<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Peter L. Rousseau | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/peter-l.-rousseau/</link><description>Peter L. Rousseau</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/peter-l.-rousseau/index.xml" rel="self" type="application/rss+xml"/><item><title>AI and task efficiency</title><link>https://macropaperwarehouse.com/papers/ai-and-task-efficiency/</link><guid>https://macropaperwarehouse.com/papers/ai-and-task-efficiency/</guid><description>&lt;p&gt;AI can improve decisions, raise firm productivity, and accelerate human capital growth through its effect on signal quality in problem-solving tasks, but the consequences are heterogeneous across the skill distribution and depend on how AI changes the hierarchy within firms. This paper proposes a framework in which AI improves the accuracy of the signals that guide human decisions—individually and in groups—and derives implications for firm organization, wages, and productivity. It also examines preliminary evidence: a cross-sectional regression of changes in TFP growth (2024 versus 2022) on sectoral AI exposure (Eisfeldt et al. 2024) for Compustat firms yields a positive relationship, statistically significant at the 10% level at the 3-digit NAICS sector level and at the 5% level at the firm level, with a slope coefficient of 0.206 for the firm-level regression. The paper compares AI to earlier general purpose technologies (GPTs)—electricity and information technology—finding that if there is a productivity delay for AI it appears shorter than the five- and eight-year delays documented for electrification and IT.&lt;/p&gt;</description></item></channel></rss>