Written by: Cathy

On April 9, B.AI (Chinese name: Bai B.AI) officially made its debut.

It defines itself with just one sentence: the underlying financial infrastructure for the AI Agent era. Simply put, it creates a dedicated payment and identity channel for AI, allowing machines to complete transactions autonomously without relying on human bank accounts. Its broader ambition is to become the foundational economic engine driving AGI evolution.

Notably, Sun Yuchen, the founder of TRON, participated in B.AI as an advisor, making it easier for the outside world to understand it within the context of TRON's ongoing efforts around "AI + payment networks" in recent years. After the product announcement, Sun Yuchen also publicly stated on platform X, "B.AI drives AGI to arrive as soon as possible; this is my only mission and goal!" This positions B.AI not just as a product launch, but as part of a long-term strategic plan.

From the development path of AI, this emergence is not a coincidence.

Discussions about AI in the industry have never ceased, with new terms like models, parameters, reasoning, and Agents emerging almost weekly. However, one question is rarely asked: as AI becomes stronger, who provides the infrastructure for its real operation?

It’s not computing power, nor data; it's that deeper layer. When an Agent needs to make hundreds of calls per second, pays for each call, and proves its identity to another Agent, what path should it take?

B.AI aims to tackle this layer.

01Why Now, Why Payments

Superficially, this layout can easily be understood as a cross-industry attempt. However, if we extend the time dimension, it resembles a natural extension of infrastructure capabilities.

The meaning of the term AI Agent has subtly changed over the past two years. It's no longer just a conversational assistant but is evolving into an executor that autonomously calls tools, makes decisions, and completes tasks. It can book flights for you, conduct transactions, and work for another Agent. Once it starts "doing things on its own," it means it requires funding, settlement, and payment for each API call.

Traditional payment gateways cannot support this. Systems like Stripe are designed for human use, needing accounts, KYC, card linkage, and fixed fees of $0.30 + 2.9% for credit cards. Asking an AI Agent to fill out a form and then pay a card fee for a $0.001 query is entirely misaligned.

B.AI has made a very practical choice regarding entry barriers: it integrates multiple mainstream wallets, allowing users to access it directly through on-chain addresses. More notably, it supports email login as well. This means that even a Web2 user who has never interacted with a wallet can directly enter B.AI to access AI services. The intention behind this design is clear—lower the barriers as much as possible, expanding the user base from on-chain natives to a broader internet audience.

B.AI stakes its resources on four key areas: an agent identity system, a stablecoin payment channel, tokenization of real-world assets, and development tools for autonomous financial systems. None of these are focused on creating models; all are centered on the "infrastructure needed for the machine economy."

In simpler terms, B.AI is not creating another AI model; it is building the financial track that AI must traverse on its way to autonomy.

02Embedding "Banking" into API

B.AI's product system can be broken down into three main pillars: a payment network for AI agents on the chain, a calling interface covering multiple top-tier large models, and a plug-and-play smart assistant called BAIclaw.

The first pillar: the on-chain payment network for AI agents. This is the core of B.AI and the key that distinguishes it from all other AI products. Two protocols are at work here, x402 and 8004.

The core idea of x402 is not complicated: it embeds payment capabilities directly into the network calling process, enabling the Agent to complete settlement when requesting resources without human intervention. An Agent calls a paid interface, the server returns a 402 response, the Agent automatically signs a transaction to pay with on-chain stablecoin, re-initiates the request, and acquires the resource. The entire process is a closed loop completing within seconds, with no human involvement.

8004 addresses another issue: who is this Agent? Does it have credibility? What has it done in the past? Through on-chain identity registries, reputation registries, and verification registries, each Agent possesses a readable "on-chain business card." B.AI has also added an event reporting registry specifically to record violations and anomalies.

This payment network allows Agents to achieve true economic independence: they can recharge autonomously, purchase computing power autonomously, and settle with other Agents, forming a complete commercial cycle without needing a human account to back them.

The second pillar: a gateway to access world-leading large models. B.AI's LLM Service integrates multiple industry-leading large language models, including OpenAI, Claude, Gemini, z.ai, MiniMax, and Kimi, allowing users to choose the best-suited model from a single entry point without needing to register on each platform separately.

This service covers two usage scenarios: multi-model AI dialogues for ordinary users and a complete API interface for developers and Agents. Chat addresses "how humans use AI," while APIs solve "how systems invoke intelligence." B.AI does not choose between the two; instead, it paves both paths—personal users wanting to experiment with different models can switch directly in the dialogue interface; while developers or automated processes can embed intelligence into any backend running code through the API.

What truly distinguishes the LLM Service from traditional AI platforms is its underlying Web3 native experience. Users can complete login authentication by signing with mainstream Web3 wallets, supporting multi-chain mainstream token payments, boasting advantages such as quick confirmations and low fees. This means that with just a wallet address, you can anonymously access the world's strongest models—without registering an account, linking a card, or leaving any payment traces or behavioral footprints. Through resource optimization and efficient on-chain interactions, the LLM Service is also more cost-competitive. This experience resembles OpenRouter but includes a Web3 native path focusing on extreme privacy and low cost.

The third pillar: BAIclaw and the Agent toolbox. BAIclaw is B.AI's plug-and-play AI smart assistant, where developers only need to call one interface, and the system automatically dispatches the request to the most suitable model based on the task type.

Around BAIclaw, B.AI is equipped with a complete set of Agent-oriented tools. Skills are a set of pre-configured skill packages covering the most common needs for Agents operating on-chain: DeFi and DEX operations (such as executing trades on SunSwap, managing positions on SunPerp), payment settlements based on the x402 protocol, account recharging, multi-signature permission management, and on-chain data querying and analysis. An Agent arriving at B.AI doesn’t have to start from scratch; basic financial operation skills are readily available.

OpenClaw is a plug-and-play extension, allowing developers to integrate payment capabilities and identity registration into their Agents with just a line of code; the MCP Server enables large models to understand on-chain states, treating on-chain data as context when generating responses.

For Agents, B.AI acts as a birthplace. A newly generated Agent can obtain its on-chain ID and autonomous funding account here, granting it the ability to spend money and providing a basis for trust.

03Agent's Payment and Identity: Why They Are True Long-Term Value

At this point, a trend begins to emerge: in the Agent era, infrastructure factors beyond model capabilities are becoming equally important.

Progress over the past two years has made this quite clear. GPT, Claude, Gemini, and various open-source models are continually reaching similar abilities, and the gaps are rapidly closing. In the future, models will become increasingly homogeneous, just like the reducing differences among cloud computing providers after 2010.

What will truly persist are not the parameters but three things: invocation history, payment accumulation, and identity credibility.

Once these three things sprout on a certain network, they will create an infrastructure effect. The longer an Agent operates on a specific chain, the more valuable its credibility becomes, and the more complete its payment history accumulates; it becomes increasingly difficult to migrate. This stickiness cannot be created by product features; it can only be produced by time and network effects.

Currently, however, nearly all AI Agents are still parasitic in human account systems. They rely on human credit cards, human API keys, human KYC qualifications. This means Agents can never truly "operate independently"; every expansion must seek a human account for backing.

B.AI bets that this situation will change. When the number of Agents grows from today's thousands to millions in the future, a model relying on human accounts will inevitably collapse. What is needed is a financial layer natively prepared for machines where address equals identity, signature equals authorization, and payment equals settlement.

Whether this judgment is correct will unfold over time. But at least at the product level, AI Detective is already demonstrating the feasibility of this on a small scale. This system processes case data involving amounts exceeding $1 billion through on-chain data analysis and has established a $100 million bounty pool relying on B.AI's payment capabilities, automatically allocating funds to whistleblowers and law enforcement agencies providing leads. Once an Agent truly possesses identity and a wallet, what it can handle goes beyond mere demonstrations.

In a nutshell, this is an early bet on "where future AI should put its money."

04Continuously Evolving Aspects

Zooming out, there are still some aspects along B.AI's path that will evolve alongside the maturation of the Agent economy.

One area of focus is the boundary of Agent autonomy. When Agents possess on-chain identities and funding accounts, alongside the opening of execution capabilities, how to provide them with a reasonable "operating radius" has become a new challenge. B.AI consistently emphasizes that users retain ultimate control and treats permission granularity and threshold settings as continuous refinement stages. This is something that will become clearer as real-world scenarios evolve, and it is a direction collectively explored by the entire industry.

Another direction is the evolution of underlying computing power. Logically, everything on-chain is decentralized, and Agents operate on computing power, which itself is continually evolving in global supply systems. B.AI's choice is to first establish the most critical track of payments and identity, ensuring that the underlying financial foundation is ready to support the upper layer of computing power as it matures over time.

Regarding different routes within the industry, several paths currently appear to be jointly unlocking this ecosystem. Ethereum is promoting a decentralized coordination layer standard through ERC-8004; Solana is showcasing some cases in the early implementation of the x402 protocol with 400 millisecond-level block times; and TRON's uniqueness lies in the depth of stablecoins and the high-frequency economy of payments, with these various paths complementing each other in different directions.

B.AI is betting on a long-cycle judgment: when AI truly enters the stage of autonomous execution, that financial track specifically prepared for machines will become indispensable. This is being validated step-by-step by an increasing number of products and data.

05Conclusion

While everyone focuses on model capabilities, B.AI bets on something else.

It is not creating a smarter brain, but rather a more streamlined pipeline. This may not seem flashy externally, but as AI transitions from being merely a tool to autonomous execution in the coming years, that pipeline will be harder to replace than the model itself.

What B.AI explores is laying the groundwork for that financial and operational channel designed specifically for machines before AI moves towards autonomous execution.

In the long run, the importance of this channel may be reinterpreted as being on par with model capabilities themselves.

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