AI-driven layoffs disproportionately affect entry-level and young workers
AI is reshaping the workforce right now, and the first wave of change is hitting junior roles the hardest. Companies are using generative AI to eliminate repetitive tasks, and a lot of those tasks used to be handled by entry-level workers. If you’re 22, fresh out of college, and looking for that first job in data entry, customer support, or admin tasks, you’re likely out of luck. Those opportunities are either disappearing or getting reshaped into roles that require AI fluency from day one.
When a business replaces hundreds of low-skill jobs with a few dozen engineers and system designers, it’s not just about efficiency, it’s about future-proofing. That’s good for profit margins, bad for workforce entry points. And that imbalance won’t fix itself. If we want to build resilient companies, we need to think critically about who’s getting hired and how they’re trained to operate in an AI-centric environment.
Bharat Chandar, a postdoctoral fellow at the Stanford Digital Economy Lab, pointed out that AI is meaningfully slowing entry-level hiring. He’s also urging the U.S. Department of Labor to start tracking this properly, which, frankly, should already be happening. McKinsey’s report confirms the same trend: a third of organizations expect headcount reductions due to AI; very few expect to grow because of it.
For executives, this isn’t just a hiring issue, it’s strategic risk. You can’t scale leadership without growing talent from the ground up. If you eliminate the junior bench, who exactly are you promoting in five years?
AI as a cost-cutting and workforce reduction tool
AI is helping companies cut costs fast, often through layoffs. That’s what makes it a powerful, if controversial, efficiency lever. Corporate overhead gets reduced because bots don’t sleep, don’t take days off, and don’t need benefits. Businesses under pressure, whether that’s from shrinking margins, inflation, or shifting customer demands, are leaning hard into automation.
October 2024 was a milestone month for this trend. According to the firm Challenger, Gray & Christmas, over 153,000 jobs were cut, a 175% increase from October the year before. Cost-cutting was the number one driver. Right behind it? AI-related automation and restructuring, which led to over 31,000 documented layoffs that month. That figure includes roles where AI tools replaced human workloads directly.
Now, are all these decisions smart long-term moves? Not always. Cutting staff too fast can hollow out your company’s ability to innovate. But when applied thoughtfully, reducing workforce size using automation can free up resources for strategic investments, like scaling product development or entering new markets.
Andy Challenger, the firm’s Chief Revenue Officer, said AI has accounted for more than 48,000 job cuts this year alone. Those numbers will likely grow as tools get more advanced, less expensive, and easier for mid-size firms to deploy.
If you’re responsible for your company’s future direction, this is the phase where decisions get real. You need to be thinking not just about cutting costs, but repositioning your workforce for what’s next. Use AI smartly. Don’t amputate, restructure.
Simultaneous creation of demand for AI-skilled roles
While AI is taking out some jobs, it’s also creating demand, but not evenly. New roles are emerging, and they’re centered on using, managing, and building AI technologies. We’re seeing a sharp increase in demand for skilled professionals in areas like data science, software engineering, predictive analytics, and systems architecture. These aren’t niche trends, they’re structural changes in the global labor market.
It’s not just about developers. Fields where AI integrates seamlessly, such as digital marketing, claims processing, or wealth management, are all seeing an uptick in demand for hybrid talent: people who understand the job and the tech. That’s how value’s being created right now.
Ger Doyle from ManpowerGroup highlighted this shift. Mentions of AI skills in online job postings rose 16% in just three months. At the same time, overall tech hiring dropped by 27%, marking the steepest decline in the last three years. That contrast reveals a clear pattern: we’re not hiring fewer tech workers, we’re hiring different ones.
This creates a priority for C-suite leaders. You need to reassess where your talent strategy is focused. Traditional IT recruitment policies won’t scale unless you’re feeding them through the filter of AI fluency. Upskilling programs shouldn’t be optional, they’re essential if you want to maintain performance and relevance in a tech-forward economy.
If you want to be resilient long-term, ensure your infrastructure supports roles that align with how AI systems are deployed and optimized, not just supported.
Uncertain long-term labor impact of AI amidst noticeable short-term effects
Right now, the labor market is feeling AI’s impact clearly, but mostly in fragments. What’s missing is a full picture of what’s coming next. Companies are cutting roles that are easy to automate, experimenting with AI in isolated functions, and reallocating human capital where it still adds clear value. But the broader labor impact, creation vs. displacement, net productivity gains, overall economic impact, is still being built out.
McKinsey’s “State of AI in 2025” report captures this tension. One-third of leaders surveyed see a workforce reduction ahead. Only a small portion expect headcount growth. That tells you where their confidence lies, AI is primarily viewed as a subtractive force right now, not an additive one.
At the policy level, experts are calling for better visibility. Researchers like Bharat Chandar at Stanford are urging regulatory bodies, particularly the U.S. Department of Labor, to start tracking AI’s role in job churn, across industries and demographics. Without that visibility, decision-making is being done in the dark.
From an executive standpoint, this means planning under conditions of uncertainty. You can’t wait for perfect information, you need to act now, while building enough flexibility to adjust with new data. Put systems in place that allow your workforce strategy to evolve without disruption. That includes upskilling initiatives, scenario-based workforce planning, and more dynamic hiring practices.
Lareina Yee, Senior Partner at McKinsey Global Institute, put it simply: we still don’t fully understand how AI will shape work over the next five to ten years. But the signals coming in say the pressure is real, and it’s rising fast.
Rising AI investments coexist with workforce reductions
What’s happening now is straightforward: companies are investing more in AI while cutting human headcount. That’s not a contradiction. It’s a deliberate strategy. Resources are shifting from manual complexity to scalable automation. This isn’t just happening in startups, it’s happening at the top of the food chain.
Tech giants are prioritizing AI development while reducing traditional personnel layers. Meta laid off 600 employees from its AI division in October, right after offering high-end salaries to bring in elite AI talent for its superintelligence labs. Amazon is cutting 14,000 roles to reduce bureaucracy and reposition investments. Salesforce dropped 4,000 people, with CEO Marc Benioff citing AI-enabled automation as a direct reason.
These moves aren’t just reactive. They reflect a conscious effort to colocate talent, tools, and capital in areas that drive maximum impact. Beth Galetti, SVP of People, Experience and Technology at Amazon, noted that AI is enabling innovation at speeds no previous technology has matched, so they’re shifting accordingly. The goal isn’t headcount preservation. It’s capability expansion.
From an operational leadership standpoint, this raises critical choices. If you’re cutting jobs and pumping money into AI, you better be clear on how AI is returning value. Headcount reductions may improve quarterly metrics, but unless the AI investment is producing consistent, measurable throughput improvements, customer acquisition, product efficiency, infrastructure optimization, you’ve simply compressed risk into a smaller team.
This is where strategic discipline matters. Don’t chase efficiency in one line item and lose resilience in another. Restructure with intent, align AI investments with actual business use-cases, and ensure accountability for the operational outcomes you’re targeting. That’s how AI becomes part of your core value engine, not just another expense category.
Key takeaways for leaders
- Entry-level roles face highest disruption risk: AI is rapidly replacing routine work, making junior roles and young professionals especially vulnerable. Leaders should reevaluate early-career hiring and invest in scalable talent development programs aligned with AI-enhanced workflows.
- AI drives aggressive cost-cutting strategies: Tech-driven automation is now the second most common reason cited for job cuts. Executives should integrate AI deliberately, focusing on restructuring roles, not just reducing them, to maintain capacity while increasing efficiency.
- AI talent is rising while general tech hiring falls: Demand for AI-skilled professionals is increasing in key roles like data science and software development, even as overall hiring slows. Prioritize hiring or upskilling for AI-aligned positions to stay competitive in a leaner, more specialized job market.
- Long-term labor market impact is still unclear: While AI is already shifting job dynamics, its full effect on labor creation vs. displacement isn’t yet measurable. Leaders should build flexible workforce strategies and urge policymakers to advance metrics that track AI’s role in employment.
- Workforce reductions coexist with AI investment: Companies are cutting roles while investing heavily in AI capabilities, with major tech players reallocating talent and resources to automation. Decision-makers must ensure these investments are tied to measurable outcomes and not just short-term margin gains.
