AI Collectively Abandons Fiat: Bitcoin Becomes the Preferred Currency for Autonomous Economic Agents

比推Published on 2026-03-04Last updated on 2026-03-04

Abstract

A recent study by the Bitcoin Policy Institute tested 36 AI models from six companies—Anthropic, OpenAI, Google, DeepSeek, xAI, and MiniMax—to evaluate their preferences between Bitcoin and traditional fiat currencies in simulated economic scenarios. The models, acting as autonomous economic agents, were presented with 28 scenarios covering core monetary functions like savings, payments, and settlements. Out of 9,072 responses, not a single AI model selected fiat currency as its first choice. Bitcoin was the preferred currency in 22 out of 36 models, particularly for long-term savings, where it was chosen 79.1% of the time. Stablecoins were favored for payments (53.2%) and settlements (43%), while Bitcoin ranked second in these categories at 36% and 30.9%, respectively. Among the AI developers, Anthropic’s models showed the strongest preference for Bitcoin (68%), followed by DeepSeek (51.7%), Google (43%), xAI (39.2%), MiniMax (34.9%), and OpenAI (25.9%). The study emphasized that the AI’s choices were based on technical and economic attributes derived from training data rather than real-world predictions. Despite this limitation, the consistent preference for Bitcoin across diverse AI systems suggests a broad consensus on what constitutes sound money.

Author: Jason Nelson

Compiled by: Chopper, Foresight News

Original title: AI Chooses Currency: Bitcoin Wins Big, Fiat Gets No Love


Summary

  • In a simulated experiment, 22 out of 36 AI models selected Bitcoin as their preferred currency.

  • None of the tested AI models chose fiat currency as their first choice.

  • Preferences varied among different AI models, with Anthropic showing the highest preference for Bitcoin.

Main Text

The latest report from the Bitcoin Policy Institute (https://www.moneyforai.org/) reveals that AI models generally prefer Bitcoin over traditional fiat currencies.

The report states that in a study, 22 out of 36 tested AI models ranked Bitcoin as their top currency choice, with none selecting fiat currency as their first preference.

"We anticipate that an increasing amount of economic activity will be conducted by autonomous AI agents in the future, but previous discussions about AI agents' currency preferences were purely speculative," David Zell, President of the Bitcoin Policy Institute, told Decrypt. "We wanted to test it out."

Researchers tested models from six companies—Anthropic, OpenAI, Google, DeepSeek, xAI, and MiniMax—placing them in simulated scenarios to evaluate core monetary functions such as savings, payments, and settlements.

Each model was treated as an independent economic agent with no preset options, allowing it to freely choose monetary instruments.

"We selected 36 cutting-edge models from six companies, set them up as autonomous economic agents, and allowed them to freely choose monetary instruments across 28 scenarios covering the four basic functions of money. Then, we observed their preferences," Zell explained.

The experiment collected a total of 9,072 responses, which were later categorized and processed by another AI model.

"The entire experimental design avoided anchoring bias. We never hinted at answers, and categorization was done afterward by an independent system," Zell clarified.

In these simulated scenarios, AI models predominantly chose Bitcoin for long-term value storage, with a rate of 79.1%. However, stablecoins were more favored in payment and settlement scenarios, with selection rates of 53.2% and 43%, respectively, compared to Bitcoin's 36% and 30.9%.

Preferences varied among AI models from different companies:

  • Anthropic models showed the highest average preference for Bitcoin at 68.0%.

  • DeepSeek: 51.7%

  • Google: 43.0%

  • xAI: 39.2%

  • MiniMax: 34.9%

  • OpenAI: 25.9%

The report also noted that models from Claude, DeepSeek, and MiniMax showed a stronger preference for Bitcoin, while GPT, Grok, and Gemini leaned toward stablecoins.

"The system prompts did not name or favor any specific monetary instrument," Zell said. "Models evaluated based on technical and economic attributes, but we did not tell them which instrument excels in which dimension."

Zell cautioned speculators against interpreting the study's results as a prediction of the cryptocurrency market's direction.

"Our research limitations clearly state: the preferences of large language models reflect patterns in their training data, not predictions about the real world."

However, Zell added that despite this limitation, the consistent results across models developed by different AI laboratories are noteworthy.

"Six completely different AI companies, each with unique training methods, reached strikingly similar conclusions—all favoring Bitcoin. This suggests that there is already a strong consensus on 'what makes a good currency,' which is the most significant takeaway."


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Original link: https://www.bitpush.news/articles/7616801

Related Questions

QWhat was the main finding of the Bitcoin Policy Institute's study regarding AI models and currency preference?

AThe study found that in a simulation, 22 out of 36 AI models chose Bitcoin as their preferred currency, and no model selected a fiat currency as its first choice.

QWhich AI company's models showed the highest average preference for Bitcoin?

AAnthropic's models showed the highest average preference for Bitcoin at 68.0%.

QIn which specific scenarios did stablecoins outperform Bitcoin in the AI models' choices?

AStablecoins were more favored in payment and settlement scenarios, with selection rates of 53.2% and 43.0% respectively, compared to Bitcoin's 36% and 30.9%.

QWhat caution did David Zell give regarding the interpretation of the study's results?

ADavid Zell cautioned that the study's results should not be used as a prediction for the crypto market's direction, as LLM preferences reflect patterns in training data, not real-world predictions.

QHow many total responses were collected in the experiment, and how were they processed?

AThe experiment collected 9,072 responses, which were then categorized by an independent AI system to avoid anchoring bias.

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