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

Erdem Dogukan Yilmaz, Ivana Naumovska, Vikas A. Aggarwal · Management Science · 14 Oct 2026

Where this sits in the debate

Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

Using difference-in-differences on transaction data from an online translation labour market, the study compares two AI shocks. Google's neural machine translation cut demand for routine translation but not for creative transcreation, while ChatGPT produced larger and more uniform demand reductions across both task types.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

Do different generations of AI replace different kinds of human knowledge work?

The approach

Two natural experiments in a large online labor market for translation: the launch of Google Neural Machine Translation and of ChatGPT. Difference-in-differences models on transaction-level data from 2016-2017 and 2022-2023 compare regular translations with transcreations, which need creative and cultural adaptation.

The findings

Google's task-specific translation AI reduced demand for regular translation tasks by economically meaningful amounts but had no detectable effect on transcreation. ChatGPT produced larger and more uniform reductions in demand for both kinds of task.

How to read it: observational. Analysis of real-world firm, worker, or market data — realistic settings; causal claims depend on the identification strategy.

Limitations and counterpoints AI-written

Evidence comes from one occupation on one online platform, so effects in other markets or in conventional employment may differ.

Abstract

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 .

About this publication

Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2026 (recently)
Citations: 0 (OpenAlex)

0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.