A new study by Choukhmane et al. (2025) analyzes the financial advice provided by large language models (LLMs) like ChatGPT 5.2 and Gemini Flash 3. The research combines prompts written by a representative sample of individuals with a life-cycle model to quantify the impact of following AI financial advice over a lifetime. The study finds that following LLM advice would move most people closer to saving, spending, and investing patterns recommended by standard life cycle theory relative to their current behavior. However, the advice leans on simple rules of thumb and diverges from theory on subtler margins such as smoothing consumption after a job loss. The advice also varies systematically across people by gender, financial literacy, and prior AI experience, reflecting both what different people ask (demand) and how the model answers identical questions (supply). The researchers surveyed a nationally representative sample of around 1,000 US adults, asking each to write the prompts they would actually use to seek spending and investing advice from an LLM. Within a few years, over half of adults in the US and the UK report having used AI tools for financial guidance.