    y confundes brillo y legado.

    Si el juicio queda apagado,

    ¿qué queda del ser pensado?»

    Siguió el ave parlanchina

    con su música divina;

    el Mono, siempre encantado,

    creyó saber lo escuchado.

    Mas todo era emitido,

    sin juicio ni sentido.

    Moraleja

    Quien su juicio abandona

    por voz que solo razona

    según patrón repetido,

    descubre, tarde y herido,

    que pensar no es lo oído.

    La mente que no ejercita

    la duda que la acredita

    termina por delegar

    no solo el cómo expresar,

    sino el arte de juzgar.

    Y allí donde el juicio muere

    y la comodidad prefiere,

    no avanza la inteligencia:

    se atrofia la conciencia.

  • Günther Anders and the Promethean Gap in the Age of Artificial Intelligence

    Günther Anders, the twentieth-century philosopher of technology, coined a concept that feels increasingly relevant: the Promethean disparity. By this, he meant the growing gap between what human beings are technically capable of producing and what they are morally and imaginatively capable of understanding.

    We can build systems whose consequences we struggle to foresee. We can optimise processes faster than we can reflect on their implications.

    For Anders, the danger was not simply technological misuse. It was an anthropological transformation: the gradual reshaping of human self-understanding under the pressure of technical possibility.

    Artificial intelligence brings this insight directly into education.

    Our technical capacity to automate feedback, generate explanations, personalise learning pathways and evaluate performance has expanded at remarkable speed. Yet our reflection on how these capabilities may redefine the meaning of teaching and learning often lags behind.

    Anders’ warning highlights three interrelated concerns.

    First, humanity must remain primary.

    AI can assist, extend and analyse. It cannot exercise ethical judgement, assume responsibility or interpret the moral texture of a classroom. Education is not merely the transmission of information; it is the formation of persons. When optimisation becomes the dominant lens, the risks to formation become secondary.

    Second, we must resist what Anders described elsewhere as a form of “technological shame”: the tendency to measure ourselves by the efficiency of our own machines. In education, this may manifest as evaluating teachers primarily through metrics aligned with machine logic — speed, output, standardisation — rather than through intellectual and relational depth.

    Third, integration requires deliberation.

    Innovation in education cannot be automatic. It demands anticipation: What habits does this tool cultivate? What forms of dependency might it create? What aspects of human judgement might it erode or strengthen?

    Artificial intelligence is not inherently dehumanising. It becomes so only when adopted uncritically.

    Anders reminds us that technical progress does not guarantee moral progress. The faster our tools evolve, the more disciplined our reflection must be.

    At its core, education is not about optimising systems.

    It is about forming human beings capable of judgment within themselves.

  • Education in the Intention Economy

    For two decades, we have spoken of the “attention economy”. Platforms competed to capture our attention, maximise engagement and monetise distraction.

    We are now entering a more subtle phase.

    Emerging research describes what is increasingly called the intention economy: a technological environment in which artificial i not merely react to our behaviour but anticipates, shapes and commercialises our future decisions. Predictive systems learn to identify emerging preferences before they are consciously articulated. Desire becomes data. Intention becomes an asset.

    The shift is significant.

    Attention concerns what we look at.

    Intention concerns what we choose.

    In educational contexts, the implications are profound. Students are not fully formed consumers; they are individuals in the process of constructing values and direction. An ecosystem that places economic value on anticipating and influencing its emerging intentions introduces a new layer of vulnerability.

    Teaching digital skills is therefore no longer sufficient. Competence in using tools does not guarantee autonomy in forming desires.

    The more pressing question is both meta-cognitive and ethical:

    How do I decide what I want?

    How do I recognise when my preferences are being nudged or pre-shaped?

    How do I distinguish between authentic aspiration and algorithmically amplified impulse?

    These are not technical questions. They are anthropological.

    Education must create deliberate spaces for reflection on the architecture of digital environments. Not merely how platforms function, but how they shape perception, preference formation and self-understanding. Students need conceptual tools to interpret influence, not merely to navigate interfaces.

    This reframes the educator’s role.

    In the intention economy, teachers are not only transmitters of knowledge or facilitators of skills. They are agents of empowerment, responsible for helping students cultivate reflective will — the capacity to pause, examine motives, and decide deliberately rather than react predictably.

    Algorithmic systems segment, predict and optimise.

    Education must form people capable of resisting automaticity.

    If the attention economy risked distraction, the intention economy risks something deeper: the gradual outsourcing of desire.

    Education cannot remain peripheral to this transformation.

    Its task is not merely to prepare students for a digital world but to ensure they remain sovereign within it — capable of taking their intentions seriously rather than allowing them to be silently shaped.

  • El taller renacentista y los límites de la inteligencia artificial

    Por qué la ejecución puede delegarse, pero el juicio no.

    Durante el Renacimiento, la creación artística rara vez respondía al mito moderno del genio solitario. Los grandes pintores trabajaban en talleres. Concebían la composición, realizaban los primeros bocetos, fijaban las proporciones y aseguraban la coherencia de la obra. Los aprendices del taller realizaban gran parte del trabajo: preparaban las superficies sobre las que pintar, aplicaban los pigmentos y completaban las figuras secundarias.

    Lo decisivo no era quién empuñaba el pincel, sino la idea que daba vida a la obra. No era el número de pinceladas aplicadas lo que la definía, sino su concepción intelectual.

    La ejecución podía delegarse. La idea original, la visión, la creación intelectual, no.

    Lo que distinguía a un Leonardo o a un Rafael nunca fue la exclusividad manual, sino la arquitectura intelectual: la capacidad de concebir una forma, organizar la complejidad e imponer unidad a la multiplicidad.

    La analogía entre el taller renacentista y la inteligencia artificial no es superficial; es estructural.

    Hoy los sistemas de IA generan textos, imágenes, código y análisis con una rapidez extraordinaria. Amplían la capacidad productiva del mismo modo que lo hicieron los aprendices del taller. Aceleran la ejecución, incrementan la producción y facilitan el perfeccionamiento.

    Pero no originan la visión.

    No determinan qué preguntas merece la pena formular, qué distinciones son relevantes ni hacia dónde debe orientarse la indagación. Operan dentro de parámetros fijados por la intención humana. Procesan patrones. No habitan el significado. Ahí reside su límite.

    O la arquitectura intelectual sigue siendo nuestra o se diluirá.

    Esta distinción adquiere una urgencia especial en el ámbito educativo porque surge por doquier una inquietud recurrente: cuando los estudiantes utilizan herramientas de IA en su trabajo académico, ¿desaparece la autoría? ¿Hay plagio? ¿Queda comprometida la originalidad?

    La preocupación es comprensible, pero es también incompleta.

    El plagio, en su sentido clásico, implica la apropiación indebida de palabras o ideas identificables. Los sistemas generativos complican esta definición porque producen formulaciones nuevas en lugar de copiar de fuentes rastreables. Desde un punto de vista estrictamente textual, el resultado puede ser técnicamente original.

    Ocurre que el núcleo de la educación nunca ha consistido en la originalidad textual. Ha consistido, sobre todo, en adquirir una formación intelectual.

    La autoría de una obra, en un sentido más profundo, no reside en la mera formulación lingüística, sino en la apropiación de las preguntas, la responsabilidad por los argumentos y la capacidad de justificar las conclusiones. Se sitúa en la dimensión tácita de la comprensión: la integración de evidencias, contexto y juicio que no puede reducirse por completo a procedimientos explícitos.

    Si un estudiante define el problema, evalúa críticamente las evidencias y asume la responsabilidad de la interpretación, la IA puede funcionar como un instrumento —como el aprendiz en un taller renacentista—. Pero si la máquina “piensa” y el humano se limita a presentar el resultado, la autoría se vacía, aunque las palabras sean nuevas.

    La distinción decisiva no es, por tanto, entre «usar IA» o «no usar IA». Es entre asistencia o sustitución.

    Utilizada de forma reflexiva, la IA puede aclarar estructuras, revelar debilidades en el razonamiento y estimular nuevas indagaciones. Puede actuar como andamiaje cognitivo. Utilizada sin reflexión, corre el riesgo de fomentar la pasividad, sustituyendo precisamente los procesos que el trabajo académico pretende cultivar: la duda, la revisión, el discernimiento y el juicio.

    La inquietud ante la IA suele ocultar una incomodidad más profunda. Tememos que los estudiantes dejen de pensar por sí mismos. Sin embargo, la historia sugiere que no son las herramientas las que determinan los resultados, sino los marcos en los que se utilizan. Las calculadoras no abolieron las matemáticas. Los archivos digitales no abolieron la investigación. Cada cambio tecnológico obligó a precisar mejor en qué consiste la responsabilidad intelectual.

    La inteligencia artificial desempeña hoy una función similar. Actúa como prueba de resistencia para el diseño educativo. Si una tarea se puede externalizar por completo en un sistema de IA generativo sin pérdida intelectual, quizá nunca hubo juicio auténtico.

    Esta es la lección incómoda.

    Nunca existió una edad dorada del pensamiento crítico destruida de repente por los algoritmos. Lo que tenemos, de nuevo, es una tecnología que pone al descubierto fragilidades. La IA no crea la debilidad; la revela.

    La cuestión ética central ya no es si un texto es original. Es si el pensamiento que lo sustenta es genuinamente humano: reflexivo, responsable y asumido como propio. El juicio no es solo una operación cognitiva; es una forma de responsabilidad.

    En el taller renacentista, la grandeza no residía en el pincel, sino en la mente que lo guiaba. En la era de la inteligencia artificial, la misma verdad sigue vigente.

    La tecnología amplía lo que podemos calcular. La educación debe fortalecer nuestra capacidad de juzgar.

    La ejecución puede delegarse. El juicio no.

    En la era de las máquinas inteligentes, preservar el juicio humano no es resistirse a la tecnología. Es la condición para utilizarla bien.

  • The Renaissance Workshop and the Limits of Artificial Intelligence

    Why execution can be delegated, but judgment cannot.

    Artificial intelligence challenges traditional notions of authorship and judgment. This foundational essay explores why execution can be delegated — but genuine intellectual responsibility cannot.

    During the Renaissance, artistic creation rarely conformed to the modern myth of solitary genius. Master painters worked in workshops. They conceived the composition, drafted initial sketches, set proportions, and ensured coherence. Apprentices executed large sections of the work — preparing surfaces, applying pigment, and completing secondary figures.

    What mattered was not who held the brush, but who held the vision.

    Authorship did not depend on the number of brushstrokes personally applied.

    It depended on intellectual architecture.

    Execution could be delegated.

    Conception could not.

    What distinguished a Leonardo or a Raphael was not manual exclusivity but intellectual architecture: the capacity to conceive a form, organise complexity, and impose unity upon multiplicity.

    The analogy with artificial intelligence is not superficial; it is structural.

    Today, AI systems generate text, images, code and analysis at extraordinary speed. They extend productive capacity as much as apprentices once did. They accelerate execution, expand output and assist refinement.

    But they do not originate the vision.

    They do not determine which questions are worth asking, which distinctions matter, or which direction inquiry should take. They operate within parameters set by human intention. They process patterns. They do not inhabit meaning. There lies the limit.

    The architecture remains ours — or it dissolves.

    This distinction becomes especially urgent in education.

    A recurring anxiety has emerged: when students use AI tools for academic work, does authorship disappear? Has originality been compromised?

    The concern is understandable, but it is also incomplete.

    Plagiarism, in its classical sense, concerns the misappropriation of identifiable words or ideas. Generative systems complicate this definition because they produce novel formulations rather than copying from traceable sources. From a purely textual perspective, the output may be technically original.

    But education has never been merely about textual originality. It has been about intellectual formation.

    Authorship, in its deeper sense, lies not in phrasing but in ownership of questions, responsibility for arguments, and the capacity to justify conclusions. It resides in the tacit dimension of understanding — the integration of evidence, context and judgement that cannot be fully reduced to explicit procedure.

    If a learner defines the problem, evaluates evidence critically, and assumes responsibility for interpretation, AI may serve as an instrument — like the apprentice in a Renaissance workshop. If the machine performs the thinking and the human merely submits the output, authorship becomes hollow, even if the wording is new.

    The decisive distinction is therefore not between “AI” and “no AI.”

    It is between assistance and substitution.

    Used reflectively, AI can clarify structure, expose weaknesses in reasoning and stimulate further inquiry. It can serve as cognitive scaffolding. Used uncritically, it risks encouraging passivity — replacing the very processes academic work is meant to cultivate: doubt, revision, discernment and judgement.

    The anxiety surrounding AI often masks a deeper discomfort. We fear learners will cease to think for themselves. Yet history suggests that tools do not determine outcomes; frameworks do. Calculators did not abolish mathematics. Digital archives did not abolish research. Each technological shift compelled a clearer articulation of what intellectual responsibility entails.

    Artificial intelligence now performs a similar function. It serves as a stress test for educational design. If a task can be fully outsourced to a generative system without intellectual loss, perhaps it never required genuine judgment in the first place.

    This is the uncomfortable implication.

    There was no golden age of critical thinking suddenly destroyed by algorithms. What exists is a moment that exposes fragilities. AI does not create the weakness. It reveals it.

    The central ethical question is therefore no longer whether a text is original. It is whether the thinking behind it is genuinely human — reflective, accountable and internally owned. Judgement is not merely a cognitive operation; it is a form of responsibility.

    In the Renaissance workshop, greatness lay not in the brush but in the mind that guided it. In the age of artificial intelligence, the same truth holds.

    Technology expands what we can calculate. Education must strengthen our capacity to judge.

    Execution can be delegated. Judgment cannot.

    In the age of intelligent machines, preserving human judgment is not resistance to technology. It is the condition for using it well.

    Developing this argument

  • The Bic Cristal and the Quiet Transformation of Education

    On 3 September 1965, France officially lifted the ban on ballpoint pens in schools. Until then, students had been required to write with metal-nib pens and ink. The change, as recently recalled by Le Monde, may seem trivial from a contemporary perspective. Yet it signalled more than a practical adjustment. It marked a cultural transition.

    The move from inkwells to the Bic Cristal was not merely a change of instruments. It altered rhythm, posture and expectations. Writing became faster, cleaner and more accessible, and less ceremonious.

    The metal nib demanded deliberation. Ink required attention. Blots were visible and consequential. Writing carried a certain ritual weight. The ballpoint pen reduced friction. It simplified execution and widened access.

    Neither development was inherently superior. Each fostered different habits.

    The Bic Cristal democratised writing. It lowered costs and increased reliability. It allowed millions to write without fear of spills or technical inconvenience. At the same time, something of the older aesthetic discipline — the visible trace of careful calligraphy — receded.

    This historical moment offers a modest yet instructive lesson.

    Technological innovations in education rarely announce themselves as revolutions. They arrive as improvements in convenience. Yet convenience reshapes practice, which reshapes habits, which reshape cognition.

    The lesson is neither to romanticise the inkwell nor to celebrate the ballpoint uncritically. It is to recognise that tools alter the texture of learning.

    Today’s debates about artificial intelligence often assume the scale is unprecedented. In one sense, it is. In another, education has always absorbed technologies that reconfigure its daily rhythms — from printing presses to pencils, from blackboards to laptops.

    The question is not whether tools change education. They do.

    The question is whether we remain attentive to what changes in them.

    The Bic Cristal did not destroy literacy, but it did change how writing was experienced. Likewise, contemporary digital tools will not abolish thinking. They will, however, shape its tempo, friction, and perhaps its depth.

    Sometimes the most consequential transformations begin with the simplest objects.

  • The Cloister and the Spaceship: Neil Ferguson on the Future of the University

    In a recent article in El Mundo, British historian Niall Ferguson advances a deliberately stark proposal for the future of higher education. He suggests that the university of tomorrow may need to be divided into two complementary spaces.

    The first he calls the “cloister”: a technology-free environment inspired by medieval monasteries, where reading, writing, debate and sustained problem-solving occur without digital mediation. The second is the “spaceship”: a space where artificial intelligence is used intensively and students learn to interact fluently with advanced technologies.

    The proposal is architectural, but its core is pedagogical.

    Ferguson’s central insight is that asking good questions requires prior formation. Intellectual depth does not arise from immediate access to answers. It is cultivated in silence, through effort, and through the disciplined confrontation with difficulty.

    The cloister, in this sense, is not a nostalgic retreat. It is a preparatory space. It recognises that judgement precedes automation.

    Artificial intelligence can expand inquiry. It cannot replace the habits that make inquiry meaningful. Without prior immersion in texts, arguments and conceptual struggle, AI risks becoming not a tool of amplification but a mechanism of shortcuts.

    The distinction recalls the classical idea of paideia: education as the formation of the whole person through rigorous engagement with language, logic and moral reasoning. In a culture increasingly structured by algorithmic immediacy, defending such formative spaces becomes countercultural.

    The spaceship, by contrast, acknowledges reality. Students must learn to navigate and shape technological systems. Fluency in AI interaction will be indispensable, but it must rest on intellectual foundations laid elsewhere.

    The deeper question concerns sequencing.

    Do we expose students first to optimisation or to formation?

    Do we train them to generate or to think?

    Ferguson’s proposal suggests that universities must resist the temptation to collapse these stages. If everything becomes a spaceship, the cloister disappears — and with it, the conditions for meaningful questioning.

    In that sense, the debate is not about technology versus tradition. It is about safeguarding the formative conditions that make technological mastery responsible rather than reactive.

    The future university may indeed require both spaces.

    But without the cloister, the spaceship has no pilot.