DWF Ventures Decodes DeFAI: You should pay attention to core projects in these four directions

Original source: DWF Ventures X account

Author: DWF Ventures

Compiled by: Shenchao TechFlow

In just over a week, DeFAI has quickly emerged as a high-profile project area and is expected to show strong performance in the coming months.

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

introduction

DeFi has grown significantly in recent years – from the first wave of protocols (such as Maker (now @SkyEcosystem ), @Uniswap, and @compoundfinance ) to over 3,000 different DeFi protocols today.

Although the advancement of DeFi is of great significance to the entire industry, some key challenges have been exposed along the way.

challenge

The first major issue is the increasing operational complexity of DeFi products. Whether it is due to the complexity of the underlying architecture or the number of steps required to participate, these have led to low user acceptance of some DeFi products.

The second problem is that the process of finding the most capital efficient and attractive yield strategies is manual and inefficient. For example, products such as centralized liquidity provision and lending even require ongoing active management by depositors.

While solutions such as automated liquidity management protocols and account abstraction have helped reduce operational friction, DeFAI promises to completely solve these problems.

In order to solve the above two major challenges, a new paradigm emerged.

DeFAI is a combination of artificial intelligence (AI) and decentralized finance (DeFi), aiming to simplify and automate complex DeFi operations and bridge the gap between existing solutions and user-friendly experiences.

Through the form of AI agents, DeFAI can automatically perform tasks for users based on preset parameters. Not only can these agents interact with smart contracts and accounts without human intervention, they can also learn user preferences and behavioral habits to further optimize the user experience over time.

@danielesesta : “@DWFLabs were the first team to spot the DeFAI trend and act quickly. Today, the crypto space welcomes a brand new category – DeFAI.

What started out as just a fun attempt to combine my love for DeFi with the emerging technologies we develop at @heyanonai has now 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 divided into the following categories, each of which addresses different issues faced by DeFi:

• Abstraction

• Analysis

• Optimization

• Infrastructure ​ ​

abstraction

Projects in the abstraction category strive to simplify DeFi and make it easier for users to get started even as product complexity increases.

These projects achieve their goals in a variety of ways, such as supporting text-to-action capabilities and automating multi-step and multi-chain processes.

These methods actually simplify the process of participating in DeFi into two simple steps: the first step is to identify the best opportunities based on the user's needs and interests; the second step is to let the agent complete all necessary operations through a single instruction.

Some projects extend these capabilities even further.

For example, @HeyAnonai not only provides research tools and automatic execution functions, but also provides developers with a framework that allows them to integrate their own DeFi protocols directly into the agent's ecosystem, thereby expanding the agent's service capabilities.

@griffaindotcom has introduced a variety of specialized agents that users can use to further simplify specific processes, such as quickly completing token sniping.

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analyze

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 multiple sources to identify trends and opportunities in DeFi and Tokens.

Through a user interface, users can query the agent for relevant information such as project technical indicators (technical aspects), basic attributes (fundamental aspects), and market sentiment. In addition, most such agents will also operate their own accounts on the X platform, actively share analysis results and interact with the community.

@aixbt_agent is one of the leaders in this category, featuring a custom large language model (LLM) framework, data indexer, and proprietary algorithms for identifying trends. It quickly integrated into the CT community culture and gradually established a reputation similar to an opinion leader (KOL) with its relatively accurate predictions.

Another emerging agent, @AcolytAI , provides dynamic interaction capabilities through its unique Oracle (oracle), which can collaborate with a group of agents 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 use AI to optimize returns and portfolio allocation.

Protocols usually have built-in AI models that directly deploy users' deposits based on their previous backtest strategies. The intelligent agent focuses more on providing flexibility, allowing users to customize their own investment strategies and methods.

For example, @SturdyFinance ’s SN10 (based on the Bittensor subnet) is an AI-driven revenue optimization engine that can independently decide how to allocate users’ deposits to different lending pools to provide lenders with the best returns while achieving complete global Automated operations.

@getaxal ’s flagship product, Autopilot, allows users to set parameters to automate portfolio rebalancing and return harvesting. This not only helps users maintain risk exposure at any time, but also avoids irrational decisions caused by emotional fluctuations, while achieving automatic compound interest growth in returns.

infrastructure

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

ConsoleKit launched by @BrahmaFi helps agents achieve safe and efficient asset management and operations by introducing functions such as pre-execution simulation, custom smart accounts, and modular strategy engines.

@OmoProtocol is a comprehensive multi-agent collaboration layer that allows users and developers to create collaborative dedicated agent networks to support more complex interaction and strategy design. In addition, it also provides an aggregation toolkit to facilitate users to quickly create agents.

in conclusion

Although the DeFAI field is still in the early stages of development, with many projects not yet fully mature and lacking clear differentiation, there is no doubt about the potential of this field.

Although it will take some time to fully realize the possibilities that DeFAI can bring, it has already demonstrated its ability to solve some of the most pressing problems in the DeFi field.

The value of DeFAI is not just about simplifying complexity or improving user experience – it also plays a crucial role in promoting the popularity of DeFi, making it more friendly to both novice and experienced users. As the DeFAI ecosystem gradually improves, we can expect DeFi to become more intuitive, efficient, and user-friendly, laying the foundation for deeper innovation and broader user participation.