DeFi+AI has arrived, a panoramic view of the four major areas of DeFAI

원저자: Poopman

원문: TechFlow

What kind of sparks will emerge 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) together.

I hope this article can provide you with some inspiration!

(*I鈥檒l be publishing a 20-page in-depth analysis on Medium soon. Today鈥檚 post is just a quick overview to get you up to speed on this emerging field.)

Why should you care about DeFAI?

The combination of artificial intelligence (AI) and blockchain is not new. From the early decentralized model training in the Bittensor subnet, to decentralized GPU and computing resource markets such as Akash and io.net , to the combination of AI and memecoin on Solana, each stage has demonstrated how blockchain can supplement 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 value of DeFAI has reached approximately US$1 billion. Among them, Griffain accounts for 45% of the market share, while $ANON accounts for 22%.

Starting from December 25, 2024, as frameworks and platforms such as Virtual and ai16z ushered in the return of American funds after the Christmas holidays, the DeFAI industry began to accelerate its development.

This is just the beginning. DeFAIs potential goes far beyond its current performance.

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

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

How AI Agents Work in DeFi

An AI agent is a program that performs tasks on behalf of a user according to a specific workflow. At their core, these agents are powered by a Large Language Model (LLM) that is able to generate responses based on their training data.

In blockchain, agents can interact with smart contracts and accounts and handle complex tasks without constant intervention from users.

예를 들어:

• Simplify the DeFi user experience: complete multi-step cross-chain bridging and liquidity mining operations with one click

• Optimizing liquidity mining strategies: providing users with higher returns

• Automated trade execution: buying or selling assets based on market analysis (whether third-party or your own models)

에 따르면 @threesigmaxyz 鈥檚 research, AI models usually follow the following 6 core workflows:

• Data Collection

• Model Inference

• Decision Making

• Hosting and Operation

• Interoperability

• Wallet Management

Once you have collected the above 6 core elements, you can build your own autonomous intelligent entities on the blockchain. These intelligent entities can play different roles in the DeFi ecosystem, thereby improving on-chain efficiency and user trading experience.

Explore the world of DeFAI v2

In general, I divide the combination of DeFi and AI (DeFAI) into four main categories:

• Abstraction/User-friendly AI

• Yield Optimization and Portfolio Management

• DeFAI Infrastructure or Platform

• 시장 analysis and forecast

Abstract AI or AI ChatGPT

In this area, an ideal AI solution should have the following capabilities:

• Automatically execute multi-step transactions and staking operations without requiring any user expertise.

• Conduct market research in real time and provide key information and data users need to make informed trading decisions.

• Obtain data from multiple platforms, identify market opportunities, and provide users with comprehensive analysis.

Next, let鈥檚 look at some of the popular tools in this space:

Griffain

@griffaindotcom is the first and best performing abstract AI tool on the Solana blockchain, supporting multiple functions such as transaction execution, wallet management, NFT minting, and token quick purchase.

Its main functions include:

• Use natural language input to complete transaction operations

• Launch 토큰 projects through Pumpfun, mint NFTs, and support airdrops to selected addresses

• Multi-agent collaboration function

• Agents can post tweets on behalf of users

• Grab the newly launched 밈 코인s on Pumpfun based on specific keywords or conditions

• Automated Staking and DeFi Strategy Execution

• Task scheduling, users can customize personalized agents by inputting memory data

• Obtain data from multiple platforms for market analysis, such as identifying the main holders of a token

Wallet features:

When creating an account, the system automatically generates a wallet through Privy. Users can authorize their accounts to the agent, which will autonomously execute transactions and manage the portfolio. For enhanced security, private keys are stored in a split manner using Shamir secret sharing technology, ensuring that neither Griffain nor Privy can independently control the wallet.

Anon

@HeyAnonai was created by well-known developer @danielesesta , who created DeFi protocols Wonderland and MIM. Anons goal is to simplify the interactive experience of DeFi, making it easy for both new and experienced users to get started.

Key features include:

• Realizing cross-chain asset bridging based on LayerZero

• Real-time price and data updates via Pyth

• Provides automated operations and triggers based on time and Gas prices

• Real-time market insights, such as sentiment analysis and social data analysis

• Supports lending operations in cooperation with protocols such as Aave, Sparks, Sky and Wagmi

• Natural language trading capabilities supporting multiple languages (including Chinese)

Additionally, Anon recently released two important updates:

• Automation Framework

• Focus on Gemma research agent functions

These updates make Anon one of the most anticipated abstraction tools available today.

Slate (not yet issued)

Slate is backed by BigBrain Holdings and its founder @slate_ceo positions it as an Alpha AI that can trade autonomously based on on-chain data signals. Currently, Slate is the only abstract AI tool that can automate trading on the @hyperliquidX platform.

One thing worth noting is their fee structure.

In Slates service, fees are mainly divided into two categories:

• General operations: Slate does not charge any fees for regular transfers or withdrawals. However, when performing more complex operations such as swaps, bridges, claims, borrowing, lending, repaying, staking, unstaking, long, short, lock, and unlock, the platform will charge a 0.35% handling fee.

• Conditional operations: If a user sets a conditional order (such as a limit order), Slate will charge a fee based on the condition type:

• A fee of 0.25% is charged for conditional operations based on Gas;

• A 1.00% fee is charged for all other conditional actions.

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

• @AIWayfinder

• @orbitcryptoai

• @dolion_ai

• @askthehive_ai

• @HeyElsaAI

• @스펙트럴_랩스

• @Infinit_Labs

• @ProjectPlutus_

• @bankrbot

And many more projects are under development…

Here is a comparison table comparing multiple abstract AI tools:

Figure: Compiled by TechFlow

Automated yield optimization and investment management: Different from 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.

T3A

@trustIn Web3 is a lending protocol that supports undercollateralized lending, using AI as an intermediary and risk management engine.

T3AI鈥檚 AI agent can monitor the health of loans in real time and ensure that loans are always repayable through its risk indicator framework. This is an interesting application example of AI in DeFi.

Kudai

@Kudai_IO is an experimental intelligent entity focusing on the GMX ecosystem, developed by GMX Blueberry Club with the help of EmpyrealSDK toolkit. Currently, $KUDAI Token is traded on the Base network.

The following is Kudai鈥檚 development roadmap:

The core idea of Kudai is to use all transaction fees earned through $KUDAI to fund agents that perform autonomous trading operations, and return the profits generated by these operations to token holders.

In the upcoming second phase of four, Kudai will have the following features, which users can trigger through natural language commands on Twitter:

• Buy and stake $GMX to generate new income streams

• Invest in GMXs GM pool to further increase your returns

• Buy GBC NFT at the reserve price to expand their portfolio

Sturdy Finance V2

@SturdyFinance is a protocol that combines lending and yield aggregation functions. It dynamically allocates funds between different whitelisted isolated pools through an AI model trained by Bittensor SN 10 subnet miners to achieve yield optimization.

Sturdys architecture is divided into two layers: isolated pools and aggregation layer.

• Isolated pool: This is a single asset pool where users can only lend one asset or borrow with one collateral, reducing the mutual risk between assets.

• Aggregation layer: Built on Yearn V3, users assets are allocated to whitelisted isolated pools based on usage and returns. The Bittensor subnet provides the best allocation strategy for the aggregation layer. When users lend assets to the aggregation layer, their risk is limited to the selected collateral type, avoiding the risks brought by other lending pools or collateral assets.

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

• @derivexyz

• @Thales_ai

• @Mozaic_Fi

• @boltrade_ai

• @vainguard_ai

• @Ensofi_xyz

• @0x ARMAgeddon

• @glamsystems

And many more projects are under development…

Market Sentiment Analysis AI Agent

AIXBT

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

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

AIXBT鈥檚 functionality is not limited to providing market insights, it is also interactive, able to answer user questions, and even issue tokens through the Twitter platform. For example, the $CHAOS token was created by AIXBT in collaboration with another interactive robot, Simi, through the @EmpyrealSDK toolkit.

Other market analysis agents include:

• @tri_sigma_

• @ASYM 41 b 07

• @kwantxbt

• @gemach_io

DeFi infrastructure and ecological platform

The realization of Web3 AI agents is inseparable from decentralized infrastructure. These projects not only provide model training and inference services, but also provide data, verification mechanisms, and coordination layers for the development of AI agents.

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

We discussed the following in depth on Medium:

• How to create a model

• Provision of data and computing resources

• The role of the authentication mechanism

• How Trusted Execution Environment (TEE) works

Since there is a lot of content, please pay attention to the article on Medium for specific details.

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

Key players in this space include:

Trusted Execution Environment (TEE)

• @PhalaNetwork

• @MarlinProtocol

• @AutomataNetwork

액자

• @arcdotfun

• @ai16z dao

Platform/ Integrated Solution

• @virtuals_io

• @aisweatshop

• @Almanak__

• @autonolas

• @Cod 3 xOrg

• @crestalnetwork

• @CreatorBid

• @openservai

• @WaveformBackup

• @getaxal

• @EmpyrealSDK

General infrastructure

• @joinFXN

• @TheoriqAI

• @hyperbolic_labs

• @BagelOpenAI

• @Hive_Intel

도구전부

• @sendaifun

• @lexiconinfra

The future of DeFi AI

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

The development of DeFi AI will go through 4 specific stages.

Phase 1: Focus on improving efficiency and launching tools to simplify complex DeFi operations. For example:

• AI that can understand imperfect input

• Tools to quickly complete transactions

• Real-time market research helps users make more informed decisions based on their goals

Phase 2: Agents will be able to trade autonomously and execute strategies based on third-party data or insights from other agents. Advanced users can fine-tune models and build agents to optimize returns for themselves or their clients.

Phase 3: Users will focus on wallet management and AI verification issues. Trusted Execution Environment (TEE) and Zero-Knowledge Proof (ZKP) will ensure the transparency and security of AI systems.

Phase 4: Eventually, 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 암호화폐currency transactions.

Although this vision is exciting, there are still some pressing issues to be addressed:

• Many current tools are simply wrappers of ChatGPT and lack clear evaluation criteria.

• The fragmentation trend of on-chain data may cause AI models to be more centralized rather than decentralized, and there is currently no clear solution.

This article is sourced from the internet: DeFi+AI has arrived, a panoramic view of the four major areas of DeFAI

Related: HTX Growth Academy | DeSci Research Report: Blockchain Reshapes Scientific Research

1. Background and Introduction Since the Enlightenment, scientific research has promoted the rapid development of human civilization. However, with the continuous centralization of the modern scientific system, many challenges have gradually emerged, including uneven distribution of scientific research resources, disputes over the ownership of intellectual property rights, insufficient data transparency, and academic monopoly. These problems have hindered the efficiency of scientific discovery to a certain extent, and even affected the fairness and inclusiveness of science. Decentralized Science (DeSci) is an emerging concept based on blockchain technology, which aims to transform the existing scientific ecology through a transparent and decentralized technology system, giving researchers and the public more rights and choices. DeSci has brought revolutionary changes to the governance model, knowledge sharing mechanism, and funding model of scientific research, and its…