From Capability Opening to Capability Constraint: AI Payment Enters a New Stage

In the past few years, the core breakthrough of AI technology has been the continuous release of capabilities—from information generation and content understanding to decision support, AI has gradually become an important tool for improving efficiency. With the rise of AI Agents, this trend is further evolving: AI is no longer just providing suggestions but is starting to directly participate in task execution.

In this process, a key capability has been rapidly amplified: AI has begun to possess the ability to initiate payments and mobilize resources.

Whether it's automatically calling APIs, completing computing power procurement, or handling subscriptions and settlements, AI is gradually moving from internal systems into real economic activities. But it is precisely at this moment that problems begin to arise: When AI can spend money autonomously, how can we ensure it does not go out of control?

The stronger the capability, the higher the requirements for constraints

The core advantage of AI Agents lies in their automation and continuous execution capabilities. They can optimize decision paths based on established goals without human intervention. However, this efficiency also means that AI does not inherently possess risk awareness or a sense of boundaries.

For example, in scenarios like advertising placement and resource scheduling, an AI Agent may continuously amplify the optimal strategy, increasing investment rapidly until it reaches or even exceeds the original budget boundaries.

At the same time, AI's execution relies on API keys and system permissions. Once these credentials are misused or leaked, related interfaces may be called frequently, leading to uncontrollable expenses.

Such risks are not hypothetical. There have been cases where some AI tools experienced abnormal calls and cost surges due to improper permission management or key exposure. Behind these issues lies a more fundamental change: as AI transitions from a tool to an executor, the opening of its capabilities is forcing the establishment of constraint mechanisms.

One of the most direct manifestations of this change is occurring in the critical payment process—when AI begins to possess the ability to autonomously mobilize resources and initiate transactions, the preconditions that the original payment system relied on are also being impacted.

After AI begins to spend autonomously, the payment system must also upgrade

Current payment infrastructure is essentially designed around human behavior: authorization relies on manual confirmation, risk control is based on behavioral judgment, and responsibility attribution is established on clear identities. This system has worked well in the past, but when the transaction initiator shifts from a person to an AI Agent, these rules may not necessarily apply.

AI Agents neither possess traditional identity boundaries nor share the same responsibility mechanisms as humans. Once endowed with execution capabilities, they resemble a high-speed automated system rather than a decision-making entity with judgment and restraint capabilities. This creates a clear misalignment in the existing system when facing AI: we are trying to constrain a non-human executor with a system designed for humans.

At the same time, as AI gradually enters practical application scenarios, its autonomous payment capabilities are beginning to demonstrate real value—whether it's 24/7 computing power procurement, SaaS subscriptions, and API call fee settlements, or collaboration and resource allocation among multiple agents, all are driving the realization of "payment capabilities without human intervention."

Based on this, the real challenge is not to enable AI to spend money but to ensure it spends money according to rules.

In this context, a type of payment control mechanism aimed at AI Agents is beginning to take shape. Its core is to build a programmable and constrained payment system, ensuring that AI operates within clear boundaries while executing tasks.

From a practical perspective, such mechanisms typically need to cover several key dimensions:

• Permission control: Clearly define which agents can initiate payments and under what scenarios they have execution capabilities, preventing permission abuse from the source.

• Quota management: Set clear spending boundaries for AI through single transaction limits, periodic budgets, and total amount controls.

• Usage scope: Restrict payment behaviors to specific merchants or service ranges.

• Behavioral rules: Establish execution logic based on different scenarios, such as whether automatic execution is allowed, whether additional confirmation is needed, and whether to trigger an exception interruption mechanism.

• Process traceability: Ensure all transactions have recording and tracking capabilities, allowing for path tracing, problem localization, and responsibility delineation in case of anomalies.

The common goal of these capabilities is not to weaken AI's efficiency but to establish controllable boundaries for its execution capabilities, thus achieving a balance between automation and safety.

Transitioning from "capability layer" to "control layer," AI payment infrastructure is being restructured

If the focus of AI development in the past was on capability enhancement, then after entering the execution phase, its underlying demands are also changing.

Beyond "what can be done," the market is increasingly concerned with whether AI is controllable, manageable, and traceable during execution.

Currently, a series of exploratory paths have emerged in the industry. For example, incorporating AI's payment behaviors into a programmable framework through virtual cards; combined with permission control and dynamic risk control mechanisms, to constrain and manage its execution process, thereby establishing clear boundaries for AI's autonomous execution.

In this direction, Interlace is conducting ongoing exploration. Its payment solution for AI Agents is built on virtual cards and on-chain wallets, attempting to introduce interaction and control mechanisms more suited for AI, such as transforming consumption scenarios, amount limits, and time windows into executable rules through a structured instruction system; simultaneously integrating real-time risk control and transaction recording capabilities, allowing AI to autonomously execute payments 24/7 while still operating within a clear and controllable rule system.

In this process, the significance of AI payment infrastructure is also undergoing a transformation—from supporting transactions to ensuring that transactions occur within boundaries.

Final thoughts

As AI gradually gains the ability to spend money, it also begins to become a part of economic activities. Any system that can operate long-term needs to establish boundaries and constraints beyond efficiency.

The transition from "capability openness" to "capability constraints" is not only the development path of AI payments but may also be a necessary stage for the entire AI Agent ecosystem to mature. In this process, how to enable AI to execute autonomously without going out of control will become a question that all participants need to answer together.