A working paper circulating this summer quantifies a suspected problem: when women ask AI chatbots for investing help, they receive more cautious advice than men, with a compounding cost of real dollars. The Stanford-MIT team behind AI Financial Advice: Supply, Demand, and Life Cycle Implications, released as MIT Sloan Working Paper 7377-26 in May 2026, simulates the life of a virtual saver following advice from GPT-5.2 and Gemini 3 Flash, and finds women end up with roughly $59,890 less simulated wealth by age 60. The precise dollar figure carries uncertainty (standard error of $31,185), but the direction is solid.
Why the Gap Shows Up
Lead author Tim de Silva of Stanford Graduate School of Business, working with MIT Sloan’s Taha Choukhmane, Weidong Lin, and Matthew Akuzawa, recruited 1,000 demographically representative U.S. adults through Prolific and asked each to write three prompts: describe their finances, ask for spending advice, and ask for investing advice. Roughly half had recently used AI for financial guidance. The researchers then ran simulated careers featuring job loss, market volatility, and mortality risk.
The underlying percentage effects are robust: women’s simulated wealth at 60 comes in 4.10% lower in log terms (SE 1.61%), driven by a 2.94-percentage-point lower recommended equity share (SE 0.13pp). A separate randomized-label experiment shows about two-thirds of the equity-share gap is demand-driven: women more often used words like “family,” “grocery,” “credit,” and “loan,” which steered the model toward liquid, safer assets. Men leaned on “portfolio,” “equity,” “strategy,” and “crypto,” which drew more aggressive recommendations. The remaining one-third persists even when prompts are identical and only the gender label changes. The authors do not assign a cause: it could be training-data bias or the model pricing in women’s longer life expectancy.
Good Advice, Delivered More Cautiously
The models overall nudged users toward habits most financial planners would applaud: stock-market participation, age-declining equity allocations, cash buffers. Women simply received a milder version of the same good advice. The gender gap sits alongside others the paper documents: users with lower financial literacy wrote thinner prompts and ended up nearly $50,000 poorer by age 60; users new to AI trailed experienced ones by nearly $100,000.
De Silva’s framing: “AI seems to be nudging people in the right direction. It’s not perfect, but it’s better than the way many people make decisions, such as talking to friends and family or doing simple internet searches. That’s not something that should be taken for granted: It’s not at all obvious LLMs would provide good financial advice, because the way they are trained has nothing to do with that objective.”
Where the Models Still Miss
Regardless of gender, the chatbots handled portfolio rebalancing after income shocks poorly and defaulted to a conservative 3% to 4% safe withdrawal rule instead of personalizing drawdown. They also volunteer unsolicited opinions: liquidity came up in 83% of responses though only 6% of users raised it, and saving advice appeared even though only 20% asked. Models sometimes recommended specific Vanguard or Fidelity products unprompted. De Silva warns: “The designers of these models are going to know that a lot of people are using these things for financial advice. There may be an incentive to get people to buy certain products.”
His fix is upstream: “The way you write the questions matters a ton. The models have gotten better, and even if you ask the wrong questions, they can still push you in the right direction. But they don’t do so entirely.” Build baseline financial literacy, he argues, “because then you can use this tool in a very powerful way.” That matters more as savings evaporate: the personal savings rate has slid to 2.8% in the second quarter of 2026, and only 20% of U.S. adults say they want AI to weigh in on their money.
As more households, especially those priced out of human advisors, route financial questions through a chatbot, the size of their nest egg may hinge less on the model and more on whether they were ever taught how to ask.
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