Evolving Narratives in AI and Crypto: Challenges and Opportunities

According to PANews, discussions with entrepreneurs and venture capitalists reveal a steadfast belief in the potential of the AI and crypto sectors, despite some confusion surrounding the evolution of web3 AI Agent narratives. Several potential shifts in AI narratives have been identified for consideration: Firstly, the trend of issuing tokens through AI Agents as a meme has lost its appeal. Projects lacking a product-market fit and relying solely on tokenomics are often dismissed as mere speculative ventures, with little connection to AI. Secondly, the traditional implementation sequence of AI Agent, AI Framework, AI Platform, and AI DePIN might be disrupted. As the AI Agent market bubble bursts, these agents may become carriers for refined models and data algorithms. Without core technological support, AI Agents alone may struggle to demonstrate their capabilities. Thirdly, projects focused on AI data, computing power, and algorithms are likely to gain more attention than AI Agents. New AI Agents developed by these platform projects may have greater market credibility, as the teams behind AI platforms generally possess more reliable technical foundations than those deploying low-cost frameworks. Fourthly, web3 AI Agents must differentiate themselves from web2 counterparts. While web2 Agents emphasize utility and low-cost deployment, web3 Agents should focus on tokenomics. Overemphasizing low-cost deployment could lead to asset bubbles. Web3 AI Agents should innovate by integrating blockchain's distributed consensus architecture. Fifthly, the primary advantage of AI Agents is their 'application-first' approach, following a 'fat protocol, thin application' logic. The challenge lies in enhancing protocols to leverage idle computing resources and drive low-cost algorithm applications in vertical sectors like finance, healthcare, and education. Applications should gradually evolve to manage assets autonomously, engage in self-directed transactions, and facilitate multimodal interactions. Lastly, web2 protocols like MCP and Manus's automated multimodal execution offer insights for web3 innovation. Extending these protocols for web3 applications or enhancing them with distributed collaboration frameworks can be beneficial. Rather than aiming for total disruption, optimizing existing product protocols to highlight web3's unique advantages is sufficient. Both web2 and web3 are part of the ongoing AI LLMs revolution, where ideological differences are secondary to advancing AI technology.