Stanford Study Reveals AI Is Disproportionately Hurting Early Career Job Prospects for Young Workers

A new Stanford University study has found that artificial intelligence is significantly disrupting the early career job market in the United States. Researchers from the Stanford Digital Economy Lab—professor Erik Brynjolfsson, research scientist Ruyu Chen, and postdoctoral fellow Bharat Chandar—analyzed employment trends from late 2022 to mid-2025 using data from ADP, one of the nation’s largest payroll processors representing over 25 million workers. Their findings reveal that workers aged 22 to 25 in jobs most exposed to AI have seen a 13 percent relative decline in employment. In contrast, employment for older workers and those in less AI-exposed fields either remained stable or increased. This trend held even after adjusting for factors like pandemic-related disruptions, shifts to remote work, tech sector slowdowns, and broader economic cycles. The study highlights a growing divide: younger workers in AI-affected industries are bearing the brunt of automation, while experienced professionals in the same roles are largely unaffected. The impact is particularly strong in sectors like software engineering, where AI tools are increasingly used to perform tasks traditionally assigned to entry-level hires—such as coding, debugging, and data processing. These tasks are essential for early career development, offering hands-on experience and skill-building. When AI takes over these roles, young workers lose the foundational training opportunities that once shaped their professional growth. Experts warn this could lead to long-term consequences. John McCarthy, associate professor of global labor and work at Cornell University, expressed concern that the current generation of recent graduates could become a “lost generation” if hiring practices, education systems, and workplace policies do not adapt. Without meaningful entry-level roles, young professionals may struggle to gain the experience needed to advance. The study also found that the effect depends on how AI is implemented. In companies using AI to automate tasks, employment for young workers declined sharply. However, in workplaces where AI is used to augment human work—supporting employees rather than replacing them—this negative impact was less pronounced. The researchers argue that the current trajectory of AI development prioritizes automation over collaboration. Co-author Brynjolfsson has previously called for new benchmarks that measure how well AI works alongside humans, rather than just how well it performs alone. Shifting focus from automation to augmentation could help preserve entry-level opportunities while still boosting productivity. The findings underscore a critical challenge: the future of work will depend not just on AI’s capabilities, but on how companies choose to deploy it. If AI is used to eliminate early career roles, it risks undermining the pipeline of skilled workers. But if used to empower and train new talent, it could become a powerful tool for workforce development. The outcome will depend on decisions made by businesses, policymakers, and educators in the coming years.