Original author: Kevin, the Researcher at BlockBooster
DeFAI is another hot topic in the market after Framework. According to Kaitos data on January 15, DeFAIs mindshare has reached the same level as Meme. Although Meme has been a little quiet in the Agent boom in the past two months, it still shows the market popularity of DeFAI as the latest narrative. DeFAI is a combination of DeFi and AI Agent. Currently, many protocols cant wait to combine Agent with DeFi, a traditional narrative, in the hope of sparking new sparks.
AI Abstraction is expected to become the mainstream direction of DeFAI applications
A few days ago, @poopmandefi sorted out the application mapping of DeFAI. I think AI Abstraction DeFAI applications are more likely to create bubbles and have a greater chance of producing high-quality applications. Although portfolio management and market analysis DeFAI applications are equally attractive, they have less room for imagination and rely more on trust assumptions than abstract applications.
Portfolio management applications that focus on agent automation can be traced back to the last cycle. Automated applications can be simple scripts or complex algorithms, but the core remains the pursuit of user customization, that is, users can DIY their own strategies based on their trading habits and risk preferences from the options provided by the platform. Therefore, the goal of automated applications is to allow users to rest assured after running the program.
This means that the imagination space for automated applications is limited. They focus more on the vertical refinement of the user experience, and the moat between protocols is often reflected in the design of the algorithm. The competition for automated portfolio management and yield optimization applications is essentially the teams strategy formulation ability, competing on when to trigger arbitrage, when to reduce the risk of liquidation, how to allocate positions and maximize farming returns.
I think the opportunity for Agents to participate is not as big as the market expects. The reason is that it is difficult for private Agents that are fine-tuned and trained by users to outperform the algorithms that are rapidly iterated by professional teams. At the current stage, it is difficult for Agents to help themselves find trading opportunities on the chain without becoming the exit liquidity for others. Therefore, the narrative of letting Agents become their own money printing machines while sleeping may only seem idealistic.
There are mixed bag of DeFAIs in the market analysis category because any agent can express opinions on token prices, but most of the opinions are stereotyped, resulting in no attention. In these analyses, applications with self-developed frameworks such as Zara AI analyze specific indicators through continuous training and optimization; and AIXBT, as the industry leader, has long occupied the top spot on Kaito mindshare and become a top KOL. There is a large deviation in the market analysis category DeFAI. The vast majority of agents are just cannon fodder, full of bubbles, and it is difficult to generate commercial value. From users recognizing the agents market analysis to the agent forming a business model and realizing traffic monetization, this may be the short-term ceiling of the market analysis category DeFAI.
However, the public analysis of Agents can be either Buy Signal or Sell News. This may be the reason why top KOLs such as AIXBT have not started to manage user assets on their own. Because Agents’ analysis is based on public data, unlike human KOLs who work with the team to push up prices by posting articles. The difference between the two is one of the reasons why the imagination of market analysis DeFAI is limited.
So why is AI Abstraction DeFAI different? I think its characteristics are low expectations and high growth. The low expectations come from the objective limitations of Web3 AI. From the AI bot in 2023, the GPT Wrapper in the first half of 2024, to the fine-tuning Agent in recent months, there are many small fish projects in Web3. These projects are centered on ChatGPT, encapsulating the input and output of the model in the front end of the application. Users can prompt in natural language during initial use. However, due to the lack of performance moat, there is a lot of friction in the actual experience. This poor user experience for more than a year is the reason why abstract applications have low expectations.
The definition of abstract applications is to abstract complex on-chain operations through artificial intelligence, thereby simplifying the experience of novice users and allowing entry-level users to deeply experience the DeFi protocol. Although these applications are similar to a large number of small fish projects in terms of simplification, users interact with the Agent front-end through natural language and call various APIs, and the Agent completes the operation on the back-end, but the interaction method has not been significantly upgraded. Therefore, most users, or the general perception of the market, tend to have low expectations for abstract applications.
However, as more and more Web2 developers enter this field, the development of abstract applications is accelerating, which provides huge growth potential for such applications. Currently, abstract applications are in a very high growth stage and are expected to achieve breakthroughs in the future.
High growth comes from the fact that abstract applications can fully optimize the user experience, while poor user experience usually comes from two aspects:
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 make users feel amazing.
Users overestimate the capabilities of the application and enter complex instructions. However, for a single model, such instructions are often difficult to execute accurately, resulting in an error in a step in the Pipeline workflow.
The current version of the Agent application still has ample room for growth and can overcome the above problems. Taking Questflow as an example, the abstract application combines multiple Agents into a Swarm to optimize the user experience. In a Swarm, the more Agents are used, the more refined the users use case is. 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 Swarm, users can quickly understand its purpose: monitoring currency prices, analyzing projects, and refining Alpha information to push to Telegram groups. Therefore, when interacting with the Swarm, users expectations can be fully met and actual feedback can be consistent with expectations. More importantly, complex instructions will not be simplified or omitted, because the users instructions will be split and assigned to different Agents, and each Agent only completes its own tasks, making the entire workflow more efficient and concise.
The bubble and chaos in the abstract application track are gradually fading, and the market has begun to turn to more positive and serious development. A new way of interaction is about to begin to truly help users solve problems and improve efficiency. This new way of interaction will bring a new trading paradigm. In the process of accelerating the evolution of the AI Agent track, abstract applications are expected to become a forerunner in capturing the market value of DeFAI.
Solana Ecosystem Actively Embraces DeFAI
Solana and Base are the two main battlefields of the AI Agent track, but the development directions of these two ecosystems are completely different. Virtuals, relying on a mature token model, occupies the vast majority of the market value of the Base AI Agent track; while in Solana, despite the participation of ai16z, due to weak fundamentals and the influence of the Solana memecoin atmosphere, Solanas share in the AI Agent track is relatively low.
For Solana, the current flourishing ecosystem is not the most ideal situation. Solana needs to have a weighty narrative label to move towards the next market value milestone. Against the backdrop of Depins failure, DeFAI is undoubtedly Solanas best opportunity at the moment. From the distribution of DeFAI applications summarized by Solana Daily, it can be seen that many DeFAI applications have chosen the Solana platform. This may be related to Solanas frequent hosting of Agent hackathons and its Grant issuance initiatives. In general, Solana is in the lead in the DeFAI track, surpassing Base.
Solana released the DeFAI Landscape on Solana last week, and I selected projects with a market cap of more than $10 million as of January 19th and made a brief summary of their core features and classifications.
About BlockBooster: BlockBooster is an Asian Web3 venture studio backed by OKX Ventures and other top institutions, committed to becoming a trusted partner for outstanding entrepreneurs. Through strategic investment and in-depth incubation, we connect Web3 projects with the real world and help high-quality entrepreneurial projects grow.
Disclaimer: This article/blog is for informational purposes only and represents the personal opinions of the author and does not necessarily represent the views of BlockBooster. This article is not intended to provide: (i) investment advice or investment recommendations; (ii) an offer or solicitation to buy, sell or hold digital assets; or (iii) financial, accounting, legal or tax advice. Holding digital assets, including stablecoins and NFTs, carries a high degree of risk and may fluctuate in price or become worthless. You should carefully consider whether trading or holding digital assets is appropriate for you based on your financial situation. If you have questions about your specific situation, please consult your legal, tax or investment advisor. The information provided in this article (including market data and statistics, if any) is for general informational purposes only. Reasonable care has been taken in preparing these data and charts, but no responsibility is assumed for any factual errors or omissions expressed therein.
