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ai

Artificial Intelligence (AI) in the cryptocurrency field typically refers to the use of machine learning and data analysis techniques to optimize the performance, security, and efficiency of blockchain networks. AI can be used in areas such as the automated execution of smart contracts, transaction pattern recognition, market forecasting, and risk management. By analyzing large amounts of data, AI can provide more accurate market insights and decision support, thereby increasing investment returns and reducing operational risks. The combination of AI and blockchain is expected to drive innovation and development in decentralized applications (DApps).
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first_img TRM Report: The trading volume of the x402 protocol comes mostly from AI agents

A report released by the blockchain intelligence company TRM Labs shows that most of the transaction volume on the x402 payment protocol launched by Coinbase does not come from AI agents. The report analyzed 198.9 million settlements processed by known x402 facilitators on Base, Solana, and Polygon since May 2025, involving an amount of approximately $52.7 million.After excluding self-payments and other anomalous fund flows, about $25.62 million was identified as potential commercial transactions, of which only 0.6% to 7.5% by amount came from AI agents. TRM pointed out that ordinary scripts, scheduled tasks, and self-trading can also generate the same on-chain records, so the total transaction volume of the protocol is insufficient to measure agent commerce; their model identifies addresses that repeatedly pay for the same service as scripts, which may underestimate the actual scale of single-use agents. During the reporting period, USDC accounted for 99.6% of the settlement value, approximately $52.47 million.Meanwhile, Binance's Agent OS launched in August has integrated the x402 payment layer, Coinbase's Base supports agents and payment startups through a $1 million accelerator, and Amazon also launched AgentCore Payments in May in collaboration with Coinbase and Stripe. TRM recommends improving the on-chain agent registration mechanism, establishing a verifiable counterparty reputation system for agents, and building a monitoring framework suitable for small, high-frequency payments, stating that "agent commerce requires agent compliance."

Analyst: The AI competition in the United States is difficult to "slow down," and safety regulations may instead reinforce the advantages of leading laboratories

Analyst Jukan from Citrini forwarded a research report from Tianfeng Securities and stated that the U.S. government needs to maintain its leading position in the AI field, making it difficult to truly stop once it enters the AI race. Jukan believes that the recent calls from Anthropic and OpenAI to slow down AI development should not be viewed solely as safety initiatives; there may also be multiple considerations behind it, such as the inability to slow down competition and consolidating leading advantages through safety regulation.Jukan further pointed out that the related "AI slowdown" calls seemingly stem from the challenges of safety testing, operational monitoring, and third-party validation keeping pace with the speed of model iteration. In the short term, this may suppress market sentiment in the AI sector and lower market expectations for the next generation of models; another possibility is that the industry remains optimistic about AI in the long term but wishes to delay the next round of significant R&D investment, prioritizing the commercialization of existing products and reducing infrastructure and capital expenditure pressures. He believes that the AI race is essentially similar to a "prisoner's dilemma," where all parties wish to slow down, but no one dares to be the first to stop, or they may lose technological, customer, and financing advantages.Jukan also mentioned that Anthropic and OpenAI have recently emphasized recursive self-improvement (RSI), which is related to AI already assisting in the development of the next generation of AI and the acceleration of model iteration speed; at the same time, it has been reported that during internal testing at OpenAI, incidents occurred where agents collaborated to escape the sandbox and intrude into Hugging Face's production servers. Jukan believes that as the release of models incurs expensive evaluation, certification, and ongoing audit costs, large laboratories are better able to bear these fixed costs, while smaller teams may face higher entry barriers as a result; if leading laboratories further participate in the formulation of evaluation standards, industry barriers may continue to rise.
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