AI Means the End of Entry-Level Jobs
Companies will have to find ways of giving junior employees less grunt work and more responsibility.
By Richard R. Smith and Arafat Kabir
Chad CroweThis month’s lackluster employment numbers spurred talk that artificial intelligence is destroying jobs.
Whether or not that is showing up in the statistics, AI presents a different challenge than past technological disruptions—in large part because it is eliminating the entry-level positions that traditionally served as stepping stones to career advancement.
This shift helps explain a troubling pattern in workforce anxiety.
A recent Pew Research survey shows that more than half of employed adults worry about how AI may be used in the workplace.
A September Deutsche Bank survey reports that 24% of workers under 35 express high concern about losing their jobs to AI, compared with only 10% of those over 55.
It’s been historically true that younger workers embrace new technologies while older workers resist change.
But AI seems to have flipped this dynamic.
When AI automates routine tasks, organizations often find they need experienced employees who can combine AI capabilities with years of business knowledge.
What those organizations don’t need is entry-level employees learning the basics.
Data shows rising unemployment since 2022 among 22- to 25-year-olds in AI-affected sectors—even while employment for older workers remains stable.
The traditional bottom rung of the career ladder is disappearing.
We need to think about how younger workers will be affected in an AI-driven future to ensure that we have enough talent to replace retiring workforces.
This begins with companies recognizing that AI represents a fundamental shift rather than merely another tool.
One example could be focusing on “AI native” tracks in which, instead of starting new employees with routine tasks that AI can handle, they begin with AI oversight and optimization roles.
They learn to train, monitor and improve AI systems while simultaneously building domain expertise—combining technical fluency with business acumen.
A second option can be a mentor-intensive development program that pairs junior workers directly with senior professionals—letting AI handle the routine tasks that used to fill a junior employee’s day.
Instead of learning by doing grunt work, juniors learn judgment and strategy by working alongside experienced colleagues on higher-level problems from day one, building the business acumen and strategic thinking that AI can’t replicate.
This can extend to project-based progression, in which new workers help implement AI initiatives across departments rather than filling traditional roles.
They learn technology adoption, gain client exposure, and observe business dealings typically reserved for more senior employees, while developing both technical and leadership capabilities across multiple business areas.
At the heart of this new kind of career progression lies a reprioritization of demonstrated competency over tenure.
Companies can develop employee metrics that are guided by their needs and talent pools, allowing faster advancement based on proven capability rather than on time served in roles that may no longer exist.
As AI continues to evolve, the distinction between implementation of AI and everyday work will increasingly blur.
Forward-thinking organizations are already requiring such change, embedding adaptability into their cultural DNA.
AI skills can’t be developed in isolation.
Rather than treating AI as a separate skill set or capability, leading organizations will integrate AI training with employee development, ensuring that workers understand both the technology and its strategic application.
This means teaching how to prompt AI alongside financial analysis.
It means that employees learn AI model evaluation alongside customer relations and master algorithmic bias detection alongside market research.
The goal is developing professionals who think about AI as part of business strategy rather than as a separate technical domain.
This approach represents the future of work.
Organizations that build these capabilities now will be better positioned to capitalize on emerging technologies while maintaining employee engagement and trust.
As AI transforms work, the competitive advantage will increasingly go to organizations that excel not only at deploying technology but at developing the human capabilities that make the technology valuable.
By addressing AI adoption with the same rigor these employers already apply to technical deployment, leaders can build stronger workforces for the future and create workplaces where humans and AI together achieve outcomes neither could realize alone.
The missing lower rung in today’s career ladder isn’t a problem to be solved.
It presents an opportunity to build something better.
Organizations that act now to create these new pathways will define what success looks like in the emerging AI era.
Mr. Smith is a professor of practice and faculty director of the Human Capital Development Lab at Johns Hopkins University. Mr. Kabir is a writer specializing in AI.
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