A brief sharing of the investment thinking logic for various categories of AI Agents:
1) Standalone AI: Strong user perception, vertical application scenarios, and short product verification cycles, but the ceiling is limited. Investment must be based on the premise of experiential application; for example, the emergence of new strategy analysis standalone AIs. No amount of others' boasting can compare to practical experience; for example: $AIXBT, $LUNA;
2) Framework and Standards: The technical threshold is high, the vision and goals are grand, and the degree of market (developer) adoption is crucial. Moreover, the ceiling is very high. Investment should 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 effects, and will stimulate a positive flywheel effect. However, if there are no blockbuster products for an extended period, it will severely damage market expectations. It is advisable to consider following the upward channel when market enthusiasm is high and innovations are frequent, but to adopt a wait-and-see attitude during collective downturns; for example: Virtual, $MetaV;
4) DeFi Trading AI Agent: Agents landing in the Endgame form of Crypto offer immense imaginative space, but there are uncertainties in intent identification, Solver execution, and trading result accuracy. Therefore, it is essential to experience it first before deciding whether to follow up; for example: $BUZZ, $POLY, $GRIFT, $NEUR
5) Creative Specialty AI Agent: The sustainability of the creativity itself determines everything. User stickiness is high, with IP value attributes, but the initial momentum often affects later market expectations. This tests the team's ability for continuous updates and iterations; for example: $SPORE, $ZAILGO;
6) Narrative-Oriented AI Agent: Attention should be paid to whether the project team background is legitimate, 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 Promotion AI Agent: This tests the coverage of resources on the B-end project, the degree of product and strategy promotion, and the imaginative space for continuously refreshing new milestones. Of course, actual platform data indicators are also key; for example: ZEREBRO, GRIFFAIN, $SNAI, $fxn
8) AI Metaverse Series AI Agent Platform: AI Agents have advantages in promoting 3D modeling and metaverse application scenarios, but the commercial vision is overly ambitious, heavily reliant on hardware, and has a long product cycle. Attention should be paid to the project's continuous iteration and landing situation, especially the manifestation of 'practicality' value; for example: $HYPER, $AVA
9) AI Platform Series: Whether dealing with data, algorithms, computing power, inference fine-tuning, DePIN, etc., all are 'consumer-level' markets that require the introduction of a massive demand-side market. Undoubtedly, AI Agents represent a market with explosive potential, so how to connect with AI Agents is crucial; for example: @hyperbolic_labs, @weRoamxyz, @din_lol_, @nillionnetwork;
Note: The above is only an incomplete summary of AI Agent categories. The listed Tickers are for research and learning reference only and should not be considered investment advice. DYOR!