A recent investigation conducted by the Bitcoin Policy Institute reveals that top-tier artificial intelligence models exhibit a marked preference for Bitcoin and other forms of digital-native currencies when tested in simulated economic environments.
Bitcoin emerged as the most favored monetary asset overall, being chosen in nearly half of all AI-generated responses.
AI systems demonstrated a strong inclination towards cryptocurrencies over traditional fiat money, with over 90% of selections favoring crypto-based options.
While stablecoins were predominantly preferred for transactional purposes, Bitcoin was overwhelmingly selected as the optimal long-term store of value.
Analysis of 36 AI Models Highlights Bitcoin’s Role as Prime Store of Value
The study, published on MoneyForAI.org, assessed 36 advanced AI models through 9,072 carefully designed prompts aimed at evaluating monetary decision-making without bias toward any particular currency.
The findings indicated that Bitcoin (BTC) stood out as the single most popular monetary instrument overall, accounting for 48.3% of all choices made by these models.
In scenarios emphasizing long-term wealth preservation, Bitcoin’s dominance became even more pronounced—79.1% identified it as their preferred store-of-value asset.
The research further revealed that more than 91% of model responses favored digital-native currencies such as Bitcoin and stablecoins instead of conventional fiat money.
A notable functional distinction appeared: stablecoins were typically selected for short-term payments and transactions whereas Bitcoin was predominantly chosen to serve as a savings or reserve asset.
The researchers suggest these outcomes imply that when AI evaluates key monetary characteristics like scarcity, neutrality, and durability, decentralized digital assets tend to be the natural choice.
Interestingly enough, some models proposed alternative units based on energy consumption or computational power when not restricted to existing currency frameworks.
The authors propose these insights could influence future autonomous AI agents and machine-to-machine economies where native digital currencies might integrate more seamlessly compared to traditional financial infrastructures.