The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
This addresses the problem of balancing efficiency and usability in API design for enterprise developers, though it is incremental as it builds on existing AI-assisted design methods.
The study tackled the tension between rapid feature delivery and usability standards in enterprise API design by evaluating an AI-assisted workflow, finding that AI-generated specifications outperformed human ones in 10 of 11 usability dimensions and reduced authoring time by 87%, but experts misidentified AI work as human with 19% accuracy and described it as unsettlingly perfect.
Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals (AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly "perfect." We characterize this as a "Perfection Paradox" -- where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer's role from the "drafter" of specifications to the "curator" of AI-generated patterns.