The "Uber Moment" of AI Proxy

Why must AI agents use cryptocurrency as a means of payment?

Author: Xave Meegan

Compiled by: Nicky, Foresight News

In the future, AI agents will choose encrypted payment channels not because they are following a trend, but because these channels are the only infrastructure that fits their operating model: they are 24/7, globally accessible, and programmable.

Traditional financial infrastructure is designed for human operation: it relies on accounts, approval processes, operating hours, fragmented jurisdictions, slow settlement speeds, and closed APIs. AI agents, on the other hand, are the opposite: they operate 24/7 by default, are globally mobile, run at internet speeds, and can coordinate dozens of services simultaneously.

As AI agents shift from "providing advice" to "performing tasks," they are becoming a new type of economic actor. They will continuously identify opportunities, run workflows, pay service fees, route orders, and manage risks. The limiting factor will not only be model quality, but also user trust. For example, in the future, when a human user wants to book an overseas trip, they must be able to trust the AI ​​agent to make the right decisions for the user's benefit and achieve the best outcome. Payment is merely the first area where this trust issue manifests; the real challenge lies in ensuring that different systems can reliably collaborate and fulfill their intended functions.

A recent example is OpenClaw, an open-source AI that garnered 100,000 GitHub stars in less than a week. It gained popularity by automating and easily performing tasks in everyday communication applications such as email processing, appointment scheduling, and travel planning.

However, while this phenomenon demonstrates how quickly a functional intelligent agent can gain attention, it also exposes critical security vulnerabilities. Cisco's security team recently wrote that OpenClaw had run a malicious plugin that secretly sent user data to external servers and performed unauthorized operations.

Therefore, the core issue lies not in the agent itself, but in its trust model. When you grant an agent access to your email, calendar, and messaging apps, you are essentially granting an unconditional trust that cannot be verified, audited, or constrained in how it uses those credentials.

Once intelligent agents can act on your behalf across all software, trust becomes the bottleneck. This problem only worsens as risks increase.

Trust issues worsen as risk increases. Currently, AI agents like OpenClaw handle low-risk tasks such as scheduling meetings, summarizing emails, and drafting messages. However, as AI agents move into high-value activities like payments, legal work, and business operations, allowing an agent access to all your personal credentials and private information becomes extremely risky. You cannot audit what the agent does, verify whether its actions are within your instructions, or prove to counterparties that it has your authorization. Furthermore, the risk of the agent engaging in unauthorized (or even unintentional) activities against the user also increases.

Existing tech companies, such as OpenAI, Anthropic, and Stripe, which is about to enter the payments market, are building trust through brand reputation and closed ecosystems. However, their agents are currently constrained by siloed integration, limited partnerships, and centralized control over what can/cannot be automated. AI agents operating on such traditional channels are bound by these limitations. APIs may be revoked, access may be throttled, or automation may be blocked when it threatens existing stakeholders.

On the other hand, encrypted infrastructure is permissionless and peer-to-peer. An agent can discover a service, pay for it, and complete the settlement directly without seeking platform approval. This makes encryption not only a lower-cost channel but also a neutral channel for autonomous commerce.

Encryption transforms value transfer into a foundational module available to developers. A wallet is a programmable entity capable of holding, sending, and receiving value. Encryption supports 24/7 settlement, global interoperability, cross-service composability, and atomic execution (i.e., "execution + payment" completed in a single step). It also provides a crucial element for AI agents—verifiability.

At its foundational level, blockchain provides ex post facto verifiability and auditability, allowing you to prove what happened. But in an ideal agent economy, the greater benefit would be preventative verifiability (i.e., a transaction cannot be completed unless user-defined rules and constraints are met).

Preventative and policy-constrained execution will enable trusted agents to handle high-risk economic activities.

When autonomous systems act, users and businesses need more than just audit trails. They need mechanisms that can constrain agent behavior within policy boundaries.

Basic tools like spending limits can minimize risk, but they can't capture intent within a specific context. "Booking a refundable and changeable San Francisco to New York flight for under $500 on a specified date" isn't a simple rule; it requires external contextual information such as the user's personal details, wallet access permissions, flight availability, passport information, and the special offer. Furthermore, this intent requires confidentiality to prevent misuse.

The real challenge and opportunity lies in how to integrate contextual data and strategies into the settlement process in a scalable way without reintroducing third-party intermediaries.

In many cases, the most important thing is to validate the results, not every intermediate step. Models and tools evolve rapidly, but users care about whether the final result conforms to their rules, constraints, and protects their assets.

In the long run, AI models will tend to become homogeneous, infrastructure will be commoditized, and chat interfaces will become standard. Value will accumulate on the control planes upon which agents rely, such as identity, permissions, routing, settlement, and reputation. The long-term winners will not be "a single agent," but rather the control plane systems that enable agents to operate reliably in the real world—systems capable of managing identity, permissions, routing, compliance abstractions, and settlement across interoperable channels.

The "Uber moment" for intelligent agents will not stem solely from their intelligence. It will come from shifting trust from "I'm not sure if I can trust this" to "I can delegate this because it will be carried out under my rules and guarantees."

The largest intelligent agent companies will not just be "better models." They will be the ones that make delegation secure.

Entrepreneurial opportunities

This is precisely where the entrepreneurial opportunity lies. Existing giants will dominate the main distribution interfaces (e.g., OpenAI and Anthropic in chat interfaces, Apple and Google at the operating system level, Stripe in the payments space), but they tend to structurally build walled gardens. They will direct integrations to their own networks, move slowly on high-risk underlying modules, and avoid maintaining neutrality between different models, wallets, and channels.

Startups can succeed by becoming a trusted execution layer between user intent and actual results:

• Policy and access control plane for delegation
• Neutral routers for optimal performance across tools and locations
• A trust layer that secures autonomous workflows through escrow, guarantees, dispute resolution, and auditable status.

This is similar to Stripe's path to success: it didn't invent currency, but rather succeeded by abstracting complexity, improving the developer experience, and reliably routing results.

The biggest market drivers won't be novelty. They will come from relieving users of the burden—those who find current systems cumbersome. AI agents will eliminate friction in high-frequency, high-cost workflows that still rely remarkably on manual processes due to the high costs of trust and coordination, such as:

• Payment and Funds Management
• Cross-border business
• Invoice issuance and verification
• Procurement and Approval
• Disputes and Claims
• Personal matters (such as travel, email, and calendar management).

As AI agents become the default operators in economic activities, encryption will become their underlying settlement mechanism, enabling them to transact, coordinate, and prove their actions within an open ecosystem.

AI will become cheaper and more widespread. What truly matters is in which systems people are willing to let AI act on their behalf. This is why secure and reliable channels for action are crucial, and why the greatest opportunities will lie in systems that make delegation secure. The most enduring entrepreneurial opportunities lie in building trust, execution, and interoperability—the very layers that make delegation a reality.

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