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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.12475 |
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| _version_ | 1866910051492429824 |
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| author | Ahmad, Mak Macvean, Andrew Geewax, JJ Karger, David |
| author_facet | Ahmad, Mak Macvean, Andrew Geewax, JJ Karger, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12475 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Perfection Paradox: From Architect to Curator in AI-Assisted API Design Ahmad, Mak Macvean, Andrew Geewax, JJ Karger, David Software Engineering Artificial Intelligence Human-Computer Interaction H.5.2; D.2.2 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. |
| title | The Perfection Paradox: From Architect to Curator in AI-Assisted API Design |
| topic | Software Engineering Artificial Intelligence Human-Computer Interaction H.5.2; D.2.2 |
| url | https://arxiv.org/abs/2603.12475 |