AI Financial Advice: Supply, Demand, and Life Cycle Implications

Taha Choukhmane, Tim de Silva, Weidong Lin and Matthew Akuzawa

No 35574, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We ask a representative sample to write prompts seeking spending and investing advice from LLMs, then simulate the lifetime effects of following the advice under realistic asset and labor market conditions. Applying this method to GPT-5.2, we find following the advice would move respondents toward life cycle theory: broader participation in diversified equity funds, age-declining equity shares, and larger savings buffers. Recommendations vary systematically by gender, prior AI experience, and financial literacy. For gender, two-thirds of recommended equity-share differences arise from men and women writing different prompts (demand), while one-third arise from gender labels attached to otherwise identical prompts (supply).

JEL-codes: D15 G11 G5 G51 G53 (search for similar items in EconPapers)
Date: 2026-08
Note: AG AP PR
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