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Faculty & Research


AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT

Working Paper
Although artificial intelligence (AI) has the potential to significantly disrupt businesses across a range of industries, there is limited empirical evidence for its substitution effect on human labor. The authors use Google’s introduction of neural network-based translation (GNNT) in 2016-2017 as a natural experiment to examine the substitution of human translators by AI in the context of a large online labor market. Using a difference-in-differences design, the authors show that the introduction of GNNT reduced the number of (human translation) transactions at both the overall market and individual translator levels. In addition, they show that GNNT had a stronger effect on translation tasks with analytical elements, as compared to those with cultural and emotional elements. In supplemental analyses, the authors document a similar pattern after the launch of ChatGPT using question and answer patterns in Stack Exchange forums. This study thus offers robust and causal empirical evidence for a heterogeneous substitution effect of human tasks by skilled knowledge workers. The authors discuss the relevance of our findings for research on competitive advantage, technology adoption, and strategy microfoundations.

Associate Professor of Entrepreneurship and Family Enterprise

Associate Professor of Entrepreneurship and Family Enterprise