Lately, in the quiet corridors of academia, I’ve sensed a small but telling drift: a mix of fatigue and a diminishing sense of duty when it comes to training graduate students. Why spend long hours walking a doctoral student through rough drafts and ham-handed analysis when an AI tool can clean data and produce analyses with unnerving speed — no hassle to arrange meetings or bruised student egos to mend?

That mood in universities is a microcosm of a much larger shift. Outside the ivory tower, companies are one step closer to workplaces where dexterous robot arms, automated warehouses and AI-driven operations rival the assembly lines of the industrial past. The workforce isn’t just being optimized; it’s being transformed, and people are bound to lose jobs.

The classic cycle of capitalism — jobs lead to income, income fuels consumption, profits create more jobs — has always been a virtuous (imperfect but functional) ecosystem. Yet AI and robotics increasingly sever the link between production and (often unionized) human labor. More troublingly, automation is not only replacing tasks; it is removing the entry rungs that once made social mobility plausible. If the first steps into a profession vanish, the aforementioned loops face a continuity problem: Who, exactly, gets to enter the system and learn how to contribute?

Inside corporate organizations, the risks compound. Companies do not run on explicit rules alone; they survive on tacit knowledge — culture, norms and the quiet know-how passed down through the trial-and-error of junior staff. If AI leapfrogs this intermediate stage, if firms stop hiring juniors because software is cheaper and “good enough,” we invite a kind of “social memory” loss. Without the clumsy, error-prone phase of the novice, where does the wisdom of future leaders come from? You can’t download it. You can’t prompt it into existence. You earn it by doing the work badly at first, then less badly, then well — under the guidance of others and real-world consequences.

The long-term outcome of “AI-first staffing” is a hollow middle. We may end up with a thin layer of senior decision-makers and a thick layer of automated execution, but fewer people in between who can learn to diagnose problems, interpret context, or hold relationships together. Leadership and organizational culture may erode not because anyone intended harm, but because the apprenticeship pipeline that formed them was quietly dismantled.

Recent organizational research, including work discussed in Harvard Business Review by Edmondson and Chamorro-Premuzic, points to a striking paradox: When AI handles all entry-level work, human members may end up responsible for outcomes they do not fundamentally understand. Meanwhile, the junior employee, often dismissed as “the one who doesn’t know the rules,” is frequently the source of fresh perspective. Newcomers ask inconvenient questions. They notice absurdities veterans have normalized. They disrupt stale routines simply by not being fully socialized into them. They argue in their article that automating novice work doesn’t merely reduce inefficiency; it silences a key engine of renewal.

This is why we should rediscover the moral value of human growth. Mastering basics is not just skill acquisition; it is self-formation. Repetitive work has its own dignity, even if boredom is real — not because repetition is inherently noble, but because it can cultivate self-control, attention, patience and responsibility for others. Failure is not a detour from learning; it is how we map the boundaries of our knowledge and develop humility about what we don’t yet see.

So our educational philosophy must shift — not necessarily away from technology, but toward learning in which humans are the actors. Even if a student uses AI to arrive at an answer, they still have to sit with it: why it emerged, what assumptions it rests on, what it leaves out, and what evidence would falsify it. We should cultivate citizens capable of thought, reflection and action, rather than passive consumers of AI recommendations and decisions.

This brings me to a question I want to ask plainly of the big tech leaders, executives and policymakers who help lead today’s world: When you picture the future your own children will inherit, what do you envision and what are you truly passionate about? Most people want their children to be recognized for real merit, to work hard to create value, and to gather meaningful experiences — not just to prompt an AI and wait for output. If that is the world we want, we cannot be miserly in investing in humans. We must be willing to pay for the inefficiency of human learning — for the privilege of letting a person be slow, clumsy and wrong, so that they may eventually become competent, wise and good.

The true test of our time will not be how fast our machines can think for us. It will be whether we still value the slow, messy, magnificent process of a human mind growing up — and whether we are brave enough, and generous enough, to protect the conditions that make that growth possible.

Lim Woong

Lim Woong is a professor at the Graduate School of Education at Yonsei University in Seoul. The views expressed here are the writer’s own. — Ed.


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