Younger Gen Z professionals are encountering heightened obstacles when seeking entry-level positions. Recent insights from The Washington Post highlight a growing preference among employers for seasoned workers, sidelining fresh graduates and early-career individuals. This trend is backed by data from Revelio, a workforce analytics company, which reveals that the typical age of newly hired employees climbed to 42 in 2025, marking a noticeable increase from 40.5 just three years prior in 2022.
This shift isn’t arbitrary; it stems from a confluence of economic and societal factors. As hiring slows across industries, companies are adopting a more cautious approach to recruitment.
Economists point to heightened risk aversion, particularly in the face of rapid technological advancements like artificial intelligence, which are reshaping workflows and skill requirements. Employers appear reluctant to invest in training inexperienced talent when uncertainties loom large.
Additionally, demographic changes play a role: improved health outcomes and extended life expectancies mean older employees are remaining in the workforce longer than previous generations.
Compounding this, ongoing financial strains—such as inflation, inadequate retirement savings, and economic volatility—discourage many from retiring, further crowding out opportunities for younger applicants.
The implications are profound for Gen Z, a cohort already grappling with student debt, housing affordability, and the aftermath of pandemic-disrupted education.
This age bias not only delays career progression but also risks stifling innovation, as diverse perspectives from younger voices could invigorate stagnant sectors.
A key driver of this employer caution is the accelerating adoption of AI, which is disrupting traditional job structures and prompting a reevaluation of hiring priorities.
In high-growth fields like web3, fintech, and crypto, AI’s integration could significantly alter key roles, often to the detriment of entry-level positions while favoring those with proven expertise.
In the web3 space, encompassing blockchain and decentralized technologies, AI tools are increasingly automating routine tasks such as code auditing, smart contract optimization, and data analysis for decentralized apps (dApps).
For instance, AI-driven platforms can generate basic blockchain protocols or detect vulnerabilities faster than manual reviews, potentially reducing the demand for junior developers or entry-level blockchain engineers.
This shift might elevate the value of experienced professionals who possess not just technical skills but also strategic insight into regulatory compliance and ecosystem integration—areas where AI alone falls short.
As a result, Gen Z entrants could find fewer “learning-on-the-job” opportunities, with employers opting for veterans capable of overseeing AI systems to mitigate risks in volatile decentralized environments.
Similarly, in fintech, AI is enhancing areas like algorithmic trading, fraud detection, and personalized financial advising.
Roles such as junior financial analysts or compliance officers may see automation through machine learning models that process vast datasets in real-time, crunching numbers that once required human oversight.
However, this creates a premium on seasoned experts who can interpret AI outputs, ensure ethical deployment, and navigate complex financial regulations.
For young job seekers, breaking into fintech might become tougher, as companies prioritize hires with years of experience to handle AI’s “black box” decisions and prevent costly errors in high-stakes transactions.
The crypto sector faces parallel transformations.
AI applications in market forecasting, sentiment analysis from social media, and wallet security enhancements could streamline operations, diminishing the need for support staff.
Predictive algorithms might outperform entry-level analysts in spotting trends, but they require human experts to validate results amid crypto’s inherent unpredictability and regulatory scrutiny.
Consequently, firms may lean toward older candidates with battle-tested knowledge of market cycles and risk management, using AI as a tool rather than a replacement for experience.
Overall, while AI promises efficiency and innovation in these sectors, its adoption risks widening the generational divide.
By automating foundational tasks, it elevates the bar for entry, demanding a blend of domain expertise and AI literacy that Gen Z often lacks without practical exposure.
To counter this, policymakers and educators must advocate for upskilling programs, apprenticeships, and inclusive hiring practices. Otherwise, the job market’s tilt toward maturity could hinder long-term economic vitality, leaving a talented young generation on the sidelines.