The market for AI memes changes rapidly, which may not only be a shift in the meme paradigm, not just a shift in the Web3 narrative paradigm, but also a shift in Web3 investment methods.
The simple reason many meme players miss out on AI memes is that they are unprepared for the rapid iteration in the AI industry.
Even those who caught the wave with AI16z, goat, and virtual may still miss the next wave of rapidly evolving narratives.
This reason is the lack of preparation for the fast iterations in the AI industry.
To delve deeper, it can be divided into three parts:
1. Insufficient preparation for the rapid iteration of AI meme gameplay brought about by model quick iterations.
Leveraging the wealth effect from memes, the AI meme sector is quickly catching up with the latest advancements in various AI tools and development ideas.
To make money, players must (have to) quickly keep up and even anticipate trends.
This requires a substantial understanding of various aspects of the AI industry's development.
2. Insufficient ability to judge the functional complexity of new targets.
AI memes are becoming increasingly complex with rapid iterations, and simply relying on so-called community and culture for AI memes can no longer sustain enough attention to maintain market value over the long term. This makes the functionality of memes increasingly stronger while development costs are rising. Therefore, more and more so-called VC funds will enter to create memes and then raise industry entry barriers through innovative gameplay.
Whether for meme developers or those who fully understand these gameplay mechanics, there needs to be a transition from "finding angles" to "analyzing project fundamentals."
This requires a considerable understanding of the current AI technological advancements.
For example, to anticipate what the agent's next step is, such as agent swarm, workflows, or Agent Driven Organization (ADO), one should at least have a basic knowledge.
3. Insufficient preparation for the rapid iteration of investment tools and methods.
The information that AI agents can generate, resembling research reports, has left many research-oriented influencers shocked that they might be eliminated in the future. Little do they know, in research, AI has already surpassed the capabilities of graduate students and even PhD candidates in aspects like topic selection, writing, and experimentation.
Using AI for quick research on various meme targets will become a key means for profit or loss (possibly the only way).
But to use AI effectively, one must have a sufficiently deep understanding of the industry and large models.
For example, if you want to mentor graduate students, you should at least have the level of a graduate advisor, right?