Author: Haotian, source: author's Twitter @tmel0211

Briefly share the "targets" of each category of AI Agent 》Investment thinking logic:

1) Single AI: strong user perception, vertical application scenarios, short product verification cycle, but the ceiling is limited, investment must be based on On the premise of experiencing the application, for example, the emergence of some new strategic analysis units AI, no matter how many times you listen to other people bragging about it, it can’t compete with one actual practice; 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 a comprehensive inspection of the project's technical quality, founder's background, narrative logic, application implementation, etc.; for example: $arc, $REI , $swarms , $GAME ;

3) Launchpad platform: Tokenomics is perfect and has strong ecological synergy, which will generate a positive flywheel effect. However, if there are no hits 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 changing frequently, and you should choose to wait and see when there is a collective decline. For example: #Virtual, $MetaV;

4) DeFi transaction AI Agent: Agent is implemented in Crypto’s Endgame form, with a huge space for imagination, but the intention is to identify and match There are uncertainties in Solver execution, accuracy of transaction results, etc., so be sure to 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. User stickiness is high and it has IP value attributes, but the potential energy in the early stage is often lost. It affects the height of market expectations in the later period and tests the team's ability to continuously update and iterate; for example: $SPORE, $ZAILGO;

6) Narrative-oriented AI Agent: You need to pay attention to whether the background of the project team is decent, whether it can continue to launch iterative updates, whether the white paper plan 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 sustainability The 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 data, algorithm, computing power and inference fine-tuning ,DePIN All "consumer-level" markets 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 integrate 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, including Ticker as an example For research and study reference only, not as investment advice, DYOR!