Author: YBB Capital Researcher Ac-Core
I. What story does DeFAI tell
1.1 What is DeFAI
DeFAI can be simply and clearly described as AI+DeFi. The market has already hyped up AI from AI computing power to AI Meme, from different technical architectures to different infrastructures. Although the overall market capitalization of AI Agents has generally declined recently, the concept of DeFAI is becoming a new breakthrough trend. The current DeFAI can be broadly divided into three categories: AI abstraction, autonomous DeFi agents, and market analysis and forecasting, with specific subdivisions within these major categories as shown in the figure below.
Source: Created by the author
1.2 How does DeFAI work
In the DeFi system, the core of the AI Agent is the LLM (Large Language Model), and its operation involves multi-level processes and technologies, covering all aspects from data collection to decision execution. According to the research of @3sigma in the IOSG article, most models follow six specific workflows: data collection, model inference, decision making, custody and operation, interoperability, and wallet, which are summarized as follows:
1. Data Collection: The primary task of the AI Agent is to fully understand its operating environment. This includes obtaining real-time data from multiple sources:
On-chain data: Obtain real-time blockchain data such as transaction records, smart contract status, and network activity through indexers and oracles, helping the Agent stay synchronized with market dynamics;
Off-chain data: Obtain price information, market news, and macroeconomic indicators from external data providers (such as CoinMarketCap, Coingecko) to ensure the Agent's understanding of external market conditions. These data are usually provided to the Agent through API interfaces;
Decentralized data sources: Some Agents may obtain data from decentralized data feed protocols to ensure the decentralization and credibility of price oracle data.
2. Model Inference: After data collection is complete, the AI Agent enters the inference and computation stage. Here, the Agent relies on multiple AI models to perform complex reasoning and prediction:
Supervised and unsupervised learning: By training on labeled or unlabeled data, AI models can analyze market and governance forum behavior. For example, they can predict future market trends by analyzing historical transaction data, or infer the results of a governance proposal by analyzing governance forum data;
Reinforcement learning: Through trial-and-error and feedback mechanisms, AI models can autonomously optimize their strategies. For example, in token trading, an AI Agent can simulate multiple trading strategies to determine the optimal buy or sell timing. This learning method allows the Agent to continuously improve in changing market conditions;
Natural Language Processing (NLP): By understanding and processing natural language inputs, Agents can extract key information from governance proposals or market discussions to help users make better decisions. This is particularly important when scanning decentralized governance forums or processing user instructions.
3. Decision Making: Based on the collected data and inference results, the AI Agent enters the decision-making stage. At this stage, the Agent not only needs to analyze the current market conditions, but also weigh multiple variables:
Optimization engine: The Agent uses an optimization engine to find the best execution plan under various conditions. For example, when providing liquidity or implementing arbitrage strategies, the Agent must consider factors such as slippage, transaction fees, network latency, and capital size to find the optimal execution path;
Multi-agent system collaboration: To deal with complex market conditions, a single Agent may not be able to fully optimize all decisions. In such cases, multiple AI Agents can be deployed, each focusing on different task domains, to improve the overall decision-making efficiency of the system. For example, one Agent may focus on market analysis, while another Agent focuses on executing trading strategies.
4. Custody and Operation: Since AI Agents need to handle a large amount of computation, they are usually hosted on off-chain servers or distributed computing networks:
Centralized hosting: Some AI Agents may rely on centralized cloud computing services like AWS to host their computing and storage needs. This approach helps ensure the efficient operation of the models, but also brings potential centralization risks;
Decentralized hosting: To reduce centralization risks, some Agents use decentralized distributed computing networks (such as Akash) and distributed storage solutions (such as Arweave) to host their models and data. These solutions ensure the decentralized operation of the models while providing persistent data storage;
On-chain interaction: Although the models are hosted off-chain, the AI Agents need to interact with on-chain protocols to execute smart contract functions (such as trade execution, liquidity management) and manage assets. This requires secure key management and transaction signing mechanisms, such as MPC (Multi-Party Computation) wallets or smart contract wallets.
5. Interoperability: The key role of AI Agents in the DeFi ecosystem is to seamlessly interact with various DeFi protocols and platforms:
API integration: Agents connect to decentralized exchanges, liquidity pools, and lending protocols through API bridges to exchange data and perform interactive operations. This allows Agents to access real-time market prices, counterparties, lending rates, and make trading decisions accordingly;
Decentralized messaging: To ensure the synchronization of Agents with on-chain protocols, Agents can receive updates through decentralized messaging protocols (such as IPFS or Webhooks). This allows AI Agents to process external events in real-time, such as governance proposal voting results or liquidity pool changes, and adjust their strategies accordingly.
6. Wallet Management: AI Agents must be able to execute actual operations on the blockchain, which all depend on their wallet and key management mechanisms:
MPC wallet: Multi-Party Computation wallets split the private key among multiple participants, allowing Agents to securely execute transactions without the risk of a single point of failure. For example, Coinbase Replit's wallet demonstrates how to use MPC to achieve secure key management, allowing users to delegate some autonomous operations to AI Agents while maintaining a certain degree of control;
TEE (Trusted Execution Environment): Another common key management approach is to use TEE technology, storing private keys in a protected hardware enclave. This allows AI Agents to perform transactions and make decisions in a fully autonomous environment without relying on third-party intervention. However, TEE currently faces issues of hardware centralization and performance overhead, but once these challenges are resolved, fully autonomous AI systems will become possible.
1.3 The Origin of the Doctrine? From Intent to DeFAI
Source: Created by the author
If the vision of DeFAI is: to allow users to autonomously manage their investment portfolios and easily participate in the crypto market through AI agents and various AI platforms, does this vision naturally lead us to the concept of "intent"?
Let's review the "intent" concept first proposed by Paradigm. In our normal transactions, we need to specify the exact execution path, like exchanging Token A for Token B on Uniswap. But in the intent-driven scenario, the execution path is determined by the solver and AI working together. In other words: Transaction = I specify the execution method of the TX; Intent = I only care about the TX result, not the execution process. From a retrospective perspective, the narrative of DeFAI not only approaches the ultimate vision of AI Agents, but also perfectly follows the realization of the intent vision, combining AI and intent. Comprehensively, DeFAI seems more like a new path added to the intent concept.
The ultimate version of realizing large-scale blockchain application in the future may be: AI Agent + Solver + Intent-Centric + DeFAI = Future?
II. DeFAI-related Projects
Source: Author-made
2.1 Griffain
@griffaindotcom $GRIFFAIN: It is an innovative platform that combines AI Agent and blockchain, which can help users issue AI Agents, focusing on creating a powerful and scalable decentralized finance (DeFi) solution, supporting seamless token swaps, liquidity provision and ecosystem growth. It can easily manage wallets, trading and NFTs, and automatically execute Memecoin issuance and airdrops.
2.2 Hey Anon
@HeyAnonai $ANON: It is an AI-driven DeFi protocol that can simplify interactions, aggregate real-time project data and execute complex operations through natural language processing, and provide a convenient DeFi abstraction layer for users. DWF Labs announced that it will support the DeFAI project Hey Anon through its AI Agent fund and launched Moonshot on January 14.
2.3 Orbit
@orbitcryptoai $GRIFT: It has simplified the complex DeFi interface and operations, lowering the participation threshold for ordinary people.
It currently supports more than 100 blockchains (EVM and Solana) and more than 200 protocols, and the GRIFT token is used to inject vitality into the platform.
2.4 Neur
@neur_sh $NEUR: It is an open-source full-stack application that integrates LLM models and blockchain technology functions, designed specifically for the Solana ecosystem, and uses the Solana Agent Kit to achieve seamless protocol interaction.
2.5 Modenetwork
@modenetwork $MODE: It positions itself as the center platform for AI x DeFi innovation on the Ethereum Layer2, and holders can stake MODE to obtain veMODE, thereby enjoying AI agent airdrops, aiming to become the DeFAI Stack.
2.6 The Hive
@askthehive_ai $BUZZ: It is built on Solana and integrates multiple models including OpenAI, Anthropic, XAI, Gemini, etc. to achieve complex DeFi operations such as trading, staking, and lending.
2.7 Bankr
@bankrbot $BNKR: It is an AI-driven cryptocurrency companion, where users can easily buy, sell, exchange, place limit orders and manage wallets by sending a single message, and plans to add token swap and on-chain tracking functions in the near future, with the vision of enabling everyone to use DeFi and achieve automated trading.
2.8 HotKeySwap
@HotKeySwap $HOTKEY: It provides an AI-driven DEX aggregator and analysis tools, cross-chain trading and other DeFi tools.
2.9 Gekko AI
@Gekko_Agent $GEKKO: It is an AI agent created by the Virtuals protocol, focusing on providing comprehensive automated trading solutions, specially designed for the prediction market. The GEKKO token's automated trading strategies include automatic rebalancing, yield harvesting and creating new token index functions.
2.10 ASYM
@ASYM41b07 $ASYM: It provides an AI-driven DEX aggregator and analysis tools, which can identify high-return investment opportunities and settle the generated profits in $ASYM.
2.11 Wayfinder Foundation
@AIWayfinder $Wayfinder: It is an AI full-chain interactive tool launched by the card game chain game Parallel, to help Agents navigate the on-chain environment, execute transactions and interact with decentralized applications.
2.12 Slate
@slate_ceo $Slate: It is a general-purpose AI agent and agent connectivity infrastructure layer, which can translate natural language commands into on-chain operations, focusing on the execution of automated trading strategies, buying or selling under specific conditions, making on-chain operations as simple as thinking.
2.13 Cod3x
@Cod3xOrg $Cod3x: It is a Solana AI hackathon project that provides no-code development tools to build agents that can automate DeFi strategies, and its agent interface (Agentic Interface) is a tool that can execute complex operations using only intent expression.
2.14 Almanak
@Almanak__ $Almanak: It is an AI Agent with self-learning capabilities that can autonomously execute tasks, using agent-based modeling to optimize DeFi and gaming projects, with the mission of using data science and trading knowledge to maximize the profitability of protocols while ensuring their economic security.
2.15 HIERO
@HieroHQ $HTERM: It is a multi-chain smart tool for the Solana and Base networks, allowing users to use natural language commands to have agents autonomously complete transactions, including buying and selling tokens, performing simple token analysis, etc.
III. Where Will the AI Agent Ultimately Belong?
Source: Author-made
Time is precious, and DeFAI projects have been emerging like bamboo shoots after the rain. After Bitcoin fell sharply below $90,000 on January 13, the next day CoinGecko data showed that DeFAI-related tokens rose against the trend by 38.73%, with $GRIFT, $BUZZ and $ANON seeing the largest gains. But where should the AI Agent's financial direction go? The current crossroads point to the left for Game and the right for DeFi.
3.1 Towards Game on the Left:
M3 (Metaverse Makers _) (@m3org) may be the most promising representative, the project is composed of artists and open-source hacker communities behind what is believed to be a16z, with core team members including JIN (@dankvr), Reneil (@reneil1337), Saori (@saori_xbt), Shaw (@shawmakesmagic) and others. But the biggest real obstacle for Game is that in the resource-rich Web2 market, there has not yet been a truly explosive AI game. The highly anticipated "Illuvium" in January 2024 sparked controversy over whether AI design was used, but the CEO ultimately denied this claim. In addition, the long development cycle required for games, compared to the right-hand DeFi, AI Game seems to require more market enthusiasm.
3.2 Towards DeFi on the Right:
The project market caps are ranked as $GRIFFAIN, $ANON, $OLAS, $GRIFT, $SPEC, $BUZZ, $RSS3, $SNAI, $GATSBY, among which GRIFFAIN and ANON account for 37.29% of the total DeFAI market cap.
GRIFFAIN: Built on Solana, it currently has a market cap advantage of $457M and 103,000 Twitter followers, ranking first in the DeFAI market cap rankings. Its core function is to complete transactions by generating wallets and enabling fast trading, and users can currently mint The Agent Engine NFTs for 0.01 Sol.
Hey Anon: It adopts a multi-chain model, currently supporting Sonic Insider, Solana, EVM, opBNB and other different public chains. The sudden surge of $ANON is completely driven by the aura of the founder Daniele (@danielesesta), who is also the founder of Wonderland, Abracadabra and WAGMI. Relying solely on traffic has already injected a lot of vitality into $ANON, and Hey Anon, as his next entrepreneurial project, currently ranks second with a market cap of $248M.
IV. Summary
The emergence of DeFAI is not accidental, as the core characteristics of blockchain are well-suited for strong financial scenarios. Currently, whether it is the left-hand GameFAI or the right-hand DeFAI, both are showing considerable market potential. On the left-hand Game direction, the future may see the continuation of the metaverse, with the help of AI, managing virtual assets, characters, economies and other aspects, and borrowing the elements of AI Agent's Meme propagation to achieve the self-governance and prosperity of the metaverse.
The development of DeFi to the right will inevitably move from the passionate emotional speculation to a value-oriented endpoint. The value of AI Agents cannot rely solely on issuing Memes to cater to market trends, but the continuation of the AI Agent story must have the support of DeFi-like yield stacking. The victorious king will not always be clad in armor, and the ultimate result of market competition is worth our eager anticipation.
