Why this article matters
- We are not just losing jobs; we are losing the training ground that shapes future leaders.
- In emerging markets, employers are increasingly choosing US$20 monthly AI subscriptions over graduate trainees.
- By automating junior tasks, companies are putting pressure on senior staff and leaving them without a pipeline of support.
- If success in the 2026 labor market is not about competing with machines, can we still afford to define “entry-level” using the traditional definitions of the corporate ladder?
Brynn Pule, a 2025 marketing graduate, applied for 200 jobs between May and September 2025, without securing a single offer of work. Her experience is becoming increasingly common and labor market data suggests it may reflect a broader structural shift.
University career centers report that the average time it takes for a student to land their first job has doubled in recent years. A recent survey suggests that only 30% of 2025 graduates secured an entry-level role in their field compared to roughly 40% the previous year.
The direction of change appears to be consistent: entry into the workforce is taking longer and is less certain. But whether this marks a lasting transformation or a short-term disruption remains an open question.
Why the decline?
Vahid Haghzare of Silicon Valley Associates Recruitment notes that while there was fierce competition for IT skills five years ago, the rapid rise of AI tools has brought about a downturn in junior hiring. According to the Stanford Digital Economy Lab, following ChatGPT’s introduction in late 2022, early-career employment in AI-exposed sectors decreased by 13% in comparison to less-exposed fields.
This shift is often attributed to large language models, code generators, and automation tools being capable of handling a wide range of tasks, including data entry, rudimentary coding, routine customer queries, and basic report creation, tasks that were traditionally assigned to junior employees.
In recent years, generative AI systems have been able to create marketing content, summarize legal documents, and even correct software bugs – tasks that formerly provided juniors with practical experience.
In fact, companies adopting generative AI saw 7.7% fewer junior hires and no significant change in senior hires over the four quarters immediately after the introduction of ChatGPT. When a company adopts AI tools, it often does not replace senior staff, but instead reduces the number of juniors it hires or stops hiring them completely. In some emerging markets, like Nigeria, employers describe AI as a cost-saving alternative to graduate trainee programs, with basic AI tools costing about US$20 per month.
Why entry-level roles?
The CEO of AI company Anthropic, Dario Amodei, has warned that technology could eliminate half of all entry-level office positions within five years, potentially increasing unemployment by between 10% and 20%.
This is because entry-level roles disproportionately consist of exactly the type of work that AI excels at automating. For example, new AI “digital assistants” can manage inboxes, draft emails or summaries, schedule meetings, thereby reducing the need for administrative support roles.
These changes suggest that the traditional entry-level roles (data entry, filing, basic analysis) are gradually disappearing. A report by CBRE similarly noted that roles such as office administrators, secretaries, and support staff have been in long-term decline, with generative AI accelerating this trend. AI tools can now also handle travel bookings with the most affordable hotel being identified within minutes, a new ability that travel agents cannot match.
Industry case studies
Technology sector
The technology sector provides a clear example of this shift. In early 2025, Isaac, a mid-level developer, noticed that the company he worked for had completely stopped hiring junior members of staff which increased pressure on senior staff. “Seniors are burning out, and when they leave, there’s no rush to replace them, because ‘AI will do it’!” he explained.
This sentiment is reflected in macroeconomic data across the globe. In India, consultancy company EY reports 20–25% fewer entry-level positions in IT services firms, attributing this to automation and AI. LinkedIn and EU platforms also saw a 35% drop in junior tech posts across Europe in 2024.
At the same time, the proportion of roles requiring AI-related skills has grown significantly, suggesting a shift in hiring requirements rather than the replacement of human skills.
Administration, retail and services
Traditional entry-level jobs, such as clerks, receptionists, retail assistants, and food service workers, are also evolving. Many involve routine interactions or data handling which makes them susceptible to automation.
Retail chains are increasingly using self-checkouts, inventory automation, or outsourcing to online platforms, reducing demand for cashiers. In offices, scheduling, document preparation, and basic customer enquiries are often handled by chatbots and virtual assistants.
The emerging talent gap
While businesses and AI investors may benefit from efficiency gains, some experts are concerned about the impact on the workforce. Entry-level roles have historically served as training grounds, allowing workers to develop foundational skills. The erosion of this “first rung” of the career ladder may create a long-term problem with fewer workers gaining practical experience early in their careers.
This raises a higher risk: if junior employees are no longer trained, the pipeline that produces mid-level and senior talent may begin to break down. It seems that in attempting to eliminate inefficiency, companies may also be weakening the systems that produce their future workforce.
What happens next: collapse or restructuring?
The challenge lies in determining whether current labor market signals indicate short-term disruptions or a deeper restructuring.
One possibility is that the labor market is experiencing a period of adjustment, a pattern historically seen during major economic and technological shifts. Contemporary data supports this view. Charter Global, a leading IT services and digital engineering company, estimates that technological change is likely to both displace and create jobs in roughly comparative numbers over time. Similarly, the World Economic Forum noted that trends in AI were expected to create 11 million jobs as opposed to the 9 million lost.
Another, less discussed outcome is a delayed talent gap. If junior employees do not develop into mid-level professionals, organizations may struggle to replace experienced workers in the future. Some tech companies warn of a potential “lost generation” of workers who lack hands-on experience. Early signs of this dynamic are already emerging in fields such as cybersecurity, where reduced junior hiring is raising concerns about future skills shortages.
How are employers responding?
Faced with this uncertainty, employers must work out their strategies.
In some cases, entry-level work is being redesigned so that junior employees oversee AI systems, validate the outputs, and handle exceptions rather than perform routine execution. Price Waterhouse Coopers put this as a broader shift toward “augmentation” rather than replacement.
For other employers, maintaining junior hiring remains a strategic choice. Continuing to recruit early-career workers — despite lower immediate productivity — may act as a hedge against future talent shortages and weakened succession pipelines, a risk highlighted in workforce analyses undertaken by the World Economic Forum.
Since AI is about heightened efficiency, the question is about capability, and it seems that its evolution will depend on employers’ ability to redefine capability. It might well be that, as leadership coach Joshua Miller argues, the companies that succeed will be those that use AI to make their entry-level employees more productive, rather than making them redundant. In practice, this could mean that instead of a junior marketer spending 20 hours a week drafting basic social media copy, their role shifts towards auditing the AI-generated output for brand accuracy or handling the complex ethical principles that AI cannot yet navigate.
