This study explores the semantic differences between human-written and AI-generated academic texts by applying word embedding techniques to a curated corpus of 325 introductions from linguistic articles. The corpus includes human-authored texts and AI-generated texts produced by six language models (OpenAI, Google, and DeepSeek; base and advanced). Each topic was prompted in two different ways: plain and academic. Using cosine similarity, the most frequently occurring lemmas were grouped into semantic categories. The analysis reveals that AI-generated texts, especially under academic prompts, overuse positive-evaluative and methodological vocabulary (e.g., central, crucial, analysis, methodology) and explicitly refer to text structure more often than the plainly prompted texts (e.g., section, chapter). In contrast, human authors employ more epistemically cautious, critical, evaluative, and connective language (e.g., possibly, inconsistent, by no means). I propose that the relative absence of such epistemic markers in AI texts, combined with their tendency to exaggerate the importance of certain topics or data, reflects a pattern of pseudo-commitment: the models produce syntactically assertive, formally academic prose but only weakly modulate epistemic stance and critical engagement, which may contribute to the reported sense of weirdness in AI-generated academic writing.