Returning to the Value Anchor: How Can Web3 AI Agents Overcome the MEME Trap?
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Returning to the Value Anchor: How Can Web3 AI Agents Overcome the MEME Trap?
Everyone remains confident about the AI + Crypto赛道, but there's some uncertainty about the evolving narrative around web3 AI Agents.
Navigating Web3 tides with focused insightsAfter recent conversations with several founders and VCs, there's a shared sentiment: confidence in the AI + Crypto赛道 remains strong, but there's noticeable confusion about how the Web3 AI Agent narrative will evolve. So what should we do? I've outlined several potential directions for future AI narratives as reference:
1) Launching tokens via meme-driven strategies is no longer an advantage for AI Agents—in fact, people are becoming wary of anything token-related. Projects lacking product-market fit (PMF) and relying solely on circular tokenomics will naturally be labeled as pure meme plays—wolves in sheep’s clothing, with little real connection to AI;
2) The original expected progression—AI Agent > AI Framework > AI Platform > AI DePIN—may shift. When the AI Agent market bubble bursts, Agents will become mere carriers built atop mature technologies like fine-tuned large models, data, and algorithms. Without core technical support underneath, standalone AI Agents will have few opportunities to prove their value;
3) Projects focused on AI infrastructure such as data, computing power, and algorithm platforms may surpass AI Agents as focal points of attention. Even if new AI Agents emerge, those developed by established AI platform projects will carry far greater credibility. After all, teams capable of building full AI platforms typically possess stronger technical foundations and team experience than developers who simply deploy low-cost framework-based agents;
p>4) Web3 AI Agents can no longer compete head-on with Web2 teams using brute-force development tactics. Instead, they must pursue differentiated paths unique to Web3. Web2 Agents prioritize utility, making low-cost deployment and platform development logical. But Web3 Agents center around tokenomics—overemphasizing low-cost deployment only fuels more asset issuance bubbles. Undoubtedly, Web3 AI Agents should innovate by leveraging blockchain’s distributed consensus architecture (as detailed in my pinned article);5) The greatest strength of AI Agents lies in "application-first" design, following the "fat protocol, thin application" logic. But how should protocols become “fatter”? By mobilizing idle computing resources and using distributed architectures to drive cost-efficient algorithm applications across vertical sectors like finance, healthcare, and education. And how should applications become “thinner”? Features like autonomous asset management, intent-based transactions, and multimodal interactions cannot be achieved overnight. We must break down complex demands into smaller parts and implement them gradually; otherwise, even maturing a single DeFi scenario could take one or two years;
6) Protocols from the Web2 space—such as MCP and Manus for automated execution of multimodal tasks—offer valuable inspiration for Web3 innovation. Directly extending MCP + Manus for Web3 use cases, or enhancing business scenarios atop MCP using decentralized collaboration frameworks, are viable paths. There's no need to talk about disrupting everything from day one. Sufficient progress means making meaningful optimizations atop existing protocols and highlighting Web3’s irreplaceable, differentiated advantages. Whether Web2 or Web3, both are part of this ongoing AI LLM revolution. Ideology doesn't matter—what counts is advancing AI technology in tangible ways.
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Haotian | CryptoInsight
