[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 73.6K followers Created: 2025-07-16 12:53:00 UTC Money habits differ worldwide, yet nobody knows which habits shape LLM advice. This study asked X major chatbots and humans from XX countries the same XX finance questions. Each model answered XXX times, researchers kept the median answer for every prompt, and compared it with the INTRA survey medians. When the authors ran that check on the XX finance questions, every large language model landed in the same tight group, and the only human data that fell into that pocket came from Tanzania The models almost always choose, or price, the gamble right at that average. In plain terms, they treat a risky $XXX at XX% chance exactly the same as a sure $XX. Most real people are risk-averse. They prefer a smaller certain gain over a larger but shaky one, so their bids usually drop below the average. The paper notes that the models skip that human caution and act risk-neutral, which is uncommon in the survey data On time choices several models returned discount factors above 1, which violates basic discounting logic. Gemini's present bias score topped X too, meaning it liked waiting more than receiving cash now. Across loss tasks bots priced insurance close to strict math, while humans overpaid for safety. The Tanzania tie likely reflects East African raters who guide model training feedback. So current chatbots act as cool calculators but still carry hidden cultural fingerprints and occasional math slips. ---- Paper – arxiv. org/abs/2507.10933 Paper Title: "Artificial Finance: How AI Thinks About Money"  XXXXX engagements  **Related Topics** [finance](/topic/finance) [countries](/topic/countries) [llm](/topic/llm) [money](/topic/money) [Post Link](https://x.com/rohanpaul_ai/status/1945466698913681563)
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Rohan Paul @rohanpaul_ai on x 73.6K followers
Created: 2025-07-16 12:53:00 UTC
Money habits differ worldwide, yet nobody knows which habits shape LLM advice.
This study asked X major chatbots and humans from XX countries the same XX finance questions.
Each model answered XXX times, researchers kept the median answer for every prompt, and compared it with the INTRA survey medians.
When the authors ran that check on the XX finance questions, every large language model landed in the same tight group, and the only human data that fell into that pocket came from Tanzania
The models almost always choose, or price, the gamble right at that average.
In plain terms, they treat a risky $XXX at XX% chance exactly the same as a sure $XX.
Most real people are risk-averse. They prefer a smaller certain gain over a larger but shaky one, so their bids usually drop below the average. The paper notes that the models skip that human caution and act risk-neutral, which is uncommon in the survey data
On time choices several models returned discount factors above 1, which violates basic discounting logic.
Gemini's present bias score topped X too, meaning it liked waiting more than receiving cash now.
Across loss tasks bots priced insurance close to strict math, while humans overpaid for safety.
The Tanzania tie likely reflects East African raters who guide model training feedback.
So current chatbots act as cool calculators but still carry hidden cultural fingerprints and occasional math slips.
Paper – arxiv. org/abs/2507.10933
Paper Title: "Artificial Finance: How AI Thinks About Money"
XXXXX engagements
