AI and the End of Upward Mobility: A Major Challenge to Meritocracy

AI Is Dismantling the Traditional Path to Upward Mobility

For a long time, education and personal effort have been seen as the primary means of changing one's fate. A young person from an ordinary background could attend a prestigious university, land a white-collar job, gradually accumulate experience, and ultimately climb the social ladder. This logic forms the core belief of what modern society calls "meritocracy" — the idea that hard work and talent will be rewarded.

Yet the article The End Of Upward Mobility – AI is coming for the meritocracy makes a pointed argument: artificial intelligence is shaking the very foundation of this belief. As AI becomes capable of handling large numbers of tasks that once belonged to entry-level knowledge workers, the traditional career ladder — built on accumulated experience and gradual advancement — is having its lowest rungs pulled out from underneath it.

What the Disappearance of Entry-Level Jobs Really Means

In the past, young people typically entered the workforce by starting with the most basic, repetitive tasks: organizing data, drafting documents, conducting foundational analysis, and handling standardized paperwork. These jobs weren't particularly valuable on their own, but they were the necessary starting point for gaining experience and advancing to higher levels.

The problem is that these are precisely the tasks AI is best at replacing. When companies realize that one engineer working with AI tools can produce what previously required three to five junior employees, the incentive to hire for entry-level positions drops significantly. This creates a structural dilemma: without entry-level jobs, how do young people accumulate the experience needed to become senior professionals?

This isn't simply a narrative about "AI stealing jobs." It's a deeper concern about the career development pipeline itself being severed. When the lower half of the ladder disappears, those who can still reach the top are increasingly likely to be people who already have resources, networks, and a head start.

A Crisis of Faith in Meritocracy

The legitimacy of meritocracy rests on an assumption: that opportunity is broadly open, and that effort and talent can translate into success. The article's central warning is that AI may shatter this assumption, transforming upward mobility from "effort creates opportunity" into "starting point determines destination."

If high-quality growth opportunities become increasingly scarce and concentrated among a select few, society will face more than just an employment problem — it will face a deep crisis of fairness and collective belief. When younger generations find that no amount of effort can break through existing class barriers, the shared conviction that "hard work pays off" — the very glue holding society together — may begin to dissolve.

This is why the topic extends far beyond technical discussion. It touches on fundamental questions of social mobility, intergenerational equity, and institutional trust.

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The concept of "meritocracy" itself carries a profound internal contradiction. The term was coined by British sociologist Michael Young in his 1958 satirical novel The Rise of the Meritocracy as a critique — he predicted that a society equating intellect and effort with virtue and distributing resources accordingly would ultimately produce a new self-enclosed, empathy-deficient privileged class. Yet the word was "positively appropriated" as it spread, gradually becoming a core piece of rhetoric used to justify modern capitalist society. Harvard professor Michael Sandel, in The Tyranny of Merit, goes further, arguing that meritocracy not only fails to achieve genuine equality of opportunity but causes winners to believe their success is entirely self-made — leading them to morally look down on those who fail. AI's assault on entry-level positions is adding pressure to an institutional framework that was already cracking. When even the pathway to "start by working hard" is blocked, the self-justifying narrative that meritocracy relies on becomes even harder to sustain.

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Worth Watching, But the Verdict Is Still Out

It's worth noting that perspectives like this are currently more of a trend warning than an established fact. Every major technological revolution in history has triggered fears about disappearing jobs — yet new positions and new forms of work have typically emerged alongside the disruption. Whether AI will repeat this pattern, or whether this time is truly different, remains to be seen.

That said, the question raised in the article remains highly valuable: when AI takes over the work that was once used to train newcomers, how should society rebuild pathways for upward mobility? This will likely require a systemic response from educational institutions, corporate training programs, and public policy alike — not simply a hope that markets will self-correct.

For individuals, rather than waiting passively, the most realistic strategy is to proactively understand and harness AI — to be on the side of those who use the tools, rather than those who are replaced by them.

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Among economists, there is genuine serious debate about whether "this time is different." The traditional view, grounded in the Lump of Labour Fallacy argument, holds that technological progress always destroys old jobs while creating an equal or greater number of new ones — the Industrial Revolution and the computing revolution both serve as evidence. But recent research by MIT economist Daron Acemoglu and others raises challenges: the key difference between AI and previous general-purpose technologies is that AI replaces cognitive tasks far faster than new demand can form, and the newly created positions typically require skills entirely different from those being displaced, making retraining extremely costly. Furthermore, the employment restructuring that has followed every past technological revolution has typically played out over decades — and that "transition period" itself generates severe intergenerational injustice. The generation before completed its accumulation under the old system, while the generation just entering the workforce bears the full friction of the transition. This makes the reassurance that "things will be fine in the long run" of limited comfort to today's young people.

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Conclusion

This article poses a disturbing yet important proposition: what AI disrupts may not just be specific jobs, but the very mechanism of upward mobility that society depends on to function. Whatever the final verdict, thinking ahead about how to maintain equality of opportunity in the age of AI is an unavoidable challenge for every member of society and every policymaker.