Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Consulting, Data Analyst, and Management Tasks

• URL: http://arxiv.org/abs/2512.21316v1
• Date: Wed, 24 Dec 2025 18:24:29 GMT
• Title: Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Consulting, Data Analyst, and Management Tasks
• Abstract summary: We find each year of AI model progress reduced task time by 8%.<n> productivity gains were significantly larger for non-agentic analytical tasks.<n>These findings suggest continued model scaling could boost U.S. productivity by approximately 20% over the next decade.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper derives `Scaling Laws for Economic Impacts' -- empirical relationships between the training compute of Large Language Models (LLMs) and professional productivity. In a preregistered experiment, over 500 consultants, data analysts, and managers completed professional tasks using one of 13 LLMs. We find that each year of AI model progress reduced task time by 8%, with 56% of gains driven by increased compute and 44% by algorithmic progress. However, productivity gains were significantly larger for non-agentic analytical tasks compared to agentic workflows requiring tool use. These findings suggest continued model scaling could boost U.S. productivity by approximately 20% over the next decade.

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