關於AI生產力提升的說法

Claims about AI productivity improvements

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 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.

That is from Ali Merali of Yale University.

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