Why cutting junior jobs is quietly deepening techs AI skills shortage

Date:
Tue, 05 May 2026 11:00:26 +0000

Description:
The technology sector has a habit of spotting contradictions everywhere
except in its own workforce strategies.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter The technology sector has a habit of spotting contradictions everywhere except in its own workforce strategies. Todays is particularly stark.

Globally, 74% of employers struggle to find qualified talent, with $11.5 trillion in annual productivity lost to skills gaps. Yet at the same time, overall tech hiring remains materially below prepandemic levels, with entrylevel roles contracting far faster than the rest of the market as AI absorbs routine work. Article continues below You may like Why hands-on digital skills will define the value of AI Why early-career investment and AI training matter for tackling the productivity crisis Why AI adoption isnt
just a tech problem, but a retention risk Paramita Chatterjee

VP at Cornerstone. Nowhere is that tension clearer than in AI. There are around 1.6 million unfilled AI roles worldwide, even as the earlycareer jobs that once allowed people to build those skills are quietly disappearing.

Employers are calling for advanced capability while narrowing one of the main routes through which future expertise is created. AI isnt creating AI jobs its reshaping every job Demand reflects that shift. Our data found that AI
and machine learning skills have grown 245%, making them one of the fastestgrowing technical skill categories. At the same time, the traditional split between technical and human work has collapsed; roles now require a neareven mix of both.

These capabilities do not appear fully formed. They are developed through exposure to real work, judgement calls and context - historically gained through earlycareer roles. Increasingly, those are precisely the activities being absorbed by AI tools . Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. The impact of Entrylevel role exposure Entrylevel roles are especially exposed because so much junior work is structured and repeatable. Tech job adverts have declined 50% since 2019/20, with junior roles among the hardest hit. Projections suggest a 45% decline in junior developer roles, alongside steeper falls in
AI QA testing and basic IT support.

Both external research and our own data show organizations can already automate around 30% of entrylevel work hours, including tasks that once acted as informal apprenticeships into more complex roles. On paper, this looks
like progress as productivity improves, whats harder to see is the delayed cost.

When fewer juniors are hired, and those who encounter thinner, more automated roles, fewer people accumulate the experience needed to step into senior positions later. A few years on, employers find themselves searching for advanced AI capability in a market that has quietly produced too little of
it. What to read next 'If organizations focus only on short-term
efficiency... they risk hollowing out the next generation of technical leaders': Microsoft execs say senior workers must mentor juniors to fix AI mistakes The AI gap nobody's talking about Closing AI learning gaps between leaders and employees

This is the structural problem beneath the AI talent shortage that were finding - companies are accelerating output today while eroding the learning capacity their future workforce depends on. AI is reshaping jobs as we know them AI is not eliminating jobs wholesale, its reshaping the work inside
them. Our analysis shows job titles remain stable while skills within them change rapidly - a form of substitution that headcount figures alone fail to capture.

For example, architects worried when AutoCAD architecture software arrived - manual drafting disappeared, but the profession strengthened. AI has a
similar effect in technology - used well, it can remove friction and free up time so people can focus on the more human elements of the role.

The mistake is treating productivity as the only outcome that matters. Shortterm efficiency gains can coexist with longterm capability loss, and leaders rarely see the latter until it becomes expensive. From AI literacy to AI excellence A more sustainable approach treats AI adoption as a maturity journey, not a switch.

It starts with AI literacy - understanding what tools can and cannot do,
where they add value and where they introduce risk. Literacy is no longer optional and needs to be encouraged, but its only the first step.

From there comes fluency, where AI is used inside real workflows and decisions, with humans still accountable for outcomes. Finally comes excellence, where AI is embedded into operating models, governed properly and continuously improved.

Earlycareer roles matter at every stage of that journey. The task now is to protect the learning value inside work. That does not mean preserving routine tasks, but rather identifying which activities still matter because they help lessexperienced employees understand the very foundations of their roles.

In many cases, AI should sit alongside junior staff, with explanation, review and correction built into the process so that learning continues rather than quietly disappearing. What tech leaders should do next If leaders want the AI dividend to last, workforce design needs as much attention as tooling. Automating tasks without redesigning roles is an incomplete strategy, particularly at the entry level where capability is formed.

Rebuilding junior work starts with deliberately protecting its learning
value, even when AI is used to assist. Where automation is introduced, explanation and review need to be built into the workflow so that judgement continues to develop.

Training must also move beyond basic awareness. Organizations should support progression from literacy into fluency, where AI is used inside real
decisions and workflows, and ultimately into excellence, where humanAI collaboration is embedded into how work gets done.

This is also where skills strategy needs to mature. In a market shaped by constant change, skills should be treated like R&D rather than a compliance exercise. Leaders need clearer visibility into which capabilities are rising, which are fading and where adjacent skills already exist inside the organization.

Workforce intelligence belongs at the center of growth planning, with success measured by whether capability is expanding over time, not just whether
output ticks up in the next quarter.

Reskilling pathways and internal mobility also need to be normalized rather than treated as an exception. Automation will continue to remove parts of roles, particularly at the execution layer.

What matters is whether people have credible routes into the work that replaces them. Redundancies may be a shortterm outcome of rapid change, but the longterm requirement is reskilling. The warning and the opportunity AI will continue to raise expectations of speed and sophistication in technology work, but capability still has to be grown.

We must invest in earlycareer development, redesign the work that teaches people, and treat skills as the growth asset they are.

AI can accelerate technology, but only if leaders stop eating their young. We feature the best recruitment platforms, to make it simple and easy to manage vacancies and hire staff. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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