Will AI agents prefer to use Bitcoin and Crypto or Traditional Banks?
AI Agents converge on two-tiers: Stable Coins for Short-term and Bitcoin for Long-term
Q: If AI performs monetary transactions, is it expected to use Bitcoin or Crypto, and if so how?
A: When it comes to autonomous AI agents performing monetary transactions, there is a very strong expectation—and an accelerating trend—that they will use cryptocurrency and stablecoins rather than traditional bank accounts.
In fact, controlled studies on “agentic payments” (such as a large-scale experiment by the Bitcoin Policy Institute) reveal that when advanced AI models are tasked with navigating financial scenarios autonomously, they reject traditional fiat money over 90% of the time. Instead, they naturally converge on a two-tier digital economy: Bitcoin for long-term savings/value preservation and stablecoins for daily transactional payments.
Here is a breakdown of why this expectation exists and exactly how it works in practice.
Why AI Rejects Traditional Banking
Traditional banking infrastructure was built for humans, by humans. It relies on identity verification (SSNs, passports), physical signatures, business hours, and manual fraud checks.
The “No-ID” Problem: An AI agent cannot walk into a bank branch, fill out a KYC (Know Your Customer) form, or open a checking account.
MoonPay
Speed and Scale: AI operates 24/7 and can execute thousands of micro-transactions per second. Visa, Mastercard, and ACH networks are too slow, charge high flat fees for tiny transactions, and are plagued by chargeback risks.
Crypto networks are permissionless, API-driven, and run 24/7/365, making them the native “money layer” for software.
How AI Performs Transactions (The Mechanics)
For an AI to actually buy data, pay for API access, or rent server space, a specific infrastructure has been built to bridge the gap between machine intelligence and blockchain networks.
1. AI Web3 Wallets
Developers equip AI agents with non-custodial crypto wallets (often on fast, low-cost Layer 2 blockchains or networks like Solana and Base). The AI holds its own private keys. Using smart contracts, developers set strict guardrails. For example, a developer might program an AI agent with a rule: “You have permission to spend up to $50 worth of stablecoins per day on data scraping APIs, but any transaction over that requires human cryptographic approval.”
2. The Two-Tier System: Bitcoin vs. Stablecoins
AI agents generally differentiate between the types of crypto they use based on the task:
Stablecoins (USDC/USDT) for Spending: For buying API access, paying another AI for a service, or handling micro-payments, AI relies on dollar-pegged stablecoins. This avoids the volatility of traditional crypto and aligns with the pricing of most digital services.
Bitcoin (BTC) for Wealth/Savings: When AI agents are tasked with holding corporate treasuries or preserving capital over long horizons, they heavily favor Bitcoin. They recognize its hard-capped supply, decentralization, and immunity to inflation.
3. Machine-to-Machine Protocols
Specialized protocols (like the AP2 standard or agentic payment networks) allow AIs to transact directly with other machines without needing to create user accounts, fill out credit card forms, or deal with billing cycles. The AI simply attaches a micro-fraction of a cent to its API request, paying exactly for what it consumes in real-time.
💡 The Emergence of “Compute Money”
Fascinatingly, when AI models are put into economic simulations without access to existing human currencies, they frequently attempt to invent their own money based on energy or compute resources—pricing things natively in kilowatt-hours (kWh) or GPU-hours. Until the world runs on “compute tokens,” however, crypto and stablecoins remain their practical standard.
QUESTIONS:
In the traditional banking system there is the concept of the global reserve currency. Should we expect the standard for AI money to be the same, or different due to issues with
computers geting KYC for accounts, as well as
speed and 24/7 transactions?
How much money do we anticipate existing in the Agentic AI system?
What sort of split should we anticipate be between short and long term usage?
DETAILS:
What is an AI agent? (Google)
AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt.
Their capabilities are made possible in large part by the multimodal capacity of generative AI and AI foundation models. AI agents can process multimodal information like text, voice, video, audio, code, and more simultaneously; can converse, reason, learn, and make decisions. They can learn over time and facilitate transactions and business processes. Agents can work with other agents to coordinate and perform more complex workflows.
Key features of an AI agent
As explained above, while the key features of an AI agent are reasoning and acting (as described in ReAct Framework) more features have evolved over time.
Reasoning: This core cognitive process involves using logic and available information to draw conclusions, make inferences, and solve problems. AI agents with strong reasoning capabilities can analyze data, identify patterns, and make informed decisions based on evidence and context.
Acting: The ability to take action or perform tasks based on decisions, plans, or external input is crucial for AI agents to interact with their environment and achieve goals. This can include physical actions in the case of embodied AI, or digital actions like sending messages, updating data, or triggering other processes.
Observing: Gathering information about the environment or situation through perception or sensing is essential for AI agents to understand their context and make informed decisions. This can involve various forms of perception, such as computer vision, natural language processing, or sensor data analysis.
Planning: Developing a strategic plan to achieve goals is a key aspect of intelligent behavior. AI agents with planning capabilities can identify the necessary steps, evaluate potential actions, and choose the best course of action based on available information and desired outcomes. This often involves anticipating future states and considering potential obstacles.
Collaborating: Working effectively with others, whether humans or other AI agents, to achieve a common goal is increasingly important in complex and dynamic environments. Collaboration requires communication, coordination, and the ability to understand and respect the perspectives of others.
Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust their behavior based on feedback, and continuously enhance their performance and capabilities over time. This can involve machine learning techniques, optimization algorithms, or other forms of self-modification.