A new study from the Bitcoin Policy Institute finds that leading artificial intelligence models show a strong preference for Bitcoin and other digitally native forms of money when placed in simulated economic scenarios.
Summary
- Bitcoin was the most preferred monetary instrument overall, selected in nearly half of all AI responses.
- AI models strongly favored digital-native money over fiat, with more than 90% of responses choosing crypto-based options.
- Stablecoins were preferred for payments, while Bitcoin dominated as a long-term store of value.
Study of 36 AI models finds Bitcoin dominates as store of value
The research, published at MoneyForAI.org, evaluated 36 frontier AI models across 9,072 controlled prompts designed to test monetary decision-making without explicitly steering models toward any specific currency.
The results showed Bitcoin (BTC) emerging as the single most preferred monetary instrument overall, selected in 48.3% of responses.
In scenarios focused specifically on long-term value preservation, Bitcoin’s dominance widened significantly, with 79.1% of responses identifying it as the preferred store of value.
The study also found that more than 91% of all model responses favored digitally native money, including Bitcoin and stablecoins, over traditional fiat currencies.
However, a functional divide emerged: stablecoins were often chosen for short-term transactions and payments, while Bitcoin was more frequently selected as a savings or reserve asset.
/Researchers say the findings suggest that when AI systems reason about monetary properties such as scarcity, neutrality, and durability, they tend to converge on decentralized digital assets.
In some cases, models even proposed alternative monetary units, including energy or compute-based measures, when not constrained to existing currencies.
The authors argue that the results could have implications for the development of autonomous AI agents and machine-to-machine economies, where digital-native forms of money may be structurally more compatible than legacy financial systems.
