How recent employment changes vary with work experience and exposure to AI (digitaleconomy.stanford.edu)

Recent research utilizing the Canaries Dashboard highlights how varying levels of exposure to AI impact employment across different occupations and age groups. A study by Brynjolfsson, Chandar, and Chen (2025) reveals that employment growth is slowest for the most AI-exposed occupations, notably among early-career workers (ages 22-25), who have experienced stark declines, especially in roles like software development and customer service. In contrast, less exposed roles, such as home health aides, demonstrate employment gains for younger workers. These findings suggest a growing divide in job security based on AI exposure, indicating that the effects of AI-driven changes are likely to intensify in the coming years. This research is significant for the AI/ML community as it emphasizes the nuanced impacts of AI across different sectors and demographics, providing early indicators for labor market trends. The utilization of ADP payroll data allows for detailed insights into employment variations based on AI exposure and its applications, namely automation versus augmentation. The report underscores the critical need for policymakers and industry leaders to address the ramifications of AI on the job market, especially for vulnerable groups, as well as the importance of understanding how the type of AI usage can influence employment dynamics across various occupations.