What advantages does blockchain have over mobile payments? This is a question that has embarrassed many blockchain experts. In 2015, renowned blockchain evangelist Andreas Antonopoulos was challenged by an audience member during a speech: mobile payments are convenient and fast, while Bitcoin is slow and cumbersome; what advantages does it have over other payment methods? He didn't defend himself, but instead presented a vivid scenario: if a driverless taxi operated independently, needing to collect fares and pay for its charging, wouldn't Bitcoin be a more ideal payment solution?
This is a thought-provoking question. In fact, over the past decade, many thinkers in the blockchain field, such as Zhu Jiaming and Xiao Feng, have considered this question and raised a bold question: perhaps blockchain was never intended for humans, but rather for AI and robots.
Ten years ago, AI agents and driverless cars were still cutting-edge research in laboratories, but now, these technologies are being applied in various ways.
Currently, the development of AI multi-agent systems has become a hot topic. Will it open up new avenues for blockchain applications and ultimately lead to the integration of AI and blockchain? Multi-agent systems are becoming the hottest AI application direction. Starting in the second half of 2025, multi-agent systems suddenly became one of the most popular directions for AI implementation. A significant proportion of the most watched AI projects recently belong to this direction. - Anthropic, a leader in the Agentic AI field, has continuously launched products such as Cowork, Agent Teams, and Managed Agents, clearly demonstrating its leadership in this direction. Google's Agent Development Kit (ADK) provides a standardized framework to help developers quickly build layered, scalable multi-agent systems, supporting parallel, sequential, and cyclic orchestration. OpenClaw allows ordinary users to deploy a "Lobster team" on their local machines, collaborating to complete multi-step workflows, sparking heated discussions about "AI employees" and "one-person companies." ByteDance's DeerFlow 2.0, after being open-sourced, quickly topped GitHub Trending. It's a super-agent runtime infrastructure that orchestrates sub-agents, long-term memory, and Docker sandboxes to autonomously complete long-duration, complex tasks ranging from minutes to hours, completely solving the pain point of traditional AI's "manual handover." - Gary Tan, CEO of Y Combinator, a well-known Silicon Valley incubator, open-sourced gstack, turning Claude Code into a virtual startup team. It includes 23 professional roles such as CEO, designer, engineer, QA, and security officer, along with slash commands, allowing one person to simulate the operation of an entire startup.
- The TradingAgents project, a collaboration between UCLA and MIT, allows multiple agents to act as fundamental analysts, sentiment analysts, technical analysts, and risk managers, making investment decisions through debate and collaboration, simulating the organizational processes of a real trading company.
- Paperclips.AI focuses on organizing a group of agents into a complete architecture capable of autonomously operating a company, including organizational charts, budgets, governance, and goal setting, achieving a closed-loop business model with zero human intervention. These projects collectively point to a clear trend: people are no longer satisfied with individual AI assistants, but are beginning to build real work teams and collaborative networks using multi-agent architectures. In the past, we mainly used AI to "ask questions and get answers"; now we are starting to let AI "work as a team"—dividing tasks, collaborating, supervising each other, and making autonomous decisions to complete complex tasks that only human organizations can handle. This is not a simple tool upgrade, but a crucial turning point for AI, moving from a personal efficiency tool to an organizational-level technology. The core of a multi-agent system is that multiple agents are no longer isolated "role-playing," but form a dynamic network that can create accounts, scale, recombine, and even collaborate across organizational boundaries based on task requirements. They can handle high-frequency, small-scale, cross-entity, and cross-jurisdictional value exchanges, while triggering complex contract enforcement conditions. These characteristics naturally necessitate a completely new payment and transaction infrastructure for multi-agent organizations. Why must multi-agent applications be equipped with a new payment system? When AI multi-agent organizations move from the laboratory to real-world tasks, payment and value exchange are no longer optional add-ons, but rather the lifeblood of the system. Once a multi-agent network begins processing actual business, it generates a large number of high-frequency, small-scale, cross-entity, and even cross-jurisdictional payment demands. Agent A might complete content generation in seconds, and Agent B immediately needs to call a paid model for it; Agent C, after processing logistics data, needs to immediately pay Agent D for data usage; in cross-border collaborations, an agent in Singapore might need to pay an agent on a US server for computing power. These payments can occur dozens of times per minute, with amounts as small as 0.1 cents, and the participants may be completely different organizations or individuals. These exchanges are often accompanied by complex value flows: not only money, but also data, computing power, model access rights, fragments of intellectual property, etc. More importantly, the dynamism of multi-agent organizations far exceeds that of traditional organizations. Intelligent agent accounts can be created at any time, and organizations can scale and recombine at any time. One minute a task might require a small team of five intelligent agents, while the next minute another task could require twenty agents to be instantly reorganized, some even from external partners. When intelligent agents across organizations make payments to each other, complex contract execution conditions inevitably arise, with payments only automatically executed when thresholds are reached. These conditions can be nested, multi-layered, and real-time, defying traditional contracts and beyond the real-time response capabilities of traditional banking systems. Traditional banking systems are ill-suited for this scenario. They are accustomed to large-scale, batch, manually reviewed netting settlements, with response times measured in hours or even days. They cannot provide instant account opening services for thousands of constantly created and destroyed intelligent agent accounts, cannot handle the complex conditions attached to each transaction, and cannot offer the kind of "personalized service"—intelligent agents need code-level, millisecond-level automated execution, not phone calls to customer service or paper applications. Bank rules are designed for people, and processes are designed for stable institutions. However, when faced with a globally distributed network of intelligent agents that can instantly reorganize, never sleep, and operate at full speed, its limitations become immediately apparent. The advantages of blockchain as a next-generation financial market infrastructure are highlighted in this scenario. Blockchain is essentially a distributed ledger, allowing all participants to share the same real-time updated public ledger without repeated reconciliation. Smart contracts directly translate contract terms into code, automatically executing once conditions are met, without third-party intervention. Programmability transforms payments from simple transfers into automated processes that can incorporate arbitrarily complex logic: triggered upon condition fulfillment, executed atomically, and rolled back upon failure. Full settlement on a transaction-by-transaction basis replaces traditional netting, with each transaction clearing and settling simultaneously upon confirmation. Atomic cash-on-delivery ensures that value transfer and asset delivery occur simultaneously, preventing default by either party. Instantaneous finality means that once a transaction is recorded on the blockchain, it is irreversible and tamper-proof. These characteristics are a near-perfect fit with the operational logic of multi-agent organizations. Agents need to create accounts at any time, and the cost of generating blockchain addresses is close to zero; agent organizations need to scale constantly, and smart contracts can deploy new rules instantly; cross-organizational collaboration requires complex triggering conditions, and smart contracts are inherently designed for this; high-frequency micro-payments require low-cost, instant settlement, and blockchain's gas fees and Layer 2 solutions are reducing costs to negligible levels. Traditional infrastructure is centralized, rigid, and slow, while blockchain is disintermediation-free, flexible, and real-time. We are increasingly seeing that multi-agent organizations are not simply piecing together AI tools, but rather building a completely new collaborative paradigm. This paradigm places unprecedented demands on payment and transaction infrastructure, and blockchain is currently the only mature technology system that can meet these requirements. It is not icing on the cake, but an infrastructure-level necessity. When AI agents truly begin to work as a team, blockchain will no longer be an option, but a necessity. Multi-agent applications will become the "base" for blockchain payments. In traditional C2C payment scenarios, blockchain's performance is not outstanding. For ordinary people, transferring money via WeChat or Alipay takes only seconds—entering the amount and scanning a code for confirmation. Blockchain wallets, however, require copying addresses, verifying gas fees, and waiting for block confirmation, resulting in a significantly inferior user experience. For the past decade, blockchain has struggled to compete with mobile payments in human-dominated scenarios such as daily small-amount transfers and face-to-face payments. However, in high-frequency, automated, contract-driven payment scenarios involving AI agents, blockchain's advantages are far superior. Intelligent agents do not require QR codes or manual confirmation. They require payments to be executed automatically. Once preset conditions are met, the smart contract immediately triggers the transfer, without any intermediary intervention. Programmability allows payments to embed complex logic: funds are only released when agent A delivers the specified content, agent B completes data verification, and an external oracle confirms that the market price has reached a threshold. If any step fails, the transaction automatically rolls back. Blockchain supports 24/7 operation, instant settlement between any addresses globally, and instant finality for every transaction. These capabilities are currently completely unavailable from traditional banking systems and mobile payment platforms. Multi-agent organizations will become the "main arena" for blockchain payments. Mobile payments initially had no significant advantage in face-to-face payments. At that time, people were still accustomed to cash and card payments, and mobile payments were even considered redundant in small shops. However, it found a breakthrough in e-commerce scenarios. Order payments on Taobao and JD.com require instant online settlement and support for massive concurrency, where mobile payments quickly gained a foothold. It first refined the e-commerce payment experience to its extreme, accumulating users, merchants, and network effects before giving back to the whole society. The ease with which we make payments today stems from mobile payment's initial success in e-commerce. AI multi-agent organizations will become the most solid and explosive foundation for blockchain payments and value exchange. Here, payment is no longer an occasional human act, but a regular part of system operation. It could be micro-payments occurring every second, computing power rental fees, model call fees, data usage fees, or intellectual property sharing. These payments require embedded complex conditions and atomic-level execution. Traditional payment infrastructure struggles to cope, while blockchain is naturally suited. It doesn't need to change user habits because the intelligent agent itself is code. It doesn't need customer service support because everything is guaranteed by contracts. It doesn't need centralized risk control because trust is provided by cryptography and distributed consensus. I believe this precisely reflects the penetrating nature of blockchain technology. It establishes irreplaceable structural advantages in the scenarios where it is most needed. Blockchain does not need to completely replace existing payment systems. It only needs to take root in places where humans don't yet need it, building a completely new value network. As AI agents work in large groups, this network will grow rapidly, gradually extending from micro-payments between agents to broader economic activities, ultimately benefiting human society. Mobile payment proved itself with e-commerce; blockchain will prove itself with AI multi-agent organizations. When the base of agent payments is firmly established, the position of blockchain in the entire digital economy will be completely different. The New Form of the AI Economy The combination of AI multi-agent organizations and blockchain is far more than just a technological overlap. It will open up a completely new economic landscape, profoundly changing resource allocation, social exchange, individual income, and the innovation ecosystem. The following analysis will explore this from four dimensions. First, it will significantly improve the overall performance and resource allocation efficiency of AI multi-agent systems. Currently, most multi-agent applications are still in the "playing house" stage. Developers mainly rely on agent skills, hooks, MCP (Multi-Channel Programming), prompt engineering, and other methods to simulate and customize personalized "digital employees." This is essentially a primitive role-playing state; everyone is using prompts to create seemingly professional AI characters, then having them chat and divide tasks to simulate a multi-step workflow. It looks lively, but the actual advantage compared to using a single, all-powerful AI assistant is quite limited. True multi-agent organizations are completely different. Some agents will possess unique resources and capabilities that cannot be easily imitated or replaced through simple customization. These capabilities may include proprietary datasets, exclusive model weights, real-time data sources specific to a particular domain, high-precision simulation environments, or industry experience accumulated through long-term training. They can only be developed, cultivated, and released by institutions with unique resources. Calling these advanced agents inevitably involves real payments. The key role of smart contracts in blockchain lies in this. They can encapsulate call rules, pricing mechanisms, quality verification, and fee settlement all within the code. Once conditions are met, payment is automatically triggered, and resources are automatically delivered; if conditions are not met, funds are automatically rolled back. The entire process is efficient, secure, programmable, and auditable. The inefficient methods of manual negotiation, email confirmation, and post-event reconciliation will completely disappear. Resource allocation efficiency will thus be significantly improved, and the overall performance of the multi-agent system will reach a new level. This is not simply a cost reduction, but a true expansion of the system's capabilities. Secondly, it significantly promotes the scale of social exchange and the speed of economic growth. Traditional financial infrastructure sets extremely high barriers to transactions with small, frequent, and complex conditions. Bank transfers have minimum amount limits, clearing has time windows, and cross-border payments involve exchange rates and compliance costs. These frictions directly exclude a large number of potential transactions. When blockchain provides AI agents with a low-friction micro-payment and value exchange network, the situation will fundamentally change. Agents can easily complete instant settlements for a few cents at a time for computing power leasing, data calls, model fine-tuning services, and even single API calls. Transactions that were previously suppressed due to high costs are now feasible. Massive amounts of previously impossible exchanges will be released, significantly accelerating the speed and scale of economic cycles. Imagine: a content creation agent automatically pays a small copyright fee to the material-providing agent for every piece of high-quality text it generates; an investment analysis agent pays a fee to the data source agent for every real-time market data it accesses; a logistics optimization agent pays a corresponding reward to the map service agent for every route planning it completes. These micro-payments accumulate to form an extremely large value flow network. The density and frequency of economic activity will increase, and overall economic growth will gain new momentum. Third, enabling ordinary people to truly earn money through AI agents will address the structural gap in supply and demand matching in the AI era. One of the most prominent contradictions in the AI era is that large model companies possess core capabilities, while the supply and demand of a large number of ordinary people are difficult to effectively match. Many people have unique data, experience, or scenarios, but lack the ability to transform them into AI services; at the same time, there are many tasks that require specialized agents to complete, but suitable service providers cannot be found. The "one person, multiple agents" model will become a new form of employment. Ordinary people can deploy and operate their own agent networks, encapsulating their personal knowledge, data, or industry insights into callable agent modules, and then providing services and automatically collecting payments through a blockchain network. Some people are skilled in local life services and can train regional life assistant agents; others are familiar with a niche field and can develop specialized analytical agents for that field. These agents are no longer free toys, but economic units capable of autonomously generating income. In this way, a new balance mechanism will be formed in the supply and demand matching of the AI era. The supply side will no longer be dominated by a few large companies, and the demand side can also accurately reach the most suitable intelligent agent services through micro-payments. Ordinary people will no longer just be consumers of AI, but can become contributors and beneficiaries of the AI value network. This will greatly alleviate the employment pressure brought about by AI, while allowing the innovation vitality of the whole society to be more fully released. Fourth, prevent large AI model companies from evolving into new economic oligarchs. Currently, there is a serious imbalance in the AI ecosystem. All technologies such as prompt engineering and skills that embody knowledge and experience exist almost entirely in the form of free and open source, making it difficult to obtain sustained economic incentives. Developers are desperately burning tokens, only to receive cheap cheers on social networks, which are difficult to convert into income. Only large model companies have clear business models, and they control the underlying computing power and basic models. Even more dangerous is that once large model companies observe a successful pattern, they can easily replicate or even surpass existing AI startups with just a slight adjustment at the model layer. This leads to the rapid elimination of numerous innovative teams, and the innovation ecosystem faces the risk of being exploited. When Anthropic released its Managed Agents product in early April, some lamented that at least 1,000 startups woke up to find their value zero. The situation will change when the Agent itself becomes an economic element capable of autonomously receiving payments. The value network will be decentralized and restructured. Each intelligent agent can independently price, settle, and accumulate reputation and assets through the blockchain. Successful Agents will no longer be dependent on a large model but will become independent nodes in the network. Developers can earn direct revenue by continuously iterating their Agents without having to relinquish all value to the underlying model provider. Large model companies will remain important, but they will transform from rule-makers into infrastructure providers. Their overwhelming advantages will be effectively balanced, and the diversity and vitality of the innovation ecosystem will be protected. This is not a denial of large-scale companies, but rather a move to make the entire AI economic system healthier and more sustainable. The combination of AI and blockchain is painting an unprecedented economic picture for us. In this picture, resource allocation is more precise, exchanges are smoother, ordinary people have new sources of income, and the innovation ecosystem remains open and vibrant. The benefits brought by this integration far exceed the technology itself. It will profoundly affect our production methods and wealth distribution patterns over the next twenty years. Short-term obstacles and realistic prudence: While the trend is clear, the implementation process will not be smooth sailing. The combination of AI multi-agent organizations and blockchain faces several real and thorny obstacles. We must face them squarely to avoid blind optimism. First, although the US has taken the lead in digital asset legislation, it has not yet been fully implemented. The CLARITY Act is stalled, with the Treasury and regulatory agencies continuing to push forward with details on anti-money laundering and reserve asset management. Traditional powers like banks still face resistance to stablecoin issuance and smart contract payments. The refinement of the regulatory framework takes time, and uncertainties in its implementation will continue to constrain large-scale application in the short term. Secondly, other countries, including China, maintain a conflicted regulatory stance. Many economies are concerned about monetary sovereignty, capital flows, and financial stability, making it difficult to quickly introduce clear and favorable frameworks in the short term. Legislative delays and policy instability will create additional friction for cross-jurisdictional collaboration among intelligent agents. Thirdly, AI practitioners generally lack understanding of blockchain and even hold cognitive biases. In the AI community, blockchain is often simply equated with speculation or even fraud. Many developers only see on-chain gas fees and confirmation delays, rarely understanding the structural value of distributed ledgers and smart contracts for multi-agent organizations. This cognitive gap leads to a reluctance of top AI talent to invest in the field, slowing down the progress of integration projects. Meanwhile, the blockchain industry is currently experiencing a downturn in funding, talent, and confidence. The shadows cast by fraud and project failures following the bursting of the speculative bubble in recent years have not yet fully dissipated. High-quality projects are struggling to secure funding, top engineers are leaving in large numbers, and the overall industry confidence will take time to recover. If these problems are not handled properly, they could amplify external biases and further slow down the integration with AI. These obstacles are real and unlikely to be completely eliminated in the short term. They remind us that any major technological integration is not a linear process but requires repeated breakthroughs in cognition, regulation, and practice. However, it is precisely because these obstacles exist that the strategic significance of this integration becomes even more prominent. Whoever can break through cognitive bottlenecks first, proactively fill regulatory gaps, and invest resources in cultivating cross-disciplinary talent will gain a competitive advantage in the new paradigm of AI and blockchain integration. Returning to the essence of technology, setting aside short-term noise, and investing in genuine cognitive upgrades and practical exploration is the only way to address this challenge. History has repeatedly proven that at major technological turning points in a great era, those who hesitate and observe often miss the window of opportunity, while those who dare to face obstacles and continuously iterate ultimately become the driving force behind the wave. The trend is set. The development of AI multi-agent organizations will inevitably lead to the deep integration of AI and blockchain. This is not a possibility, but a historical inevitability driven by technological logic. Intelligent agents need automatic payments triggered by high-frequency, minute, and complex conditions, and blockchain provides the most suitable infrastructure. Due to its relatively advanced digital asset legislation, the United States has already accumulated a significant first-mover advantage in stablecoins and on-chain infrastructure. In the coming years, they are likely to transform this integration into real productivity and economic control. China must not be complacent in this integration. Considering the talent pool, technological reserves, and resource investment of China and the United States in core AI technologies, the gap between the two sides is not significant in the competition of models and applications alone. If, several years from now, China's AI ecosystem lags significantly behind that of the United States, it will undoubtedly be because the US has done things that China cannot or is unable to do. Currently, blockchain payments seem to be such a case.
Kikyo
Weatherly
Catherine
Anais