Chainfeeds Introduction:
This article deeply studies the diverse applications of AI Agent in Web3, from Web3 infrastructure, middleware, application level to data and model market and other dimensions, aiming to identify and evaluate the most promising project types and application scenarios, so as to deeply understand the deep integration of AI and Web3.
Source:
https://mp.weixin.qq.com/s/y7TVIP9QMtA1eyno41K4Kg
Article author:
James
Viewpoint:
ArkStream Capital: We believe that AI Agent projects should focus on the following aspects: - Ecosystem construction: Go beyond a single application and build an ecosystem with multiple services and functions to promote interaction and value-added between different components. - Token economic model: Design a reasonable token economic model to incentivize users to participate in network construction and contribute data and computing power. - Cross-domain integration: Explore the application potential of AI Agent in different fields and create new usage scenarios and value through cross-domain integration. We also provide some forward-looking suggestions for project parties with different focuses. - For non-AI core application products: Maintain long-termism, focus on its core products while integrating AI technology, and follow the times to wait for the wind. We believe that in the uncertainty of the market, it may be a strategic decision for non-AI core application products to consider introducing AI Agent in a timely manner. It can not only increase the market exposure of the product at present, but also bring new growth points to the product in the continuous development of AI technology. - For native projects focusing on AI Agent: Balancing technological innovation and market demand is the key to success. We suggest that project parties should pay attention to market dynamics while ensuring product quality, and learn from projects that have achieved results in the Web3 market. Finally, we analyze the Web3 AI Agent track from multiple perspectives: - Capital investment and market attention: Although AI Agent projects in the Web3 industry do not have an advantage in the number of listings, they account for nearly 50% of the market valuation, showing that the capital market highly recognizes this track. With more capital investment and increased market attention, it is a foregone conclusion that more highly valued projects will appear in the AI Agent track. - Competitive landscape and innovation capabilities: The competitive landscape of the AI Agent track in the Web3 industry has not yet been fully formed. At present, there are no phenomenal leading products in the application layer, which gives new project parties a lot of room for growth and innovation. With the maturity of technology and innovation before the project, the track is expected to develop more competitive products and drive the valuation of the entire track. - Emphasis on token economy and user incentives: The significance of Web3 is to reshape production relations, making the originally centralized process of deploying and training AI models more decentralized. Through reasonable token economic design and user incentive schemes, idle computing power or personal data sets can be redistributed, and data privacy can be protected through solutions such as ZKML, further reducing computing power and data costs, and allowing more individual users to participate in the construction of the AI industry.
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