Author: Haotian
A simple sharing of the investment logic of the "targets" of various categories of AI Agents:
1) Single AI: Strong user perception, vertical application scenarios, short product verification cycle, but limited ceiling. Investment must be based on the premise of experiencing the application. For example, some new strategy analysis single AI, no matter how much others boast, cannot be better than trying it out once; for example: $AIXBT $LUNA;
2) Frameworks and standards: High technical threshold, grand vision and goals, the degree of market (developer) adoption is crucial, and the ceiling is very high. Investment should be based on a comprehensive examination of the project's technical quality, founder background, narrative logic, and application landing; for example: $arc, $REI, $swarms, $GAME;
3) Launchpad platforms: Tokenomics are well-designed, with strong ecological synergies, which will generate a positive flywheel effect. But if there is no blockbuster for a long time, it will seriously damage market expectations. It is recommended to consider following the upward channel when market enthusiasm is high and innovation is frequently replaced, and to choose to wait and see when the collective decline occurs. For example: #Virtual, $MetaV;
4) DeFi trading AI Agents: The Agent's landing on Crypto's Endgame form has great imaginative space, but there is uncertainty in intent matching, Solver execution, and trading result accuracy. Therefore, it is necessary to experience it first before deciding whether to follow up; for example: $BUZZ, $POLY, $GRIFT, $NEUR;
5) Creative and distinctive AI Agents: The sustainability of the creativity itself determines everything. High user stickiness, with IP value attributes, but the initial momentum often affects the subsequent market expectation height. It requires the team's continuous update and iteration capabilities; for example: $SPORE, $ZAILGO;
6) Narrative-oriented AI Agents: It is necessary to pay attention to whether the project team's background is upright, whether they can continue to launch iterative updates, and whether the whitepaper's plan can be gradually implemented. The key is whether they can maintain the leading position in a round of narrative; for example: #ai16z $Focai;
7) Business organization promotion AI Agents: It is more demanding on the coverage of B-end project resources, the progress of product and strategy promotion, and the continuous refreshing of new Milestone imagination space. Of course, the actual platform data indicators are also very important; for example: #ZEREBRO, #GRIFFAIN, $SNAI, $fxn
8) AI Metaverse series AI Agent platforms: AI Agents do have advantages in promoting 3D modeling and metaverse application scenarios, but the commercial vision ceiling is too high, hardware dependence is large, and the product cycle is long. It is necessary to pay attention to the project's continuous iteration and landing, especially the manifestation of "practical" value; for example: $HYPER, $AVA
9) AI Platform series: Whether it is data, algorithms, computing power, or reasoning fine-tuning, DePIN, etc., they all need to introduce a huge demand-side market. Undoubtedly, AI Agents are a potential market with huge potential, so how to interface with AI Agents is crucial; for example: @hyperbolic_labs, @weRoamxyz, @din_lol_, @nillionnetwork;
Note: The above is only an incomplete summary of the categories of AI Agents, and the Tickers mentioned are for research and learning purposes only, not as investment recommendations. DYOR!
