AI Agent's investment logic for each category of targets

Author: Haotian, Source: Author's Twitter @tmel0211

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, for example, 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: high technical threshold, grand vision and goals, market (developer) adoption is critical, and the ceiling is very high, investment should be based on the actual comprehensive investigation of the project's technical quality, founder background, narrative logic, application landing, 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 product for a long time, it will seriously damage market expectations. It is recommended to consider following the rising channel when the market is hot and innovations are frequently replaced, and choose to wait and see when there is a collective decline. For example: #Virtual, $MetaV; 4) DeFi trading AI Agent: The Agent has landed in the Endgame form of Crypto, with great imagination space, but there are uncertainties in intent identification and matching, Solver execution, and accuracy of transaction results. Therefore, you must experience it first before deciding whether to follow up; For example: $BUZZ, $POLY, $GRIFT, $NEUR; 5) Creative feature AI Agent: The sustainability of the 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 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:

7) Business organization-driven AI Agent: It tests the coverage of B-side project resources, the degree of advancement of products and strategies, and the continuous refreshing 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 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 Platform Series: Whether it is data, algorithms, computing power, reasoning fine-tuning, DePIN, etc., they are all "consumer-level" markets, and they need to introduce a huge demand-side market. There is no doubt that AI Agent It is a market with great potential to explode, so how to connect with AI Agent is crucial; for example: @hyperbolic_labs, @weRoamxyz, @din_lol_, @nillionnetwork;

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