Space Review | B.AI Officially Launches: Financial Infrastructure in the AI Agent Era, How to Accelerate the Arrival of AGI?

Recently, B.AI (Chinese brand name: Bai B.AI) officially announced its launch, dedicated to building the underlying financial infrastructure for the AI Agent (intelligent agent) era. Over the past two years, breakthroughs have been made in large model technology, but as applications become more deeply integrated, issues such as AI Agents lacking independent payment systems, verifiable identities, and closed-loop execution capabilities have become increasingly prominent, leading to their continued heavy reliance on manual operations in real business scenarios. The launch of B.AI aims to fill this systemic gap by endowing AI Agents with comprehensive economic execution power, promoting their transformation from passive information exchange nodes to autonomous new economic entities participating in global value flows, thereby laying a solid business foundation and operational cornerstone for the full arrival of the AGI era.

At this critical juncture where the industry shifts from “intelligence competition” to “execution capability competition,” how will the launch of B.AI reshape the future business landscape? Recently, several industry veterans gathered for an in-depth Space roundtable discussion. The guests engaged in exciting debates around the core topic of “How B.AI Accelerates the Arrival of AGI.” Below is a highlights review of this Space session.

From “Thinking” to “Doing”: Why Is Financial Infrastructure Key to Breaking Through AI?

After experiencing two years of rapid development, the “intelligence” level of large models has reached astonishing heights. However, when the industry attempts to bring AI Agents into real business environments, the path to deployment is not smooth. When discussing “the core issues that truly determine the long-term development of the AI industry,” many guests’ views are highly aligned: the industry’s focus has quietly shifted from “intelligence” competition to “execution ability” competition. The key to bridging this practical execution gap lies in building a dedicated underlying financial infrastructure for AI Agents.

Wang Feng Anc and Teacher Xiaohai both pointed out that the current AI competition has moved beyond merely comparing model parameters and intelligence. As major companies’ models become increasingly similar, the real ceiling lies in the AI Agent’s ability to connect to the real world and complete closed-loop execution.

Wang Feng Anc emphasized that the ability of an intelligent agent to think and answer questions does not mean it can act independently. In a complete task flow (e.g., booking flights, on-chain transactions), AI Agents lack stable wallet permissions, settlement capabilities, verifiable identities, and cross-tool collaboration at the execution layer. Teacher Xiaohai also believes that models only solve “IQ” issues, but for AI Agents to participate in genuine value creation, they must have their own identities, reliable credit relationships, and payment and settlement capabilities. Without a set of financial and economic infrastructure, AI Agents cannot become true economic participants.

Grace, from the perspective of practical applications in trading, confirmed the pain points caused by the lack of infrastructure. She stated that current large models are already excellent at generating strategies and conducting research and backtesting, but they struggle to operate independently over the long term and stably in real funds and complex market environments because this requires strong constraints, controls, and risk management mechanisms. Therefore, the next phase of industry competition will shift from model intelligence alone to the execution capabilities of AI Agents and the construction of infrastructure.

Among many consensus points, Teacher Damo offered a more unique and divergent perspective. As an industry practitioner, Damo pointed out that the real factor limiting the deployment speed of AI across various industries is actually the industry’s own level of informatization. The more software- and information-driven an industry is, the easier its workflows can be summarized into standardized capabilities, and the faster AI can replace and reshape them. He also reminded everyone that current intelligent agents (such as L2/L3 level) mostly operate according to human instructions and do not yet possess true “independent thinking” capabilities, which serve as a safety boundary. Facing the irreversible wave of AI, he called on everyone to actively learn, embrace change, and try new infrastructure solutions like B.AI that can solve practical problems.

B.AI Officially Launches: Building the Financial Foundation for AI Agents’ Economic Operations

It is precisely under this industry consensus and urgent demand that B.AI announced its official launch. Its core positioning is very clear: rather than competing in the “intelligence” race of large models, it aims to build a set of critical infrastructure directly targeting the pain points of “financial execution ability.” B.AI’s main goal is to endow AI Agents with underlying economic capabilities, including: seamless access to top global models, payment and settlement functions, establishing independent identities and trust mechanisms, and supporting AI Agents to independently complete complex asset transactions and cross-entity business collaborations.

Regarding the implementation path, OxPink further broke down the “three core capability bases” supporting this infrastructure:

1. LLM Service Platform: Developers no longer need to connect to multiple models and manage multiple bills. B.AI already supports over 15 top large models worldwide, including GPT-5, Gemini, Claude, MiniiMax, and Kimi, enabling “single account management and on-demand multi-model invocation,” greatly reducing development barriers and costs.

2. x402 Payment Protocol and Complete Financial Operating System: In traditional scenarios, even if AI analyzes excellent market opportunities, final execution still requires manual order placement and payment. To break this bottleneck, B.AI innovatively introduced the x402 payment protocol based on the HTTP 402 standard, combined with MCP Server and Skills core components, directly enabling AI Agents to automate handling of encrypted asset payments and complex DeFi operations. This underlying architecture not only makes AI Agents well-suited for high-frequency, small-value, real-time settlement scenarios but also achieves full-chain integration from autonomous decision-making and automatic payments to profit strategy execution, truly closing the business logic loop among intelligent agents.

3. On-Chain Identity and Credit System: B.AI has established a dedicated ID and credit score for AI Agents, recording their transaction history, default situations, and objective evaluations. This functions like a credit system in the AI world—high-credit AI Agents can gain more employment opportunities, facilitating mutual employment and transactions among intelligent agents, ultimately forming a self-sustaining AI Agent economy.

On top of this solid infrastructure, B.AI also launched an out-of-the-box AI Agent application—BAIclaw. As a bridge connecting the technical foundation and users, BAIclaw supports seamless multi-model switching and multi-agent collaboration, deeply integrated with tools like Telegram and Discord. Users can give commands in natural language, and BAIclaw will automatically perform complex operations including DEX swaps, data queries and analysis, and perpetual contract trading. If the first three modules provide the “hardcore foundation” for enabling agents to participate in value flows, then BAIclaw offers an efficient and smooth “interaction engine,” allowing developers and users to naturally embed AI Agents into real business operations and daily collaboration.

As infrastructure like B.AI matures, user experience and roles will undergo disruptive changes. Wang Feng Anc and Teacher Xiaohai believe that the biggest change will be the “disappearance of latent friction.” Users will be liberated from the previous tedious manual operations and platform switching, shifting to a “goal-oriented” experience—simply issuing commands, with complex execution, payments, and settlements automatically completed by the infrastructure in a closed loop at the bottom. The financial infrastructure built by B.AI not only breaks the last barrier for intelligent agents to enter reality but also signals that an “Agent Economy” driven by AI Agents’ trading and collaboration is accelerating toward reality.