AI-assisted programming is rapidly reshaping software development, with large language models (LLMs) enabling new paradigms such as vibe coding and agentic coding. While prior works have focused on prompt design and code generation quality, the broader impact of LLM-driven development on the iterative dynamics of software engineering remains underexplored. In this paper, we conduct large-scale experiments on thousands of algorithmic programming tasks and hundreds of framework selection tasks to systematically investigate how AI-assisted programming interacts with the software ecosystem. Our analysis reveals \textbf{a striking Matthew effect: the more popular a programming language or framework, the higher the success rate of LLM-generated code}. The phenomenon suggests that AI systems may reinforce existing popularity hierarchies, accelerating convergence around dominant tools while hindering diversity and innovation. We provide a quantitative characterization of this effect and discuss its implications for the future evolution of programming ecosystems.

翻译：人工智能辅助编程正在迅速重塑软件开发，大型语言模型（LLM）催生了诸如氛围编码和智能体编码等新范式。尽管先前研究主要关注提示设计和代码生成质量，但LLM驱动开发对软件工程迭代动态的更广泛影响仍未得到充分探索。本文通过对数千个算法编程任务和数百个框架选择任务进行大规模实验，系统研究了人工智能辅助编程如何与软件生态系统相互作用。我们的分析揭示了一个显著的**马太效应：编程语言或框架越流行，LLM生成代码的成功率就越高**。这一现象表明，人工智能系统可能强化现有的流行度层级结构，加速围绕主流工具的收敛，同时阻碍多样性和创新。我们对该效应进行了量化表征，并讨论了其对编程生态系统未来演化的影响。