Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback

TL;DR

This paper examines how self-selection in seeking AI feedback influences individual learning, skill gaps, and intellectual diversity, revealing that motivated individuals benefit more, which can lead to increased inequality and reduced diversity.

Contribution

It demonstrates that self-selection biases in AI feedback use distort perceived effectiveness and cause broader societal impacts like widening skill gaps and decreasing diversity.

Findings

Motivated, higher-skilled individuals self-select into AI feedback and benefit more.

Apparent learning gains from AI disappear when accounting for self-selection.

Self-selection into AI feedback widens skill gaps and reduces intellectual diversity.

Abstract

Feedback from artificial intelligence (AI) is increasingly easy to access and research has already established that people learn from it. But individuals choose when and how to seek such feedback, and more engaged and motivated individuals may seek it more, creating an illusion of effectiveness that masks self-selection. We investigate how the endogenous choice to seek AI feedback shapes both individual learning and collective outcomes. Using data from over five years and 52,000 individuals on an online chess platform, we show that motivated and higher-skilled individuals self-select into AI feedback use-and use it more productively. This self-selection creates an illusion of AI effectiveness: apparent learning gains disappear once endogenous motivation is accounted for. This same selection mechanism drives two population-level consequences. Because motivated, higher-skilled individuals benefit disproportionately, AI access widens the skill gap. And because individuals exposed to centralized AI feedback converge on common input from a centralized AI source, intellectual diversity declines. Leveraging 42 platform-level natural experiments, we show this diversity reduction is causal. Self-selection into AI use thus connects individual-level learning dynamics to collective-level consequences-a micro-macro linkage with implications for organizational learning, human capital development, and the design of AI-augmented work.