Core Viewpoint - The article discusses the potential integration of AI in financial transactions, particularly focusing on the challenges and opportunities presented by traditional payment systems and the suitability of cryptocurrency for AI-driven payments [1][3]. Group 1: Challenges of Traditional Payment Systems - Traditional payment systems are not designed to accommodate AI, leading to issues such as compliance, security, and transaction speed [2][4]. - The current payment industry standards, such as PCI DSS, impose strict requirements on the handling of cardholder data, making it difficult for AI to interact with traditional payment methods [5][6]. - The experience and risk management in traditional payments are structured to counteract automated systems, which does not align with the needs of AI-driven transactions [6]. Group 2: Advantages of Cryptocurrency for AI Payments - Cryptocurrency can facilitate immediate payment and delivery (DvP), aligning well with the operational model of AI services, where payment is made for specific outputs [7]. - The concept of tokenization in cryptocurrency allows for a direct correlation between AI tokens and crypto tokens, enabling precise billing and microtransactions that are challenging to implement in fiat systems [7][8]. - The separation of transaction construction and signing can enhance security, allowing AI to identify payment scenarios without compromising sensitive data [10][11]. Group 3: Future Payment Relationships - Payment relationships can be categorized into three types: human-to-machine, machine-to-human, and machine-to-machine, with the latter being more naturally suited to a digital economy [8][9]. - Scenarios such as automated payments for services or rewards between AI agents illustrate the potential for a decentralized payment ecosystem [9]. - The integration of AI and cryptocurrency could lead to a more efficient and secure payment infrastructure, reducing reliance on centralized systems [13]. Group 4: Legal and Identity Considerations - For AI agents to operate independently in financial transactions, they must utilize cryptocurrency, which grants them a form of digital identity and civil capacity [14][15]. - The legal framework of "code is law" can establish binding agreements through smart contracts, ensuring that AI actions are recognized as legitimate transactions [15][16]. Group 5: Technical and Compliance Challenges - The article emphasizes the need for robust technical and compliance frameworks to ensure that AI's payment capabilities are reliable and accountable [17]. - The integration of signature and rule layers is crucial to mitigate the uncertainties associated with AI reasoning in critical financial operations [17].

Group 1 - The core viewpoint of the article highlights the ongoing competition between Tencent and Alipay in the AI payment space, particularly focusing on the introduction of the Model Context Protocol (MCP) to facilitate easier payment integration for developers [1][4][12] - The MCP allows large models to call various external tools under a unified standard, enabling the creation of familiar agent products [3][12] - The rise of agents is seen as a transformative phase in the AI industry, with predictions that 2025 will be the year of agents, driven by advancements in reasoning models [5][9] Group 2 - Both Tencent and Alipay are vying for dominance in the AI payment entry point, which is viewed as a new battleground for application ecosystems [14][17] - The user base for online payment in China has grown from 854 million in 2020 to 1.029 billion in 2024, with WeChat and Alipay reaching approximately 1 billion and 900 million monthly active users, respectively [19] - The competition has intensified as both platforms have reached user growth saturation, prompting them to innovate payment methods like Alipay's "tap to pay" and WeChat's palm payment [20][21] Group 3 - Despite the potential of AI agents to create new payment channels, significant challenges remain in establishing a commercial closed-loop system [25][28] - The industry faces difficulties in attracting users to AI applications that are engaging and frequently used, with a prediction that 99% of AI startups may fail within a couple of years [26][27] - The integration of agents with existing applications raises questions about how to balance the convenience of agents with the revenue models of traditional applications, creating uncertainty in the evolution of the market [27][28]