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

Briefly share the investment thinking logic of each category of AI Agent:

1. Single AI: strong user perception, vertical application scenarios, short product verification cycle, but the ceiling is limited, investment must be based on the premise of experiencing the application, such as the emergence of some new strategies to analyze single AI, no matter how much others brag about it , is no match for a practical operation; for example: $AIXBT$LUNA;

2. Framework and standards: The technical threshold is high, the vision and goals are ambitious, the degree of market (developer) adoption is critical, and the ceiling is very high. Investment must be based on the project’s technical appearance, founder’s background, narrative logic, application implementation, etc. Actual comprehensive inspection; for example: $arc, $REI, $swarms, $GAME;

3. Launchpad platform: Tokenomics is perfect and the ecological synergy is strong, which will generate a positive flywheel effect. However, if there are no hits for a long time, market expectations will be seriously damaged. It is recommended to consider the rising channel to follow when the market is hot and innovations are frequently replaced. You should choose to wait and see when it falls. For example: #Virtual, $MetaV;

4. DeFi trading AI Agent: The Agent is implemented in Crypto’s Endgame form, and there is a lot of room for imagination. However, there are uncertainties in intent screening and matching, Solver execution, and the accuracy of transaction results, so you must experience it first before judging whether to follow up; For example: $BUZZ, $POLY, $GRIFT, $NEUR;

5. AI Agent with creative characteristics: The sustainability of the creativity itself determines everything. It has high user stickiness and IP value attributes. However, 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 background of the project team is decent, whether it can continue to launch iterative updates, whether the plan of 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. Commercial organization-promoted AI Agent: Comparatively tests the coverage of B-side project resources, the degree of promotion of products and strategies, and the continuously refreshed 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 business vision is too high, 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 "practical" value; for example: $HYPER, $AVA

9. AI Platform platform series: Regardless of the "consumer-level" market for data, algorithms, computing power, inference fine-tuning, DePIN, etc., a huge demand-side market needs to be introduced. There is no doubt that AI Agent is a market with potential to explode. , so how to connect with AI Agent is very important; 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!