The Impact of Generative AI on Employment and Labor Productivity

Motivation: After the recent introduction of ChatGPT, the rise of generative artificial intelligence (GAI) has ignited discussions about its potential to disrupt employment and impact labor productivity. Our paper empirically examines the potential effects of GAI. Premise: Our paper showcases GAI's impact on employment and labor productivity in the United States. Approach: We investigate the 100 largest publicly traded U.S. companies. The key variable of GAI exposure is from Eisfeldt, Schubert, and Zhang (2023). Employment is measured by the number of employees or the number of employees scaled by total assets. Labor productivity is assessed using real sales per employee or operating income per employee. We employ a difference-in-differences methodology, comparing changes in firms with high GAI exposure to those with low GAI exposure, before and after the launch of ChatGPT. Results: Our findings indicate that the introduction of GAI has not had a negative impact on employment. Furthermore, GAI has created positive and statistically significant effects on labor productivity. Conclusion: We conclude that GAI has not decreased employment but has increased labor productivity. The impact of GAI extends beyond the business world. Our discovery highlights the revolutionary potential of GAI and encourages policy makers to utilize it to benefit and advance society. Consistency: Our research provides the latest empirical evidence on GAI's impact on employment and labor productivity. The findings suggest that GAI's adoption enhances labor productivity for businesses and creates employment stability. Our discovery has prevalent and profound influence on the present and future of our world.