Space Review | B.AI Officially Launches: How Does Financial Infrastructure for the AI Agent Era 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 era. Over the past two years, large language model technology has achieved breakthrough progress. However, as applications deepen, the issues of AI Agents lacking an independent payment system, verifiable identity, and closed-loop execution capabilities have become increasingly prominent, causing them to remain highly reliant on manual operations in real business scenarios. The launch of B.AI aims to fill this systemic gap. By endowing AI Agents with robust economic execution capabilities, it aims to elevate them from passive information interaction nodes to new economic entities that autonomously participate in global value circulation, thereby constructing a solid commercial foundation and operational cornerstone for the comprehensive arrival of the AGI era. 

At this critical juncture where the industry is shifting from "competing on intelligence" to "competing on execution," how will the launch of B.AI reshape the future business landscape? Recently, several senior industry practitioners gathered for an in-depth Space roundtable discussion. The guests engaged in a brilliant exchange around the core topic of "How B.AI Accelerates the Arrival of AGI." Below is a summary of the key highlights from this Space session.

From "Thinking" to "Doing": Why Financial Infrastructure is the Key to AI's Breakthrough?

After the rapid advancement of the past two years, the "intelligence" level of large models has reached astonishing heights. However, as the industry attempts to deploy AI Agents in real business environments, it finds the path to implementation is not smooth. When discussing "the core proposition that truly determines the long-term development of the AI industry," multiple guests shared a highly consistent view: The industry's focus has quietly shifted from competing on "intelligence" to competing on "execution capability." The key to bridging this gap in real-world execution lies in building a dedicated underlying financial infrastructure for AI Agents.

Both Wang Feng Anc and Xiao Hai pointed out that the current AI competition has moved beyond the stage of simply comparing model parameters and intelligence. As the capabilities of major vendors' models gradually converge, the real ceiling lies in the ability of AI Agents to connect to the real world and complete closed-loop execution.

Wang Feng Anc emphasized that an agent's ability to think and answer questions does not mean it can act independently. In a complete task flow (e.g., booking a flight ticket, on-chain transaction), AI Agents lack stable wallet permissions, settlement capabilities, verifiable identities, and an execution layer for cross-tool collaboration. Xiao Hai similarly believes that models only solve the "IQ" problem, but for AI Agents to participate in real commercial value creation, they must have their own identity, reliable credit relationships, and payment, clearing, and settlement capabilities. Without a set of financial and economic infrastructure, AI Agents cannot become true economic participants.

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

Amidst these shared views, Da Mo offered a more unique and divergent perspective. As a practitioner, Da Mo stated that what limits the speed of AI adoption across various industries is actually the level of informatization within each industry itself. Industries with higher levels of softwareization and informatization have workflows that are more easily summarized into standardized capabilities, making them faster to be replaced and reshaped by AI. At the same time, he reminded everyone that current agents (such as L2/L3 level) mostly operate in compliance with human instructions and do not yet possess true "independent thinking" capabilities, which also serves as a layer of safety boundary. Facing the irreversible tide of AI, he called on everyone to proactively learn, embrace change, and actively try new infrastructure like B.AI that can solve real-world problems.

B.AI Officially Launches: Building the Financial Foundation for AI Agent 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: not to participate in the "intelligence" arms race of large models, but to build a set of key infrastructure that directly addresses the pain point of "financial execution capability." What B.AI fundamentally aims to solve is to endow AI Agents with underlying economic capabilities, including: seamless access to global top-tier models, enabling payment and settlement, establishing independent identity and trust mechanisms, and supporting AI Agents to independently complete complex asset transactions and cross-entity commercial collaborations.

Regarding the implementation path, OxPink further deconstructed the "three core capability foundations" supporting this infrastructure: 

1. LLM Service Platform: Developers no longer need to tediously integrate multiple models and manage multiple bills. B.AI already supports over 15 top global large models, including GPT-5, Gemini, Claude, MiniiMax, and Kimi, enabling "unified management with one account, on-demand invocation of multi-model capabilities," significantly lowering development barriers and costs.

2. x402 Payment Protocol and Complete Financial Operating System: In previous scenarios, even if traditional AI analyzed excellent market opportunities, it ultimately required humans to manually place orders and make payments. To break this bottleneck, B.AI innovatively introduced the x402 payment protocol based on the HTTP 402 standard, combined with core components like MCP Server and Skills, directly empowering AI Agents with the ability to automate crypto asset payments and execute complex DeFi operations. This underlying architecture not only perfectly adapts AI Agents to high-frequency, small-amount, real-time settlement trading scenarios but also achieves end-to-end integration from autonomous decision-making and automatic payment to yield strategy execution, truly running through the closed-loop commercial logic between agents.

3. On-Chain Identity and Credit System: B.AI establishes a dedicated ID card and credit score for AI Agents, recording their transaction history, default situations, and objective evaluations. This is akin to a credit reporting system for the AI world. AI Agents with good credit can obtain more employment opportunities, thereby facilitating mutual hiring and transactions among agents, ultimately forming a self-operating AI Agent economic circle.

Built upon this solid underlying infrastructure, B.AI has also launched a ready-to-use AI Agent application — BAIclaw. As a bridge connecting the technical foundation with users, BAIclaw supports seamless switching between multiple models and collaborative work among multiple agents (Multi-Agent), and is deeply integrated with daily collaboration tools like Telegram and Discord. Users only need to issue instructions in natural language, and BAIclaw can automatically complete complex operations including DEX swaps, data querying and analysis, and perpetual contract trading. If the first three modules provide agents with the "hardcore foundation" to participate in value circulation, then BAIclaw offers users an efficient and smooth "interaction engine," allowing developers and users to integrate AI Agents into real business operations and daily collaboration in the most natural way.

As infrastructure like B.AI matures, user experience and roles will also undergo disruptive changes. Wang Feng Anc and Xiao Hai believe the biggest change lies in the "disappearance of hidden friction." Users will be liberated from the tedious manual operations and platform switching of the past, transitioning to a "goal-oriented" experience. Users only need to issue commands, and complex execution, payment, and settlement will be automatically completed in a closed loop by the underlying infrastructure. The underlying financial infrastructure built by B.AI not only breaks the final barrier for agents to step into reality but also signals the accelerated arrival of an "Agent Economy" era driven by AI Agents for transactions and collaboration.