The Successive Rise of ChatGPT, DeepSeek, and Manus: What Impact Does It Have on Web3 DeFAI?

#ChatGPT #DeepSeek #Manus #DeFAI

The recent integration of AI Agents with DeFi opened up a new narrative track. Although the initial hype had somewhat cooled, the emergence of Manus has brought it back into the spotlight.

Manus has become so sought-after that activation codes are in extremely high demand. The reason is simple: Manus is the first product truly tailored to the needs of AI Agents.

What are the needs of AI Agents?
AI aims to mimic human actions through the PDCA (Plan — Do — Check — Act) cycle, which is executed collaboratively by multiple large models. Each model specializes in a specific phase, reducing decision-making risks for individual models while enhancing overall execution efficiency.

In simple terms, AI Agents must be capable of independent thinking, planning, executing complex tasks, and delivering complete results.

From GPT to DeepSeek to Manus, the evolution of AI products has been remarkable. On the surface, AI applications have significantly expanded to include tasks like resume screening, stock research, and real estate purchases. However, the true breakthrough lies in the underlying framework and execution system.

The latest Manus model is driven by a multimodal large model and introduces an innovative multi-signature system. This so-called “multi-signature system” is essentially a decision validation mechanism for multi-model collaboration. By requiring multiple specialized models to jointly confirm decisions, it ensures the reliability of both decision-making and execution — making it highly aligned with the needs of AI Agents.

The core objective of the DeFAI field is to optimize financial decision-making through AI technology and achieve more efficient decentralized financial services. The maturity of AI Agents will significantly enhance the automation level of DeFi, enabling intelligent portfolio management, on-chain financial risk control, and automated claims processing in decentralized insurance. For instance, AI systems like Manus can assist DeFi protocols in analyzing on-chain data, predicting market trends, and providing dynamically adjusted investment recommendations. This reduces the need for human intervention and improves decision-making efficiency.

Secondly, the advancement of AI Agents and multimodal large models will further drive the intelligent evolution of the Web3 ecosystem. While current sectors such as DeFi, NFT, and GameFi have already established decentralized ecosystems, they still lack in terms of user experience and intelligence. For example, in DeFi lending protocols, AI Agents can analyze users’ asset conditions, market liquidity, and historical borrowing data in real time to offer personalized loan recommendations and dynamically adjust interest rates. Similarly, the NFT market can leverage AI models for intelligent price evaluations and trend predictions, improving overall market efficiency.

Moreover, the impact of AI advancements on DAO (Decentralized Autonomous Organization) governance cannot be overlooked. At present, many DAO governance processes rely on community voting, but due to information asymmetry and complexity, ordinary users often struggle to make accurate decisions. AI Agents can act as decentralized advisors, assisting community members in analyzing proposals and predicting the potential impact of different options, thereby improving the rationality and efficiency of DAO governance.

However, everything has two sides. The decentralized nature of DeFAI requires AI decision-making processes to be transparent and traceable. Yet, mainstream AI models still rely on centralized computing resources, posing a technical challenge in deploying AI within decentralized networks. Furthermore, does automated AI decision-making truly align with the principles of decentralization? If AI models gain excessive decision-making power, could they introduce new centralization risks? These questions still require further exploration.

Specifically, the following are the core impacts that the AI boom may have on DeFAI:
More Accurate Market Analysis: AI can process vast amounts of on-chain data, predict market trends, and reduce decision-making errors caused by human emotions. For example, over the past year, Bitcoin’s price volatility has had a correlation of over 80% with global macroeconomic trends. AI models can identify these relationships much faster.
Smarter Risk Control Systems: On-chain financial risk management has always been a core issue in the DeFi sector. In 2024, losses due to smart contract vulnerabilities exceeded $1 billion. AI, leveraging deep learning technology, can quickly detect potential vulnerabilities and enhance security.
More Efficient Lending and Trading: AI can adjust interest rates or trading strategies on DeFi platforms based on market liquidity and user behavior, making lending markets more balanced. For instance, after integrating an AI risk control system, a DeFi protocol saw its default rate decrease by 15%.
Challenges in Decentralized Data Processing: AI models currently rely on centralized servers for training, whereas Web3 emphasizes decentralization. The conflict between these two paradigms remains unresolved. Finding ways to enable AI to operate effectively in a decentralized environment is a crucial technical direction for the future of DeFAI.
Overall, the rise of AI models such as ChatGPT, DeepSeek, and Manus has opened new opportunities for the development of DeFAI. The intelligence of AI Agents is reshaping the decentralized financial ecosystem, improving the efficiency of financial transactions and governance, while also challenging the fundamental decentralization principles of Web3.