# Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation

arXiv: https://arxiv.org/abs/2409.02391 · HTML: https://mlsanity.com/paper/2409.02391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- ["It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models](https://mlsanity.com/paper/2411.13032/) (2024)
- [Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains](https://mlsanity.com/paper/2605.26146/) (2026)
- [The Open Source Economic Index of AI Adoption and Capability](https://mlsanity.com/paper/2606.26118/) (2026)
- [Will Scaling Improve Social Simulation with LLMs?](https://mlsanity.com/paper/2607.02464/) (2026)

## Abstract (arXiv)

This paper derives "scaling laws"--empirical relationships between the training compute of Large Language Models (LLMs) and their performance--for economic outcomes. In a preregistered online experiment, 300 professional translators completed 1,800 tasks using one of 13 LLMs (or a control). A tenfold increase in model compute improved task completion speed by 12.3%, grades by 0.18 standard deviations, and earnings per minute by 16.1%. Gains were four times larger for lower-skilled workers. These findings suggest continued model scaling could boost U.S. productivity by at least 6.9% over the next decade.