The accelerating integration of artificial intelligence into the workforce has reignited an old question in fundamental economics: does technology replace workers or empower them? A recent study by economists Seyed M. Hosseini and Guy Lichtinger, titled “Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data,” suggests that this newest wave of automation may be unlike any before it. Rather than favoring high-skilled labor over low-skilled, as economists once argued, AI appears to favor seniority over youth.
Using résumé and job posting data from over 60 million U.S. workers and 285,000 firms between 2015 and 2025, Hosseini and Lichtinger identify a structural transformation in hiring patterns. Firms that adopted “AI integrator” or “machine learning operations” roles significantly reduced hiring for junior positions while maintaining or even expanding senior employment, not stemming from layoffs but from slower inflows of new workers.
As AI systems took on tasks once assigned to entry-level employees such as drafting documents, collecting information, organizing reports, the need for large pools of junior labor largely decreased. Senior workers, meanwhile, benefited from AI’s ability to handle routine work, allowing them to pass entry level work and focus on higher-level decision-making, management, and strategic planning.
The result, the authors claim, is a new form of “seniority-biased technological change.” Unlike past industrial revolutions that replaced manual labor or rewarded specialized education, this one consolidates value among experienced employees while narrowing the traditional entry points for younger workers. Historically, the concept of skill-biased technological change dominated labor economics: new technologies increased demand for highly educated workers capable of operating advanced systems. The personal computer, for instance, created opportunities for software engineers and data analysts while displacing manual occupations.
With AI, however, the emphasis has shifted. The new technology rewards not just skill or education, but more of contextual expertise and judgment, qualities that can only come through experience. Tasks like supervising junior employees, developing strategies, and assuming the role as an ambassador for the company, rely on institutional knowledge and critical reasoning, strengths that typically belong to senior employees. The adoption of AI, therefore, compresses the early stages of career development, removing many of the “learning years” that once allowed young professionals to grow into their roles.
This loss of entry-level opportunity is not just an American problem. My father, Roy Lu, has seen a similar trend unfolding across the world, particularly in China.
“Due to a sharply slowing economy burdened by excessive debt and industrial overcapacity, China is facing broad deflationary pressures and a shrinking population, casting a dark shadow over its job market,” he said.
“Although official data from universities are unavailable, it is widely believed that as many as 40 percent or even more of recent college graduates are unemployed upon graduation,” Lu added. “Widespread closures and layoffs among private and foreign-owned enterprises have increasingly become the new norm. ‘You’re lucky that you still have a job’ is one of the most common remarks heard today among the working class. And the situation is still getting worse.”
His observation reflects a reality confirmed by multiple reports. According to Bloomberg and the South China Morning Post, China’s urban youth unemployment rate has hovered near 18 percent in 2025, even as a record 11.8 million college graduates enter the workforce. Private and foreign-owned companies have cut staff or closed entirely, and slowing domestic demand has left many graduates without opportunities. Deflation, overcapacity, and weak consumer confidence have further worsened the labor market. In this climate, a first job has become a privilege rather than a given.
Although differing in many areas, technological transformation in the U.S. versus economic stagnation in China do have strikingly similar outcome: a generation of young people struggling to find promising entry points into professional life. Both cases point to an erosion of the first rung on the career ladder. Without entry-level positions, new graduates lose the chance to develop experience, and societies risk a widening gap between established professionals and those trying to begin their careers.
For students preparing to enter this rapidly changing world, adaptability and creativity may matter more than technical credentials alone. As entry-level positions disappear, employers increasingly seek applicants who can contribute immediately in hybrid human-AI workplaces. This means education must evolve, too. Internships, apprenticeships, and project-based learning will become essential for gaining real experience before graduation. Schools must begin to bridge the gap between theory and practice earlier, fostering problem-solving and collaboration rather than rote knowledge.
At a further standing point, these shifts may deepen intergenerational inequality. If the lower rungs of the career ladder vanish, economic mobility will stagnate, and future leadership pipelines could weaken. Both the United States and China are confronting versions of the same challenge shaped by rapid technological progress.
Preparing for this future requires reimagining not just what students learn, but how they learn. Courses emphasizing critical thinking, creative application, and cross-disciplinary learning may prove increasingly important to navigating the unpredictable job market ahead. In an age when machines can analyze data faster than humans, the most valuable workers will be those who can think independently, connect ideas across fields, and build new paths when the old ones have disappeared.
