Recently, B.AI officially announced its launch, dedicated to building underlying financial infrastructure for the era of AI Agents. Over the past two years, significant breakthroughs have been made in large model technology, but as applications have deepened, issues such as the lack of an independent payment system, verifiable identities, and closed-loop execution capabilities for AI Agents have become increasingly prominent, leading to their high dependence on manual operations in real business scenarios. The launch of B.AI is to fill this systematic gap by empowering AI Agents with comprehensive economic execution capabilities, allowing them to leap from passive information interaction nodes to new economic entities autonomously participating in the global value circulation, thereby building a solid commercial foundation and operational cornerstone for the full arrival of the AGI era.

At this critical juncture where the industry shifts from "competing in intelligence" to "competing in 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 dialogue. The guests engaged in exciting discussions around the central topic of "How B.AI Accelerates the Arrival of AGI." Here is a summary review of this Space session.

From "Thinking" to "Doing": How Can Financial Infrastructure Become the Key to AI Breakthroughs?

After experiencing rapid advancement over the past two years, the "intelligence" level of large models has reached an astonishing height. However, when the industry tries to bring AI Agents into real business environments, it finds that the path to implementation is not smooth. In discussing the "core proposition that truly determines the long-term development of the AI industry," many guests shared highly consistent views: the focus of the industry has quietly shifted from competitions of "intelligence" to battles of "execution ability." The key to bridging this gap in real-world execution capability lies in building a dedicated underlying financial infrastructure for AI Agents.

Wang Feng Anc and Xiaohai both pointed out that the current AI competition has passed the stage of simply competing on model parameters and intelligence. As the model capabilities of major manufacturers gradually converge, the real ceiling is the ability of AI Agents to access the real world and complete closed-loop execution.

Wang Feng Anc emphasized that while agents can think and answer questions, it does not mean they can operate independently. In a complete task flow (for example, booking flights or conducting on-chain transactions), AI Agents lack stable wallet permissions, settlement capabilities, verifiable identities, and execution layers for cross-tool collaboration. Xiaohai also believes that the models only address the "IQ" issue, but for AI Agents to engage in real commercial value creation, they must possess their own identity, reliable credit relationships, and payment settlement capabilities. Without a financial and economic infrastructure, AI Agents cannot become true economic participants.

Grace, drawing from practical applications in the trading field, confirmed the pain points brought by the lack of infrastructure. She stated that current large models excel in generating strategies and conducting investment research backtesting but struggle to operate independently for long periods in real capital and complex market environments, as this requires strong constraints, control, and risk management mechanisms. Therefore, the focus of competition in the next stage of the industry will shift from purely the intelligence of models to the execution capabilities of AI Agents and the construction of infrastructure.

Among the many agreements, Damo provided a more unique and divergent perspective. As a practitioner, Damo indicated that the speed of AI's integration across various industries is actually limited by the industry's own level of informatization. The more software-oriented and digitized an industry is, the more its workflows can be summarized into standardized capabilities, which leads to a faster pace of replacement and reformation by AI. At the same time, he reminded everyone that current agents (such as L2/L3 level) are more accommodating to human directives and have not yet achieved true "independent thinking" capabilities, which serves as a layer of safety boundary. In the face of the irreversible tide of AI, he called on everyone to actively learn, embrace change, and try out new infrastructure like B.AI that can solve practical problems.

B.AI Officially Launches: Building an Economic Foundation for AI Agents

Amid this industry consensus and urgent demand, B.AI announced its official launch. Its core positioning is very clear: it does not aim to participate in the "intelligence" competition of large models, but rather to build key infrastructure that directly addresses the pain points of "financial execution capabilities." The core issue B.AI aims to resolve is to empower AI Agents with underlying economic capabilities, including: seamless access to the world's top models, realization of payment settlement, establishment of independent identities and trust mechanisms, and support for AI Agents to independently complete complex asset transactions and cross-entity commercial collaborations.

In terms of implementation paths, OxPink further broke down the "three core capability bases" that support this infrastructure:

1. LLM Service Platform: Developers no longer need to cumbersome connect multiple models and manage multiple bills, as B.AI now supports over 15 top-tier models globally, including GPT-5, Gemini, Claude, MiniiMax, and Kimi, enabling "one account for unified management and multi-model capabilities on demand," significantly reducing development thresholds and costs.

2. x402 Payment Protocol and Complete Financial Operating System: In previous scenarios, traditional AI, even when identifying excellent market opportunities, 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 the MCP Server and Skills core components, directly granting AI Agents the ability to automate the processing of cryptocurrency payments and execute complex DeFi operations. This underlying architecture allows AI Agents to perfectly adapt to high-frequency small amount and real-time settlement trading scenarios, achieving a full-link connection from autonomous decision-making and automatic payments to profitability strategy execution, truly closing the business logic loop between agents.

3. On-Chain Identity and Credit System: B.AI has established dedicated identification and credit scores for AI Agents, recording their transaction histories, default situations, and objective evaluations. This functions like a credit system in the AI world, enabling AI Agents with good credit to gain more employment opportunities, thereby facilitating mutual employment and transactions between agents, ultimately forming a self-operating AI Agent economic circle.

On a solid foundational infrastructure, B.AI has also launched a ready-to-use AI Agent application - BAIclaw. Serving as a bridge between the technical foundation and users, BAIclaw supports seamless switching between multiple models and collaborative work among multiple agents (Multi-Agent), and deeply integrates 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 exchanges, data queries and analysis, and perpetual contract trading. If the first three modules provide agents with the "hardcore foundation" to participate in value transfer, then BAIclaw offers users an efficient and smooth "interaction engine," allowing developers and users to drive AI Agents into real business operations and daily collaborations in the most natural way.

As infrastructures like B.AI gradually mature, user experiences and roles will also undergo transformative changes. Wang Feng Anc and Xiaohai believe the biggest change will be the "disappearance of implicit friction." Users will be liberated from the cumbersome manual operations and platform switching of the past, transforming into a "goal-oriented" experience where users only need to issue commands, and complex executions, payments, and settlements will be automatically completed by the underlying infrastructure in a closed loop. The foundational financial infrastructure built by B.AI not only breaks through the final barrier for agents entering reality but also signifies the accelerating arrival of an era driven by AI Agents engaged in trade and collaboration, the "Agent Economy."