Artificial Intelligence (AI) is a cutting-edge technology dedicated to endowing computer programs with human-like intelligence. It enables computer programs to perform tasks that would otherwise require human intelligence, such as learning, reasoning, planning, and prediction, by simulating the "neural networks" in the human brain. Currently, the most widely discussed core AI technologies include Large Language Models (LLM) and inference systems.
Another important concept closely related to AI technology is the " AI Agent ." It can be seen as an application of AI technology; it's an intelligent system with autonomous perception, prediction, decision-making, and execution capabilities. If AI is a capability, then an AI Agent is an AI application that can truly take action.
Because of these powerful characteristics, AI technology has been introduced into various application scenarios. However, traditional AI systems have some potential structural problems during operation. For example, AI models have extremely high demands for computing resources, and their training costs are relatively high; the inference process of AI models is difficult to verify; and providers of data, models, and computing power cannot participate fairly in the distribution of value. These pain points can be precisely addressed by the inherent advantages of Web3 technology.
Against this backdrop, the Web3+AI sector has entered a period of rapid development. This research report will take some projects that have recently announced financing or made significant progress as a starting point to deeply analyze the causes, current status and future development direction of this sector, helping you to further understand its underlying logic and ecological value.
Author: Shirley Li , Researcher at Web3Caff Research
Cover: Photo by Andrew Kliatskyi on Unsplash , Typography by Web3Caff Research
Word count: 14,800+ words in total
Table of contents
- background
- Background and Logic of Web3+AI Integration
- Distribution of Web3+AI tracks
- Data services
- Threshold AI Oracle s
- Ocean
- MCP protocol
- AI computing power/training network
- Gensyn
- GAIB
- AI Inference Network
- EigenAI
- Inference Labs
- Crunch
- Judge
- AI Trust and Identity Framework
- Trusta.AI
- Keycard
- AI Payment Protocol
- x402 protocol and its derivatives
- AP2 protocol
- other
- Multi-Agent Architecture
- Virtuals Protocol
- AI-native blockchain
- Talus Network
- Hyperion
- Web3+AI Browser
- Donut Labs
- AI ecosystem
- MyShell
- summary
- Multi-Agent Architecture
- Data services
- Web3+AI: "Technological Restructuring" or "Narrative Bubble"?
- Future Outlook
- Key Points Structure Diagram
- References
background
In 2022, ChatGPT emerged, showcasing the immense potential of AI technology in the Web2 world. Subsequently, this wave quickly swept through the Web3 world, with developers beginning to explore the value of AI technology in Web3.
In 2024, the Web3+AI narrative experienced explosive growth, with concepts such as decentralized AI computing power market, decentralized AI inference network, AI payment protocol, and AI native blockchain emerging one after another, gradually filling the gaps in various sub-fields of Web3+AI infrastructure.
So why does Web3 need AI, and why does AI need Web3 technology?
First, for Web3, the intelligent characteristics of AI can help users complete many complex operations in an "abstract" way, which coincides with the "abstract" solutions that Web3 is advocating. Moreover, AI Agents can simulate the user's way of thinking, automatically perform certain operations in place of humans, and provide predictive functions for users based on a wider range of data.
Secondly, for AI technology, the reasoning process of Large Language Models (LLMs) developed by traditional Web2 companies is a "black box" for users. Users find it difficult to verify whether the model performed the inference as required, whether the chosen model was used to complete the inference task, and whether the final result provided by the LLM was manipulated. Therefore, the transparency, traceability , and distributed nature of Web3 can provide solutions to these problems.
In short, the introduction of AI technology has injected new growth momentum, not only improving the user experience in various application scenarios such as DeFi , gaming, and social networking, but also enhancing the application's capabilities in data analysis and prediction. Conversely, for AI technology, Web3 has brought a development model different from the traditional one. The two complement each other, and we will now delve into the current development status of the Web3+AI infrastructure sector to understand its future direction.
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