DeFAI is another hot topic in the market after Framework. According to Kaito's data on January 15th, DeFAI's mindshare has reached the same level as Meme. Although Meme has been somewhat quiet during the recent Agent craze in the past two months, it still shows that DeFAI is the hottest topic in the market as the latest narrative.

DeFAI is the combination of DeFi and AI Agent, and many protocols are eager to combine Agent with the traditional narrative of DeFi, hoping to spark new ideas.

AI Abstraction is expected to become the mainstream direction of DeFAI applications

A few days ago,@poopmandefiOrganized the application mapping of DeFAI, among which I believe that DeFAI applications in the AI Abstraction category are more likely to create bubbles and have a greater potential to produce high-quality applications. Although DeFAI applications in the portfolio management and market analysis categories are equally attractive, compared with abstract applications, they have less imaginative space and rely more on trust assumptions.

The portfolio management application that focuses on Agent automation can be traced back to the previous cycle. Automation applications can be a simple script or a complex algorithm, but the core remains the pursuit of user customization, that is, users can customize their own strategies based on their trading habits and risk preferences among the choices provided by the platform. Therefore, the goal of automation applications is to allow users to rest assured after running the program.

This means that the imagination space for automated applications is limited. They are more focused on the vertical fine-grained experience of users, and the moat between protocols often reflects in the design of algorithms. The competition of automated portfolio management and yield optimization applications is essentially the team's ability to formulate strategies, competing on when to trigger arbitrage, when to reduce the risk of liquidation, how to allocate positions, and maximize Farming yield.

I believe that the opportunities for Agent's participation in it are not as great as market expectations. The reason is that it is difficult for users to train and fine-tune their private Agents to outperform professional teams' rapidly iterating algorithms. It is difficult for Agents to help themselves find trading opportunities on the chain without becoming someone else's exit liquidity at this stage. Therefore, the narrative of making Agents one's own 'money-printing machine' may only appear ideal.

The market analysis of DeFAI in the Chinese Simplified language is mixed. The reason is that any agent can express their views on token prices, but most of the opinions are repetitive and receive little attention. In these analyses, applications like Zara AI, which have self-developed frameworks, continuously train and optimize to analyze specific indicators. AIXBT, as an industry leader, has long occupied the top spot in the Kaito mindshare and become a top KOL. The market analysis of DeFAI has significant deviations, with the majority of agents being cannon fodder and filled with bubbles, making it difficult to generate commercial value. From the market's recognition of agent-based market analysis to agents forming business models and realizing traffic monetization, this may be the short-term ceiling of the market analysis of DeFAI.

However, the public analysis of Agent can be both a Buy Signal and a Sell News. This may be one of the reasons why top KOLs like AIXBT haven't started independently managing user assets. Because Agent's analysis is based on public data and doesn't artificially drive up prices like human KOLs do through articles and team collaboration. The difference between the two is one of the reasons why DeFAI market analysis has limited imagination space.

So, why is AI Abstraction class DeFAI different? I think its characteristics lie in low predictability and high growth. The low predictability comes from the objective limitations of Web3 AI, with many 'garbage projects' in Web3 from the 'AI bot' in 2023, 'GPT Wrapper' in the first half of 2024, to the recent fine-tuned Agent in the past few months. These projects, with ChatGPT as the core, encapsulate the input and output of the model in the application front-end, and users can use natural language prompts when using it for the first time. However, due to the lack of performance protection, there is significant friction in actual experience. This more than one-year-long poor user experience is the reason why abstract application expectations are low.

The definition of abstract application is to abstract the complex on-chain operations through artificial intelligence, thus simplifying the experience of novice users, so that entry-level users can also deeply experience DeFi protocols. Although these applications are similar to a large number of 'fishy projects' in the way they simplify, users interact with the Agent frontend through natural language and call various APIs, and the Agent completes the operation in the backend, but the interaction method has not significantly improved. Therefore, most users, or the general market perception, often consider the expectations of abstract applications to be low.

However, as more and more Web2 developers enter this field and the development of abstract applications accelerates, this provides huge growth potential for such applications. Currently, abstract applications are in a phase of rapid growth and are expected to achieve breakthroughs in the future.

High growth comes from abstract applications that can fully optimize the user experience, while poor user experience usually comes from two aspects:

1. Users lack understanding of the actual capabilities of the application. When entering commands such as Swap, Staking, etc., although these operations can be successfully executed, this interaction method does not impress users.
2. The user overestimated the ability of the application, inputting complex instructions that are often difficult to execute accurately for a single model, leading to errors in a certain step of the Pipeline workflow.

The current version of the Agent application still has ample room for growth and can overcome the above issues. Taking Questflow as an example, the abstract application combines multiple Agents into a Swarm to optimize user experience. In a Swarm, the more Agents used, the more refined the user's use cases become. For example, the 'Crypto Token Signal Swarm' on the Questflow platform consists of five Agents: Schedule Agent, Telegram Agent, Techcrunch Agent, OKLink Agent, and Aggregated Web3 Information Agent. Through the introduction of the Swarm, users can quickly understand its purpose: monitoring token prices, analyzing projects, and delivering refined Alpha information to Telegram groups. Therefore, when interacting with this Swarm, users' expectations can be fully met, and the actual feedback can match their expectations. More importantly, complex instructions are not simplified or omitted because user instructions are divided and assigned to different Agents, with each Agent only completing its own tasks, making the entire workflow more efficient and concise.

The bubble and chaos in the abstract application track are gradually receding, and the market has begun to turn to more positive and serious development. A brand new way of interaction is about to truly help users solve problems and improve efficiency. This new way of interaction will bring new trading paradigms, and in the process of accelerating evolution in the AI Agent track, abstract applications are expected to become the pioneers capturing the value of the DeFAI market.

Solana ecosystem actively embraces DeFAI

Solana and Base are the two main battlefields in the AI Agent track, but the development directions of these two ecosystems are completely different. Virtuals, relying on a mature token model, occupy the vast majority of the market value in the Base AI Agent track; while in Solana, despite the participation of ai16z, due to the weak fundamentals and the influence of the Solana memecoin atmosphere, Solana's market share in the AI Agent track is relatively low.

For Solana, the current diverse ecosystem is not the most ideal situation. Solana needs a substantial narrative tag to reach the next market value milestone. In the context of Depin's failure, DeFAI is undoubtedly Solana's best opportunity. From the DeFAI application distribution summarized by Solana Daily, many DeFAI applications have chosen the Solana platform. This may be related to Solana's frequent hosting of Agent hackathons and its grant initiatives. Overall, Solana is leading the race in DeFAI, surpassing Base.

Solana released the DeFAI Landscape on Solana last week. I have selected projects with a market cap of over $10 million as of January 19th and provided a brief summary of their core functions and categories.

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