AI Benefits Senior Staff? 40% of CEOs Plan to Cut Junior Positions, Young People's Jobs Are More at Risk

marsbitPublished on 2026-05-18Last updated on 2026-05-18

Abstract

The traditional assumption that senior employees are first in line during layoffs is being inverted in the AI era. A survey of 415 CEOs by Oliver Wyman and the NYSE reveals 43% plan to cut entry-level positions in the next 1-2 years to shift towards a mid-to-senior talent structure, a sharp rise from 17% last year. The logic is that AI excels at automating routine, cognitive tasks typically handled by junior staff (e.g., coding, data review), while the experience and judgment of senior employees remain harder to replicate. Research indicates this shift primarily manifests as a hiring freeze for junior roles rather than mass layoffs. Goldman Sachs estimates AI currently nets a loss of about 16,000 US jobs monthly, disproportionately impacting Generation Z concentrated in highly automatable white-collar roles. This raises long-term concerns about a broken talent pipeline, as companies risk having no future senior managers trained internally. Despite the dominant trend, a minority of successful AI adopters, like IBM and Salesforce, are expanding junior hiring, arguing these employees are adept at using and building AI tools. However, most companies are still in early AI deployment phases, with 67% in planning/pilot stages and many reporting returns below expectations. The overarching reality is a weakening of job security across all levels, as organizations reshape for an AI-augmented, leaner future.

Author: Claude, Shenchao TechFlow

Shenchao's Insight: A recent survey of 415 global CEOs by Oliver Wyman in partnership with the New York Stock Exchange shows that 43% of CEOs plan to reduce junior positions in the next one to two years, shifting their workforce composition towards mid- to senior-level talent. This figure has more than doubled from 17% last year. AI is systematically replacing routine tasks typically handled by junior employees, while experienced employees become more valuable due to their judgment and expertise. Goldman Sachs previously estimated that AI is eliminating a net of approximately 16,000 US jobs per month, with Generation Z being the most affected.

In past waves of layoffs, older, higher-paid employees were often the first targets. But the logic of the AI era is reversing this trend.

According to a Bloomberg report on May 16th, the 2026 CEO Agenda survey released by management consultancy Oliver Wyman and the New York Stock Exchange reveals that over 40% of CEOs plan to reduce junior positions in the next one to two years, tilting their workforce structure towards more mid- to senior-level employees; only 17% of CEOs indicated they would increase the proportion of junior roles. A year ago, these numbers were almost the opposite.

The survey covered 415 CEOs (266 from public companies, 149 from private companies), with the public company sample representing about 10% of global market capitalization, including 65 Fortune 500 CEOs.

John Romeo, Head of the Oliver Wyman Forum, told Bloomberg in an interview, "The difficulty for junior employees to enter the workforce is indeed increasing. CEOs now place greater emphasis on mid- to senior-level employees to drive productivity."

43% of CEOs Plan to Cut Junior Positions, AI's 'Experience Bias' Effect Emerges

The logic behind this shift is not hard to understand: the tasks that AI agents are currently capable of performing are highly concentrated in the typical scope of work for junior employees. Writing code, evaluating sales leads, reviewing documents, compiling data reports—these routine cognitive tasks previously handled by junior staff are being rapidly replaced by AI systems.

However, what AI currently cannot replicate is the judgment formed through years of industry experience. Future of work advisor Ravin Jesuthasan told Bloomberg that the corporate attitude is shifting to "I need someone who has actually done this, because her experience, judgment, and problem-solving ability make her more valuable than the AI."

This phenomenon has data support in academia. A paper by Harvard University researchers Seyed M. Hosseini and Guy Lichtinger analyzed resume and hiring data covering 62 million employees and 285,000 US companies. The results show that since early 2023, among companies actively adopting generative AI, the number of junior employees decreased by 7.7% over six quarters compared to non-adopting firms, while the number of senior employees was largely unaffected. A key finding is that this decline primarily stems from a hiring slowdown, not large-scale layoffs. In other words, they are not firing people, but simply not hiring them.

The Oliver Wyman report states the implication of this trend more bluntly: "CEOs with the longest planning horizons are the most likely to plan for headcount reductions. This suggests they anticipate that organizations will become leaner, enhanced by AI, not as a cost-cutting measure, but as an end-state."

Goldman Sachs Estimates: AI Net Eliminates 16,000 US Jobs Monthly, Generation Z Bears the Brunt

Goldman Sachs economist Elsie Peng estimated in an April research report that the AI substitution effect eliminated about 25,000 jobs per month over the past year, while the AI augmentation effect created about 9,000 jobs per month in the same period, resulting in a net elimination of about 16,000 jobs. Frontierbeat

The impact is highly unevenly distributed. In occupations with the highest exposure to AI substitution, the unemployment rate gap between entry-level workers under 30 and experienced workers aged 31 to 50 has widened significantly compared to pre-pandemic levels. The wage gap has also worsened concurrently; Goldman's regression analysis estimates that for every one standard deviation increase in AI substitution exposure, the wage gap between entry-level and experienced workers widens by approximately 3.3 percentage points. Fortune

Generation Z is disproportionately concentrated in routine white-collar roles such as data entry, customer service, legal support, and billing processing—precisely the areas AI is most adept at automating. They lack the experiential buffer that senior employees possess. Fortune

A Stanford University study from last November further confirms this: in fields with the highest AI exposure, young employees are 16% more likely to be unemployed than other groups. Fortune

Long-Term Risks of a Broken Talent Pipeline

While cutting junior positions can lower costs and improve efficiency in the short term, the hidden costs are raising alarms.

Helen Leis, Global Head of Leadership and Change at Oliver Wyman, pointed out to Bloomberg that if companies want to have mid- to senior-level talent to manage AI-driven workflows in the future, "these people need to first learn the job within the company." Not hiring junior employees is akin to cutting off one's own talent pipeline.

Andrew McAfee, co-director of MIT's Initiative on the Digital Economy, previously expressed similar concerns to the Harvard Business Review: "Other than on-the-job learning and apprenticeship training, how are people supposed to learn how to do a job?"

Monster survey data shows that nearly 90% of the class of 2026 are worried that AI or automation will replace entry-level positions, a sharp rise from 64% in 2025. Fortune

These concerns are not unfounded. According to a SignalFire report, from 2023 to 2024, the hiring volume for junior positions at the 15 largest US tech companies fell by 25%. The situation in the UK is more severe, with graduate roles in the tech sector decreasing by 46% in 2024, projected to drop another 53% by 2026. IEEE SpectrumRezi

A Few Companies Go Against the Grain: AI 'Winners' Actually Value Junior Employees More

Interestingly, companies most successful in AI deployment are adopting a different talent strategy.

The Oliver Wyman report points out that "a cohort of contrarian, advanced AI adopters believe this technology is increasing the value of junior talent, not displacing them." Among companies with higher ROI from AI investments, the proportion leaning towards increasing junior positions is higher than among those not yet seeing returns.

IBM announced in February this year its plan to triple its hiring of US junior positions and rewrite job descriptions for the AI era. Salesforce CEO Marc Benioff announced this week the hiring of 1,000 recent graduates and interns to build its AI systems, writing on platform X: "They say AI will kill junior jobs. But these grads & interns are BUILDING AI." Amazon Web Services CEO Matt Garman publicly stated that replacing junior employees with AI is "one of the dumbest decisions a company can make," reasoning that junior employees are often the most proficient users of AI tools.

However, these cases remain the minority in the overall trend. The Oliver Wyman survey shows that 74% of CEOs are freezing or reducing headcount, up from 67% last year. The most aggressive cuts are happening in the technology, media, and telecom industries.

AI's ROI Dilemma: Most Companies Still in the 'Money-Burning Experimentation' Stage

There is a significant gap between CEOs' confidence in AI's ability to change workforce structures and the actual returns AI is delivering.

The Oliver Wyman survey reveals that 67% of corporate AI deployments remain in the planning and pilot stages. 53% of CEOs say it is too early to assess AI investment returns, a figure that has increased from 41% last year. Only 27% of CEOs report that AI investment returns meet or exceed expectations, down from 38% last year. Nearly a quarter say AI has had no impact on revenue whatsoever.

The report describes this as "not a crisis of confidence, but a recognition that redesigning work at scale is slower and more difficult than initial enthusiasm anticipated."

However, the report also notes that companies deploying AI across more than two application areas report cost savings and revenue growth roughly double that of companies with single-scenario deployments. The value curve for AI is non-linear, with real returns concentrated after scaled deployment.

A statement by economist Teresa Ghilarducci of The New School to Bloomberg perhaps sums up the current situation: Even if AI tilts the workplace balance in favor of experienced employees, it does not mean they have gained job security. "Companies are increasingly less committed to their employees."

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The article, "Wang Chuan: How to Remain Unanxious After Neighbor Lao Wang's Thirty-Fold Gain on Storage Stocks (Part 6) - The Trap of Commoditized Goods," analyzes the cyclical and perilous nature of the data storage industry through historical and current case studies. It begins with the example of Iomega, whose Zip drives led to a stock surge of over 160x in the mid-1990s before collapsing over 97% from its peak due to competition from cheaper CD-R technology. This pattern is characteristic of storage, where products like DRAM are highly commoditized, leading to extreme price volatility. The sector has seen prices crash over 80% multiple times, with companies often facing bankruptcy. The core dynamic is "elastic demand facing heavy-asset, long-cycle, rigid supply." High prices attract new capacity, but the long lead time means supply eventually overshoots, causing sharp price corrections. The current AI-driven boom, exemplified by surging demand for High-Bandwidth Memory (HBM), has led to skyrocketing prices and profit margins for companies like SanDisk and Micron, despite relatively flat production volumes. However, the author warns this high-margin environment is self-defeating. The high profits are already triggering massive new capacity investments (hundreds of billions starting 2026), with supply expected to ramp up by late 2027. When supply catches up, total revenue and profits may fall even as more units are sold. Long-term supply agreements offer little protection, as buyers can find ways to renegotiate if market prices drop, similar to fragile political treaties. Key risks include economic downturns, cuts in AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and innovations in chip/algorithm design that reduce memory needs. A critical trap is that at the cycle's peak, storage stocks often appear cheap with low P/E ratios, luring value investors just before an impending downturn where profits evaporate. The conclusion cautions that for commoditized goods like storage, high margins inevitably destroy themselves, and the current asymmetry favors downside risk over further upside. The neighbor's dream of easy wealth from storage stocks is portrayed as a precarious illusion.

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Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

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