x402 V2 Release: When AI Agents Gain Credit Access, Which Projects Will Be Revalued?

The cryptocurrency market has entered a typical low-activity phase, with sentiment oscillating between weakness and anxiety.

When markets lack an obvious wealth effect, focusing on localized narrative opportunities can prove cost-effective. This applies particularly to infrastructure projects that continue building and iterating despite limited market attention.

One to two months ago, Coinbase released the x402 protocol along with its associated standard, ERC-8004. During a brief one to two week window, tokens related to this narrative delivered strong gains.

The core value proposition of x402 V1 addressed AI ownership of wallets. The protocol enabled AI agents to execute on-chain payments through simple API calls. Many viewed this as a strategic move by the Base network in the AI sector.

The underlying logic proved sound. However, x402 faced constraints from its single-chain environment and single settlement model, which prevented large-scale adoption.

Yesterday, the development team quietly released x402 V2, a development that largely went unnoticed.

If V1 was an experimental tool allowing AI to make payments, the V2 update log reveals far greater ambition.

Full multi-chain compatibility, hybrid crypto and non-crypto payment rails, and a credit-based mechanism enabling task completion before payment represent more than minor fixes. These features suggest an attempt to build a financial foundation suited for a machine-driven commercial society.

This direction aligns well with the core AI narrative.

The crypto market may no longer offer strong profit opportunities. However, staying informed about actively progressing protocol upgrades remains valuable.

Understanding these developments early often provides the competitive advantage needed to identify emerging opportunities.

Enabling "Buy Now Pay Later" for AI

The original purpose of the 402 protocol can be explained simply. It revives the long-dormant 402 HTTP status code and allows AI to use a crypto wallet to call APIs and automatically purchase data and services, much like a human swiping a card.

V1 successfully proved this concept but demonstrated limitations in real-world applications.

When an AI agent must sign a transaction on-chain and pay gas for every inference, data request, and API call, this atomic exchange model becomes extremely inefficient and costly. Immediate payment for each action creates bottlenecks that prevent scalability.

Consequently, V1 functioned as a technical demonstration rather than infrastructure capable of supporting substantial commercial traffic.

V2 addresses this challenge by normalizing AI commercial behavior.

The most important upgrade introduces a delayed payment mechanism. The official documentation describes this feature as follows:

In financial terms, this establishes a ledger-based relationship between service providers and AI. An AI agent can access and consume services after verification, such as making one thousand consecutive calls to a compute API. The system records usage in the background and performs a single consolidated settlement at the end.

While this appears to simply reduce gas costs, the implications extend much further. AI agents begin to possess a form of credit.

Deferred payment introduces new market dynamics. The market must assess the default risk of an agent, creating natural demand for entities that can provide guarantees or underwriting for newly created agents.

This lays the groundwork for AgentFi. What began as a payment tool evolves toward credit and financial infrastructure.

Beyond the hidden credit layer, V2 introduces two major infrastructure-level upgrades.

1. Multi-Chain Compatibility: V1 focused on the Base ecosystem as an experimental platform. V2 defines a universal HTTP header interaction standard that enables integration across any compatible network, including Solana, Ethereum mainnet, and Layer 2 solutions. This removes cross-chain capital silos.

2. Hybrid Payment Rails: V2 bridges fiat and cryptocurrency systems. An agent can pay in USDC while traditional cloud service providers such as AWS and Google Cloud receive fiat directly through the x402 gateway. This enables AI to move beyond on-chain experimentation into real-world procurement.

The comparison table below highlights the core differences between V1 and V2.

V2 positions itself beyond a Base ecosystem tool. It increasingly resembles a Visa-style settlement network for the AI economy, attempting to issue AI universally accepted credit access across networks.

The core objectives are twofold. Deferred payment mechanisms address efficiency issues in high-frequency transactions, while multi-chain compatibility resolves constraints related to capital sources and network fragmentation.

This development may signal potential revaluation of two emerging sectors.

1. First, the need for credit ratings and guarantees for AI agents points to the rise of an AI-native credit and reputation layer.

2. Second, the opportunity to sell compute to AI through streaming or session-based payments likely intersects with real-world payment adoption within the DePIN sector.

Which Projects are Positioned to Benefit from x402 V2 Upgrade?

Once the core upgrade logic is understood, the approach to identifying potential targets becomes straightforward.

If x402 V2 functions as a Visa-style settlement network within the AI economy, three categories of protocols form the key nodes that enable this network to operate.

Credit Assessment and Fulfillment Layers for AI

The subscription model that allows services to be used before settlement creates a fundamental challenge. Service providers need assurance that anonymous AI agents will pay as promised at the end of the billing cycle.

Addressing this requires two layers of assurance. The first is a credit score determining whether the agent has the ability to pay. The second is fulfillment verification confirming whether the required work has been completed.

This is where the narratives of x402 and ERC-8004 intersect.

Some projects stand out as particularly well aligned with this narrative.

Spectral ($SPEC) @Spectral_Labs

Spectral operates as an on-chain credit scoring and machine intelligence network.

Its core product, the MACRO score, functions similarly to an on-chain FICO score. Within an x402 V2 environment, service providers can set access thresholds so that only agents with sufficient credit scores are allowed to use deferred payment models. This addresses a foundational requirement for any credit-based settlement system.

Spectral is also developing Inferchain, which focuses on agent verification. This directly complements the settlement and trust requirements introduced by x402 V2.

Bond Credit @bondoncredit

Bond Credit provides a credit and lending layer designed specifically for AI agents.

The project is one of the few explicitly centered on credit for agents. When a newly created agent wants to access cloud compute through x402 V2 but lacks upfront capital, Bond Credit can use trusted execution environments to monitor historical performance and provide credit guarantees. This allows service providers to confidently enable deferred payment.

This project remains at an early stage and requires independent research. However, its focus addresses a clear gap in AI-native credit infrastructure.

CARV（$CARV) @carv_official

CARV operates as a modular data and identity layer.

The protocol addresses agent identity by defining who an agent is. With x402 V2 supporting multi-chain environments, CARV's identity standard allows an AI agent to maintain consistent identity across different blockchains.

According to official communications, CARV has already conducted real-world payment scenario testing, indicating early practical adoption.

The Relationship Between x402 V2 and ERC-8004

The fulfillment verification logic reinforces an important distinction. x402 V2 handles settlement and fund flows, while ERC-8004 focuses on verifying business logic and service execution.

Only after service delivery is confirmed does deferred payment become executable. The sectors that benefit from ERC-8004 adoption remain equally relevant in this x402 V2 upgrade cycle. Check out the chart below:

（相关阅读：x402 逐渐内卷，提前挖掘 ERC-8004 里的新资产机会）

Core Utilities and Verification Layers for AI

The session-based settlement model introduced in x402 V2 significantly reduces friction for high-frequency payments. This benefits DePIN networks that sell compute resources and verification protocols that prove the authenticity of delivered compute.

Akash Network ($AKT)

Akash Network operates as a decentralized compute marketplace.

Compute rental represents a classic usage-based billing scenario, often charged per second or per unit of consumption. x402 V2 allows AI agents to pay for compute using USDC or fiat rails through streaming or aggregated settlement, substantially lowering the barrier for AI-driven compute procurement.

The relationship here is less direct than the credit layer protocols. Akash benefits from reduced payment friction rather than being deeply integrated into the x402 V2 core logic.

Giza ($GIZA) @gizatechxyz

Giza operates as a verifiable machine learning protocol using zero-knowledge machine learning technology.

At the infrastructure level, Giza acts as a verification layer before payment settlement. Prior to paying high inference costs through x402, Giza's zero-knowledge machine learning technology can verify that a model has executed exactly as specified.

At the application level, Giza's flagship products, such as the ARMA DeFi agent, rely on payment rails like x402 to operate.

The Asset Layer and Execution Layer for AI

The narrative logic here is straightforward. If x402 V2 makes payments more efficient for AI, the next questions are who is producing these agents and who is using them to generate returns for users.

Virtuals Protocol ($VIRTUAL)

As a leading AI agent issuance platform, x402 V2 effectively provides agents on Virtuals with cross-chain capability. Users who hold Virtuals-based agents may eventually be able to use the x402 protocol to direct their agents to participate in token launches on Solana or pursue arbitrage opportunities on Ethereum mainnet.

Brahma，@BrahmaFi

Brahma operates as an on-chain execution and strategy orchestration layer.

The protocol focuses on automating the execution of complex DeFi strategies for users. By integrating x402, Brahma can unify the payment of gas fees and execution fees for automated keepers, enabling fully automated strategy execution.

This represents a broader category of DeFAI use cases and critical infrastructure for the transition of DeFi toward AgentFi.

Conclusion

The release of x402 V2 sends a significant signal worth examining.

From a technical standpoint, x402 is a payment protocol. In practice, it extends into a wide range of financial applications.

By introducing deferred payment mechanisms based on credit and enabling multi-chain account support, V2 effectively allows AI agents to adopt the concept of a balance sheet. When an agent receives services before payment, it assumes liabilities. When it holds assets across multiple chains, it gains equity.

With both assets and liabilities, an AI agent is no longer simply code. It functions as an independent economic entity, and the range of possible financial behaviors expands significantly.

This represents the true starting point of the AgentFi narrative.

During periods of market weakness, the focus should be on observing changes in fundamental narrative logic rather than projecting complex commercial empires for AI.

Previously, investing in AI meant investing at the model layer, focusing on which system was smarter. Increasingly, this shifts toward the financial layer, focusing on which entities are wealthier.

x402 V2 serves as an early signal. When market conditions improve, attention can be directed toward projects that issue identities to AI, provide credit assessment and guarantees for AI, or transform compute resources into standardized products.

These projects share common characteristics. They address emerging infrastructure needs, operate in nascent markets with limited competition, and align with forward-looking narratives around AI economic activity.

As AI evolves from a tool into an economic entity, these infrastructure providers are positioned to capture value regardless of broader market conditions.

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