Web3 + AI: Building sovereign AI to meet the interests and demands of the Crypto community

Author: IOBC Capital

When Huang Renxun spoke at WGS in Dubai, he proposed the term "sovereign AI". So, which sovereign AI can meet the interests and demands of the Crypto community?

Maybe it needs to be built in the form of Web3 AI. Vitalik described the synergy between AI and Crypto in the article "The promise and challenges of crypto AI applications": The decentralization of Crypto can balance the centralization of AI; AI is opaque, Crypto brings transparency; AI requires data, and blockchain is conducive to data storage and tracking. This kind of collaboration runs through the entire industrial landscape of Web3 AI.

Most Web3 AI projects use blockchain technology to solve the construction problems of infrastructure projects in the AI ​​industry, and a few projects use AI to solve certain problems in Web3 applications.

Web3 AI industry picture is roughly as follows:

AI production and The workflow is roughly as follows:

In these links, the combination of Web3 and AI is mainly reflected in four aspects:

1. Computing power layer: Capitalization of computing power

In the past two years, the computing power used to train large AI models has increased exponentially , basically doubling every quarter, growing at a rate far exceeding Moore's Law. This situation has led to a long-term imbalance in the supply and demand of AI computing power, and the prices of hardware such as GPUs have risen rapidly, thus raising the cost of computing power.

But at the same time, there is also a large amount of idle mid- to low-end computing hardware in the market. It is possible that the single computing power of this part of mid-to-low-end hardware cannot meet high-performance needs. However, if a distributed computing power network is built through Web3 and a decentralized computing resource network is created through computing power leasing and sharing, it can still meet the needs of many AI applications. Because it uses distributed idle computing power, the cost of AI computing power can be significantly reduced.

The computing power layer breakdown includes:

• General decentralized computing power (such as Arkash, Io. net, etc.);
• Decentralized computing power for AI training (such as Gensyn, Flock. io, etc.);
• Decentralized computing power used for AI reasoning (such as Fetch. ai, Hyperbolic, etc.);
• Decentralized computing power used for 3D rendering (such as The Render Network, etc.).

The core advantage of Web3 AI’s computing power assetization lies in decentralized computing power projects. Combined with token incentives, it is easy to expand the network scale, and its computing resource cost is low, and it has high Cost-effective, it can meet some mid-to-low-end computing power needs.

2. Data layer: Data assetization

Data is the oil and blood of AI. If you do not rely on Web3, generally only giant companies have a large amount of user data. It is difficult for ordinary startups to obtain extensive data, and the value of user data in the AI ​​industry is not fed back to users. Through Web3 AI, processes such as data collection, data annotation, and distributed data storage can be made cheaper, more transparent, and more beneficial to users.

Collecting high-quality data is a prerequisite for AI model training. Through Web3, distributed networks can be used, combined with appropriate Token incentive mechanisms, and crowdsourcing collection methods. Get high-quality, broad-based data at a lower cost.

Based on the purpose of the project, data projects mainly include the following categories:

• Data collection projects (such as Grass, etc.);
• Data transaction projects (such as Ocean Protocol, etc.);
• Data annotation projects (such as Taida, Alaya, etc.) ;
• Blockchain data source projects (such as Spice AI, Space and time, etc.);
• Decentralized storage projects (such as Filecoin, Arweave, etc.).

Data-based Web3 AI projects are more challenging in the process of designing the Token economic model because data is more difficult to standardize than computing power.

3. Platform layer: Capitalization of platform value

Most platform projects will target Hugging Face, focuses on integrating various resources in the AI ​​industry. Establish a platform that aggregates links to various resources and roles such as data, computing power, models, AI developers, and blockchain to more conveniently solve various needs with the platform as the center. For example, Giza, focuses on building a comprehensive zkML operation platform, aims to make Machine learning inference becomes trustworthy and transparent because data and modelsBlack boxes arecurrently in AICommon problems can be solved by Web3using cryptography technologies such as ZK and FHE The reasoning of the verification model is indeed executed correctly, sooner or later will called upon by in the industry.

There are also layer1/layer2 for Focus AI, such as Nuroblocks, Janction, etc. The core narrative connects various computing power, data, models, AI developers, nodes and other resources, and helps Web3 AI applications to achieve rapid construction and development by packaging common components and common SDKs.

There are also platforms like Agent Network. Based on this type of platform, AI Agents can be built for various application scenarios, such asOlas, ChainML, etc.

Platform-type Web3 AI projects mainly use Token to capture the value of the platform and encourage all participants of the platform to build together. It is helpful for start-up projects from 0 to 1, and can reduce the difficulty for project parties to find partners such as computing power, data, AI developer communities, nodes, etc.

4. Application layer: AI value assetization

Most of the previous infrastructure projects use blockchain technology to solve AI problems Issues in the construction of industry infrastructure projects. Application layer projects are more about using AI to solve problems in Web3 applications.

For example, Vitalik mentioned two directions in the article, which I think is very meaningful.

First, AI serves as a Web3 participant.For example: In Web3 Games, AI can act as a game player, and it canquickly Understand the rules of the game, and complete the game tasks most efficiently;In DEX, AIHasplayed a role in arbitrage trading for many years;Predictionmarkets(Prediction market), AI Agent can widely accept a large amount of data, knowledge base and information to train the analysis and prediction capabilities of its model, And provide it to users as a product to help users make predictions of specific events through model reasoning, Such as sports events, presidential elections, etc..

The second is to create scalable decentralized private AI. Because many users are worried about the black box problem of AI and the system is biased; or they are worried that some dApps use AI technology to deceive users to make profits. Essentially, this is because users do not have review and governance rights over the AI ​​model training and inference process. But if you create a Web3 AI, like the Web3 project, the community has distributed governance rights for this AI, which may be more easily accepted.

As of now, there are no white horse projects with high ceilings in the Web3 AI application layer.

Summary

Web3 AI is still in its early stages, and the industry is divided on the development prospects of this track. We will continue to pay attention to this track. We hope that the combination of Web3 and AI can create products that are more valuable than centralized AI, allowing AI to get rid of the labels of "giant control" and "monopoly" and "co-govern AI" in a more community-based way. Perhaps in the process of closer participation and governance, humans will be more "awe" and less "fear" of AI.

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