Simply share the investment thinking logic of various categories of AI Agent 'targets':
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, for example, some new strategy analysis of single AI. No matter how much others boast, it can't compare to one practical operation; for example: $AIXBT, $LUNA;
2）Framework and standards: The technical threshold is high, the vision and goals are grand, the degree of adoption in the market (developers) is critical, and the ceiling is very high. Investment must be based on a comprehensive assessment of the project's technical quality, founder background, narrative logic, application landing, etc.; for example: $arc, $REI, $swarms, $GAME;
3）Launchpad platform: Complete Tokenomics, strong ecological synergy effect, will generate a positive flywheel effect, but if there are no blockbuster products for a long time, it will severely damage market expectations. It is recommended to consider following the rising channel when the market is hot and innovation is frequent, and to choose to wait and see during collective declines. For example: Virtual, $MetaV;
4）DeFi trading-type AI Agent: The Agent lands in the Endgame form of Crypto, with a huge imagination space, but there are uncertainties in intention identification, Solver execution, transaction result accuracy, etc. Therefore, it is essential to experience first before judging whether to follow up; for example: $BUZZ, $POLY, $GRIFT, $NEUR
5）Creative characteristic AI Agent: The sustainability of creativity determines everything, high user stickiness, has IP value attributes, but the initial momentum often affects the later market expectation height, which tests the team's ability for continuous updates and iterations; for example: $SPORE, $ZAILGO;
6）Narrative-oriented AI Agent: Attention needs to be paid to whether the project team background is upright, whether it can continuously launch iterative updates, whether the white paper's plans can gradually land, etc. The most critical factor is whether it can maintain a leading position in a round of narratives; for example: ai16z $Focai;
7）Business organization promotion type AI Agent: Tests the coverage of resources on the B-end project, the degree of product and strategy advancement, and the ongoing refreshing new Milestone imagination space. Of course, actual platform data indicators are also critical; for example: ZEREBRO, GRIFFAIN, $SNAI, $fxn
8）AI Metaverse series AI Agent platform: AI Agent promotes 3D modeling and metaverse application scenarios, which indeed has advantages, but the commercial vision ceiling is too high, hardware dependence is large, product cycle is long, and attention needs to be paid to the continuous iteration and landing situation of the project, especially the manifestation of 'practicality' value; for example: $HYPER, $AVA
9）AI Platform series: Whether it's data, algorithms, computing power, and inference fine-tuning, DePIN, etc., all belong to the 'consumer-grade' market, which needs to introduce a large demand-side market. Undoubtedly, AI Agent is a market with potential waiting to explode, so how to connect with AI Agent is key; for example: @hyperbolic_labs, @weRoamxyz, @din_lol_, @nillionnetwork;
Note: The above is only an incomplete summary of AI Agent categories, where the listed Ticker is for research and learning reference only and does not constitute investment advice, DYOR!