Overview of AI Agent sub-tracks: investment logic of various targets

Let me briefly share the investment thinking logic of each category of AI Agent "target":

1) Single AI: strong user perception, vertical application scenarios, short product verification cycle, but limited ceiling, investment must be based on the premise of experience application, such as the emergence of some new strategy analysis single AI, no matter how much others brag, it can't beat the actual operation; for example: $AIXBT$LUNA;

2) Framework and standards: The technical threshold is high, the vision and goals are grand, the market (developer) adoption is critical, and the ceiling is very high. Investment should be based on a comprehensive investigation of the project's technical quality, founder background, narrative logic, application implementation, etc. For example: $arc, $REI, $swarms, $GAME;

3) Launchpad platform: Tokenomics is perfect, and the ecological synergy is strong, which will give rise to a positive flywheel effect. However, if there is no hit for a long time, it will seriously damage market expectations. It is recommended to consider when the market is hot and innovations are frequently replaced, and when the upward channel follows, and when the collective decline occurs, you should choose to wait and see. For example: #Virtual, $MetaV;

4) DeFi trading AI Agent: The Agent is implemented in the Endgame form of Crypto, and there is great room for imagination, but there are uncertainties in intent identification and matching, solver execution, and transaction result accuracy, so be sure to experience it first before deciding whether to follow up; for example: $BUZZ, $POLY, $GRIFT, $NEUR;

5) Creative AI Agent: The sustainability of creativity itself determines everything. It has high user stickiness and IP value attributes, but the potential energy in the early stage often affects the height of market expectations in the later stage, which tests the team's continuous update and iteration capabilities; for example: $SPORE, $ZAILGO;

6) Narrative-oriented AI Agent: It is necessary to pay attention to whether the project team has a decent background, whether it can continue to launch iterative updates, whether the plan in the white paper can be gradually implemented, etc. The most important thing is whether it can continue to maintain its leading position in a round of narrative; for example: #ai16z$Focai;

7) Business organization-driven AI Agent: This is more about the coverage of B-side project resources, the degree of advancement of products and strategies, and the continuous refresh of new Milestone imagination space. Of course, the actual platform data indicators are also critical; for example: #ZEREBRO, #GRIFFAIN, $SNAI, $fxn

8) AI Metaverse Series AI Agent Platform: AI Agent does have advantages in promoting 3D modeling and Metaverse application scenarios, but the ceiling of the business vision is too high, the hardware dependence is large, and the product cycle is long. It is necessary to pay attention to the continuous iteration and implementation of the project, especially the manifestation of "practicality" value; for example: $HYPER, $AVA

9) AI Platform Series: Whether it is data, algorithms, computing power, reasoning fine-tuning, DePIN, etc., they are all "consumer-level" markets, and they all need to introduce a huge demand-side market. There is no doubt that AI Agent is a market with potential to explode, so how to connect with AI Agent is critical; for example: @hyperbolic_labs, @weRoamxyz, @din_lol_, @nillionnetwork;

Note: The above is only an incomplete category summary of AI Agent. The example of Ticker is only for research and study reference, not as investment advice, DYOR!