Large language models (LLMs) constitute a transformative advancement in natural language processing, yet their development remains disproportionately skewed toward English. Despite the global linguistic landscape, non-English languages – including major languages like Spanish, French or Chinese – are effectively treated as low-resource in current LLM training paradigms. This study analyses two linguistic traits of AI-generated texts which mimic human-authored German newspaper articles and compares them with a purpose-built corpus of real journalistic texts. These features are (i) word order and (ii) pre-field occupation. Through quantitative and qualitative analyses of the outputs of four distinct LLMs, three key phenomena in AI’s German outputs were identified: (i) a marked preference for SVO word order; (ii) reduced syntactic variability compared to human-authored texts; and (iii) the emergence of stylistically marked constructions which mirror English linear progression rather than native German sentence bracketing. While some models approximate human-like syntactic patterns for certain variables, this equivalence remains limited and context-dependent, which may suggest a cross-linguistic interference from the overwhelming English predominance in LLM training data. The study emphasises the linguistic implications of LLM architectures and calls attention to the urgent need for more equitable representation of world languages in natural language processing development.