The emergence of DeFAI is not a coincidence; the core feature of blockchain adaptation is strong financial scenarios. Currently, whether it is the leftward GameFAI or the rightward DeFAI, both exhibit comparable market potential.

Author: YBB Capital Researcher Ac-Core

1. What Story Does DeFAI Tell?

1.1 What is DeFAI?

In simple terms, DeFAI refers to AI + DeFi. The market has already gone through several rounds of hype regarding AI, from AI computing power to AI memes, and from different technical architectures to various infrastructures. Although the overall market value of AI agents has recently seen a decline, the concept of DeFAI is becoming a new breakthrough trend. Currently, DeFAI can be broadly categorized into three types: AI abstraction, autonomous DeFi agents, and market analysis and prediction. The specific divisions within these categories are shown in the diagram below.

Image source: Created by the author

1.2 How Does DeFAI Work?

In the DeFi system, the core behind AI agents is LLM (Large Language Model), and its operation involves multi-layered processes and technologies, covering all aspects from data collection to decision execution. According to the research by @3sigma in the IOSG article, most models follow six specific workflows: data collection, model inference, decision-making, hosting and operation, interoperability, and wallet management. Below is a summary:

1. Data Collection: The primary task of the AI agent is to gain a comprehensive understanding of its operating environment. This includes obtaining real-time data from multiple sources:

● On-chain data: Real-time blockchain data such as transaction records, smart contract status, and network activity is obtained through indexers, oracles, etc. This helps the agent stay synchronized with market dynamics;

● Off-chain data: Price information, market news, and macroeconomic indicators are obtained from external data providers (such as CoinMarketCap, Coingecko) to ensure the agent understands external market conditions. This data is typically provided to the agent via API interfaces;

● Decentralized data sources: Some agents may obtain price oracle data through decentralized data feed protocols, ensuring the decentralization and reliability of the data.

2. Model Inference: After data collection is complete, the AI agent enters the inference and computation phase. 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 behaviors. For example, they can predict future market trends by analyzing historical trading data or infer the outcome of a voting proposal by analyzing governance forum data;

● Reinforcement learning: Through trial and error and feedback mechanisms, AI models can autonomously optimize strategies. For instance, in token trading, the AI agent can determine the best time to buy or sell by simulating various trading strategies. This learning method allows the agent to continuously improve under changing market conditions;

● Natural language processing (NLP): By understanding and processing user natural language input, the agent can extract key information from governance proposals or market discussions, helping users make better decisions. This is particularly important when scanning decentralized governance forums or processing user commands.

3. Decision-Making: Based on the collected data and inference results, the AI agent enters the decision-making phase. In this stage, the agent needs to analyze the current market situation and 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 executing 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 cope with complex market conditions, a single agent may not be able to optimize all decisions comprehensively. In such cases, multiple AI agents can be deployed, each focusing on different task areas, to improve the overall decision-making efficiency of the system. For example, one agent focuses on market analysis while another agent focuses on executing trading strategies.

4. Hosting and Operation: Since AI agents need to handle a large amount of computation, they typically require their models to be 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 risks of centralization;

● Decentralized hosting: To reduce centralization risks, some agents use decentralized distributed computing networks (like Akash) and distributed storage solutions (like Arweave) to host models and data. These solutions ensure the decentralized operation of the models while providing data storage persistence;

● 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 and 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 multiple different DeFi protocols and platforms:

● API integration: Agents exchange data and interact with various decentralized exchanges, liquidity pools, and lending protocols through API bridges. This allows agents to access key information such as market prices, counterparties, and lending rates in real-time, enabling them to make trading decisions accordingly;

● Decentralized messaging: To ensure the synchronization of agents with on-chain protocols, agents can receive updates through decentralized messaging protocols (like IPFS or Webhook). This allows AI agents to process external events in real-time, such as voting results of governance proposals or changes in liquidity pools, thereby adjusting their strategies.

6. Wallet Management: AI agents must be able to perform actual operations on the blockchain, and all of this relies on their wallet and key management mechanisms:

● MPC wallets: Multi-Party Computation wallets split private keys among multiple participants, allowing agents to conduct transactions securely without a single point of key risk. For example, the wallet from Coinbase Replit demonstrates how to achieve secure key management using MPC, enabling users to maintain some control while delegating partial autonomous operations to AI agents;

● TEE (Trusted Execution Environment): Another common key management method is to use TEE technology to store private keys in a protected hardware enclave. This allows AI agents to conduct 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 Source of the Sect? From Intent to DeFAI

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If the vision of DeFAI is to enable users to autonomously manage their portfolios through AI agents and various AI platforms, allowing everyone to easily participate in cryptocurrency market trading, then does this vision naturally lead us to the concept of "intent"?

Let’s revisit the concept of "intent" first proposed by Paradigm. When we trade normally, we need to specify a clear execution path, just like exchanging Token A for Token B on Uniswap. However, in an intent-driven scenario, the execution path is matched and ultimately determined by solvers and AI. In other words: trading = I specify how the TX is executed; intent = I only care about the TX result but not the execution process. From a retrospective perspective, the narrative of DeFAI not only approaches the ultimate concept of AI agents but also perfectly aligns with the vision of realizing intent while being closely tied to AI. Overall, DeFAI seems more like a new added path for intent.

The ultimate version of achieving large-scale application landing on blockchain in the future will be: AI Agent + Solver + Intent-Centric + DeFAI = Future?

2. DeFAI Related Projects

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2.1 Griffain

@griffaindotcom $GRIFFAIN: An innovative platform that combines AI agents with blockchain, enabling users to issue AI agents, focusing on creating a powerful and scalable decentralized finance (DeFi) solution that supports seamless token swaps, liquidity provision, and ecosystem growth. It allows easy management of wallets, trading, and NFTs, and automatically executes tasks such as Memecoin issuance and airdrops.

2.2 Hey Anon

@HeyAnonai $ANON: An AI-driven DeFi protocol that simplifies interactions, aggregates real-time project data, and executes complex operations through natural language processing, facilitating users' DeFi abstraction layer. DWF Labs announced support for the DeFAI project Hey Anon through its AI Agent fund, which launched on Moonshot on January 14.

2.3 Orbit

@orbitcryptoai $GRIFT: Simplifies complex DeFi interfaces and operations, lowering the participation threshold for ordinary people. It currently supports over 100 blockchains and more than 200 protocols (EVM and Solana), with the GRIFT token used to energize the platform.

2.4 Neur

@neur_sh $NEUR: An open-source full-stack application that integrates LLM models and blockchain technology capabilities, designed specifically for the Solana ecosystem, achieving seamless protocol interaction using the Solana Agent Kit.

2.5 Modenetwork

@modenetwork $MODE: Its positioning is as a central platform for AI x DeFi innovation on Ethereum Layer 2, where holders can stake MODE to obtain veMODE, thereby enjoying airdrops from AI agents, aiming to become the DeFAI Stack.

2.6 The Hive

@askthehive_ai $BUZZ: Built on Solana, it integrates multiple models including OpenAI, Anthropic, XAI, and Gemini to facilitate complex DeFi operations such as trading, staking, and lending.

2.7 Bankr

@bankrbot $BNKR: An AI-driven cryptocurrency companion that allows users to easily buy, sell, swap, place limit orders, and manage wallets with just a message. It plans to add token swapping and on-chain tracking features soon, with a vision to enable everyone to use DeFi and achieve automated trading.

2.8 HotKeySwap

@HotKeySwap $HOTKEY: Provides a complete set of DeFi tools including an AI-driven DEX aggregator and analytics tools, supporting cross-chain trading and analysis.

2.9 Gekko AI

@Gekko_Agent $GEKKO: An AI agent created by the Virtuals protocol, focused on providing comprehensive automated trading solutions, specifically designed for prediction markets. The automated trading strategies of the GEKKO token include automatic rebalancing, yield harvesting, and creating new token index functionalities.

2.10 ASYM

@ASYM41b07 $ASYM: Offers an AI-driven DEX aggregator and analytics tools that can identify high return investment opportunities, settling the generated profits in $ASYM.

2.11 Wayfinder Foundation

@AIWayfinder $Wayfinder: An AI full-chain interactive tool launched by the card game chain Parallel, designed to help agents navigate the on-chain environment, execute trades, and interact with decentralized applications.

2.12 Slate

@slate_ceo $Slate: A universal AI agent and agent connection infrastructure layer that translates natural language commands into on-chain operations, focusing on executing automated trading strategies for buying or selling under specific conditions, making on-chain operations as simple as thinking.

2.13 Cod3x

@Cod3xOrg $Cod3x: A Solana AI hackathon project that provides no-code development tools to build agents that can automate DeFi strategies. Its agent interface (Agentic Interface) is a tool that can execute complex operations using only intent expressions.

2.14 Almanak

@Almanak__ $Almanak: An AI agent with self-learning capabilities that can autonomously execute tasks, utilizing agent-based modeling to optimize DeFi and gaming projects. Its mission is to maximize the profitability of protocols while ensuring their economic security through data science and trading knowledge.

2.15 HIERO

@HieroHQ $HTERM: A multi-chain smart tool for Solana and Base networks that allows users to use natural language commands to enable agents to autonomously complete transactions, including buying and selling tokens and performing simple token analyses.

3. What System Will AI Agents Ultimately Belong To?

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Time is of the essence, and DeFAI projects are emerging like mushrooms after rain. After Bitcoin significantly fell below $90,000 on January 13, the next day, DeFAI-related tokens on CoinGecko rose against the trend by 38.73%, with $GRIFT, $BUZZ, and $ANON seeing the largest increases. However, how the financial direction of AI agents should proceed is worth our contemplation, as the current crossroads point to the left towards Game and the right towards DeFi.

3.1 Leftward Game:

M3 (Metaverse Makers ) (@m3org) may be the most promising representative, composed of artists and an open-source hacker community from an organization suspected to be behind ai16z. Key members of the team include JIN (@dankvr), Reneil (@reneil1337), Saori (@saorixbt), Shaw (@shawmakesmagic), among others. However, the biggest real-world obstacle for games is the resource-rich Web2 market, where no truly explosive AI game has emerged. The highly anticipated "Phantom Beast Palu" in January 2024 sparked controversy over whether AI design was used due to its extraordinary development efficiency, but the CEO ultimately denied this claim. Additionally, the long development cycle required for games seems to demand more market enthusiasm compared to the rightward DeFi.

3.2 Rightward DeFi:

The projects are ranked by market capitalization as follows: $GRIFFAIN, $ANON, $OLAS, $GRIFT, $SPEC, $BUZZ, $RSS3, $SNAI, $GATSBY, with the combined market capitalization of GRIFFAIN and ANON accounting for 37.29% of the total DeFAI market cap.

GRIFFAIN: Built on Solana, it currently ranks first in the DeFAI market cap leaderboard with a market cap of $457M and 103,000 followers on Twitter. Its core functionality includes completing directed transactions through wallet generation and facilitating quick trades. Currently, it costs 0.01 Sol to mint The Agent Engine's NFT.

Hey Anon: Adopting a multi-chain model, it currently supports various public chains such as Sonic Insider, Solana, EVM, and opBNB. The sudden surge of $ANON is entirely driven by the aura of its founder Daniele (@danielesesta), who is also the founder of Wonderland, Abracadabra, and WAGMI. The traffic alone has injected considerable vitality into $ANON, and Hey Anon, as his next entrepreneurial project, currently ranks second with a market cap of $248M.

4. Conclusion

The emergence of DeFAI is not a coincidence; the core feature of blockchain adaptation is strong financial scenarios. Currently, whether it is the leftward GameFAI or the rightward DeFAI, both exhibit comparable market potential. In the leftward Game direction, there may be a continuation of the metaverse, where, with the help of AI, management of virtual assets, characters, economies, and more can be achieved. Elements from the proliferation of AI agents can be borrowed to realize the autonomy and prosperity of the self-evolving metaverse.

As DeFi develops to the right, it will inevitably transition from passionate emotional speculation to a destination oriented towards actual value. The value of AI agents cannot rely on issuing memes to cater to market trends, but the continuation of the AI agent story must be supported by a kind of DeFi yield nesting doll. The victorious king will not always wear armor, and the ultimate outcome of market competition is worth our anticipation.

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