Understanding DeFAI in One Article: How AI Empowers Web3 Finance? | Nx.one Research

With the rapid expansion of Decentralized Finance (DeFi) in 2020, it has become a core pillar of the encryption ecosystem, especially in providing Financial Services to global users without intermediaries. DeFi offers users financial tools such as lending, trading, insurance, and derivation through a decentralized approach, fundamentally changing the landscape of the TradFi industry. However, as the number of DeFi applications increases and the complexity of the ecosystem continues to rise, despite many innovative protocols emerging, the complexity and fragmentation of DeFi are also intensifying, making it difficult for even experienced users to navigate the complexities of cross-chain assets, protocols, and smart contracts.

At the same time, artificial intelligence (AI) has shifted from a broad narrative in 2023 to a more specialized, agent-oriented focus. This shift has given rise to an emerging field—DeFi AI (DeFAI), which combines Decentralized Finance and artificial intelligence. This field enhances DeFi through automation, risk management, and capital optimization, helping users navigate the complexities of the ecosystem, and allowing DeFi protocols to operate more efficiently and intelligently.

The multi-layered structure of DeFAI

The architecture of DeFAI can be divided into multiple layers, each providing different functions and values. The bottom layer is the blockchain layer, as AI agents must interact with a specific chain to execute transactions and smart contracts. The blockchain provides a decentralized trust foundation, allowing AI agents to perform tasks in a trustless environment.

On top of this, the data layer and computing layer are important components of DeFAI. The data layer provides the historical data, market sentiment, and on-chain analytical data required for training AI models. By collecting and analyzing this data, AI can extract insights from it, thereby optimizing its decisions and strategies. The computing layer, on the other hand, provides the necessary infrastructure for AI, ensuring that AI agents can perform real-time data processing and predictive analysis.

Privacy and verifiable layers are essential for protecting users’ financial data. Decentralized Finance emphasizes trustless mechanisms, so ensuring that data remains decentralized while not compromising user privacy has become a key challenge. The technological advancements in this area allow DeFi AI to conduct effective data analysis and decision-making without infringing on privacy.

Finally, the agency framework layer is the core of DeFAI, allowing developers to build specific applications such as autonomous trading bots, credit risk assessors, and on-chain governance optimizers. These AI-driven agents can independently perform tasks and interact with different DeFi protocols, providing automated services.

The main categories of DeFAI

With the continuous development of the DeFAI ecosystem, more and more projects are emerging and being categorized. Currently, the most prominent projects can be divided into the following three main categories:

1. Abstract Layer

The abstract layer protocol serves as a user-friendly interface similar to ChatGPT in DeFi, simplifying complex on-chain operations and allowing users to complete tasks through simple prompts. Users only need to provide a simple instruction, and the abstract layer protocol will automatically execute a series of complex tasks, eliminating the need for manual intervention. Typical functions include cross-chain swaps, lending/borrowing, automatic execution of stop-loss and take-profit operations.

For example, suppose a user wants to withdraw ETH from the Aave platform, cross-chain it to Solana, exchange it for SOL or Fartcoin, and provide liquidity on the Raydium platform. The traditional manual operation process can be cumbersome and error-prone, while with an abstract layer protocol, the user only needs to provide a simple instruction, and the protocol can execute these operations across multiple chains without requiring manual intervention from the user.

2. Autonomous Trading Agent

Autonomous trading agents differ from traditional trading robots in that they do not merely follow preset rules for trading. Instead, these agents are capable of adjusting their trading strategies based on market changes and new information. By analyzing on-chain data and market sentiment, autonomous trading agents can continuously optimize their trading decisions, predict market trends, and execute complex Decentralized Finance trading strategies based on this.

The advantage of these agents lies in their adaptability; they can adjust their strategies according to the dynamic changes in the market, rather than rigidly executing preset rules. Autonomous trading agents continuously improve their decision-making capabilities through machine learning technologies, generating more alpha (excess returns) based on this foundation.

3. AI-driven DApps

Decentralized applications (DApps) play a crucial role in Decentralized Finance (DeFi), including lending, trading, liquidity mining, and more. However, the introduction of AI and AI agents has made these traditional DApps smarter and more efficient. With AI technology, these DApps can automatically optimize the services they provide, enhance user gains, and reduce risks.

For example, AI can optimize liquidity supply by rebalancing the positions of liquidity providers (LPs), thereby increasing users’ annual percentage yield (APY). In addition, AI can automatically detect potential risks, such as rug pulls or honeypots, by analyzing the smart contracts of tokens, helping users avoid potential scams.

Challenges faced by DeFAI

Although DeFAI projects have garnered significant attention in the market, they also face some challenges. First, DeFi protocols rely on real-time data streams to ensure optimal trade execution. Data quality directly affects the efficiency and success rate of trades; low-quality data may lead to trade failures or profit losses. The dependence of AI models on data means that the effectiveness of the models is closely related to the quality of the datasets. To ensure accurate decision-making by agents, it is essential to provide diverse and high-quality datasets.

Secondly, the volatility of the cryptocurrency market is significant, and AI agents must be able to adapt to the drastic fluctuations in the market. While historical data provides training material for AI, handling the high volatility of the encryption market is a major challenge faced by DeFAI agents. Therefore, to ensure the long-term effectiveness of AI agents, it is necessary to train on diversified data and regularly update the models.

Finally, the DeFAI agent must be able to understand the overall market conditions, including asset correlation, liquidity changes, and market sentiment. This requires the AI agent to not only have the ability to process a single data point but also to comprehensively analyze multiple data sources for global prediction and optimization.

The best paradigm for the combination of AI and Web3

The combination of AI and centralized exchanges is currently the most successful and promising direction for AI in the Web3 field. For example, Nx.one, as a globally leading AI-driven cryptocurrency exchange, aims to provide users with an intelligent and automated trading experience. The platform covers various trading products such as spot, contracts, and wealth management, and combines AI technology to offer users customized strategies, real-time data analysis, and yield optimization.

Driven by AI, Nx.one is empowered by AI in every scenario to bring users the best trading and investment experience in the market. The platform covers a variety of trading products such as spot, futures, and wealth management, and combines AI technology to provide users with customized strategies, real-time data analysis, and income optimization. Nx,one is deeply engaged in AI technology and is currently the exchange with the highest degree of AI technology adoption in the industry. Each scenario in the exchange is empowered by AI to bring users the best trading and investment experience in the market. At present, Nx.one provides users with a full range of AI trading experiences, such as AI spot, AI contracts, AI financial management, AI strategies, and AI copying. Users will be able to experience real-time market data analysis, personalized investment recommendations, automated trading services, comprehensive data interfaces, and high-frequency update AI tools on Nx.one. In particular, the Nx.oneAI strategy feature allows users to open long and short positions on the same target at the same time, helping users earn certain and stable profits in an uncertain market.

This is just the beginning. The collaboration between AI and centralized exchanges is precisely a necessary step for the integration of AI and Decentralized Finance. Through exchanges, traffic and funds can be fully combined with AI technology, while also completing the final implementation of DeFAI based on the exchange’s own DeFi ecosystem.

The future development of DeFAI

Currently, most AI agents in DeFi still have certain limitations. For example, although the abstraction layer can translate user intentions into actual execution actions, it often lacks market prediction capabilities. AI agents may generate alpha when analyzing data, but they do not have the ability to execute trades independently. While AI-driven DApps can handle specific tasks, such as liquidity provision or trade execution, they are usually in a passive state and cannot proactively predict market trends and make real-time decisions.

Therefore, the next step for DeFAI may focus on strengthening the construction of the data layer, enhancing the predictive ability and independent execution capability of AI agents. By integrating richer on-chain data and market sentiment data, DeFAI can assist users in more accurately predicting market changes, thereby improving the quality of trading decisions. Especially with the development of big data and deep learning technologies, AI agents can gain insights from a broader range of data sources, further optimizing their trading strategies and providing more personalized and efficient services.

Ultimately, the goal of DeFAI is to enable AI agents to seamlessly generate and execute trading strategies from a single interface, allowing users to make complex financial decisions through a simple interface. With the continuous advancement of AI technology, DeFi traders will be able to rely on AI agents to autonomously assess, predict, and execute financial strategies with minimal human intervention, significantly enhancing the efficiency and profit potential of DeFi trading.

Conclusion

The combination of DeFi and AI represents the future of financial technology, and DeFAI, as a pioneer of this future, is redefining the operational model of Decentralized Finance. With continuous technological advancements and innovations, DeFAI will become an indispensable part of the DeFi ecosystem, helping users find optimal strategies and achieve wealth growth in complex and volatile markets.