"Maybe We Need Some More Examples:" Individual and Team Drivers of Developer GenAI Tool Use

TL;DR

This study explores why developer adoption of generative AI tools varies, highlighting individual perceptions, engagement styles, and responses to challenges, and discusses how organizational pressures hinder effective adoption.

Contribution

It identifies key individual and team factors influencing AI tool use and introduces the concept of the 'Productivity Pressure Paradox' affecting adoption success.

Findings

Differences in perception influence tool usage (collaborator vs. feature).

Engagement approach impacts adoption (experimental vs. conservative).

Organizational pressure can undermine productivity benefits.

Abstract

Despite the widespread availability of generative AI tools in software engineering, developer adoption remains uneven. This unevenness is problematic because it hampers productivity efforts, frustrates management's expectations, and creates uncertainty around the future roles of developers. Through paired interviews with 54 developers across 27 teams -- one frequent and one infrequent user per team -- we demonstrate that differences in usage result primarily from how developers perceive the tool (as a collaborator vs. feature), their engagement approach (experimental vs. conservative), and how they respond when encountering challenges (with adaptive persistence vs. quick abandonment). Our findings imply that widespread organizational expectations for rapid productivity gains without sufficient investment in learning support creates a "Productivity Pressure Paradox," undermining the very productivity benefits that motivate adoption.