Whenever a new narrative enters public discourse, mainstream arguments are simplified to the most easily accepted form. Intuitively, when no one can empirically prove what will happen, provocation is more rewarding than meticulous analysis. The recent debate surrounding "agent commerce" is no exception. A consensus has formed in the market: the number of agents is surging; agents need to transact; agents cannot hold bank accounts but can hold e-wallets; card organizations charge 2-3% fees; therefore, stablecoins win. This chain of logic is flawed on many levels. Agents can hold bank accounts under an FBO (Financial Business Operator) structure. Furthermore, the 2-3% fee reflects credit risk and fraud risk, which blockchain cannot solve. However, the debate about "which payment method wins?" stems from a fundamental question that has been largely ignored in the discussion: will most agents actually transact? The agent economy will be enormous, but the proportion of agents actually transacting will not be that high. The agent economy is more like an organizational chart than a market. Fundamentally, artificial intelligence is an automation technology. It can accomplish certain tasks—such as searching, aggregating, and synthesizing—and more efficiently than humans. Intelligent agents are the operational derivatives of artificial intelligence. They don't simply return outputs; they perform actual actions. The underlying assumption of the entire intelligent agent business theory is that execution comes at a cost. In other words, for most intelligent agent tasks, they need to spend money to autonomously acquire external resources, pay for computing and data on a usage basis, and interact with other intelligent agents as independent economic entities. This fundamentally contradicts how intelligent agents are actually used. Generally, intelligent agent deployments can be divided into two categories: business intelligent agents deployed on behalf of enterprises, and consumer intelligent agents that enhance our personal lives. For different reasons, neither type of intelligent agent is likely to engage in autonomous transactions. Business agents are an inevitable evolution of SaaS. A reasonable concept of business agents is an inevitable evolution of SaaS. They don't enhance workflows, but rather replace existing ones. Just as over 95% of software spending comes from enterprises and governments, over 95% of large-scale agent applications are likely to be deployed within similar organizations. This is the first subtle difference that mainstream agency business theory overlooks: the vast majority of agency demand is not about agents booking tickets for consumers, but rather about top-down deployments within companies.More importantly, there is a fundamental difference between an agent automating tasks within a closed organization and an agent operating as an independent economic entity. Take a sales agent as an example. It integrates with a CRM system, researches potential customers, writes personalized marketing copy, and arranges follow-ups. It doesn't make autonomous expenditures, nor does it interact with external agents in other organizations. It simply performs a task—sales development—within a closed environment and automates it. Intuitively, this applies to almost all organizational functions. Financial agents audit and reconcile expenses; accounting agents record journal entries, reconcile accounts, and prepare reports; legal agents review contracts and identify exceptions; coding agents write code. In almost all use cases, the agent itself doesn't make expenditures, nor is it given expenditure permissions. It is deployed top-down within a controlled organizational environment with access control mechanisms. Even if it does need to interact across organizations and pay for its API calls or data, the cost may not be reflected in the form of autonomous agent payments. Any cost per use can be abstracted by the software vendor. This is precisely how enterprise software stacks operate. Platform providers negotiate customized partnerships with data vendors, computing providers, and other infrastructure partners, packaging access into the platform cost and delivering it as a single, aggregated entry. Furthermore, they achieve this at unit economics that no single agent can replicate autonomously. Computing resources are acquired through reserved capacity agreements with AWS, Azure, or GCP. Pricing for model inference is based on volume agreements with companies like Anthropic, OpenAI, or Google. Data augmentation is handled by vendors like Bombora or Clearbit. All of this is pre-negotiated and abstracted. In other words, an agent's 40,000 API calls, model inference, and data queries don't generate 40,000 payments, but rather a single invoice. The granularity of consumption is never the same as the granularity of settlement, and businesses may prefer to maintain this state. The consumer agent will be responsible for coordination, not consumption. While business agents may not trade autonomously because businesses wouldn't allow it, consumer agents also won't trade autonomously because people don't expect them to. To illustrate with an example advocated by smart commerce: you have your agent book a trip to Tokyo. It searches hundreds of hotels, cross-references reviews, checks your calendar, and applies your preferences. Then, it automatically books a room. You don't have to do anything.Of course, proponents of agency-based business models extend this user experience to almost every consumer sector, from groceries to home goods to clothing. The problem is that preferences are not static. They are reflected in the act of choosing itself. When you book a hotel, you're not just looking for the lowest price. Your judgment reflects your mood, the situation, your risk tolerance, and other qualitative factors you might not have been aware of before looking at the options. In practice, the agent searches, asks follow-up questions, and returns options. You look at pictures, ask about the surroundings, and perhaps read some reviews. Then you make a choice and authorize the agent to use the credit card information it has to make the payment. In other words, the agent is merely a research assistant, not an independent economic agent. Aside from some predictable recurring purchases, this user experience is likely to remain consistent across almost all consumer sectors precisely because consumer decisions are rarely based solely on price. The entire consumer economy is built on product differentiation. Whether it's clothing, hotels, home goods, or groceries, the decision-making process involves countless qualitative factors that not only cannot be captured by intelligent agents—but more importantly, these factors exist inherent in the user's discovery process. Intelligent agents will play a powerful coordinating role during the discovery phase, but at crucial moments, they will relinquish decision-making power to humans. Semantically, this is not an intelligent agent business, nor does it require the establishment of new payment channels. The real winner in crypto payments lies in bottom-up agents. While these two types of agents may account for more than 95% of agent deployments in the next five years, a third type deserves attention. In the past few months, a new type of bottom-up agent has begun to emerge. Driven by the OpenClaw phenomenon, these agents belong to a completely different category. Unlike the aforementioned commercial and consumer agents, they are truly autonomous actors, independent of any organizational entity. These agents require actual payments, and the granularity and frequency of these payments make manual authorization impractical. Although the bottom-up agent economy is currently very small, it is likely to grow rapidly with the emergence of some unforeseen new use cases. Therefore, only in this extremely narrow context is the debate about whether crypto payments or card networks are the best underlying architecture truly compelling. While everyone is listing the technical arguments that crypto payments are superior, in my opinion, the reason they ultimately win may be something else entirely—they are permissionless.Currently, the reality is that neither of these payment methods is technically optimized for agent-based commerce. While blockchain theoretically offers better unit economics for small payments, it lacks identity verification and risk scoring mechanisms—which may be particularly important in the future era of agents. Furthermore, while instant settlement is often mentioned, it merely means that fraudulent transactions are settled immediately on-chain. Conversely, card organizations possess complex fraud graphs and tokenized credentials that agents can inherit, but these tools are trained based on human behavioral patterns and cannot be directly mapped to autonomous agent transactions. Moreover, for cross-border transactions, agents are subject to the settlement time constraints of card organizations. Perhaps counterintuitively, the reason why crypto payment methods may become the default infrastructure for such agents is because blockchain is open, permissionless, and unregulated. This is its ultimate structural advantage. While I believe existing card organizations like Visa and Mastercard will continue to adapt through initiatives like Visa Intelligence Commerce and Mastercard AgentPay, they are, after all, publicly traded companies that must comply with compliance obligations, meet customer access requirements, and work with institutional counterparties. Blockchain, however, does not have these limitations. Anyone can develop on blockchain, any agent can transact, and no approval is required. Intuition tells us that emerging, experimental categories thrive where friction is least. The bottleneck isn't in the infrastructure, but in ourselves. However, the longer-term question is how this experimental pace of development can ultimately have a greater impact. Bottom-up agent economies will only truly flourish when autonomous agent organizations are significantly superior to human organizations augmented by agents; this advantage will not be weak, but significant enough that top-down human constraints on agents become a competitive disadvantage. At that point, agents will no longer be merely automated executors of human tasks in closed environments, but will become the organizations themselves. However, we may be far from this future. The bottleneck won't be in the technology itself. Moreover, what may truly be "unsuitable for machines" is not the payment system itself, but everything else not designed for autonomous agent economies: regulatory frameworks, institutional bureaucracy, legal structures, and the social inertia surrounding human decision-making. These constraints have far more profound implications than any technical detail in the payment stack. Unfortunately, protocol upgrades cannot solve these problems. The agent economy will be enormous, with most of it billed monthly. [ChainCatcher]