The DeFi + AI trend has arrived, understand the panoramic view of the four major fields of DeFAI in one article.

This is just the beginning; the potential of DeFAI far exceeds its current performance.

Compiled by: Deep Tide TechFlow

What kind of sparks will fly when traditional DeFi meets emerging AI? What new variants or technological innovations can we create?

Today, we will explore the early ecosystem of DeFAI (Decentralized Finance + AI).

I hope this article provides you with some inspiration!

(*I will soon publish a 20-page in-depth analysis on Medium. Today's content is just a quick overview to help you understand this emerging field.)

Why Pay Attention to DeFAI?

The combination of artificial intelligence (AI) and blockchain is not a new concept. From the early decentralized model training in the Bittensor subnet to decentralized GPU and computing resource markets like Akash and io.net, and now the rise of AI and memecoins on Solana, each stage has demonstrated how blockchain can enhance AI capabilities through resource aggregation and promote the realization of sovereign AI and consumer-level application scenarios.

According to CoinGecko data, as of January 13, 2025, the total market capitalization of DeFAI has reached approximately $1 billion. Among them, Griffain holds a 45% market share, while $ANON accounts for 22%.

Starting from December 25, 2024, with frameworks and platforms like Virtual and ai16z welcoming the return of "American funds" after the Christmas holiday, the DeFAI industry began to accelerate its development.

This is just the beginning. The potential of DeFAI far exceeds its current performance.

Although current applications are still in the proof-of-concept stage, we should not underestimate their potential to transform DeFi into a more intelligent, user-friendly, and efficient financial ecosystem through AI technology.

Before delving into the DeFAI ecosystem, we need to understand the basic principles of how AI agents operate in DeFi and blockchain environments.

The Operating Mechanism of AI Agents in DeFi

AI agents are programs that perform tasks on behalf of users according to specific workflows. The core of these agents is powered by large language models (LLM), which can generate responses based on their training data.

In blockchain, agents can interact with smart contracts and accounts to handle complex tasks without the need for continuous user intervention.

For example:

Simplifying the DeFi user experience: Completing multi-step cross-chain bridging and liquidity mining operations with one click
Optimizing liquidity mining strategies: Providing users with higher returns
Automating trade execution: Buying or selling assets based on market analysis (whether from third-party sources or their own models)

According to research by @threesigmaxyz, AI models typically follow these six core workflows:

Data Collection
Model Inference
Decision Making
Custody and Operations
Interoperability
Wallet Management

Once you have "collected" these six core elements, you can build your own autonomous agents on the blockchain. These agents can play different roles in the DeFi ecosystem, enhancing on-chain efficiency and user trading experiences.

Exploring the World of DeFAI v2

Overall, I categorize the combination of DeFi and AI (DeFAI) into four main categories:

Abstracted/User-Friendly AI
Yield Optimization and Portfolio Management
DeFAI Infrastructure or Platforms
Market Analysis and Forecasting

Abstracted AI or AI ChatGPT

In this field, an ideal AI solution should possess the following capabilities:

Automatically execute multi-step trading and staking operations without requiring users to have any specialized knowledge.
Conduct real-time market research and provide users with the key information and data they need to make informed trading decisions.
Gather data from multiple platforms, identify market opportunities, and provide users with comprehensive analysis.

Next, let's take a look at some popular tools in this field:

Griffain

@griffaindotcom is currently the first and best-performing abstracted AI tool on the Solana blockchain, supporting various functions such as executing trades, wallet management, NFT minting, and token sniping.

Its main features include:

Completing trading operations using natural language input
Initiating token projects and minting NFTs through Pumpfun, with support for selecting addresses for airdrops
Multi-agent collaboration functionality
Agents can post tweets on behalf of users
Sniping newly launched meme coins on Pumpfun based on specific keywords or conditions
Automating staking and DeFi strategy execution
Task scheduling, allowing users to customize personalized agents by inputting memory data
Gathering data from multiple platforms for market analysis, such as identifying major holders of a specific token

Wallet Functionality:

When creating an account, the system automatically generates a wallet through Privy. Users can authorize their accounts to agents, which will autonomously execute trades and manage portfolios. To enhance security, private keys are split and stored using Shamir's secret sharing technology, ensuring that neither Griffain nor Privy can independently control the wallet.

Anon

@HeyAnonai is developed by renowned developer @danielesesta, who previously created the DeFi protocols Wonderland and MIM. Anon's goal is to simplify the interaction experience in DeFi, making it easy for both newcomers and experienced users.

Main features include:

Cross-chain asset bridging based on LayerZero
Providing real-time price and data updates through Pyth
Offering automated operations and triggers based on time and gas prices
Real-time market insights, such as sentiment analysis and social data analysis
Supporting lending operations in collaboration with protocols like Aave, Sparks, Sky, and Wagmi
Supporting natural language trading functionality in multiple languages (including Chinese)

Additionally, Anon recently released two significant updates:

Automation framework
Agent functionality focused on Gemma research

These updates make Anon one of the most anticipated abstracted tools currently.

Slate (Not Yet Tokenized)

Slate is backed by BigBrain Holdings, and its founder @slate_ceo positions it as "Alpha AI," capable of autonomous trading based on on-chain data signals. Currently, Slate is the only abstracted AI tool that can achieve trading automation on the @hyperliquidX platform.

One notable aspect is their fee structure.

In Slate's services, fees are primarily divided into two categories:

General Operations: For regular transfers or withdrawals, Slate does not charge any fees. However, for executing more complex operations such as Swap, Bridge, Claim, Borrow, Lend, Repay, Stake, Unstake, Long, Short, Lock, and Unlock, the platform charges a fee of 0.35%.
Conditional Operations: If users set conditional orders (e.g., limit orders), Slate will charge fees based on different condition types:
1. A fee of 0.25% for gas-based conditional operations;
2. A fee of 1.00% for all other conditional operations.

In addition to Slate, there are many emerging abstracted AI tools in this field. Here are some representative projects:

And many more projects are under development…

Here is a comparison table of several abstracted AI tools:

Figure: Compiled by Deep Tide TechFlow

Automated Yield Optimization and Investment Management: Unlike traditional yield strategies, DeFi protocols in this field use AI to analyze on-chain data, identify trends, and provide insights to help teams develop more efficient yield optimization and portfolio management strategies.

T3AI

@trustInWeb3 is a lending protocol that supports under-collateralized loans, utilizing AI as an intermediary and risk management engine.

The AI agents of T3AI can monitor the health of loans in real-time and ensure that loans remain repayable through its risk indicator framework. This is an interesting application of AI in DeFi.

Kudai

@Kudai_IO is an experimental agent focused on the GMX ecosystem, developed by GMX Blueberry Club using the EmpyrealSDK toolkit. Currently, the $KUDAI Token is traded on the Base network.

Here is the development roadmap for Kudai:

The core idea of Kudai is to use all trading fees earned through $KUDAI to fund autonomous trading operations and return the profits generated from these operations to token holders.

In the upcoming second phase (out of four phases), Kudai will have the following features that users can trigger through natural language commands on Twitter:

Purchase and stake $GMX to generate new income sources
Invest in GM pools of GMX to further increase yields
Purchase GBC NFTs at the floor price to expand their portfolio

Sturdy Finance V2

@SturdyFinance is a protocol that combines lending and yield aggregation functions, dynamically allocating funds between different whitelisted isolated pools for yield optimization through AI models trained by Bittensor SN10 subnet miners.

Sturdy's architecture is divided into two layers: isolated pools and aggregation layers.

Isolated Pools: These are single-asset pools where users can only lend one type of asset or borrow using one type of collateral, reducing the inter-risk between assets.
Aggregation Layer: Built on Yearn V3, users' assets are allocated to whitelisted isolated pools based on utilization and yield. The Bittensor subnet provides optimal allocation strategies for the aggregation layer. When users lend assets to the aggregation layer, their risk is limited to the chosen collateral type, avoiding risks from other lending pools or collateral assets.

Other representative projects in the yield optimization and investment management field include:

And many more projects are under development…

Market Sentiment Analysis AI Agents

AIXBT

@AIXBT_agent is a market sentiment tracking agent that integrates and analyzes data from over 400 key opinion leaders (KOLs) on Twitter through its proprietary engine. AIXBT can capture market trends in real-time and provide valuable insights to users around the clock.

Among all AI agents in the DeFi space, AIXBT holds 14.76% of market attention, making it one of the most influential agents in the ecosystem.

The functionality of AIXBT is not limited to providing market insights; it is also interactive, capable of answering user questions, and even issuing tokens through the Twitter platform. For example, the $CHAOS token was created in collaboration with another interactive bot, Simi, using the @EmpyrealSDK toolkit.

Other market analysis agents include:

DeFi Infrastructure and Ecological Platforms

The realization of Web3 AI agents relies on decentralized infrastructure. These projects not only provide model training and inference services but also offer data, validation mechanisms, and coordination layers for the development of AI agents.

Whether in Web2 or Web3, models, computing power, and data are always the three core pillars driving the development of large language models (LLMs) and AI agents.

We have explored the following topics in depth on the Medium platform:

How to create models
Provision of data and computing resources
The role of validation mechanisms
How Trusted Execution Environments (TEEs) work

Due to the extensive content, please refer to the articles on Medium for specific details.

Here is a DeFi infrastructure ecosystem map created by @pinkbrains_io:

The main participants in this field include:

Trusted Execution Environment (TEE)

Frameworks

Platforms / Integrated Solutions

General Infrastructure

Kits

The Future Development of DeFi AI

I believe that the DeFi market will go through three main stages: first pursuing efficiency, then achieving decentralization, and finally focusing on privacy protection.

The development of DeFi AI will undergo four specific stages.

Stage One: Focus on improving efficiency by launching tools that simplify complex DeFi operations. For example:

AI capable of understanding imperfect inputs
Tools for quickly completing transactions
Real-time market research to help users make more informed decisions based on their goals

Stage Two: Agents will achieve autonomous trading, able to execute strategies based on third-party data or insights from other agents. Advanced users can fine-tune models to build agents that optimize yields for themselves or clients.

Stage Three: Users will focus on wallet management and AI verification issues. Trusted Execution Environments (TEEs) and Zero-Knowledge Proofs (ZKPs) will ensure the transparency and security of AI systems.

Stage Four: Ultimately, a no-code DeFi AI toolkit or AI-as-a-Service protocol may emerge, creating an agent-based economic system where users can fine-tune models through cryptocurrency trading.

While this vision is exciting, there are still some pressing issues to address:

Many current tools are merely simple wrappers around ChatGPT, lacking clear evaluation standards.
The trend of fragmented on-chain data may lead AI models to be more centralized rather than decentralized, and there is currently no clear solution.

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