AI as Equalizer or Amplifier? Task Complexity as the Moderating Factor for Human Expertise in Hybrid Intelligence Systems
Tao An Corresponding Author: Tao An, Hawaii Pacific University; E-mail: Hawaii Pacific University, Honolulu, HI, USA
Abstract
A growing body of empirical research suggests that generative AI narrows performance gaps between novice and expert workers on routine tasks—the so-called “equalizer” effect. This paper challenges the generality of that conclusion. Drawing on cognitive augmentation theory, expert-novice research, and structured observations of in-house generative-AI use across a small software product team, we argue that AI functions primarily as a cognitive amplifier: a system whose output quality depends fundamentally on the expertise of the human who directs it. We present a framework comprising three layers of human contribution (problem definition, quality evaluation, iterative refinement) and three levels of engagement (passive acceptance, iterative collaboration, cognitive direction), demonstrating that domain expertise—not prompt engineering skill—determines amplification effectiveness. We reconcile the equalizer and amplifier perspectives by proposing that AI equalizes performance on well-structured, routine tasks while amplifying pre-existing differences on complex tasks requiring deep judgment. This reconciliation carries direct implications for hybrid human-AI system design: rather than
中文速览
生成式AI（generative AI）在知识工作中究竟是"均衡器"还是"放大器"——这是本文要回答的核心问题。作者综合认知增强理论、专家-新手研究，以及对一支小型软件团队真实使用Claude、Cursor等AI工具的观察，提出AI对不同复杂度任务的作用截然不同：在格式固定、答案明确的常规任务上，AI确实能把新手的表现拉升到接近专家的水平，产生"均衡"效果；但在需要深度判断、复杂推理的高阶任务上，AI更像一个放大器——专家能精准定义问题、鉴别输出质量、反复引导迭代，而新手却因缺乏判断力而接受看似合理实则错误的结果，最终拉大了差距。作者进一步指出，LLM的"奉承偏见"（sycophancy）会让这种分化愈演愈烈：AI总是顺着用户的思路走，专家越用越精，新手越用越偏。这一发现对人机混合系统的设计极具现实意义：我们不应把AI造成替代专业判断的工具，而应让它成为培养和奖励专业能力的平台，包括为新手提供引导性摩擦、将AI评估能力嵌入领域教育，以及用衡量协作质量而非单纯输出数量的新标准来评估AI辅助工作的真实价值。
原文 arXiv:2512.10961;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2512.10961v2