Author: Chain Observer
After recent conversations with entrepreneurs and VCs, there's a shared feeling that expectations for the AI + Crypto track remain firm, but everyone seems somewhat confused about the narrative evolution of web3 AI Agents. What should be done? I've organized several potential shifts in subsequent AI narratives for reference:
1) Issuing tokens through MEME is no longer an advantage, and even "talking about coins causes panic". If a project lacks PMF support and only has a Token circulation mechanism, it will naturally be labeled as pure MEME speculation, merely a wolf in sheep's clothing, with little relation to AI;
2) The original landing sequence of Agent > AI Framework > AI Platform > AI DePIN might be adjusted. When the Agent market bubble bursts, Agents will become "carriers" after large model fine-tuning and data algorithms are established. Without core technical support, it will be difficult for an AI Agent to flex its muscles;
3) Some projects originally providing AI data, computing power, and algorithm services may surpass AI Agents and become the focus of attention. Even if new AI Agents are launched, Agents created by these AI platform projects will be more market-convincing. After all, teams capable of operating an AI platform are much more reliable than developers deploying low-cost frameworks;
4) Web3 AI Agents can no longer directly compete with web2 teams. They must find web3's differentiated direction. Web2 Agents focus on Utility, so low-cost deployment development platforms work, but web3 Agents emphasize Tokenomics. Overstressing low-cost deployment will only trigger more asset issuance bubbles. Undoubtedly, web3 AI Agents should innovate by combining blockchain distributed consensus architecture;
5) AI Agent's greatest advantage is "application-first", following the "fat protocol, thin application" logic. But how should protocols be "fat"? By mobilizing idle computing resources, driving algorithms with distributed architecture, and activating more vertical scenarios in finance, medicine, education. Applications should be "thin": autonomous asset management, autonomous intent trading, and autonomous multi-modal interaction aren't instant achievements. Demands must be segmented and gradually implemented, as even a mature DeFi scenario might take one or two years;
6) Web2's MCP protocol and Manus automation of multi-modal interactions provide inspiration for web3 innovation. Developing directly based on MCP + Manus for web3 application scenarios, or enhancing business scenarios above MCP using distributed collaborative frameworks. Don't immediately talk about overthrowing everything; optimizing existing product protocols and leveraging web3's irreplaceable differentiated advantages is sufficient. Whether web2 or web3, both are in this AI LLMs revolutionary process. Ideology is irrelevant; truly promoting AI technological development is what matters.
