AI-Driven Human Task Substitution: Evidence from Google Translate and ChatGPT

We examine how different generations of artificial intelligence (AI) substitute for human knowledge worker tasks, leveraging two sequential natural experiments in the translation industry. We study the introduction of Google Neural Machine Translation (GNMT), a predictive, task-specific AI, as well as ChatGPT, a broad, general-purpose generative AI, in a major online labor market. Distinguishing between analytical tasks (regular translations) and intuitive tasks (transcreations, which require creative and cultural adaptation), we estimate difference-in-differences models on transaction-level data from 2016 to 2017 and 2022 to 2023. We find that GNMT reduced the demand for analytical tasks by economically meaningful magnitudes, while having no detectable effect on intuitive tasks. By contrast, ChatGPT produced substantially larger and more uniform reductions for both analytical and intuitive tasks, indicating a marked expansion in the scope and magnitude of AI substitution. By comparing these two technological shocks within the same labor market, we show that conclusions about which types of human work are resilient to automation do not straightforwardly generalize from predictive to generative AI. This paper was accepted by Joshua Gans, business strategy. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06740 .