Returning to the value anchor, how can Web3 AI Agent overcome the MEME trap?

After talking with some entrepreneurs and VCs recently, I have a common feeling that everyone's expectations for the AI + Crypto track are still firm, but they are a little confused about the narrative evolution of web3 AI Agent. What should we do? I have sorted out several potential changes in the subsequent AI narrative for reference:

1) AI Agent’s use of MEME to issue tokens is no longer an advantage, and people are even afraid of tokens. If a project has no PMF support and only has a set of Tokenomics running idle, it will naturally be labeled as pure MEME hype, which is just a wolf in sheep’s clothing and has little to do with AI;

2) The original order of AI Agent > AI Framwork > AI Platform > AI DePIN may be adjusted. When the bubble of the Agent market bursts, Agent becomes the "carrier" for large model fine-tuning, data algorithm and other technologies. Without the core technology support, it is difficult for an AI Agent to show its strength again.

3) Some projects that originally provided AI data, computing power, and algorithm services will surpass AI Agents and become the focus of attention. In other words, even if new AI Agents are launched, the Agents created by these AI platform projects will have more market persuasiveness. After all, a project that can run an AI platform has a much more reliable team and technical foundation than a Dev that only deploys low-cost frameworks;

4) Web3 AI Agent can no longer compete head-on with the web2 team, and must find a direction for web3 differentiation. Web2 Agent focuses on utility, so the logic of low-cost deployment and development platform works, but web3 Agent focuses on Tokenomics. Over-emphasizing low-cost deployment will only stimulate more asset issuance bubbles; there is no doubt that web3 AI Agent should combine blockchain distributed consensus architecture for innovation and development (detailed description in the pinned article on my homepage);

5) The biggest advantage of AI Agent is "application front-end", which belongs to the logic of "fat protocol, thin application", but how should the protocol be fat? How to mobilize idle computing resources, use distributed architecture to drive the low-cost application advantages of algorithms, and activate more vertical segmented scenarios such as finance, medical care, and education. And how should the application be thin? Letting AI Agent autonomously manage assets, autonomous intention transactions, autonomous multimodal interactions, etc. are not achieved overnight. You can't try to eat a big fat man in one bite. You need to segment and disassemble the needs and slowly implement them. Otherwise, it will take one or two years for the mature standards of a DeFai scenario;

6) The MCP protocol and Manus automated multi-modal execution in the web2 field are inspiring innovations in the web3 field. Directly based on MCP + Manus, we can extend and develop scenarios suitable for web3 applications, or use a distributed collaboration framework to enhance business scenarios on top of MCP. Don't talk about subverting everything right away. It is enough to properly optimize the existing product protocols and give play to the irreplaceable differentiated advantages of web3. Both web2 and web3 are in the process of this AI LLMs revolution. Ideology doesn't matter. What matters is that they can truly promote the development of AI technology.