实验证明大模型算力每提升一年，专业效率提高8%。
Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Consulting, Data Analyst, and Management Tasks
- 通过13个大模型在500人实验中测试任务效率
- 算力提升贡献56%效率增益，算法进步占44%
- 非自动化分析任务受益更明显，适合企业决策者
本文提出了大语言模型经济影响的缩放定律——即模型训练算力与专业生产力之间的经验关系。在一项预注册实验中，超过500名顾问、数据分析师和管理人员使用13种不同大语言模型完成专业任务。结果表明，每一年的AI模型进展使任务时间平均减少8%，其中56%的效率提升来自算力增长，44%来自算法改进。然而，相较于需要工具调用的代理式工作流，非代理型分析任务的生产力提升更为显著。研究预测，未来十年持续模型缩放可能使美国生产力提升约20%。
原文摘要 · Abstract (English)
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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