AI is great for scientists, but perhaps not for science (www.programmablemutter.com)

Recent research argues that while AI, particularly large language models (LLMs), is beneficial for individual scientists by boosting productivity and career advancement, it may hinder the overall advancement of science. A study published in Nature highlights that AI-enabled research tends to cluster around popular topics, leading to a narrower scope of inquiry and less innovative knowledge production. This trend could create "lonely crowds" in scientific disciplines, where papers with overlapping themes are less likely to engage with diverse ideas and collaborations. The implications of this genre-fication of research are concerning, as LLMs further exacerbate existing problems of individual versus collective incentives in academia. While LLMs can enhance the efficiency and volume of scientific outputs, they risk reducing the richness and diversity of scientific exploration. As AI continues to evolve and integrate into research practices—including peer review and data analysis—there is growing apprehension that it may favor formulaic approaches over creative and meaningful inquiry, thereby threatening the integrity and dynamism of the scientific enterprise.