The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study the usage patterns of 147 professional developers, examining perceived correlates of AI tools use, the resulting productivity and quality outcomes, and developer readiness for emerging AI-enhanced development. We describe a virtuous adoption cycle where frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest. The study finds no perceptual support for the Quality Paradox and shows that PP is positively correlated with Perceived Code Quality (PQ) improvement. Developers thus report both productivity and quality gains. High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption, though security concerns remain a moderate and statistically significant barrier to adoption. Moreover, AI testing tools' adoption lags that of coding tools, opening a Testing Gap. We identify three developer archetypes (Enthusiasts, Pragmatists, Cautious) that align with an innovation diffusion process wherein the virtuous adoption cycle serves as the individual engine of progression. Our findings reveal that organizational adoption of AI tools follows such a process: Enthusiasts push ahead with tools, creating organizational success that converts Pragmatists. The Cautious are held in organizational stasis: without early adopter examples, they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy. Policy itself does not predict individuals' intent to increase usage but functions as a marker of maturity, formalizing the successful diffusion of adoption by Enthusiasts while acting as a gateway that the Cautious group has yet to reach.

翻译：生成式人工智能在软件开发领域的快速渗透，促使我们对其实践中的感知效应进行实证研究。我们分析了147位专业开发者的使用模式，考察了AI工具使用的感知关联因素、由此产生的生产力与质量结果，以及开发者对新兴AI增强开发的准备程度。我们描述了一个良性的采纳循环：AI工具的高频次、广泛使用是感知生产力（PP）与代码质量的最强关联因素，其中使用频率的影响最为显著。研究未发现支持"质量悖论"的感知证据，并表明PP与感知代码质量（PQ）提升呈正相关。因此，开发者报告了生产力与质量的双重提升。当前高使用率、应用广度、测试场景的频繁使用以及易用性，与未来采纳意愿呈强相关，但安全问题仍是中等程度且具有统计显著性的采纳障碍。此外，AI测试工具的采纳滞后于编码工具，形成了"测试缺口"。我们识别出三类开发者原型（热衷者、务实者、谨慎者），其分布符合创新扩散过程，其中良性采纳循环构成了个体推进的内在引擎。研究发现，组织对AI工具的采纳遵循以下路径：热衷者率先使用工具并创造组织成功案例，从而转化务实者；而谨慎者则陷入组织停滞状态——缺乏早期采纳者的示范，他们无法进入良性采纳循环，从未积累驱动使用意愿的频次，也始终无法达到高效能水平。政策本身虽不能预测个体增加使用的意愿，但可作为成熟度的标志：既正式化了热衷者推动的成功扩散，又充当了谨慎者群体尚未跨越的门槛。