Recent developments in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content, raising fundamental questions about how AI may reshape democratic information environments such as news. We develop a large-scale simulation framework to examine the system-level effects of AI-based imitation, using the full population of Danish digital news articles published in 2022. Varying imitation strategies and AI prevalence across information environments with different baseline structures, we show that the effects of AI-driven imitation are strongly context-dependent: imitating AI agents increase semantic diversity in initially homogeneous environments but can reduce diversity in heterogeneous ones. This pattern is qualitatively consistent across multiple LLMs. However, this diversity arises primarily through stylistic differentiation and variance compression rather than factual enrichment, as AI-generated articles tend to omit information while remaining semantically distinct. These findings indicate that AI-driven imitation produces ambivalent transformations of information environments that may shape collective intelligence in democratic societies.

翻译：大型语言模型（LLM）的最新进展催生了能够模仿人类生成内容的自主AI代理，这引发了关于AI如何重塑新闻等民主信息环境的基础性问题。我们开发了一个大规模模拟框架，利用2022年发布的丹麦数字新闻文章全量数据，考察基于AI的模仿在系统层面的影响。通过在具有不同基线结构的信息环境中改变模仿策略和AI渗透率，我们发现AI驱动模仿的效果具有强烈的环境依赖性：模仿AI代理能在初始均质环境中增加语义多样性，但在异质环境中可能降低多样性。这一模式在多种LLM中具有定性一致性。然而，这种多样性主要通过风格分化和方差压缩而非事实性丰富化产生，因为AI生成的文章倾向于省略信息，同时保持语义独特性。这些发现表明，AI驱动的模仿对信息环境产生矛盾性转变，可能影响民主社会的集体智能。