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 the major categories as shown in the figure below.

Source: Author-made

1.2 How Does DeFAI Work

In the DeFi system, the core behind 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 by @3 sigma in the IOSG article, most models follow six specific workflows: data collection, model inference, decision making, custody and operation, interoperability, and wallet management, 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: Obtaining 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: Obtaining 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 price oracle data through decentralized data feed protocols to ensure the decentralization and credibility of the data.

2. Model Inference: After data collection, the AI Agent enters the inference and computation stage. Here, the Agent relies on multiple AI models for 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 forum data.

• Reinforcement learning: Through a trial-and-error and feedback mechanism, 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 under changing market conditions.

• Natural Language Processing (NLP): By understanding and processing natural language input, 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 situation, 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 through collaboration. For example, one Agent may focus on market analysis, while another 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 themselves are hosted off-chain, 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 operational interactions. This allows Agents to access real-time market prices, counterparties, lending rates, and other key information to make trading decisions.

• 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 perform 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 maintain a certain level of control while delegating some autonomous operations to AI Agents.

• TEE (Trusted Execution Environment): Another common key management approach is to use TEE technology to store 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 challenges of hardware centralization and performance overhead, but once these issues are resolved, fully autonomous AI systems will become possible.

1.3 The Origin of the Sect? From Intent to DeFAI

Source: Author-made

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 naturally lead us to the concept of "intent"?

Reviewing the "intent" concept first proposed by Paradigm. In our normal transactions, we need to specify the exact execution path, like swapping Token A for Token B on Uniswap. But in the intent-driven scenario, the execution path is determined by the solver and AI 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.

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 tasks such as Memecoin issuance and airdrops.

2.2 Hey Anon

@HeyAnonai $ANON: It is an AI-driven DeFi protocol that simplifies interactions, aggregates real-time project data, and executes complex operations through natural language processing, and provides 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 launch 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 of AI x DeFi innovation on Ethereum Layer 2, and holders can stake MODE to obtain veMODE, thereby enjoying the airdrop of AI agents, and is committed to becoming the DeFAI Stack.

2.6 The Hive

@askthehive_ai $BUZZ: It is built on Solana, integrating multiple models including OpenAI, Anthropic, XAI, Gemini, etc., to achieve complex DeFi tasks 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, and supports cross-chain trading and analysis.

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 automated trading strategies of the GEKKO token include automatic rebalancing, yield harvesting, and the creation of new token index functions.

2.10 ASYM

@ASYM 41 b 07 $ASYM: It provides an AI-driven DEX aggregator and analysis tools, can identify high-return investment opportunities, and settle the generated profits in $ASYM.

2.11 Wayfinder Foundation

@AIWayfinder $Wayfinder: It is an AI cross-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 translates 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 Cod 3x

@Cod 3 xOrg $Cod 3 x: It is a Solana AI hackathon project that provides no-code development tools to build agent-automated 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 autonomously complete transactions, including buying and selling tokens, and performing simple token analysis.

Three, Where will the AI Agent ultimately lead?

Source: Author-made

Time is precious, and DeFAI projects are emerging like mushrooms after the rain. After Bitcoin plummeted 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 direction of the AI Agent in finance go? The current crossroads point to the left for Game and the right for DeFi.

3.1 Towards the left, Game

M 3 (Metaverse Makers _) (@m 3 org) may be the most promising representative, the project is composed of artists and open-source hacker communities behind what is suspected to be ai16z, with core team members including JIN (@dankvr), Reneil (@renei l1 337), 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 "Paru Fantasy" in January 2024 sparked controversy over whether AI design was used due to its development efficiency far exceeding normal, but the CEO ultimately denied this claim. In addition, the long development cycle required by games themselves, compared to the right-hand DeFI, AI Game seems to require more market enthusiasm.

3.2 Towards the right, DeFi

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, currently with 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 directed transactions and fast transactions by generating wallets, and currently 0.01 Sol can be spent to mint The Agent Engine NFT.

Hey Anon: 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, and the traffic alone has injected a lot of vitality into $ANON, and as his next entrepreneurial project, Hey Anon currently ranks second with a market cap of $248M.

Four, Summary

The emergence of DeFAI is not accidental, the core characteristics of blockchain are well suited to strong financial scenarios, and currently whether it is the left-hand Game or the right-hand DeFi, both show 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, can learn from the elements of Meme's self-evolution to realize the self-governance and prosperity of the metaverse.

The development of DeFi to the right must gradually move from the passionate emotional speculation to the direction of value-oriented. The value of AI Agent cannot rely on issuing Meme 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 armored, and the final result of market competition is worth our anticipation.