tl;dr: MIT researchers simulated life-cycle financial outcomes for 1,000 adults following advice from GPT-5 and Gemini 3 Flash, and found LLM guidance generally aligned with sound principles: save during working years, diversify, reduce equity exposure after 45. Weaknesses included poor response to shocks like unemployment and insufficient portfolio rebalancing, plus demographic disparities—prompts written by women or less financially literate users yielded ~$50K less wealth by age 60, partly from prompt phrasing and partly from the model adjusting advice based on inferred gender. Structured, detailed prompts with explicit assumptions substantially improved output quality.