Analysis of the Investment Logic of Various "Targets" by AI Agents

Author: Haotian

A brief sharing of investment thinking logic for various categories of AI Agents:

1) Standalone AI: Strong user perception, vertical application scenarios, short product validation cycles, but limited ceiling. Investment must be based on experiential applications, such as new strategy analysis standalone AI; no amount of boasting from others can compare to practical experience. For example: $AIXBT $LUNA;

2) Frameworks and Standards: High technical barriers, grand visions and goals, the degree of market (developer) adoption is crucial, and the ceiling is very high. Investment should be based on a comprehensive assessment of the project's technical quality, founder background, narrative logic, and practical application. For example: $arc, $REI, $swarms, $GAME;

3) Launchpad Platforms: Well-developed tokenomics, strong ecological synergy, can generate positive flywheel effects, but prolonged absence of blockbuster projects can severely damage market expectations. It is advisable to consider investing when market enthusiasm is high and innovation is frequent, and to observe during collective downturns. For example: #Virtual, $MetaV;

4) DeFi Trading AI Agents: Agents landing in the endgame form of Crypto, with vast imaginative space, but uncertainties exist in intent matching, solver execution, and accuracy of trading results. Therefore, it is essential to experience first before deciding whether to follow. For example: $BUZZ, $POLY, $GRIFT, $NEUR;

5) Creative Specialty AI Agents: The sustainability of the creativity itself determines everything, high user stickiness, and has IP value attributes. However, 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-Driven AI Agents: Attention should be paid to whether the project team background is reputable, whether they can continuously launch iterative updates, and whether the white paper's plans can gradually materialize. The key is whether they can maintain a leading position throughout a narrative cycle. For example: #ai16z $Focai;

7) Business Organization-Driven AI Agents: Tests the coverage of resources for B-end projects, the degree of product and strategy advancement, and the imaginative space for continuously refreshing new milestones. Of course, actual platform data indicators are also crucial. For example: #ZEREBRO, #GRIFFAIN, $SNAI, $fxn;

8) AI Metaverse Series AI Agent Platforms: AI Agents advancing 3D modeling and metaverse application scenarios do have advantages, but the commercial vision ceiling is too high, with significant hardware dependence and long product cycles. Attention should be paid to the project's continuous iteration and implementation, especially the manifestation of "practicality" value. For example: $HYPER, $AVA;

9) AI Platform Series: Whether dealing with data, algorithms, computing power, or inference fine-tuning, DePIN, etc., all are "consumer-grade" markets that require the introduction of a large demand-side market. Undoubtedly, AI Agents represent a market with potential for explosive growth, so how to connect with AI Agents is crucial. For example: @hyperboliclabs, @weRoamxyz, @dinlol_, @nillionnetwork;

Note: The above is only an incomplete summary of AI Agent categories, and the listed tickers are for research and learning reference only, not as investment advice. DYOR!