Investigating and Designing for Trust in AI-powered Code Generation Tools

This is a peer-reviewed academic research paper (arXiv) conducting qualitative investigation into developer trust in AI code generation tools. It is not a software project, deployable system, or replicable codebase—it is a research contribution documenting findings and design insights. With 0 stars and 4 forks (likely academic citations or follow-up studies), this has negligible software adoption. The work itself is academically novel in combining user research methodology with AI tool trust calibration, addressing an important but under-studied HCI dimension. However, as a paper with no production artifact, no API, no library, and no algorithmic contribution suitable for direct implementation, it scores very low on defensibility from a commercial or engineering perspective. Platform domination and market consolidation risks are negligible because this is not a competing product or service—it is foundational research. The insights may inform UI/UX design in tools built by GitHub, OpenAI, or Anthropic, but the paper itself is not at risk of displacement; it will be cited or superseded by later research. Displacement horizon is 'unlikely' because research papers do not face the same competitive pressures as software products; they are valued for their contribution to knowledge, not for being the only way to solve a problem.