# AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform

arXiv: https://arxiv.org/abs/2312.04180 · HTML: https://mlsanity.com/paper/2312.04180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 59 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [The Learnability of In-Context Learning](https://mlsanity.com/paper/2303.07895/) (2023)
- [Artificial Intelligence in the Creative Industries: A Review](https://mlsanity.com/paper/2007.12391/) (2020)
- ["Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets](https://mlsanity.com/paper/2308.05201/) (2023)

## Built on by (most cited first, 2 of 2)

- [When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine](https://mlsanity.com/paper/2509.14436/) (2025)
- [Generative AI and the Reorganization of Labor Demand](https://mlsanity.com/paper/2605.23159/) (2026)

## Abstract (arXiv)

This study investigates how artificial intelligence (AI) influences various online labor markets (OLMs) over time. Employing the Difference-in-Differences method, we discovered two distinct scenarios following ChatGPT's launch: displacement effects featuring reduced work volume and earnings, exemplified by translation & localization OLM; productivity effects featuring increased work volume and earnings, exemplified by web development OLM. To understand these opposite effects in a unified framework, we developed a Cournot competition model to identify an inflection point for each market. Before this point, human workers benefit from AI enhancements; beyond this point, human workers would be replaced. Further analyzing the progression from ChatGPT 3.5 to 4.0, we found three effect scenarios, reinforcing our inflection point conjecture. Heterogeneous analyses reveal that U.S. web developers tend to benefit more from ChatGPT's launch compared to their counterparts in other regions. Experienced translators seem more likely to exit the market than less experienced translators.