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AI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：频繁且广泛的AI工具使用是感知生产力与代码质量最显著的相关因素，其中使用频率的影响最为突出。本研究发现并未感知到支持"质量悖论"的证据，并表明感知生产力与感知代码质量的提升呈正相关。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、频繁使用AI测试工具以及易用性与未来采纳意向显著相关，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"测试鸿沟"。我们识别出与创新扩散过程相契合的三种开发者原型（热衷者、务实者、谨慎者），其中良性采纳循环构成了个体演进的内在引擎。研究发现表明，组织对AI工具的采纳遵循以下过程：热衷者率先使用工具并创造组织成功案例，从而促使务实者跟进。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，也未能达到高效能水平。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——既正式化热衷者推动的成功扩散，又充当谨慎者群体尚未跨越的门槛。