这项研究戳穿了“AI人人平等受益”的幻觉——真正拉开差距的是AI交互能力，做教育产品设计或企业培训的团队，建议看看如何用微培训和标准化流程缩小这个鸿沟。
一项随机对照实验发现，生成式AI（GenAI）能显著提升知识工作者的任务表现，但收益分布极不均匀。高AI交互能力（AIC）的参与者获得了巨大收益，而低AIC者甚至出现负回报。AIC包括引导、筛选和验证模型输出的能力，且与GPA或先验知识无关。通过概念图等脚手架干预可减少结果差异，表明标准化流程能缓解AI带来的不平等。研究建议企业将GenAI与简短AIC微培训及标准操作程序结合，以稳定获取价值。
Generative AI and the Productivity Divide: Human-AI Complementarities in Education
Generative Artificial Intelligence (GenAI) is transforming how firms create, process, and apply knowledge, yet little is known about the heterogeneity of its productivity effects across users. We report results from a randomized controlled experiment in which participants-analogs of early-career knowledge workers-were assigned to self-study a technical domain using either traditional resources or large-language-model (LLM) assistance. On average, GenAI access significantly increased task performance, but the distribution of gains was highly uneven. Improvements were not predicted by GPA or prior knowledge, but by \textit{AI Interaction Competence (AIC)} -- the ability to elicit, filter, and verify model outputs. High-AIC participants realized outsized gains; low-AIC participants saw limited or even negative marginal returns. A scaffolding intervention (conceptual maps) reduced outcome variance, indicating that standardized workflows can mitigate inequality in AI-mediated performance. We interpret these findings through the lens of human-AI complementarities: GenAI raises mean productivity while introducing a new axis of capability inequality. Managerially, firms should pair GenAI access with short AIC micro-training and simple standard operating procedures to capture value consistently and avoid uneven adoption outcomes.