Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Consulting, Data Analyst, and Management Tasks
This provides empirical evidence on AI's economic impact for professionals and policymakers, though it is incremental in quantifying scaling effects.
The paper investigates how training compute of Large Language Models (LLMs) affects professional productivity, finding that each year of AI progress reduces task time by 8%, with compute and algorithmic improvements contributing 56% and 44% respectively, and projects a 20% boost in U.S. productivity over the next decade.
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.