Yilmaz, Naumovska & Aggarwal on AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT

Erdem Dogukan Yilmaz (Erasmus Univeristy Rotterdam), Ivana Naumovska (INSEAD), and Vikas A. Aggarwal (INSEAD) have posted “AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT” on SSRN. Here is the abstract:

Although artificial intelligence (AI) has the potential to significantly disrupt businesses across a range of industries, we have limited empirical evidence for its substitution effect on human labor. We 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, we show that the introduction of GNNT reduced the number of (human translation) transactions at both the overall market and individual translator levels. In addition, we 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, we document a similar pattern after the launch of ChatGPT using question and answer patterns in Stack Exchange forums. Our study thus offers robust and causal empirical evidence for a heterogeneous substitution effect of human tasks by skilled knowledge workers. We discuss the relevance of our findings for research on competitive advantage, technology adoption, and strategy microfoundations.