AI Chooses Currency: Bitcoin Wins Big, Fiat Money Unwanted

marsbitPublished 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 various economic scenarios. In the experiment, AI models acted as autonomous economic agents and made choices across 28 scenarios covering core monetary functions like saving, payment, and settlement. Out of 9,072 responses collected, 22 of the 36 models selected Bitcoin as their preferred currency, while none chose fiat money as their first option. Bitcoin was strongly favored for long-term savings (79.1% of cases), while stablecoins were more commonly chosen for payments (53.2%) and settlements (43%). Among different AI developers, Anthropic's models showed the strongest preference for Bitcoin (68.0%), followed by DeepSeek (51.7%), Google (43.0%), 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 their training data, rather than real-world predictive capability. Despite this limitation, the consistent preference for Bitcoin across diverse AI systems suggests a growing consensus on the perceived advantages of cryptocurrency as sound money.

Written by: Jason Nelson

Compiled by: Chopper, Foresight News

Summary

  • In a simulation 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

A recent 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 them to freely choose monetary tools.

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

The experiment collected 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, accounting for 79.1% of cases. In payment and settlement scenarios, stablecoins were more favored, 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.

"System prompts did not specify or favor any monetary tool," Zell said. "Models evaluated based on technical and economic attributes, but we did not indicate which tool excels in which dimension."

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

"The limitations section of our research clearly states: the preferences of large language models reflect patterns in training data, not real-world predictions."

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

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

Trending Cryptos

Related Questions

QWhat was the main finding of the Bitcoin Policy Institute's report regarding AI model preferences for currency?

AThe main finding was that in a simulated experiment, 22 out of 36 AI models selected Bitcoin as their preferred currency, and no model chose a fiat currency as its first choice.

QWhich AI company's models had the highest average preference for Bitcoin, and what was it?

AAnthropic's models had 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% respectively, compared to Bitcoin's 36% and 30.9%.

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

AThe experiment collected 9,072 responses, which were then categorized and processed by another AI model.

QAccording to David Zell, what is the key takeaway from the consistent results across different AI models?

AThe key takeaway is that despite different training methods, the models from six different AI companies consistently favored Bitcoin, indicating a strong consensus on what constitutes good money.

Related Reads

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit1h ago

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit1h ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit1h ago

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit1h ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit3h ago

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit3h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit3h ago

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit3h ago

Trading

Spot
Futures

Hot Articles

What is $BITCOIN

DIGITAL GOLD ($BITCOIN): A Comprehensive Analysis Introduction to DIGITAL GOLD ($BITCOIN) DIGITAL GOLD ($BITCOIN) is a blockchain-based project operating on the Solana network, which aims to combine the characteristics of traditional precious metals with the innovation of decentralized technologies. While it shares a name with Bitcoin, often referred to as “digital gold” due to its perception as a store of value, DIGITAL GOLD is a separate token designed to create a unique ecosystem within the Web3 landscape. Its goal is to position itself as a viable alternative digital asset, although specifics regarding its applications and functionalities are still developing. What is DIGITAL GOLD ($BITCOIN)? DIGITAL GOLD ($BITCOIN) is a cryptocurrency token explicitly designed for use on the Solana blockchain. In contrast to Bitcoin, which provides a widely recognized value storage role, this token appears to focus on broader applications and characteristics. Notable aspects include: Blockchain Infrastructure: The token is built on the Solana blockchain, known for its capacity to handle high-speed and low-cost transactions. Supply Dynamics: DIGITAL GOLD has a maximum supply capped at 100 quadrillion tokens (100P $BITCOIN), although details regarding its circulating supply are currently undisclosed. Utility: While precise functionalities are not explicitly outlined, there are indications that the token could be utilized for various applications, potentially involving decentralized applications (dApps) or asset tokenization strategies. Who is the Creator of DIGITAL GOLD ($BITCOIN)? At present, the identity of the creators and development team behind DIGITAL GOLD ($BITCOIN) remains unknown. This situation is typical among many innovative projects within the blockchain space, particularly those aligning with decentralized finance and meme coin phenomena. While such anonymity may foster a community-driven culture, it intensifies concerns about governance and accountability. Who are the Investors of DIGITAL GOLD ($BITCOIN)? The available information indicates that DIGITAL GOLD ($BITCOIN) does not have any known institutional backers or prominent venture capital investments. The project seems to operate on a peer-to-peer model focused on community support and adoption rather than traditional funding routes. Its activity and liquidity are primarily situated on decentralized exchanges (DEXs), such as PumpSwap, rather than established centralized trading platforms, further highlighting its grassroots approach. How DIGITAL GOLD ($BITCOIN) Works The operational mechanics of DIGITAL GOLD ($BITCOIN) can be elaborated on based on its blockchain design and network attributes: Consensus Mechanism: By leveraging Solana’s unique proof-of-history (PoH) combined with a proof-of-stake (PoS) model, the project ensures efficient transaction validation contributing to the network's high performance. Tokenomics: While specific deflationary mechanisms have not been extensively detailed, the vast maximum token supply implies that it may cater to microtransactions or niche use cases that are still to be defined. Interoperability: There exists the potential for integration with Solana’s broader ecosystem, including various decentralized finance (DeFi) platforms. However, the details regarding specific integrations remain unspecified. Timeline of Key Events Here is a timeline that highlights significant milestones concerning DIGITAL GOLD ($BITCOIN): 2023: The initial deployment of the token occurs on the Solana blockchain, marked by its contract address. 2024: DIGITAL GOLD gains visibility as it becomes available for trading on decentralized exchanges like PumpSwap, allowing users to trade it against SOL. 2025: The project witnesses sporadic trading activity and potential interest in community-led engagements, although no noteworthy partnerships or technical advancements have been documented as of yet. Critical Analysis Strengths Scalability: The underlying Solana infrastructure supports high transaction volumes, which could enhance the utility of $BITCOIN in various transaction scenarios. Accessibility: The potential low trading price per token could attract retail investors, facilitating wider participation due to fractional ownership opportunities. Risks Lack of Transparency: The absence of publicly known backers, developers, or an audit process may yield skepticism regarding the project's sustainability and trustworthiness. Market Volatility: The trading activity is heavily reliant on speculative behavior, which can result in significant price volatility and uncertainty for investors. Conclusion DIGITAL GOLD ($BITCOIN) emerges as an intriguing yet ambiguous project within the rapidly evolving Solana ecosystem. While it attempts to leverage the “digital gold” narrative, its departure from Bitcoin's established role as a store of value underscores the need for a clearer differentiation of its intended utility and governance structure. Future acceptance and adoption will likely depend on addressing the current opacity and defining its operational and economic strategies more explicitly. Note: This report encompasses synthesised information available as of October 2023, and developments may have transpired beyond the research period.

402 Total ViewsPublished 2025.05.13Updated 2025.05.13

What is $BITCOIN

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of BTC (BTC) are presented below.

活动图片