DWF Ventures Decodes DeFAI: You Should Focus on the Core Projects in These Four Major Directions

Original source: DWF Ventures X account

Author: DWF Ventures

Compiled by: ShenChao TechFlow

In just over a week, DeFAI has rapidly emerged as a highly regarded project area, with strong performance expected in the coming months.

So, what makes DeFAI so important? What core issues does it address? Let’s explore together.

Introduction

In recent years, DeFi has made significant progress—from the first wave of protocols (such as Maker, now known as @SkyEcosystem, @Uniswap, and @compoundfinance) to now over 3000 different DeFi protocols.

Although the advancements in DeFi are significant for the entire industry, they have also exposed some key challenges.

Challenges

The first major issue is the increasing operational complexity of DeFi products. Whether due to the complexity of the underlying architecture or the numerous steps required to participate, this has led to lower user adoption for some DeFi products.

The second issue is that the process of finding the most capital-efficient and attractive yield strategies relies on manual operations and is relatively inefficient. For example, products like concentrated liquidity provision and lending require depositors to engage in continuous active management.

While solutions such as automated liquidity management protocols and account abstraction have helped reduce operational friction, DeFAI is expected to fundamentally resolve these issues.

To address the above two challenges, a brand new paradigm has emerged.

DeFAI is the combination of artificial intelligence (AI) and decentralized finance (DeFi), aimed at simplifying and automating complex DeFi operations, bridging the gap between existing solutions and user-friendly experiences.

In the form of AI agents, DeFAI can automatically execute tasks for users based on preset parameters. These agents can interact with smart contracts and accounts without human intervention and can learn user preferences and behaviors, further optimizing the user experience over time.

@danielesesta: " @DWFLabs was the first team to recognize the DeFAI trend and quickly take action. Today, the crypto space welcomes a brand new category—DeFAI.

Initially, it was just a fun attempt to combine my love for DeFi with the emerging technologies we are developing at @heyanonai, but now it has become a reality. DeFAI has arrived and is here to stay. The wave of DeFAI has just begun!"

Classification of DeFAI Projects

DeFAI projects can be categorized into the following types, each addressing different issues faced by DeFi:

• Abstraction

• Analysis

• Optimization

• Infrastructure

Abstraction

Projects in the abstraction category aim to simplify DeFi, making it easier for users to engage even as product complexity increases.

These projects achieve their goals through various means, such as supporting text-to-action functionality and automating multi-step and multi-chain processes.

These methods effectively simplify the process of participating in DeFi into two simple steps: first, identifying the best opportunities based on user needs and interests; second, allowing the agent to complete all necessary operations with a single command.

Some projects go further to expand these capabilities.

For example, @HeyAnonai not only provides research tools and automated execution capabilities but also offers developers a framework to integrate their own DeFi protocols directly into the agent ecosystem, thereby expanding the service capabilities of the agents.

Meanwhile, @griffaindotcom has introduced various specialized agents that users can utilize to further simplify specific processes, such as quickly completing token sniping.

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Analysis

Projects in this category share some similarities with the abstraction category, but their focus is on aggregating and analyzing on-chain data and data from various sources to identify trends and opportunities in DeFi and tokens.

Through a user interface, users can query agents for information related to project technical indicators (technical analysis), fundamental attributes (fundamental analysis), and market sentiment. Additionally, most of these agents operate their own accounts on the X platform, actively sharing analysis results and interacting with the community.

@aixbt_agent is one of the leaders in this category, characterized by its custom large language model (LLM) framework, data indexer, and proprietary algorithms for trend identification. It has quickly integrated into the CT community culture, gradually establishing a reputation similar to that of opinion leaders (KOLs) due to its relatively accurate predictions.

Another emerging agent, @AcolytAI, provides dynamic interaction capabilities through its unique oracle, enabling collaboration with agent groups to provide users with responses based on aggregated data. In the future, it will even support the use of private datasets.

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Optimization

Projects in the optimization category include agents and protocols that utilize AI to optimize yields and portfolio configurations.

Protocols typically incorporate AI models that directly deploy user deposits based on previous backtesting strategies. Agents, on the other hand, focus more on providing flexibility, allowing users to customize their investment strategies and methods.

For example, @SturdyFinance's SN10 (based on the Bittensor subnet) is an AI-driven yield optimization engine that autonomously decides how to allocate user deposits across different lending pools, providing the best yields for lenders while achieving complete automation.

@getaxal's flagship product, Autopilot, allows users to set parameters for automated portfolio rebalancing and yield harvesting. This not only helps users maintain risk exposure at all times but also avoids irrational decisions caused by emotional fluctuations while achieving automatic compounding of yields.

Infrastructure

Unlike single-function agents, projects in this category focus on providing core infrastructure for DeFAI agents. This infrastructure covers everything from model training and inference to data management, security, and even coordination and collaboration mechanisms between agents.

@BrahmaFi introduced ConsoleKit, which helps agents achieve secure and efficient asset management and operations by incorporating features such as pre-execution simulation, customizable smart accounts, and modular strategy engines.

Meanwhile, @OmoProtocol is a comprehensive multi-agent collaboration layer that allows users and developers to create collaborative dedicated agent networks, supporting more complex interactions and strategy designs. Additionally, it provides an aggregation toolkit that facilitates quick agent creation for users.

Conclusion

Although the DeFAI field is still in its early stages of development, with many projects not yet fully matured and lacking clear differentiation, the potential of this field is undeniable.

While fully realizing the various possibilities that DeFAI can bring will take some time, it has already demonstrated the ability to address some of the most pressing issues currently faced in the DeFi space.

The value of DeFAI lies not only in simplifying complexity or enhancing user experience—it also plays a crucial role in promoting the adoption of DeFi, making it more user-friendly for both newcomers and seasoned users. As the DeFAI ecosystem gradually matures, we can expect DeFi to become more intuitive, efficient, and user-friendly, laying the groundwork for deeper innovation and broader user participation.