Sun Yuchen's choice: When AI becomes a system, White B.AI prioritizes infrastructure investment first

Writing by: Cathy

On April 9th, B.AI (Chinese name: Bai B.AI) officially debuted.

Its self-positioning is summed up in one sentence: the underlying financial infrastructure for the AI Agent era. Simply put, it creates a dedicated payment and identity track for AI, allowing machines to autonomously complete transactions without relying on human bank accounts. Its broader ambition is to become the underlying economic engine driving the evolution of AGI.

It is worth noting that Justin Sun, founder of TRON, participated in B.AI as an advisor, which makes it easier for outsiders to understand it within the context of TRON’s recent focus on “AI + payment networks.” After the product announcement, Justin Sun also posted on X: “Driving AGI to arrive as soon as possible with B.AI is my only mission and goal!” This indicates that B.AI is not just a product launch but also a long-term strategic layout.

From the development path of AI, the emergence of this project is no coincidence.

Discussions about AI in the industry have never stopped—models, parameters, inference, agents—new terms emerge almost weekly. But one question is rarely asked: as AI becomes more powerful, who provides the infrastructure for its real operation?

It’s not computing power, not data, but a deeper layer. When an agent needs to make hundreds of calls per second, pay for each call, and prove its identity to another agent, which path should it take?

B.AI aims to connect to this layer.

01 Why now, why payments

On the surface, such a layout can be easily understood as a cross-industry attempt. But if we extend the timeline, it appears more as a natural extension of infrastructure capabilities.

Over the past two years, the meaning of “AI Agent” has quietly changed. It’s no longer just a chat assistant but has begun transforming into an executor capable of autonomous tool invocation, autonomous decision-making, and autonomous task completion. It will book flights for you, make trades, and work for another agent. Once it starts “doing things on its own,” it means it needs to spend money, settle payments, and pay for each API call.

Traditional payment gateways cannot support this. Systems like Stripe are designed for humans—they require accounts, KYC, card binding, and a fixed fee of $0.30 plus 2.9%. Having an AI agent fill out a form and pay a $0.001 query fee with a credit card is a mismatch.

B.AI has made a practical choice regarding access: it integrates multiple mainstream wallets, allowing users to use it directly via on-chain addresses. More notably, it also supports email login. This means a Web2 user who has never touched a wallet can directly access B.AI and invoke AI services. The underlying intention is clear—lower the barriers as much as possible, expanding the user base from blockchain natives to a broader internet audience.

B.AI’s focus is on four areas: intelligent identity systems, stablecoin payment tracks, tokenization of real-world assets, and development tools for autonomous financial systems. None of these involve building models; all are bets on the infrastructure needed for the machine economy.

In short, what B.AI is doing is not another AI model but the financial track that AI must pass through to achieve autonomous operation.

02 Embedding “banking” into APIs

B.AI’s product system can be broken down into three main pillars: an on-chain AI agent payment network, an access point for multiple top-tier large models, and an out-of-the-box intelligent assistant BAIclaw.

First pillar: AI intelligent agent on-chain payment network. This is the core layer of B.AI and the key differentiator from all other AI products. Two protocols are at work here: x402 and 8004.

The core idea of x402 is simple: embed payment capability directly into the network call process, allowing an agent to settle payments upon resource requests without human intervention. When an agent calls a paid API, the server returns 402, the agent automatically signs a stablecoin payment on-chain, re-initiates the request, and receives the resource. The entire process is a closed loop within seconds, with no human involved.

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 has a readable “on-chain business card.” B.AI also adds an event reporting registry to record violations and anomalies.

This payment network enables agents to achieve true economic independence: they can recharge, purchase computing power, settle with other agents, forming a complete commercial cycle without relying on a human account as a guarantee.

Second pillar: a single entry point to access top global large models. B.AI’s LLM Service integrates multiple industry-leading large language models such as OpenAI, Claude, Gemini, z.ai, MiniMax, Kimi, etc. Users don’t need to register separately on each platform; they can choose the most suitable model from one interface.

This service covers two usage scenarios: multi-model AI chat for ordinary users and a comprehensive API for developers and agents. Chat solves “how humans use AI,” while API addresses “how systems call intelligence.” B.AI doesn’t choose between the two but provides both pathways—individual users can switch models directly in the chat interface; developers or automation workflows can embed intelligence into any backend code via API.

What truly differentiates B.AI’s LLM Service from traditional AI platforms is the native Web3 experience. Users can log in with mainstream Web3 wallets, support multi-chain tokens, enjoy fast confirmation and low fees. This means with just a wallet address, you can anonymously access the world’s most powerful models—no account registration, no card binding, no payment traces or behavioral profiles left behind. Through resource optimization and efficient on-chain interactions, the cost is also more competitive. This experience is similar to OpenRouter but adds a Web3-native track that emphasizes privacy and low cost.

Third pillar: BAIclaw and the agent toolkit. BAIclaw is an out-of-the-box AI assistant, where developers only need to call an API, and the system will dispatch requests to the most suitable model based on task type.

Around BAIclaw, B.AI also provides a full set of tools for agents. Skills are pre-set skill packages covering common on-chain needs: DeFi and DEX operations (like trading on SunSwap, managing positions on SunPerp), payment settlement via x402, account recharging, multi-signature permissions, and on-chain data query and analysis. An agent on B.AI can perform basic financial operations without building capabilities from scratch.

OpenClaw is a plug-and-play extension that allows developers to add payment and identity registration to their agents with a single line of code; MCP Server enables large models to understand on-chain states, incorporating on-chain data into responses.

For agents, B.AI is their birthplace. A newly created agent can obtain its on-chain identity and autonomous fund account here, giving it the ability to spend and a basis for trust.

03 Why agent payments and identities are truly long-term value

A clear trend is emerging: in the agent era, infrastructure factors beyond model capabilities are becoming equally important.

The past two years’ progress has made this evident. GPT, Claude, Gemini, and various open-source models are rapidly approaching capability parity, with gaps narrowing quickly. Moving forward, models will become increasingly homogeneous, much like how cloud providers’ differences shrank after 2010.

What will truly endure are three things: call history, payment deposits, and identity reputation.

Once these three elements develop on a network, they create an infrastructure effect. The longer an agent runs on a chain, the more valuable its reputation becomes, the more complete its payment history, and the harder it is to migrate. This stickiness isn’t created by product features but by time and network effects.

Currently, almost all AI agents still rely on human account systems—using human credit cards, API keys, KYC credentials. This means agents can never truly “operate independently”; every expansion requires a human account as a guarantee.

B.AI bets that this will change. As the number of agents grows from thousands today to millions in the future, the reliance on human accounts will inevitably collapse. What’s needed is a native financial layer for machines—addresses as identities, signatures as authorizations, payments as settlements.

Whether this judgment is correct depends on time. But at least at the product level, AI Detective has already demonstrated this concept on a small scale. The system analyzes on-chain data involving over $1 billion in案件 data, and with B.AI’s payment capabilities, has established a $100 million bounty pool, automatically distributing funds to white-hat hackers and law enforcement for clues. Once agents truly have identities and wallets, their capabilities will go beyond mere demonstrations.

In short, this is an early bet on “where future AI should put its money.”

04 Parts still being refined

Looking further ahead, some aspects of B.AI will evolve alongside the maturing agent economy.

One focus is the boundary of agent autonomy. When agents have on-chain identities and funds, their execution ability is unlocked, but how to give them a reasonable “action radius” becomes a new challenge. B.AI emphasizes that user retention retains ultimate control, with permissions subdivided and thresholds set as ongoing refinements. This will become clearer with real-world scenarios and is a shared exploration across the industry.

Another focus is the evolution of underlying computing power. Logically, everything on-chain is decentralized; agents run on computing resources, and the global supply system is also continuously evolving. B.AI’s approach is to first lay down the most critical tracks—payment and identity—so that as upper-layer computing matures over time, the underlying financial infrastructure is already in place.

As for different industry paths, they currently seem to be collectively opening up this ecosystem. Ethereum is pushing a decentralized coordination layer standard via ERC-8004; Solana, with its 400-millisecond block time, has produced some early use cases for the x402 protocol; TRON’s edge lies in the depth of stablecoins and high-frequency payment economics. These routes complement each other in different directions.

B.AI’s long-term judgment is that when AI truly enters autonomous execution, this dedicated financial track for machines will become indispensable. This is being validated step by step by more products and data.

05 Conclusion

While everyone’s focus remains on model capabilities, B.AI bets on something else.

It’s not about building smarter brains but creating better channels. This may seem less flashy externally, but as AI moves from tools to autonomous executors in the coming years, this channel will be harder to replace than the models themselves.

B.AI is exploring the pre-laid financial and operational pathways designed specifically for machines before AI fully achieves autonomous execution.

In the long run, the importance of this pathway may be reinterpreted on par with the significance of model capabilities itself.