Articles
AI
AI and the skills gap: When junior roles disappear and the future weakens

AI and the skills gap

When junior roles disappear and the future weakens

When training grounds vanish

AI tools accelerate. They generate code, draft analyses, and deliver proposals in milliseconds. On the surface, this looks like a win: faster processes, lower costs, higher efficiency.

But what we sacrifice is harder to see in the moment. Junior roles, the places where people once practiced the craft, are disappearing. When entry-level positions are cut, we don’t just lose today’s workforce, we risk losing tomorrow’s expertise. And without practice, there is no foundation for the future of competence.

“When entry-level roles disappear, we don’t just lose today’s workforce, we risk losing tomorrow’s expertise.”

Research signals

Recent studies show the same pattern: AI hits hardest at entry-level positions.

Stanford University (2025): Employment among young workers (22–25) in AI-exposed jobs such as software development has dropped by 13% since 2022, while senior roles increased by 6–9%.
CIO / Computerworld (2025): Junior IT jobs are shrinking as AI takes over routine tasks. Senior roles survive but shift toward oversight and quality assurance.
World Economic Forum – Future of Jobs Report 2025: 40% of employers worldwide plan to reduce roles that can be automated, primarily entry-level positions.
Goldman Sachs (2025): Generation Z could become the first major losers as AI eliminates entry-level tech roles.
Business Insider (2025): 42% of tech professionals say junior engineers are “stuck” because AI takes over their training tasks.

Together, these findings show a clear pattern: the career ladder is being cut off at its base.

A double gap

We now see a paradoxical development:

Junior roles are eliminated because AI can perform “the simple work.”
Senior roles are squeezed, some reduced to AI overseers, others cut altogether in the pursuit of efficiency.

In between, a narrow zone of AI output auditors grows: those who can quality-check machine output. The result is a double gap: one generation never gets the chance to train, while another is undervalued despite its experience.

This is not just a recruitment issue. It is a structural risk for the entire system.

Programming as an example

In programming, this shift is clearest.

AI tools can generate code snippets and suggest solutions.
But the work is never fully done: security, architecture, and debugging remain, tasks that require experience, systemic understanding, and judgment.
Junior developers no longer get to build that experience, and seniors are reduced to cleaning up the machine’s mistakes.

The dream that machines could code on their own is enticing. But without humans trained in the craft, we risk building a system dependent on one generation of seniors, with no one prepared to take over.

Sustainability requires training

AI can generate code, proposals, and analyses faster than any human. But that does not mean we can skip training. Skills are built through practice, repetition, mistakes, and gradual deepening, not by jumping straight to the answer.

When junior coders no longer get to write functions, debug, or see the consequences of their choices, we lose the foundation for the next generation of developers. This is where sustainability breaks down: learning cannot be outsourced. If AI takes over entry-level work without humans growing alongside it, we create a gap that cannot be filled retroactively.

A Future without a foundation

This is not about slowing down AI, it is about strengthening the human foundation that makes technology sustainable.

Organizations need to create deliberate training grounds:

Structured apprenticeships where juniors can practice with guidance.
Mentorship models where seniors transfer systemic knowledge instead of only supervising AI output.
Protected practice zones where mistakes and debugging remain part of the learning process.

AI can accelerate processes, but it cannot replace the gradual growth of competence. Without humans developing in parallel, we risk building a fragile system—efficient today, brittle tomorrow.

The real challenge is not whether AI will take over tasks. It is whether we design the structures that ensure people continue to grow with it. A future without this balance is not only a skills gap. It is a structural weakness we cannot afford.

“The conversation should not be about whether AI takes over tasks, but how we ensure humans grow alongside it. That is the real sustainability challenge.”