Thoughts and judgments on the AI ​​Agent track

Author: cryptoHowe.eth

Recently, ArkStream Capital published a research report on the AI ​​Agent track. After reading it, I think it is quite pertinent, and I agree with many of the views. This time, I will also extend some of the points mentioned in the article and talk about my views and opinions. Everyone is welcome to communicate with me.

Statement: This article has a strong personal subjective consciousness. The views mentioned in the article do not contain any investment advice, but only for communication and sharing. At the same time, this article is based on personal cognition and existing data to make inferences, and will be updated at any time in the future

Why can the AI ​​Agent track occupy such a large market share

From the report, we can see that AI Agent currently occupies nearly a quarter of the market share of the entire AI track. I personally think that there are two reasons why it can occupy such a large share: 1. Agent has a wide range of applicable scenarios, a relatively low threshold, and a short product cycle. Currently, the AI ​​track is mainly composed of five aspects: data, storage, computing, algorithms, and communications. In terms of data, sufficient resource accumulation is required and it is easily affected by geopolitics. Storage and computing are currently very resource-intensive, and algorithms and communications have very high technical barriers. The Agent track is in a position where "everything is just right". It does not need to be supported by massive data, storage, and computing power like a general large model, nor does it need to have major improvements in algorithms and communications like squeezing toothpaste. As long as it can meet the development needs of the product itself, it will be fine. Therefore, compared with other AI products, Agent products do not have a high threshold, have a wide range of application scenarios, and have a relatively short overall development cycle. The project starts quickly, commonly known as "small and beautiful".

2. Agent is closer to the needs of the general public, easier to implement, and meets the narrative of Mass Adoption

The narrative of Agent products is actually very similar to the chain abstraction track, which is to allow users to focus only on their own needs without considering the implementation path and various participants in the middle. Agent has undoubtedly played a huge role in helping Web2 users enter Web3. Users no longer need to learn basic knowledge such as wallets and signatures from scratch, but can directly express their needs through natural language to automatically implement related operations. If a user wants to exchange all BTC for ETH, then the Agent will automatically plan the relevant interaction process and automatically perform cross-chain, transaction and other operations, and the user only needs to wait for the Agent operation to complete to get the desired result. Therefore, Agent can be classified as one of the directions that can truly realize Mass Adoption.

The survival dilemma of content generation agents

The report also divides agent products into infrastructure and content generation, and most products currently belong to the infrastructure category. Why is the development of content generation relatively slow? In other words, what is the survival dilemma of content generation agents? I think there are mainly two points:

1. Content generation is more of a way to meet emotional needs, and emotional needs are difficult to price

To be brief, it is difficult to close the business model. For infrastructure products, it is only necessary to provide relevant services or resources, such as providing relevant computing power or model services for AI developers. In this process, the price of the product is easy to measure, that is, what type of graphics card computing power the user uses and how long it takes, and the relevant price can be obtained through elementary arithmetic, and these prices fluctuate little.

For content generation products, it is very difficult to meet the emotional needs of users and be sustainable. This is because the emotional needs of users themselves are not stable. They may be very happy today and suddenly become very decadent tomorrow. Users' willingness to participate in product interaction is also different. Different users have different emotional needs at different stages, and their willingness and degree of payment are also different. Therefore, the price fluctuates greatly.

2. How to determine whether the content of the Agent meets the needs of users is a big problem

In content generation products, people's subjective consciousness occupies a large part of the product. For example, whether the effect of picture generation is satisfactory, there is no standard to measure it. It depends more on the user's personal feeling, unlike the computing power market, which has clear standards and market prices. Therefore, it will be more difficult to retain and convert users of such products.

Some personal judgments on AI Agent

For the future development of the AI ​​Agent track, I personally think that the following four points need to be noted:

1. It is difficult for pure Agent narratives to occupy a favorable competitive position in the market, and differentiated competition is needed.In the current environment, more and more AI projects will use Agent as one of their narratives, and it is difficult for pure Agent projects to stand out. Imagine that among hundreds or thousands of AI projects, it is difficult to attract users' attention by only talking about Agent narratives. After all, nowadays, good wine is afraid of being hidden in a narrow alley.

2. AI Agent will gradually transform from being independent to being interconnected AgentFi.Currently, the products in the Agent track are independent of each other, and their respective data or services are not interoperable. Users need to provide relevant personal data from scratch when using different Agent products. If there is a reasonable way to connect different products, that is, the Agent trained by users in product A can also be used in product B, its imagination space and user experience will be better.

3. Projects that sell water logic will be the first to come out and occupy the majority of the track market. To put it simply, everyone is developing Agent products, so I will make a tool that can efficiently develop Agents. Such products that can come out are basically stable and profitable gold shovel projects.

4. The income of Agent products mainly comes from toB, and toC is more of a strategy to accumulate word-of-mouth. This should be considered a common phenomenon in the AI ​​track. The willingness and ability of C-end users to pay are far less than those of B-end users. Therefore, if a product wants to be truly profitable, it depends more on the quality of B-end partners, but the publicity and promotion capabilities of C-end users should not be underestimated. Having enough users to use the product can be of great help to the subsequent publicity and promotion of the product.

Finally, here is a summary of some good AI Agent frameworks I saw recently