標題：DeFai：人工智慧與 DeFi 的融合

在短短 3 個月內，AI x memecoin 的市值已達到 134 億美元，其規模與 AVAX 或 SUI 等一些成熟的 L1 相當。

output: Artificial intelligence (AI) has experienced a surge in popularity within the cryptocurrency industry, particularly on the Solana blockchain, where the integration of AI and memecoins has led to a market capitalization of $13.4 billion, comparable to some mature Layer 1 protocols such as Avalanche (AVAX) or Sui (SUI).

產出：人工智慧 (AI) 在加密貨幣行業中的受歡迎程度激增，特別是在 Solana 區塊鏈上，人工智慧和 memecoin 的整合導致市值達到 134 億美元，與一些成熟的 Layer 1 協議（例如雪崩(AVAX) 或隋(SUI)。

Over the past few months, we've witnessed the merging of two powerful technologies: AI and blockchain. From decentralized model training on the early Bittensor subnet to decentralized GPU/computing resource markets like Akash and io.net and the current wave of AI x memecoins and frameworks on Solana, each stage demonstrates how cryptocurrency can complement AI to a certain extent through resource aggregation, thereby achieving sovereign AI and consumer use cases.

在過去的幾個月裡，我們見證了兩種強大技術的融合：人工智慧和區塊鏈。從早期Bittensor子網上的去中心化模型訓練，到Akash、io.net這樣的去中心化GPU/計算資源市場，再到當前Solana上的AI x memecoins和框架浪潮，每個階段都展示了加密貨幣如何透過資源聚合在一定程度上補充AI ，從而實現主權人工智慧和消費者用例。

In the first wave of Solana AI coins, some have brought meaningful utility beyond pure speculation. We’ve seen the emergence of frameworks like ai16z’s ELIZA, AI agents like aixbt that provide market analysis and content creation, or toolkits that integrate AI with blockchain capabilities.

在第一波 Solana AI 代幣中，有些代幣帶來了超越純粹投機的有意義的效用。我們已經看到了像 ai16z 的 ELIZA 這樣的框架，像 aixbt 這樣提供市場分析和內容創建的人工智慧代理，或者將人工智慧與區塊鏈功能整合的工具包的出現。

In the second wave of AI, as more tools mature, applications have become the key value driver, and DeFi has become the perfect testing ground for these innovations. To simplify the expression, in this study, we refer to the combination of AI and DeFi as "DeFai".

在人工智慧的第二波浪潮中，隨著更多工具的成熟，應用程式已成為關鍵的價值驅動因素，而 DeFi 已成為這些創新的完美試驗場。為了簡化表達，在本研究中，我們將 AI 與 DeFi 的結合稱為「DeFai」。

According to Coingecko, DeFai has a market cap of about $1 billion. Griffian dominates the market with a 45% share, while $ANON accounts for 22%. This track began to experience rapid growth after December 25, and frameworks and platforms such as Virtual and ai16z experienced strong growth after the Christmas holiday.

據 Coingecko 稱，DeFai 的市值約為 10 億美元。 Griffian 以 45% 的份額佔據市場主導地位，而 $ANON 則佔 22%。該賽道在12月25日後開始快速成長，Virtual、ai16z等框架和平台在聖誕假期後強勁成長。

This is just the first step, and the potential of DeFai goes far beyond this. Although DeFai is still in the proof-of-concept stage, we cannot underestimate its potential. It will use the intelligence and efficiency that AI can provide to transform the DeFi industry into a more user-friendly, intelligent and efficient financial ecosystem.

這只是第一步，德輝的潛力遠不止於此。儘管DeFai仍處於概念驗證階段，但我們不能低估它的潛力。它將利用人工智慧所能提供的智慧和效率，將 DeFi 產業轉變為更人性化、智慧和高效的金融生態系統。

Before we dive into the world of DeFai, we need to understand how agents actually work in DeFi/blockchain.

在深入了解 DeFai 的世界之前，我們需要了解代理在 DeFi/區塊鏈中的實際工作方式。

Artificial Intelligence Agent (AI Agent) refers to a program that can perform tasks on behalf of users according to workflow. The core behind AI Agent is LLM (Large Language Model), which can respond based on its training or learned knowledge, but this response is often limited.

人工智慧代理（AI Agent）是指能夠依照工作流程代表使用者執行任務的程式。 AI Agent背後的核心是LLM（大型語言模型），它可以根據其訓練或學到的知識做出回應，但這種回應往往是有限的。

Agents are fundamentally different from robots. Robots are usually task-specific, require human supervision, and need to operate under predefined rules and conditions. In contrast, agents are more dynamic and adaptive, and can learn autonomously to achieve specific goals.

代理與機器人有著根本的不同。機器人通常是特定於任務的，需要人工監督，並且需要在預先定義的規則和條件下運作。相較之下，智能體更具動態性和適應性，並且可以自主學習以實現特定目標。

To create more personalized experiences and more comprehensive responses, agents can store past interactions in memory, allowing the agent to learn from the user’s behavioral patterns and adjust its responses, generating tailored recommendations and strategies based on historical context.

為了創建更個性化的體驗和更全面的回應，代理可以將過去的互動儲存在記憶體中，使代理能夠從用戶的行為模式中學習並調整其回應，從而根據歷史背景生成量身定制的建議和策略。

In blockchain, agents can interact with smart contracts and accounts to handle complex tasks without constant human intervention. For example, in simplifying the DeFi user experience, including one-click execution of multi-step bridging and farming, optimizing farming strategies for higher returns, executing transactions (buy/sell) and conducting market analysis, all of these steps are completed autonomously.

在區塊鏈中，代理商可以與智慧合約和帳戶互動來處理複雜的任務，而無需持續的人工幹預。例如，在簡化DeFi 使用者體驗方面，包括一鍵執行多步驟橋接和挖礦、優化挖礦策略以獲得更高回報、執行交易（買入/賣出）和進行市場分析，所有這些步驟都是自主完成的。

According to @3sigma’s research, most models follow 6 specific workflows:

根據 @3sigma 的研究，大多數模型都遵循 6 個特定的工作流程：

1. Data Collection

1. 數據收集

First, models need to understand the environment in which they work. Therefore, they need multiple data streams to keep the model in sync with market conditions. This includes: 1) On-chain data from indexers and oracles 2) Off-chain data from price platforms, such as data APIs from CMC/Coingecko/other data providers.

首先，模型需要了解它們工作的環境。因此，他們需要多個資料流來使模型與市場狀況保持同步。這包括： 1) 來自索引器和預言機的鏈上數據 2) 來自價格平台的鏈下數據，例如來自 CMC/Coingecko/其他數據提供者的數據 API。

2. Model Reasoning

2. 模型推理

Once models have learned the environment, they need to apply this knowledge to make predictions or actions based on new, unrecognized input from the user. Models used by agents include:

一旦模型了解了環境，它們就需要應用這些知識來根據使用者新的、未識別的輸入做出預測或採取行動。代理程式使用的模型包括：

3. Decision Making

3. 決策

With trained models and data, the agent can take action using its decision-making capabilities. This includes interpreting the current situation and responding appropriately.

借助訓練有素的模型和數據，代理可以利用其決策能力採取行動。這包括解釋當前情況並做出適當反應。

At this stage, the optimization engine plays an important role in finding the best results. For example, before executing a profit strategy, the agent needs to balance multiple factors such as slippage, spread, transaction costs, and potential profits.

在這個階段，優化引擎在尋找最佳結果方面發揮著重要作用。例如，在執行獲利策略之前，代理商需要平衡滑點、點差、交易成本和潛在利潤等多種因素。

Since a single agent may not be able to optimize decisions in different domains, a multi-agent system can be deployed to coordinate.

由於單一智能體可能無法優化不同領域的決策，因此可以部署多智能體系統進行協調。

4. Hosting and operation

4. 託管及營運

Due to the computationally intensive nature of the task, AI agents often host their models off-chain. Some agents rely on centralized cloud services such as AWS, while those that prefer decentralization use distributed computing networks such as Akash or ionet and Arweave for data storage.

由於任務的計算密集性，人工智慧代理通常將其模型託管在鏈外。有些代理商依賴 AWS 等集中式雲端服務，而那些喜歡去中心化的代理則使用 Akash 或 ionet 和 Arweave 等分散式運算網路進行資料儲存。

Although the AI Agent model runs off-chain, the agent needs to interact with the on-chain protocol to execute smart contract functions and manage assets. This interaction requires a secure key management solution, such as an MPC wallet or a smart contract wallet, to process transactions securely. Agents can operate through

雖然AI Agent模型運行在鏈外，但Agent需要與鏈上協議互動來執行智慧合約功能並管理資產。這種互動需要安全的金鑰管理解決方案，例如 MPC 錢包或智慧合約錢包，以安全地處理交易。代理商可以透過