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 quantifies a substantial performance asymmetry: mainstream languages and frameworks achieve significantly higher success rates than niche ones. This disparity suggests a feedback loop consistent with the Matthew Effect, where data-rich ecosystems gain superior AI support. While not the sole driver of adoption, current models introduce a non-negligible productivity friction for niche technologies, representing a hidden bias in software evolution.

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）催生了诸如氛围编程与智能体编程等新范式。尽管已有研究主要关注提示设计与代码生成质量，但LLM驱动的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，系统性地研究了人工智能辅助编程如何与软件生态系统相互作用。我们的分析量化了显著的性能不对称性：主流语言和框架的成功率显著高于小众技术。这种差异揭示了符合马太效应的反馈循环——数据丰富的生态系统获得了更优越的人工智能支持。尽管并非技术采纳的唯一驱动因素，当前模型确实为小众技术引入了不可忽视的生产力阻力，这构成了软件演化过程中的一种隐性偏差。