Pro-AI Bias in Large Language Models

arXiv:2601.13749v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly employed for decision-support across multiple domains. We ask whether these models favor artificial intelligence (AI) itself. We examine this question through three experiments. First, in open-ended advice across investment, study, career, and startup domains, AI is prominent in most recommendation lists and is usually ranked first when it appears. Second, in salary estimations, models overestimate AI-related jobs more than they do for closely matched non-AI jobs. In a controlled follow-up, changing only the job title from the matched non-AI title to the AI-related title raises predicted salary by 10%, while all other supplied job details remain fixed. Finally, we show that the number of degrees and other credentials awarded by AI programs were 28% more overestimated than corresponding awards for matched computing programs. Overall, we show that AI is not only prominent in everyday LLM advice, but also receives systematically inflated quantitative estimates compared to non-AI cases, even against its closest disciplinary neighbors, raising concerns for high-stakes decision support.