What Are the Most Prominent DeFAI Applications?

DeFAI, following the rise of Framework, is another hot topic currently making waves in the market. According to Kaito’s data from January 15, DeFAI’s mindshare has reached the same level as Meme. Despite the recent quiet period in the Agent craze over the past two months, DeFAI remains a highly discussed topic as the latest narrative in the market. DeFAI is the combination of DeFi and AI Agents, with numerous protocols eager to combine Agents with the traditional DeFi narrative, hoping to spark new innovations.

By Kevin, Researcher at BlockBooster

It’s been a month since BlockBooster published the article “Can AI Agents Become Bubble Machines? Which Ecosystem, Solana or Base, is More Suitable for AI Agents?” As of January 16, according to CoinGecko, the market cap of AI Agents has reached $15.8 billion. At this point, we can say that AI Agents have indeed behaved differently when it comes to creating bubbles, with endless discussions popping up, attracting significant attention even in a down market. What’s even more noteworthy is that this is a new track that’s less than four months old. Although there is still a lot of bubble space awaiting market digestion, ever since the rise of Framework, developers and existing AI protocols have been advocating for “de-bubbling,” racing to develop practical Agent applications. Most applications are just “old wine in new bottles,” and some protocols with long-standing low token prices may seize the opportunity in this chaotic environment. However, what we should focus on is the potential value that new protocols could bring, whether they’ve reached PMF (Product-Market Fit), whether they’ve built a new community, and whether they possess innovative structures and directions.

AI Abstraction is Expected to Become the Mainstream Direction for DeFAI Applications

A few days ago, @poopmandefi organized a DeFAI application mapping, and in my opinion, AI Abstraction-type DeFAI applications are more likely to create bubbles and have a greater potential to produce high-quality applications. While portfolio management and market analysis DeFAI applications are also attractive, their imagination is relatively limited compared to abstraction applications and relies more on trust assumptions.

Automated portfolio management applications focused on Agent automation can be traced back to the previous cycle. These automated applications can range from simple scripts to complex algorithms, but the core remains the same: to provide users with customization options, allowing them to create strategies based on their trading habits and risk preferences. Therefore, the goal of automation applications is to let users run the program and relax with confidence.

This means the imaginative space for automated applications is limited. They focus more on providing users with vertical, detailed experiences, and the moat between protocols often lies in the design of the algorithms. The competition between automated portfolio management and yield optimization applications essentially comes down to the team’s ability to formulate strategies, focusing on when to trigger arbitrage, when to reduce liquidation risks, how to allocate positions, and how to maximize farming yields.

I believe the involvement of Agents in this space isn’t as significant as the market expects. The reason is that a private Agent fine-tuned and trained by the user is unlikely to outperform the algorithms iterated quickly by professional teams. Having an Agent assist in finding trading opportunities on-chain, at this stage, is likely to end up as exit liquidity for others. Therefore, the narrative of having an Agent act as a “money printer while you sleep” may just be an idealization that appears attractive on the surface.

Market analysis DeFAI applications are mixed, mainly because any Agent can give opinions on token prices, but most of these opinions are repetitive and thus attract no attention. Among these analyses, applications like Zara AI, which use self-developed frameworks to train and optimize specific indicators, and AIXBT, the industry leader that has long ranked first on Kaito’s mindshare, have gained recognition as top KOLs. Market analysis DeFAI is highly prone to deviation, with most Agents acting as cannon fodder and flooding the space with bubbles, making it difficult to generate commercial value. The gap between user acceptance of Agent-based market analysis and the realization of a business model through traffic monetization might be the short-term ceiling for market analysis DeFAI.

However, the analysis of an Agent could either serve as a Buy Signal or Sell News. This may be the reason why top KOLs like AIXBT have not started to independently manage user assets. Unlike human KOLs who can push prices by publishing articles and working with teams, Agents base their analysis on public data. This is one of the reasons for the limited imagination in market analysis DeFAI.

So, why is AI Abstraction different? I believe its characteristics are low expectations and high growth potential. The low expectations stem from the inherent limitations of Web3 AI. From 2023’s “AI bot” to the “GPT Wrapper” in the first half of 2024, to the more recent fine-tuned Agents, there have been many “low-quality projects” in Web3. These projects focus on ChatGPT, wrapping model inputs and outputs in the front-end of the application. Users can prompt using natural language when first using them. However, due to the lack of performance moats, the actual experience has significant friction. This prolonged poor user experience is the reason behind the low expectations for abstraction applications.

The definition of abstraction applications is to simplify complex on-chain operations using artificial intelligence, thereby improving the experience for novice users so they can deeply engage with DeFi protocols. Although these applications simplify things in a manner similar to many “low-quality projects,” where users interact with the Agent’s front-end through natural language and call various APIs, with the Agent executing tasks in the back-end, the way of interaction has not significantly upgraded. As a result, most users, or the general market perception, tends to have low expectations for abstraction applications.

However, with more and more Web2 developers entering this space, the development of abstraction applications is accelerating, providing enormous growth potential. Currently, abstraction applications are in a high-growth phase and are expected to break through in the future.

High growth potential comes from the ability of abstraction applications to optimize the user experience, and poor user experiences typically stem from two main factors:

1. Users lack an understanding of the actual capabilities of the application. When inputting commands like Swap or Staking, even though these operations can be executed successfully, the interaction doesn’t feel remarkable.
2. Users overestimate the application’s capabilities by entering complex commands, but for a single model, such commands are often difficult to execute precisely, leading to errors in the pipeline workflow.

The current version of Agent applications still has plenty of room for growth to overcome these issues. For example, in Questflow, abstraction applications combine multiple Agents into a Swarm, thereby optimizing the user experience. In a Swarm, the more Agents involved, the more refined the user use cases. For example, the “Crypto Token Signal Swarm” on Questflow consists of five Agents: Schedule Agent, Telegram Agent, Techcrunch Agent, OKLink Agent, and Aggregated Web3 Information Agent. Through the Swarm’s introduction, users can quickly understand its use case: monitoring coin prices, analyzing projects, and refining Alpha information to be pushed to Telegram groups. As a result, when interacting with this Swarm, users’ expectations are fully met, and the actual feedback aligns with those expectations. More importantly, complex instructions are not simplified or overlooked because users’ commands are broken down and assigned to different Agents, with each Agent completing only its own task, making the entire workflow more efficient and streamlined.

The bubbles and chaos in the abstraction application track are gradually fading away, and the market is now focusing on more serious and positive development. The new mode of interaction is set to help users solve problems and improve efficiency. This new mode will bring about a new transaction paradigm, and as AI Agents evolve rapidly, abstraction applications are expected to become the pioneers in capturing the DeFAI market value.

Solana Ecosystem Actively Embraces DeFAI

Solana and Base are the two main battlegrounds for the AI Agent space, but the development directions of these two ecosystems are quite different. Virtuals, relying on a mature token model, occupy the majority of the market share in Base’s AI Agent space. On the other hand, despite the involvement of ai16z in Solana, Solana has a relatively lower market share in the AI Agent space due to its weak fundamentals and the influence of the Solana memecoin atmosphere.

For Solana, the current flourishing ecosystem is not the ideal situation. Solana needs a significant narrative label to reach its next market capitalization milestone. Against the backdrop of the failure of Depin, DeFAI is undoubtedly Solana’s best opportunity at the moment. From the DeFAI application distribution summarized by Solana Daily, it can be seen that many DeFAI applications have chosen the Solana platform. This is likely related to Solana’s frequent hosting of Agent hackathons and its grant distribution efforts. Overall, Solana is leading in the DeFAI space, surpassing Base.

Last week, Solana released the DeFAI Landscape on Solana. I’ve selected projects with a market cap exceeding $10 million as of January 19th and provided a brief summary of their core features and classifications.

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