Introduction to AI Displacement Risk
The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. However, the track record of past approaches gives reason for humility. For example, a prominent attempt to measure job offshorability identified roughly a quarter of US jobs as vulnerable, but a decade on, most of those jobs maintained healthy employment growth.
Meanwhile, the government’s own occupational growth forecasts, while directionally correct, have added little predictive value beyond linear extrapolation of past trends. Additionally, studies on the employment effects of industrial robots reach opposing conclusions, and the scale of job losses attributed to the China trade shock continues to be debated.
Understanding AI Displacement Risk
Our goal is to establish an approach for measuring how AI is affecting employment, and to revisit these analyses periodically. This approach won’t capture every channel through which AI could reshape the labor market, but by laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses.
For instance, we introduce a new measure of AI displacement risk, observed exposure, that combines theoretical LLM capability and real-world usage data, weighting automated (rather than augmentative) and work-related uses more heavily. Furthermore, our work follows a task-based approach, incorporating measures of theoretical AI capability and real-world usage, before aggregating to occupations.
Measuring Exposure
Our approach combines data from three sources: the O*NET database, our own usage data (as measured in the Anthropic Economic Index), and task-level exposure estimates from Eloundou et al. (2023), which measure whether it is theoretically possible for an LLM to make a task at least twice as fast.
Therefore, our new measure, observed exposure, is meant to quantify: of those tasks that LLMs could theoretically speed up, which are actually seeing automated usage in professional settings? Theoretical capability encompasses a much broader range of tasks. By tracking how that gap narrows, observed exposure provides insight into economic changes as they emerge.
Key Findings
AI is far from reaching its theoretical capability: actual coverage remains a fraction of what’s feasible. Occupations with higher observed exposure are projected by the BLS to grow less through 2034. Workers in the most exposed professions are more likely to be older, female, more educated, and higher-paid.
However, we find no systematic increase in unemployment for highly exposed workers since late 2022, though we find suggestive evidence that hiring of younger workers has slowed in exposed occupations. Meanwhile, our measure qualitatively captures several aspects of AI usage that we think are predictive of job impacts.
Conclusion and Future Directions
In conclusion, our new measure of AI displacement risk provides valuable insights into the impact of AI on labor markets. As capabilities advance, adoption spreads, and deployment deepens, the observed exposure will grow to cover the theoretical capability.
Finally, we hope that our approach will help identify the most vulnerable jobs before displacement is visible, and that future findings will more reliably identify economic disruption than post-hoc analyses. Additionally, we will continue to monitor the situation and provide updates on the impact of AI on labor markets.
Frequently Asked Questions
Q: What is AI displacement risk? A: AI displacement risk refers to the potential for AI to automate jobs and displace workers.
Q: How do you measure AI displacement risk? A: We use a new measure called observed exposure, which combines theoretical LLM capability and real-world usage data.
Q: What are the key findings of your research? A: Our research finds that AI is far from reaching its theoretical capability, and that occupations with higher observed exposure are projected to grow less through 2034.
Q: What are the implications of your research for workers? A: Our research suggests that workers in the most exposed professions are more likely to be older, female, more educated, and higher-paid.
Q: What are the future directions for your research? A: We will continue to monitor the situation and provide updates on the impact of AI on labor markets, and hope that our approach will help identify the most vulnerable jobs before displacement is visible.
