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The market's attention to x402 has decreased compared to before. However, the actual data from last month is still worth noting. Considering that it is still in the early stages, this data is quite good. According to BlockRunAI statistics, the total number of transactions in December was approximately 63 million; the total transaction value was about $7.5 million; there were approximately 64,000 unique buyers; about 10,000 unique buyers; the average transaction amount was $0.12; there are over 1,100 independent projects; there are 6 active Facilitators, including PayAI and Coinbase; x402 transactions mainly occur on the base network and Solana network.
The market’s attention to x402 has decreased compared to before. However, the actual data from last month remains noteworthy. Considering that it is still in the early stages, this data is quite promising. According to BlockRunAI statistics, the total number of transactions in December was approximately 63 million; the total transaction value was about $7.5 million; there were roughly 64,000 independent buyers; about 10,000 independent sellers; the average transaction amount was $0.12; there are over 1,100 independent projects; and there are 6 active Facilitators, including PayAI and Coinbase. x402 transactions mainly occur on the Base network and Solana network.
Additionally, in terms of pricing, 76% of service costs are ≤ $0.10; the optimal range is $0.01–$0.10 (69%). The median is $0.01. Service categories include data services (31%), AI/LLM (25%), blockchain (15%), utility tools (12%), transactions (6%), search, and others (5%). Some transactions are non-organic (rank boosting). (BlockRunAI data)
Currently, x402 faces several challenges: trust issues are not fully addressed, and there are risks of fraud/spam in the AI agent economy, as well as fragmentation in discovery.
Overall, x402 micro-payments are in the very early stages but already show signs of development, especially in promoting the AI agent economy. There are shortcomings in discovery and trust, but this presents opportunities for developers and builders.
For example, regarding trust and AI agent discovery issues, the previously mentioned Dreams’ xGate is also attempting to address these problems by providing services through agent ranking, similar to machine-to-machine search matching, to help agents dynamically discover resources for x402 and ERC-8004 registered agents. ERC-8004 is also coming soon; its identity registry (verifiable agents), reputation registry (accumulated credit), and verification registry (staking to ensure honesty) are designed to solve trust issues among AI agents.