What impact does Manus have on Web3 DeFAI have on the popularity of Web3?

After waking up, many friends asked me to watch #manus, known as a truly universal AI agent in the world. It can realize independent thinking, plan and execute complex tasks, and deliver complete results. It sounds very cool, but in addition to the many anxious voices of unemployment in the circle of friends, what will it bring to the big explosion of the web3 DeFai scene? Let me talk about my thoughts:

1. About a month ago, OpenAI launched the same product Operator. AI can independently complete tasks including restaurant reservations, shopping, ticket ordering, takeaway ordering and other tasks in the browser. Users can visually supervise and take over control at any time.

There are not many people discussing the emergence of this Agent because it is a single model-driven or a framework called by the tool. Users lose the idea of ​​relying on their tasks when they think of key decisions that require intervention.

2. Manus seems to be much different on the surface, but there are many more application scenarios, including screening resumes, studying stocks, purchasing real estate, etc., but in fact, the differences in the framework and execution system behind it. Manus is driven by a multimodal big model and innovatively adopts a multi-signature system.

In short, AI is to imitate the PDCA cycle action of human execution (plan-execution-check-action), which will be completed by multiple large models in collaboration, each model focusing on a specific link, which can not only reduce the decision-making risk of a single model's task execution, but also improve execution efficiency. The so-called "multi-signature system" is actually a decision verification mechanism for multi-model collaboration, which ensures the reliability of decision-making and execution by requiring the joint confirmation of multiple professional models.

3. With such a comparison, the advantages of manus are obviously highlighted, and coupled with the series of operational experiences shown in the video demo, it really makes people feel an extraordinary experience. But objectively speaking, Manus' iterative innovation of Operator is just the beginning and cannot achieve subversive revolutionary significance.

The key point lies in the complexity of its execution tasks, as well as the definition of the fault tolerance and delivery result success rate of the large model after the non-unified standard user input Prompt enters. Otherwise, following this innovation, will the DeFai scenario of web3 be maturely applied immediately? Obviously, it is impossible to do:

For example: In the DeFai scenario, an agent needs to perform transaction decisions, an Oracle-level agent is responsible for on-chain data collection and verification, and perform data integration and analysis, and also monitor the on-chain prices in real time to capture transaction opportunities. This process is a great challenge for real-time analysis. There may be useful trading opportunities a second ago. After the Oracle big model is transmitted to the transaction execution agent, the trading opportunities will no longer exist (arbitrage window);

This actually exposes one of the biggest weaknesses in the execution decisions of this type of multimodal model. How to connect to the Internet and connect to the link to retrieve and analyze Real-Time-level data, analyze transaction opportunities from it, and then capture transactions. The networking environment is actually good. The order prices of many e-commerce websites do not change in real time, and it is not easy to cause huge dynamic balance troubles to the entire multi-modal collaboration. If it is on the chain, such challenges will exist almost all the time.

4. Therefore, the emergence of manus will indeed cause a wave of anxiety in the web2 field. After all, many civil and information processing jobs with high repetition may face the risk of being replaced by AI. But make them anxious about theirs.

This matter is about web3's driving effect on DeFai application scenarios. We must objectively understand:

It must be admitted that the significance is definitely significant. After all, the LLM OS and Less Structure more intelligence concepts, especially the multi-signature system, will give web3 great inspiration to expand the combination of DeFi and AI.

This actually corrects the major misunderstandings of most DeFai projects. Don’t just rely on a big model to achieve complex goals such as AI Agent’s independent thinking + decision-making. This is simply unrealistic in financial scenarios.

The realization of the true DeFai vision requires solving complex problems such as the upper limit of single AI model capabilities, the atomic guarantee of multimodal interaction and collaboration, unified resource scheduling and dominance of multimodal systems, system fault tolerance and fault handling mechanisms, etc.

For example: Oracle layer Agent is responsible for collecting on-chain data and analyzing, monitoring prices, and forming an effective data source;

The decision-making level agent, analyzes and assesses risks based on the data fed by Oracle, and develops a set of decision-making and action plans;

The execution layer agent is based on a variety of solutions given by the decision-making layer and takes into account actual conditions, including gas fee optimization, cross-chain status, transaction sorting conflicts, etc.

Only when this series of agents are synchronously powerful and have a huge system framework settled, a real DeFai revolution will be launched.