The end of labor and the rise of AI natives - Korea JoongAng Daily

TEXT START: A job seeker looks at postings in front of a university job center on July 23.

The Dissection

This is not really an analysis of the end of labor. It is a policy brief for universities. The article correctly identifies credential inflation, AI displacement and the erosion of degree-based career sorting, then converts a structural collapse into a skills shortage that retraining can supposedly solve.

Its central move is institutional self-preservation: fund universities to manufacture “AI natives,” and the labor system may continue functioning. The G.I. Bill analogy is the historical prop. It assumes an AI-era training boom can reproduce a postwar expansion, despite AI being a labor-saving supply shock rather than a labor-absorbing demand shock.

The Core Fallacy

The article confuses AI competence with economic power.

An AI-native worker may produce more, but that often means fewer workers are required. The gains accrue primarily to those who own or control the models, data, platforms, energy, robotics and distribution systems. Fluency is not ownership. Productivity is not bargaining power. A trained worker can remain a replaceable servitor with a more sophisticated interface.

The article also treats newly created jobs as evidence that mass employment will survive. That is a category error. New tasks do not guarantee stable, accessible, well-paid work for the displaced majority. Under P1, P2 and P3, AI can generate new niches while still severing the employment–wage–consumption circuit.

Hidden Assumptions

• AI will augment enough workers rather than compressing headcount.
• “AI-native” expertise will remain scarce instead of being rapidly commoditized.
• Employers will distribute productivity gains through hiring and wages.
• Corporate tax windfalls will be durable and politically captureable.
• National retraining can overcome global labor competition.
• Physical AI adoption will be slow enough to preserve large blue-collar niches.
• Universities can define and teach capabilities that remain economically valuable.
• The postwar relationship between education, employment and middle-class expansion still applies.
• More schooling can solve a problem caused by capital ownership and technological substitution.

Social Function

Classification: partial truth, transition management, ideological anesthetic and institutional self-exoneration.

The article is right that old credentials are losing value and that workers must learn to operate alongside AI. It becomes anesthetic when it presents this adaptation as a national solution rather than a selection mechanism. Public retraining can produce a minority of useful servitors, but it cannot manufacture enough sovereign owners to absorb the displaced population.

The university president’s proposed remedy also shifts blame toward inadequate education spending. That reframes labor oversupply as a failure to train workers correctly, allowing institutions and capital owners to avoid the harder question: who owns the automated productive system, and how will everyone else obtain claims on its output?

The Verdict

The text sees the corpse but prescribes better schooling.

AI-native training is a real transitional advantage for a minority. It is not a mass-employment solution. If the Discontinuity Thesis holds, retraining merely improves contestants’ chances in a shrinking servitor market while ownership remains concentrated. The survivors will be sovereigns, indispensable operators, verifiers, transition intermediaries and workers controlling energy, logistics, maintenance or automated infrastructure.

The article is not wholly false. It is structurally incomplete in the precise way an institutional survival document must be: it recognizes labor’s decline while pretending the education system can remain the mechanism that saves it.