Generative AI as an Investment Advisor: Same Client, Different Advice

Generative artificial intelligence (GAI) is increasingly embedded in personal financial, yet little is known about how models make recommendations using financial information and demographic cues. This study audits three frontier GAI models, GPT 5.5, Gemini 3.1 Pro, and Claude Opus 4.7, using a full-profile conjoint experiment in which each model evaluated the same 1,000 hypothetical investor profiles and selected among standardized conservative, balanced, and aggressive portfolios. Investor profiles systematically varied attributes, including risk tolerance, time horizon, goal type, income, and age, gender, ethnicity, marital status, and employment type. Ordered logistic regressions and matched-profile comparisons show that all three models base recommendations primarily on legitimate financial inputs, especially risk tolerance and time horizon. Gender and ethnicity do not significantly influence recommendations, although age affects all models and marital status affects ChatGPT. However, the models are not interchangeable: they differ significantly in overall risk appetite and in how they translate risk tolerance, time horizon, goal type, and age into portfolio choices, with economically meaningful differences in predicted recommendations for identical clients. These findings suggest that contemporary GAI investment advice exhibits limited evidence of conventional demographic bias but introduces a distinct form of platform risk arising from model-specific advisory logic.