Web3+AI: Artificial Intelligence with Community Sovereignty

Original title: "Web3 + AI: Community Sovereignty Artificial Intelligence"

Original source: IOBC Capital

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

Perhaps 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": Crypto's decentralization can balance AI's centralization; AI is opaque, Crypto brings transparency; AI needs data, and blockchain is conducive to data storage and tracking. This synergy runs through the entire industrial landscape of Web3+AI.

Most Web3 + AI projects are using blockchain technology to solve the construction problems of AI industry infrastructure projects, and a few projects are using AI to solve certain problems of Web3 applications.

The general picture of Web3 + AI industry is as follows:

The production and workflow of AI are as follows:

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

1. Computing power layer: computing power assetization

In the past two years, the computing power used to train large AI models has grown exponentially, basically doubling every quarter, and growing wildly 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, which has in turn raised the cost of computing power.

But at the same time, there are also a large number of idle mid- and low-end computing power hardware on the market, and the single computing power of this part of low- and mid-end hardware may not meet the high-performance requirements. However, if a distributed computing power network is built through Web3, a decentralized computing resource network can be created through computing power leasing and sharing, which can still meet many AI application needs. Since it uses distributed idle computing power, the cost of AI computing power can be significantly reduced.

The computing power layer segmentation 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 for AI reasoning (such as Fetch.ai, Hyperbolic, etc.);

· Decentralized computing power for 3D rendering (such as The Render Network, etc.).

The core advantage of Web3+AI computing power assetization is that decentralized computing power projects, combined with token incentives, can easily expand the network scale, and its computing resource cost is low, with high cost performance, which can meet some low-end computing power needs.

2. Data layer: data assetization

Data is the oil and blood of AI. Without relying on Web3, only giant companies generally 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 labeling, and data distributed storage can be made more cost-effective, transparent, and user-friendly.

Collecting high-quality data is a prerequisite for AI model training. Through Web3, we can use distributed networks, combine appropriate token incentive mechanisms, and adopt crowdsourcing collection methods to obtain high-quality and extensive data at a lower cost.

According to the purpose of the project, data projects mainly include the following categories:

· Data collection projects (such as Grass, etc.);

· Data trading projects (such as Ocean Protocol, etc.);

· Data labeling 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 Web3+AI projects are more challenging in the process of designing Token economic models because data is more difficult to standardize than computing power.

3. Platform layer: platform value assetization

Most platform projects will benchmark Hugging Face, with the integration of various resources in the AI industry as the core. Establish a platform that aggregates links between various resources and roles such as data, computing power, models, AI developers, and blockchain, and solves various needs more conveniently with the platform as the center. For example, Giza focuses on building a comprehensive zkML operating platform, aiming to make machine learning reasoning credible and transparent, because data and model black boxes are common problems in AI at present. Using cryptographic technologies such as ZK and FHE through Web3 to verify that the reasoning of the model is indeed correctly executed will sooner or later be called for by the industry.

There are also Layer1/Layer2 that focus on AI, such as Nuroblocks, Janction, etc. The core narrative is to connect various computing power, data, models, AI developers, nodes and other resources, and help Web3+AI applications to achieve rapid construction and development by packaging common components and common SDKs.

There are also Agent Network platforms, based on which AI Agents can be built for various application scenarios, such as Olas, ChainML, etc.

Web3+AI projects of the platform type mainly use tokens to capture the value of the platform and incentivize all participants in the platform to build together. It is helpful for the process of starting a project from 0 to 1, and can reduce the difficulty for the project party to find partners such as computing power, data, AI developer communities, nodes, etc.

4. Application layer: AI value assetization

Most of the infrastructure projects mentioned above use blockchain technology to solve the problem of infrastructure project construction in the AI industry. 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 are very meaningful.

The first is AI as a Web3 participant. For example: in Web3 Games, AI can act as a game player, which can quickly understand the rules of the game and complete the game tasks most efficiently; in DEX, AI has played a role in arbitrage transactions for many years; in Prediction markets, AI Agent can widely accept a large amount of data, knowledge base and information, train its model's analytical and predictive capabilities, and provide it to users in a productized manner, helping users to make predictions about specific events in the form of 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, the bias of the system; or they are worried that some dApps will deceive users through AI technology to make profits. In essence, it is because users do not have the right to review and govern the model training and reasoning process of AI. But if a Web3 AI is created, like the Web3 project, the community has distributed governance rights over this AI, which may be more easily accepted.

So far, there are no white horse projects with a high ceiling 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 "control by giants" and "monopoly" and "co-govern AI" in a more community-oriented way. Perhaps in the process of closer participation and governance, humans will have more "reverence" and less "fear" for AI.

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