The arrival of artificial intelligence (AI) in workplaces worldwide has raised concerns about the implications for workers. The author examines the relationship between AI and work in the United States through quantitative and qualitative lenses.
Specifically, the author analyzed relationships between an occupation’s exposure to AI and labor market outcomes from 2005 to 2023 and found that occupations with higher AI exposure experienced higher employment growth, although they did not experience substantial changes in mean wage. Among occupations focused on collecting, analyzing, and interpreting information, AI exposure was associated with higher employment and wage growth. However, among occupations focused on influencing and assisting people, AI exposure was associated with lower employment and wage growth.
The author also investigated the effect of AI on medical coding through qualitative interviews with leaders, medical coders, and AI vendors at hospitals. AI automates simpler tasks once performed by coders, resulting in positive and negative effects on coder well-being. However, by automating simpler tasks through which new coders have traditionally learned to code, AI threatens an already weak training pipeline. AI can increase demand for expertise while complicating the ability of training systems to produce that expertise.
Finally, the author describes gaps in existing research that prevent researchers from addressing key questions about AI and work. The author recommends steps researchers and public data collection programs can take to improve measures of AI activity, availability of task and skill data, and usability of individual employment and wage data.