Artificial intelligence is already changing the labor market in Lithuania: beginners are the first to suffer

TEXT START: The development of artificial intelligence (AI) is changing the Lithuanian and European labor markets: while there is a shortage of highly qualified specialists, workers performing routine tasks are at risk of losing their jobs.

The Dissection

The article documents the first visible fracture in the labor ladder: AI is absorbing routine cognitive work, companies are hiring fewer beginners, and experienced workers are performing tasks once delegated to junior staff. It then packages this deterioration as adaptation, reskilling, and market expansion. The evidence is more severe than the framing. Entry-level employment is not merely being reshaped; its function as the training pipeline for future specialists is being removed.

The Core Fallacy

The article treats continued demand for experienced engineers, hybrid specialists, and AI-enabled workers as evidence that technological displacement is being absorbed by the labor market. It is not. This is labor compression: fewer people, equipped with better tools, producing more output.

“Upskilling,” “hybrid talent,” and human responsibility describe temporary lag defenses, not a permanent solution. If senior workers and AI agents can perform the work previously assigned to juniors, the entry-level class loses its route to experience. The shortage of specialists therefore coexists with the destruction of the mechanism that creates specialists.

The article also confuses product-market expansion with employment expansion. AI startups may create valuable products while requiring progressively fewer human workers. Revenue can grow while labor demand contracts. Under the Discontinuity Thesis, that is not a contradiction; it is the mechanism of the transition.

Hidden Assumptions

• New AI-driven markets will create enough jobs to replace the roles automation removes.
• Retraining will convert displaced workers into scarce specialists at sufficient speed and scale.
• Human oversight, verification, and responsibility will remain labor-intensive rather than becoming further automated.
• Productivity gains will be distributed through wages instead of accruing mainly to owners of AI capital.
• The current shortage of skilled workers is permanent rather than a temporary bottleneck during automation deployment.
• “Creativity,” relationships, and complex judgment are durable moats rather than targets for the next automation cycle.
• Falling entry-level hiring is a cyclical adjustment instead of the collapse of the apprenticeship economy.
• National statistics capture the full labor market, despite direct hiring, hidden layoffs, and unrecorded task substitution.

Social Function

Classification: partial truth, transition management, and ideological anesthetic.

The article is factually useful because it records the leading indicator: beginners are being excluded first, budgets are shrinking, teams are getting smaller, and AI agents are taking over bounded professions. But its language converts structural displacement into a competency problem. The implied prescription is that workers should become more adaptable, more technologically fluent, and more responsible—while the ownership question remains untouched.

It reassures institutions that the crisis is still manageable because mass layoffs have not yet arrived. That is lag-based reasoning. A labor market can look stable while its replenishment system is being dismantled underneath it.

The Verdict

This is an early-warning document disguised as an adaptation story. It confirms the first phase of the Discontinuity Thesis: AI does not need to eliminate every job to break the post-WWII wage-consumption system. It only needs to eliminate enough junior and routine work to sever mass access to productive participation.

Lithuania and the other European markets described here are not facing a temporary shortage of the right skills. They are entering a bifurcation: sovereign owners and indispensable operators above, surplus labor below. The article sees the fracture clearly, then labels it “upskilling” to avoid naming the corpse.