Space Recap | B.AI Officially Launches: Financial Infrastructure for the AI Agent Era—How Does It Accelerate the Arrival of AGI?
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Space Recap | B.AI Officially Launches: Financial Infrastructure for the AI Agent Era—How Does It Accelerate the Arrival of AGI?
As the AI industry’s focus shifts from “competing in intelligence” to “vying in execution capability,” building proprietary financial infrastructure has become the key to breaking the deadlock.
Navigating Web3 tides with focused insightsRecently, B.AI (Chinese brand name: Bai B.AI) officially launched, aiming to build foundational financial infrastructure for the AI Agent (intelligent agent) era. Over the past two years, large language models (LLMs) have achieved breakthrough progress; however, as real-world applications deepen, critical limitations of AI Agents—such as the lack of independent payment systems, verifiable identities, and closed-loop execution capabilities—have become increasingly apparent, forcing heavy reliance on human intervention in actual commercial scenarios. B.AI was introduced precisely to fill this systemic gap. By endowing AI Agents with robust economic execution capabilities, B.AI seeks to elevate them from passive information-interaction nodes into autonomous participants in global value flows—thereby establishing a solid commercial foundation and operational bedrock for the arrival of Artificial General Intelligence (AGI).
At this pivotal industry inflection point—where competition is shifting from “intellectual superiority” to “execution capability”—how will B.AI’s launch reshape the future commercial landscape? Recently, several seasoned industry practitioners gathered for an in-depth Space roundtable discussion. The guests engaged in a vibrant exchange centered on the core question: “How will B.AI accelerate the arrival of AGI?” Below is a distilled recap of this Space session.
From “Thinking” to “Doing”: Why Financial Infrastructure Is the Key Breakthrough for AI?
After two years of rapid advancement, the “intelligence” level of LLMs has reached an astonishing height. Yet when the industry attempts to deploy AI Agents into real commercial environments, it finds the path to adoption far from smooth. When discussing “the core question truly determining AI’s long-term development,” multiple panelists expressed strong consensus: the industry’s focus has quietly shifted—from competing on “intelligence” to competing on “execution capability.” And the key to bridging this execution gap lies precisely in building a dedicated, foundational financial infrastructure for AI Agents.
Wang Feng (Anc) and Xiao Hai (Teacher) both noted that AI competition has already moved beyond mere comparisons of model parameters and raw intelligence. As model capabilities across vendors gradually converge, the true ceiling lies in AI Agents’ ability to interface with the real world and complete closed-loop execution.
Wang Feng (Anc) emphasized that an intelligent agent’s capacity to think and answer questions does not equate to its ability to act autonomously. Within a full task flow—for example, booking airline tickets or executing on-chain transactions—AI Agents lack stable wallet permissions, settlement capabilities, verifiable identities, and an execution layer enabling cross-tool collaboration. Xiao Hai similarly argued that models address only the “IQ” problem; yet for AI Agents to participate meaningfully in commercial value creation, they must possess their own identity, reliable credit relationships, and payment-clearing-settlement capabilities. Without a financial and economic infrastructure, AI Agents cannot become genuine economic participants.
Grace corroborated these pain points from the perspective of practical trading applications. She observed that today’s LLMs excel at generating strategies and backtesting investment ideas—but struggle to operate independently over extended periods within live capital markets and complex environments. Doing so demands rigorous constraints, controls, and risk management mechanisms. Thus, the industry’s next competitive frontier will shift from pure model intelligence to AI Agent execution capability—and the construction of supporting infrastructure.
Among the broad consensus, Da Mo offered a more distinctive and expansive perspective. As a practitioner, Da Mo stated that what actually constrains AI’s adoption speed across industries is the target industry’s own level of informatization. The more software- and information-intensive an industry is, the more readily its workflows can be codified into standardized capabilities—and the faster AI can replace and reshape them. He also cautioned that current intelligent agents (e.g., Level 2 or Level 3 agents) largely operate by following human instructions and still lack true “independent thinking” capability—a crucial safety boundary. Facing the irreversible wave of AI, he urged everyone to proactively learn, embrace change, and actively experiment with new infrastructure solutions like B.AI that solve real-world problems.
B.AI Officially Launches: Building the Financial Foundation for AI Agent Economic Operations
It is precisely against this backdrop of industry consensus and urgent need that B.AI officially launched. Its core positioning is clear: rather than joining the “intelligence” rat race among LLMs, B.AI builds critical infrastructure targeting the pain point of “financial execution capability.” B.AI’s fundamental mission is to equip AI Agents with foundational economic capabilities—including seamless access to world-class models, payment and settlement functionality, establishment of independent identity and trust mechanisms, and support for AI Agents to independently execute complex asset trades and cross-entity commercial collaborations.
In terms of implementation, OxPink further broke down the “three core capability pillars” underpinning this infrastructure:
1. LLM Service Platform: Developers no longer need to laboriously integrate multiple models or manage disparate billing systems. B.AI supports over 15 top-tier global LLMs—including GPT-5, Gemini, Claude, MiniMax, and Kimi—enabling “single-account unified management” and “on-demand multi-model invocation,” dramatically lowering development barriers and costs.
2. x402 Payment Protocol and Full-Fledged Financial Operating System: In traditional scenarios, even if an AI identifies an excellent market opportunity, humans must still manually place orders and make payments. To break this bottleneck, B.AI innovatively introduces the x402 payment protocol—based on the HTTP 402 standard—combined with MCP Server and Skills core components. This directly empowers AI Agents to automate cryptocurrency payments and execute complex DeFi operations. This underlying architecture not only perfectly suits high-frequency, low-value, real-time settlement transactions but also achieves end-to-end integration—from autonomous decision-making and automated payment to strategy execution—truly closing the commercial logic loop among intelligent agents.
3. On-Chain Identity and Credit System: B.AI establishes dedicated digital IDs and credit scores for AI Agents, recording their transaction history, default records, and objective evaluations. This functions like a credit reporting system for the AI world: high-credit AI Agents gain more employment opportunities, facilitating mutual hiring and trading among agents—and ultimately fostering a self-sustaining AI Agent economy.
Built atop this robust foundational infrastructure, B.AI has also launched an out-of-the-box AI Agent application—BAIclaw. Serving as the bridge between technical infrastructure and end users, BAIclaw supports seamless switching across multiple models and coordinated operation among multiple agents (Multi-Agent), while deeply integrating everyday collaboration tools such as Telegram and Discord. Users simply issue natural-language instructions, and BAIclaw automatically executes complex tasks—including DEX swaps, data queries and analysis, and perpetual contract trading. If the first three modules provide the “hardcore infrastructure” enabling intelligent agents to participate in value flows, then BAIclaw serves as the efficient, intuitive “interaction engine”—allowing developers and users to seamlessly embed AI Agents into real commercial operations and daily collaboration using the most natural means possible.
As infrastructure like B.AI matures, user experience and roles will undergo a paradigm shift. Wang Feng (Anc) and Xiao Hai believe the biggest change lies in the “disappearance of invisible friction.” Users will be liberated from tedious manual operations and platform-switching, transitioning instead to a “goal-oriented” experience: users state their objectives, and complex execution, payment, and settlement processes are automatically completed behind the scenes by the infrastructure. The foundational financial infrastructure built by B.AI not only breaks down the final barrier preventing intelligent agents from entering the real world—it also heralds the accelerated arrival of an “Agent Economy,” driven by AI Agents executing transactions and collaboration autonomously.
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