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