Regarding the research and investment in AI Agents, here are some reference suggestions (including interpretations of popular projects):
1) The AI Agent market is still very early; the overall appearance is still MEME-like. Even valuable incremental projects are cloaked in a MEME exterior, making it difficult to understand a project using traditional value judgment logic. Therefore, when encountering fun, interesting, and innovative projects, the earlier you participate, the better. How much to bet depends on a series of comprehensive factors such as on-chain Holder addresses, community reputation, and overall market value. In short, analyze projects with a 'technical mindset' and invest in projects with a playful 'MEME' attitude.
2) The chaotic battle of AI Agents will intensify, with conflicts between project teams and community capitalization (ELIZA vs eliza), debates over the advantages of TypeScript and Rust language frameworks (ELIZA vs ARC), the struggle between open-source ecological social consensus and closed ecological economic consensus (ELIZA vs Virtual), and the competition between application-oriented monolithic AIs and protocol-oriented AI Chains (AIXBT vs ZEROBRO), among others. More novel, fun, and creative projects will emerge in the future. How to judge the real from the fake? Just focus on one small innovation; generally, projects that aim to shake the world and overturn everything are paper tigers.
3) The AI Agent industry is a rapidly evolving one that reveals its true value, and ARC appears cooler compared to ELIZA, seemingly pointing towards a direction of a more secure, higher-performing, modular framework that is adaptable for AI Agent implementation. However, the activity level of early developers is even more important. Just like when EOS first emerged, it had the appearance of potentially surpassing Ethereum, but in the end, wasn't it the EVM standard that won? So while ARC certainly has its advantages, we need to observe its growth potential in practical implementation.
4) The ELIZA framework and the Virtual architecture encompass two equally significant Crypto Native intelligences: the spirit of decentralized open source and the consensus of Tokenomics. If you support ELIZA, you must recognize that, although it currently proclaims a grand vision of an open-source AI World, its organizational structure is still akin to a social consensus alliance similar to OP Stack, with much room for iteration. If you support Virtual, you must also clarify: after the economic model design similar to Pump and Fun places Virtual in an unbeatable position, what sustainable growth value can the Virtual platform ultimately bring to the AI Agent industry? I make no choice; I want both.
5) Most projects in the AI Agent field are still very 'makeshift.' Why can one idea issue a token? Why does one ecosystem issue multiple tokens? Why do traditional AI regular troops, represented by Gemini 2.0's multimodal real-time communication AI, disdain to issue tokens and play the model? Can we understand the development path of ai16z from the very beginning? And a host of other questions, all boil down to one answer: AI Agents are an accidental push for innovation in the Crypto industry, and everyone is wading through the river with muddy legs, unable to understand and unlikely to understand, which is why there are abundant opportunities.
6) AI Agents will definitely integrate with blockchain, which contradicts many people's common sense. Indeed, from the perspective of application implementation, it suffices for Agents to enhance AI intelligence levels and service experiences through powerful algorithm-driven methods. Why is it necessary to decentralize asset custody, decentralize message verification, decentralize data storage, and ask whether AI Agent + Chain is a false proposition? As we ponder, doesn't it become as powerful as the web2 mobile internet, insisting on doing blockchain? It can only be said that those who cannot recognize the essence of decentralization are destined to not go far in the Crypto industry; no further explanation.
Note: Recently, I have been researching AI Agents with an insatiable thirst, and I clearly feel that there is not enough 'source of information.' How about you recommend projects in the comments, and I'll follow up with analysis?