CZ has updated a new article on Binance Square, focusing on the relationship between AI agents and tokens. My cousin's viewpoint is very insightful and provides a rational analysis of the current frenzy of combining AI and blockchain. We must have a rational perspective on the narrative of AI and the focus on blockchain in the market. Because there are too many tokens of AI in the market, most of which consume the market (as can be seen from the AI narrative on the Sol chain), the balance between financialization and practical value for ecological sustainability is also the core issue that concerns me the most. Without sustainability support, there is no consensus among everyone. The following is a further extension analysis of Big Cousin's viewpoint: 1. The fallacy of using tokens as a solution Pseudo demand creation: Many projects use tokens as a preset answer rather than a means to truly solve the problem. This is similar to the logical fallacy of "hammer searching for nails", where AI agents require algorithm optimization and data quality, rather than a mandatory embedded token system Payment layer redundancy: Mature payment infrastructure has been established for existing stablecoins (USDC, DAI) and mainstream tokens (ETH, SOL). Unless specific incentive mechanisms are required (such as data rewards for machine learning model training), additional tokens are like recreating payment cards on top of the Visa network 2. The implicit costs of token economy Compliance Burden: The SEC regulatory risks brought by token issuance may far exceed the capacity of start-up teams, and the legal costs of the Ripple lawsuit in 2023 could reach tens of millions of dollars Liquidity Trap: According to CoinMarketCap data, tokens with a market capitalization of less than $100 million typically have an average daily trading volume of less than $1 million, and insufficient liquidity actually hinders practical applications Attention dissipation: Team resources are forced to be diverted to non core affairs such as market maker negotiations, exchange listing, and community governance. Typically, the Axie Infinity team spends 83% of their time dealing with economic model issues during token crises 3. Index level accumulation of technical bonds Introducing tokens too early can lead to distortions in the technical architecture, such as: Adopting non optimal blockchain architecture for compatibility with token economy Smart contract vulnerabilities lead to malicious manipulation of models (such as the 2022 OptiPoker incident) The complex binding of data ownership and token equity increases system entropy Summarize some rational ideas: The AI token must be integrated with the actual application technology, and there is a high requirement for the speed of technological iteration. I have previously shared this viewpoint with the community, and if the technology is not updated in real time, the coin price may be affected, which will have an impact on the hold Speaking of which, Midjourney、 http://Character.ai When top AI products achieve billions of dollars in revenue without issuing tokens, it fully proves that the essence of AI value creation lies in intelligent efficiency improvement, rather than financial engineering arbitrage. The industry needs to return to the Occam's Razor principle - do not add tokens unless necessary. Of course, I also bought a lot of Ai tokens, hoping to get good returns and build consensus together @cz_binance @heyibinance @yingbinance https://app. (binance.com)/uni-qr/cart/21675043587009? r=10033515&l=zh-CN&uco=Cl1A6w-WhF9Wss-mshej0A&uc=app_square_share_link&us=twitter