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

Authors: Ali Merali

Published: 2025

Publication: ArXiv

The paper identifies scaling laws linking LLM training compute to professional productivity gains, showing an 8% annual reduction in task time influenced by both compute and algorithmic advances, but with uneven impacts across task types.

Methods: A preregistered experiment involving professional tasks completed by consultants, data analysts, and managers using 13 different LLMs.

Key Findings: Economic productivity impacts of LLMs in professional settings, time savings across task categories, and contribution of compute versus algorithmic progress.

Limitations: Productivity gains were less pronounced for tasks requiring agentic workflows and tool use, indicating uneven applicability of LLM advancements across different types of work.

Institution: Yale University

Research Area: LLM-Assisted Economic Productivity, Consulting,Data Analysis

Discipline: Economics , Artificial Intelligence

Sample Size: 500 participants

Citations: 1