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DeFAI: How AI Reshapes the Decentralized Finance Ecosystem and Unlocks Huge Potential
DeFAI: How AI Unlocks the Potential of Decentralized Finance
Decentralized Finance ( DeFi ) has been a core pillar of the crypto ecosystem since its rapid development in 2020. Despite the establishment of many innovative protocols, it has also led to increased complexity and fragmentation, making it difficult for even experienced users to navigate the multitude of chains, assets, and protocols.
At the same time, artificial intelligence (AI) has evolved from a broad foundational narrative in 2023 to a more specialized, agent-oriented focus in 2024. This shift has given rise to DeFi AI (DeFAI) - an emerging field where AI enhances DeFi through automation, risk management, and capital optimization.
DeFAI spans multiple layers. The blockchain serves as the foundational layer, and AI agents must interact with specific chains to execute transactions and smart contracts. The data layer and computation layer provide the infrastructure necessary for training AI models, which are based on historical price data, market sentiment, and on-chain analysis. The privacy and verifiability layer ensures that sensitive financial data remains secure while maintaining trustless execution. Finally, the agent framework allows developers to build specialized AI-driven applications, such as autonomous trading bots, credit risk assessors, and on-chain governance optimizers.
As the DeFAI ecosystem continues to expand, the most prominent projects can be divided into three main categories:
1. Abstract Layer
Such protocols act as user-friendly interfaces similar to ChatGPT for DeFi, allowing users to input prompts for execution on the blockchain. They are often integrated with multiple chains and dApps, executing user intentions while eliminating manual steps in complex transactions.
Some functions that these protocols can execute include:
For example, there is no need to manually withdraw ETH from the lending platform, cross-chain it to Solana, swap for SOL, and provide liquidity on the DEX - the abstraction layer protocol can complete the operation in just one step.
2. Independent Trading Agent
Unlike traditional trading bots that follow preset rules, autonomous trading agents can learn and adapt to market conditions, adjusting their strategies based on new information. These agents can:
3. AI-driven DApps
Decentralized Finance dApp provides lending, exchanging, and yield farming functionalities. AI and AI agents can enhance these services in the following ways:
Top protocols on these layers face some challenges:
Rely on real-time data streams for optimal trade execution. Poor data quality can lead to inefficient routing, trade failures, or unprofitable outcomes.
AI models rely on historical data, but the cryptocurrency market is highly volatile. Agents must be trained on diverse, high-quality datasets to maintain effectiveness.
A comprehensive understanding of asset correlation, liquidity changes, and market sentiment is needed to grasp the overall market situation.
Protocols based on these categories have gained popularity in the market. However, to provide better products and optimal results, they should consider integrating various datasets of different qualities to elevate their products to a new level.
Data Layer - Powering DeFAI Smart Contracts
The quality of AI depends on the data it relies on. For AI agents to work effectively in DeFAI, they need real-time, structured, and verifiable data. For example, the abstraction layer needs to access on-chain data through RPC and social network APIs, while trading and yield optimization agents require data to further refine their trading strategies and reallocate resources.
High-quality datasets enable agents to better predict future price behavior, providing trading suggestions that align with their preferences for long or short positions on certain assets.
In addition to building a data layer for AI and agents, a certain blockchain is positioning itself as a full-stack blockchain for the future of Decentralized Finance AI (DeFAI). They recently deployed a terminal, which is the co-pilot for DeFAI, to execute on-chain transactions through user prompts, and it will soon be open to stakers.
In addition, the blockchain supports many AI and agent-based teams. They have made significant efforts to integrate multiple AI protocols into its ecosystem, and as more agents are developed and transactions executed, the blockchain is rapidly evolving.
These measures are implemented while they upgrade the network with AI, most notably equipping their blockchain with an AI sorter. By using simulations and AI analysis on transactions before execution, high-risk transactions can be blocked and reviewed prior to processing, ensuring on-chain security. As an L2 of the superchain, this blockchain stands in the middle ground, connecting human and agent users with the best Decentralized Finance ecosystem.
The Next Step for DeFAI
Currently, most AI agents in Decentralized Finance face significant limitations in achieving full autonomy. For example:
The abstraction layer converts user intentions into actions, but often lacks predictive capability.
AI agents may generate alpha through analysis, but lack independent trade execution.
AI-driven dApps can handle vaults or transactions, but they are passive rather than active.
The next phase of DeFAI may focus on integrating a useful data layer to develop the best proxy platform or agent. This will require deep on-chain data regarding whale activity, liquidity changes, etc., while generating useful synthetic data for better predictive analysis, and combining it with sentiment analysis from the general market, whether it is token volatility in specific categories like AI agents, DeSci, etc. ( or token volatility on social networks.
The ultimate goal is for AI agents to seamlessly generate and execute trading strategies from a single interface. As these systems mature, we may see future DeFi traders relying on AI agents to autonomously assess, predict, and execute financial strategies with minimal human intervention.
![Decentralized Finance全解：AI如何释放Decentralized Finance的潜力？])https://img-cdn.gateio.im/webp-social/moments-082e086a7d08141ddad8264adc07d48f.webp(
Conclusion
Given the significant shrinkage of AI agent tokens and frameworks, some may consider DeFAI to be just a passing fad. However, DeFAI is still in its early stages, and the potential of AI agents to enhance the usability and performance of Decentralized Finance is undeniable.
The key to unlocking this potential lies in obtaining high-quality real-time data, which will enhance AI-driven trading predictions and execution. An increasing number of protocols are integrating different data layers, and data protocols are building plugins for frameworks, highlighting the importance of data for agent decision-making.
![DeFi Unleashed: How AI Can Unlock the Potential of DeFi?])https://img-cdn.gateio.im/webp-social/moments-1df1f707fb29db4dd351d64ceb0fd8b8.webp(
Looking ahead, verifiability and privacy will become key challenges that protocols must address. Currently, most AI agent operations remain a black box, and users must entrust their funds to it. Therefore, the development of verifiable AI decision-making will help ensure the transparency and accountability of agent processes. Protocols integrated with TEE, FHE, or even zk-proofs can enhance the verifiability of AI agent behavior, thereby achieving trust in autonomy.
Only by successfully combining high-quality data, robust models, and transparent decision-making processes can DeFAI agents achieve widespread application.
![DeFi Full Analysis: How AI Unlocks the Potential of DeFi?])https://img-cdn.gateio.im/webp-social/moments-878bec495ad46b22ccff5200424900fe.webp(
![DeFAI Full Explanation: How AI Unlocks the Potential of Decentralized Finance?])https://img-cdn.gateio.im/webp-social/moments-56a89e79609d8f982d5d31dadfad9205.webp(