What advantages does blockchain have over mobile payments? This is a question that once embarrassed many blockchain experts. In 2015, the famous blockchain evangelist Andreas Antonopoulos was challenged by the audience in a speech: Mobile payment is convenient and fast, but Bitcoin is slow and cumbersome. What advantages does it have in payment? Instead of defending and defending, he threw out a vivid scenario: If a driverless taxi operates independently and needs to collect money and pay for charging, will Bitcoin be a more ideal payment solution?

This is a thought-provoking question. In fact, for more than ten years, many thinkers in the blockchain field, such as Zhu Jiaming, Xiao Feng, etc., have thought about this issue and raised a bold question: Maybe the blockchain was not prepared for people from the beginning, but for AI and robots.

Ten years ago, talking about AI agents and autonomous driving was still a trendy research in the laboratory, but now, the applications of these technologies have been implemented one after another. At present, the development of AI multi-agent organizations has become the hottest direction. Will it open up a new base for blockchain applications and eventually promote the combination of AI and blockchain?

Multi-agent systems are becoming the most popular AI application direction

Starting in the second half of 2025, multi-agent systems will suddenly become one of the hottest directions for AI implementation. A considerable proportion of the most recent projects in the AI ​​field fall into this direction.

• - Anthropic, the leader in the field of Agentic AI, has successively launched Cowork, Agent Teams, Managed Agents and other products, clarifying its leadership intentions in this direction.

• - Google's Agent Development Kit (ADK) provides a standardized framework to help developers quickly build hierarchical, scalable multi-agent systems, supporting parallel, sequential and loop orchestration.

• - OpenClaw (Lobster) allows ordinary users to deploy a "lobster team" on their local machine to complete multi-step workflows through division of labor and collaboration, triggering heated discussions among the public about "AI employees" and "one-person companies".

• - ByteDance’s DeerFlow 2.0 quickly reached the top of GitHub Trending after being open sourced. It is a super-agent runtime infrastructure that can orchestrate sub-agents, long-term memory and Docker sandbox, autonomously complete long-term complex tasks from minutes to hours, and completely solve the pain point of traditional AI “manual handover”.

• - Gary Tan, CEO of YC, a well-known incubator in Silicon Valley, has open sourced gstack, turning Claude Code into a virtual entrepreneurial team with built-in 23 professional roles such as CEO, designer, engineer, QA, security officer and slash commands. One person can simulate the operation of the entire startup;

• - The TradingAgents project jointly launched by the UCLA and MIT teams allows multiple agents to play fundamental analysts, sentiment analysts, technical analysts and risk managers respectively, making investment decisions through debate and collaboration, and simulating the organizational processes of real trading companies;

• - Paperclips.AI focuses on organizing a group of agents into a complete structure that can run the company autonomously, including organization charts, budgets, governance and goal setting, to achieve a closed business loop with zero manual intervention.

These projects collectively point to a clear trend: people are no longer satisfied with a single AI assistant, but are beginning to use multi-agent architecture to build real work teams and collaboration networks. In the past, we mainly used AI to “ask questions and get answers.” Now we are beginning to let AI “work in groups”—division of labor, collaboration, mutual supervision, independent decision-making, and completing complex tasks that can only be handled by human organizations.

This is not a simple tool upgrade, but a key transition for AI from a personal efficiency tool to an organizational technology. The core of a multi-agent system is that multiple agents no longer "role-play" in isolation, but form a dynamic network that can create accounts, scale, regroup, and even collaborate across organizational boundaries at any time according to task requirements. They can handle high-frequency, tiny, cross-subject, and cross-jurisdiction value exchanges while triggering complex contract execution conditions. These characteristics make multi-agent organizations naturally need a new payment and transaction infrastructure.

Why must multi-agent applications be equipped with a new payment system?

When AI multi-agent organizations move from the laboratory to real tasks, payment and value exchange are no longer dispensable additional functions, but the blood of the system's operation.

Once a multi-agent network starts processing actual business, it will generate a large number of high-frequency, small, cross-subject, and even cross-jurisdiction payment demands. Agent A may complete content generation within a few seconds, and agent B immediately needs to call the payment model for it; after agent C processes the logistics data, it needs to immediately pay data usage fees to agent D; during cross-border collaboration, an agent in Singapore may have to pay computing power fees to an agent in a US server. The frequency of these payments may reach dozens of times per minute, the amount may be as small as 0.1 cents, and the parties involved 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 calling rights, intellectual property fragments, etc.

More importantly, multi-agent organizations are far more dynamic than traditional organizations. Agent accounts can be created at any time, and organizations can be expanded and reorganized at any time. The task in the previous minute required five agents to form a small team, and another task in the next minute may require the instant reorganization of twenty agents, some of which came from external partners. When intelligence agents across organizations pay each other, complex contract execution conditions must be triggered. When a threshold is reached, the payment will be automatically executed. These conditions may be nested, multi-layered, and real-time. Traditional contracts cannot describe them at all, and traditional banking systems cannot respond in real time.

The traditional banking system is not up to the task in this scenario. It is accustomed to a large-amount, batch, manual-review netting clearing model, with response times measured in hours or even days. It cannot provide instant account opening services for thousands of agent accounts that come and go at any time, it cannot handle the requirement that each transaction is triggered by complex conditions, and it cannot provide that kind of "personal service" - what the agents need is automatic execution at the code level and millisecond level, rather than calling customer service or submitting paper applications. The bank's rules are designed for people, and its processes are designed for stable institutions. When faced with a network of intelligent agents that can be reorganized in an instant, never sleep, and are distributed globally, its ceiling is immediately exposed.

The advantages of blockchain as a new generation of financial market infrastructure are highlighted in this scenario.

Blockchain is essentially a distributed ledger, which allows all participants to share the same public ledger that is updated in real time without the need for repeated reconciliation. Smart contracts write contract terms directly into code and execute them automatically once the conditions are met, without the need for third-party intervention. Programmability makes payment no longer a simple transfer, but an automated process that can embed any complex logic: triggering when conditions are met, atomic-level execution, and rollback upon failure. Transaction-by-transaction full settlement replaces traditional netting, and each transaction is cleared and settled at the same time as it is confirmed. Atomic-level currency-bank settlement ensures that value transfer and asset delivery occur at the same time, avoiding default by either party. Instant finality means that once a transaction is on the chain, it is irreversible and cannot be tampered with.

These characteristics form an almost perfect fit with the operating 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 be scalable at any time, and smart contracts can deploy new rules instantly; cross-organizational collaboration requires complex conditions to be triggered, and smart contracts are born for this; high-frequency micropayments require low-cost and instant payment, and blockchain gas fees and Layer 2 solutions are reducing costs to negligible. Traditional infrastructure is centralized, rigid, and slow, while blockchain is disintermediated, flexible, and real-time.

We see more and more clearly that multi-agent organizations do not simply put AI tools together, but build a new collaboration paradigm. This paradigm has put forward unprecedented requirements for payment and transaction infrastructure, and blockchain is currently the only mature technology system that can meet these requirements. It's not a nice-to-have, but an infrastructure-level necessity. When AI agents begin to truly work in groups, blockchain is no longer an option, but a must.

Multi-agent applications will become the "base" of blockchain payment

In traditional C2C payment scenarios, the performance of blockchain is not outstanding. When ordinary people transfer money, it can be completed in a few seconds via WeChat or Alipay. Just enter the amount and scan the QR code to confirm. However, blockchain wallets need to copy addresses, check gas fees, and wait for block confirmation, and the user experience is obviously lagging behind. In the past ten years, blockchain has been difficult 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 between AI agents, the advantages of blockchain are far ahead.

The agent does not require a QR code or manual confirmation. It requires that payments must be automated. Once the preset conditions are met, the smart contract immediately triggers the transfer, and the entire process does not require any intermediary intervention. Programmability allows payments to embed complex logic: funds will only be released when Agent A delivers specified content, Agent B completes data verification, and an external oracle confirms that the market price reaches a threshold. If any step fails, the transaction is automatically rolled back. The blockchain supports 24/7 non-stop operation. Accounts can be received instantly from any address in the world, and every transaction has instant finality. These capabilities are currently completely unavailable by traditional banking systems and mobile payment platforms.

The application of multi-agent organizations will become the "home field" of blockchain payment.

In the early days, mobile payment had no obvious advantage over face-to-face payment. At that time, people were still accustomed to cash and credit cards, and mobile payment even seemed redundant in small stores. But it found a breakthrough in the e-commerce scene. Order payments on Taobao and JD.com require instant online settlement and support for massive concurrency, so mobile payments quickly gained a foothold here. It first polishes the e-commerce payment experience to the extreme, accumulates users, merchants and network effects, and then feeds back to the whole society. It is so easy for us to pay by scanning QR codes today, precisely because mobile payment has won the first place in the e-commerce base.

AI multi-agent organizations will become the most solid and explosive base for blockchain payment and value exchange.

Here, payment is no longer an occasional human behavior, but a normal part of the system's operation. It could be micropayments that occur per second, computing power rental fees, model calling fees, data usage fees, or intellectual property rights sharing. These payments need to embed complex conditions and require atomic level execution. Traditional payment infrastructure is difficult to cope with, but blockchain is naturally adaptable. It does not require changing user habits because the agent itself is code. It does not require customer service support because everything is guaranteed by the contract. It does not require centralized risk control because trust is provided by cryptography and distributed consensus.

I think this exactly reflects the penetrability of blockchain technology. It establishes irreplaceable structural advantages in the scenarios that need it most.

Blockchain does not have to completely replace existing payment systems. It only needs to take the lead in taking root in places that humans are temporarily unable to use, and build a brand new value network. When AI agents work in large-scale groups, the network will grow rapidly, gradually extending from micropayments between agents to a wider range of economic activities, and ultimately feeding back to human society. Mobile payment has proven itself with e-commerce, and blockchain will prove itself with AI multi-agent organizations.

When the base of smart payment is established, the position of blockchain in the entire digital economy will also be completely different.

The new form of AI economy

The combination of AI multi-agent organization and blockchain is far more than just a technical superposition. It will open up a new economic picture and bring about profound changes in resource allocation, social exchange, individual income and innovation ecology. The analysis will be carried out from four dimensions below.

First, significantly improve the overall performance and resource allocation efficiency of the AI multi-agent system.

Currently, the vast majority of multi-agent applications are still in the "playing house" stage. Developers mainly rely on agent skills, hooks, MCP, prompt engineering and other means to simulate and customize personalized "digital employees". This is essentially a primitive role-playing state. Everyone is using prompt words to create professional-looking AI characters, and then letting them chat with each other and divide work, simulating a multi-step workflow. It looks lively, but the actual effect is quite limited compared to using a single, all-purpose AI assistant.

True multi-agent organizations are completely different. Some agents will have unique resources and capabilities that cannot be imitated or replaced through simple customization. These capabilities may include proprietary data sets, exclusive model weights, real-time data sources in specific fields, high-precision simulation environments, or industry experience accumulated through long-term training. They can only be developed, cultivated and released externally at the expense of institutions with unique resources. To call these advanced agents, real payments must be involved.

Blockchain’s smart contracts play a key role here. It can write calling rules, pricing mechanism, quality verification, and fee settlement all into the code. Once the conditions are met, payment is automatically triggered and resources are automatically delivered; if the conditions are not met, funds are automatically rolled back. The entire process is efficient, safe, programmable, and auditable. The past inefficient way of relying on manual negotiation, email confirmation, and post-event reconciliation will completely disappear. As a result, the efficiency of resource allocation is greatly improved, and the overall performance of the entire multi-agent system will also reach a new level. This is not a simple cost reduction, but a real expansion of the boundaries of system capabilities.

Second, significantly promote the scale of social exchange and economic growth.

Traditional financial infrastructure sets extremely high thresholds for transactions with small, frequent, and complex conditions. There are minimum amounts for bank transfers, time windows for clearing, and exchange rates and compliance costs for cross-border payments. These frictions shut out a large number of potential deals.

When the blockchain provides a low-friction micropayment and value exchange network for AI agents, the situation will fundamentally change. Agents can easily complete computing power rental, data calls, model fine-tuning services, and even instant settlement of a single API call for a few cents each. Transactions that were previously inhibited because they were too costly are now feasible. Massive amounts of exchanges that could not have occurred before will be unleashed, and the speed and scale of the economic cycle will be significantly accelerated.

Imagine this: every time a content creation agent generates a piece of high-quality text, it automatically pays a micro-royalty fee to the material provider agent; every time an investment analysis agent calls real-time market data, it pays the data source agent; every time a logistics optimization agent completes a path planning, it pays corresponding compensation to the map service agent. The accumulation of these micropayments will form an extremely large value flow network. The density and frequency of economic activities will increase, and overall economic growth will gain new impetus.

Third, let ordinary people truly make money from AI agents and solve the structural gap in matching supply and demand in the AI era.

One of the most prominent contradictions in the AI era is that large model companies control core capabilities, but it is difficult to effectively connect the needs and supplies of a large number of ordinary people. Many people have unique data, experiences or scenarios, but lack the ability to convert them into AI services; at the same time, there are a large number of tasks that require specialized agents to complete, but no suitable service provider can be found.

The one-person-multiple-Agent model will become a new employment form. Ordinary people can deploy and operate their own agent networks, encapsulate personal knowledge, data or industry insights into callable agent modules, and then provide external services and automatically collect payments through the blockchain network. Someone who is good at local life services can train a regional life assistant agent; someone who is familiar with a certain niche field can develop a professional analysis agent in a vertical field. These agents are no longer free toys, but economic units capable of generating their own income.

In this way, the supply and demand matching in the AI era will form a new balancing mechanism. The supply side is no longer dominated by a few large companies, and the demand side can also accurately access the most suitable intelligent services through micropayments. Ordinary people are no longer just consumers of AI, but can become contributors and beneficiaries of the AI ​​value network. This will greatly alleviate the employment pressure caused by AI and at the same time allow the innovation vitality of the entire society to be more fully released.

Fourth, prevent large AI model companies from evolving into new economic oligarchs.

There is a serious imbalance in the current AI ecosystem. Almost all prompt engineering, skills and other technologies that condense knowledge and experience exist in the form of free open source, making it difficult to obtain sustained economic incentives. Developers are desperately burning tokens, and all they get are cheap applause on social networks, which is difficult to convert into income. Only large model companies have clear business models, and they master the underlying computing power and basic models.

What’s even more dangerous is that once a large model company observes a successful model, it can easily copy or even surpass the original AI startups with just a little follow-up at the model layer. As a result, a large number of innovation teams were quickly eliminated, and the innovation ecosystem faced the risk of being harvested. When Anthropic launched its Managed Agents product in early April, some lamented that at least 1,000 startups woke up in the morning and found that their value had gone to zero.

The situation will change when the Agent itself becomes an economic element that can collect payments autonomously. The value network will be decentralized and reconstructed. Each agent can independently price, settle independently, and accumulate reputation and assets through the blockchain. A successful Agent is no longer attached to a large model, but becomes an independent node in the network. Developers can earn direct income by continuously iterating their own Agents without having to transfer all value to the underlying model provider.

Big model companies will still be important, but they will transform from rule makers to 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 model companies, but makes the entire AI economic system healthier and more sustainable.

The combination of AI and blockchain is drawing 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 dynamic. The dividends brought by this integration far exceed the technology itself. It will profoundly affect our production methods and wealth distribution pattern in the next two decades.

Short-term obstacles and realistic prudence

Although 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 to avoid blind optimism.

First of all, although the US digital asset legislation has taken the lead, it has not yet been fully implemented. The CLARITY Act is difficult to produce. The Ministry of Finance and regulatory agencies are still continuing to promote anti-money laundering, reserve asset management and other details. Resistance from traditional forces such as banks on stablecoin issuance and smart contract payments still exists. It will take time to improve the regulatory framework, and uncertainty in implementation will still restrict large-scale application in the short term.

Secondly, other countries, including China, still have troubled regulatory attitudes. Many economies are worried about monetary sovereignty, capital flows and financial stability issues, and it is difficult to quickly introduce a clear and friendly framework in the short term. Legislative lags and policy swings will make cross-jurisdictional intelligence collaboration face additional friction.

Thirdly, AI practitioners generally lack understanding of blockchain and even have cognitive biases. In AI circles, blockchain is often simply equated with speculation or even fraud. Many developers only see gas fees and confirmation delays on the chain, but rarely have a deep understanding of the structural value of distributed ledgers and smart contracts to multi-agent organizations. This cognitive gap has led to the reluctance of outstanding AI talents to invest in related fields, and the slow progress of integration projects.

At the same time, the blockchain industry is still at a low ebb in funding, talent, and confidence. The speculative bubble bursting in the past few years has left a legacy of fraud and failed projects yet to fully dissipate. It is difficult to finance high-quality projects and there is a serious loss of top engineers. It will take time to restore overall confidence in the industry. If these issues are not handled properly, it is easy to amplify external biases and further slow down the pace of integration with AI.

These obstacles are real and difficult to eliminate completely in the short term. They remind us that any major technological convergence is not a linear advance, but requires repeated challenges at the cognitive, regulatory and practical levels.

However, precisely because of the obstacles, the strategic significance of this combination is all the more prominent. Whoever can take the lead in breaking through cognitive bottlenecks, actively filling regulatory gaps, and investing resources in cultivating cross-border talents will take the lead in the new paradigm of the integration of AI and blockchain.

The only way to deal with it is to return to the essence of technology, put aside short-term noise, and invest in real cognitive upgrades and practical exploration. History has repeatedly proven that at technological turning points in a great era, those who hesitate to wait and see often miss the window, while those who dare to face obstacles and continue to iterate will eventually become the force driving the wave.

The trend is set.

The development of AI multi-agent organizations will inevitably lead to the in-depth integration of AI and blockchain. This is not a possibility, but a historical necessity driven by technological logic. Intelligent agents need automatic payments triggered by high-frequency, tiny, and complex conditions, and blockchain provides the most matching infrastructure.

Because the United States is relatively leading in digital asset legislation, it has accumulated a clear first-mover advantage in stablecoins and on-chain infrastructure. In the coming years, they are likely to turn this combination into real productivity and economic control. China must not take this integration lightly. Judging from the scale of talent, technical reserves and resource investment in core AI technologies between China and the United States, there is not much of a gap between the two sides simply in the competition of models and applications. If there is a big gap between China's AI ecosystem and the United States in a few years, it must be because the United States has done something that China cannot or cannot do well. Now it seems that blockchain payment may be such a thing.