LinkedIn’s chief economic opportunity officer recently warned that AI is “breaking” entry-level jobs that have historically served as stepping stones for young workers. As Aneesh Raman wrote in The New York Times, “Breaking first is the bottom rung of the career ladder.”
AI tools are performing simple coding and debugging tasks that junior software developers once did to gain experience, along with work that young employees in the legal and retail sectors traditionally handled. Wall Street firms are also reportedly considering steep cuts to entry-level hiring in light of this.
The implications go way beyond individual hardship — they threaten the foundation of how organizations build expertise, maintain security and sustain innovation. However, there are things all organizations can do now to help.
The risks of AI over-reliance in the workforce
Hasty AI adoption at the expense of hiring and developing human workers creates serious security vulnerabilities. AI is only as effective as its inputs, and the interpretation and action steps that follow are only as sound as the skills and contextual understanding of the people involved. In short: reducing human oversight creates breeding grounds for security gaps.
Indeed CEO Chris Hyams recently noted that while AI can’t completely replace a job, “for about two-thirds of all jobs, 50% or more of those skills are things that today’s generative AI can do reasonably well, or very well.” These shifts underscore the urgent need for organizations to take a balanced approach, investing thoughtfully in both emerging technology and human talent. To shape a sustainable future of work, neither people nor progress can be left behind.
This challenge is particularly acute in fields like cybersecurity, where gaining hands-on experience is crucial. Today, many cybersecurity roles require experience that young professionals can’t acquire because entry-level positions that build that experience no longer exist or have been automated away. An increasing number of cybersecurity job postings list artificial intelligence skills as a requirement. Yet research shows that 44% of professionals say their companies have invested in AI across the organization while employees lack adequate skills and training to use these tools effectively — meaning professionals are being left behind and there is a gap in the skills needed to manage AI investments correctly. If this is happening now, consider how these changes could reshape our workforce in the next five years and beyond.
Heavily leveraging AI while human workers at all levels lack the training and skills to manage it appropriately — that is a recipe for significant risks. When you can’t build a pipeline of talent from the ground up, you end up with senior professionals who lack the diverse perspectives and fresh thinking that come from working alongside newer team members.
How entry-level work is being redefined
As organizations increasingly lean on AI to handle tasks once reserved for junior employees, the danger isn’t just the disappearance of foundational career steps, it’s the erosion of the very systems that foster growth, innovation and security. Overreliance on technology threatens to sever the pipeline that develops future experts, leaving critical gaps in both skills and perspective.
LinkedIn COO Dan Shapero says, “When I was at Bain, a lot of the time I spent was making slides and going to the library to figure out research reports. All of that is now automated. Bain still hires scores of recent graduates. They just do different parts of the process.”
Rather than eliminating entry-level jobs, organizations should reimagine them for a new era—where early-career professionals are empowered to work alongside AI, learning higher-order skills instead of just routine tasks. Use AI to augment new workers, not replace them. Allow junior employees to focus on strategy, creativity, relationship-building, and complex problem-solving while AI handles routine tasks. This approach requires treating AI like a new hire — every AI tool should be evaluated, supervised and developed like an employee, not simply deployed and forgotten.
Successful workforce transitions require structured evaluation processes for each potential AI use case. This evaluation should include a return-on-investment analysis not just in terms of dollars and hours saved, but also in terms of human impact. When an AI tool eliminates repetitive tasks, the affected employee should be retrained for a more strategic role, making AI a catalyst for internal mobility rather than displacement.
Organizations need cross-functional governance that ensures decisions aren’t just compliant but human-centered. Representatives from IT, privacy, product, security, and HR should collaborate to balance innovation with workforce development. When GenAI first captured widespread attention in 2023, smart companies built sustainable AI governance models, prioritized transparency and tackled employee displacement through reinvention rather than layoffs.
Making meaningful change
As leaders, we need to ask ourselves: What are we doing to ensure the next generation can build the experience they need to become our future leaders? Here is a starter kit:
Develop AI fluency as a core competency. Cultivating AI fluency must become a cornerstone skill for tomorrow’s workforce. Increasingly, job candidates will need to demonstrate not only their comfort with AI tools, but also their ability to harness these technologies as proactive problem-solvers and innovators. This is the new generation’s biggest advantage in the job market.
Invest in apprenticeships and mentorship programs. Create pathways where experienced professionals work directly with newcomers on real projects, not just theoretical training. Career pathing and goal setting can develop internal talent effectively. Support teams can allow engineers to explore different roles within the company, successfully transitioning employees into specialized roles like cybersecurity.
Embrace upskilling as a core business function. Teaching current employees new skills is a great start, but that’s not even half the challenge here. Let’s also approach this by creating new types of roles that didn’t exist before. Don’t assume competency. (You know what they say about assuming!) Recognize that professionals — in both new and legacy roles — need adequate training to use AI tools effectively.
Support workforce development initiatives. Creating curricula and engaging with educational institutions can help address skills gaps while supporting corporate social responsibility efforts. Legislative initiatives that enhance accessibility of cyber training and education through scholarship programs for two-year degrees at community colleges and technical schools can strengthen the talent pipeline.
Maintain explicit commitments to workforce retention. Even when specific jobs change, it’s important to find new places for affected employees within the organization. Why does this matter? It sends the message that reliable AI requires human oversight, and that the goal should be redefining roles rather than eliminating them. (Big difference!)
Individual company programs won’t resolve all of this, but collective action can make a difference. Instead of looking at this as a massive problem, why not see this as an opportunity to shape how AI transforms work? We can start now, acting deliberately and with the next generation in mind.
The opinions expressed in Coins2Day.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Coins2Day .
