Web3-AI Landscape Report: Technical Logic, Scenario Applications and In-depth Analysis of Top Projects

As AI narrative continues to gain traction, more and more attention is focused on this track. In-depth analysis of the technical logic, application scenarios, and representative projects in the Web3-AI track has been conducted to comprehensively present the panorama and development trends in this field.

1. Web3-AI: Analysis of Technical Logic and Emerging Market Opportunities

1.1 The Fusion Logic of Web3 and AI: How to Define the Web-AI Track

In the past year, AI narratives have been exceptionally popular in the Web3 industry, with AI projects emerging like mushrooms after rain. Although many projects involve AI technology, some projects only use AI in certain parts of their products, and the underlying token economics have no substantial connection to AI products. Therefore, such projects are not included in the discussion of Web3-AI projects in this article.

The focus of this article is on projects that use blockchain to solve production relationship issues and AI to address productivity problems. These projects themselves offer AI products while also serving as production relationship tools based on the Web3 economic model, complementing each other. We categorize such projects as the Web3-AI track. To help readers better understand the Web3-AI track, we will elaborate on the development process and challenges of AI, as well as how the combination of Web3 and AI perfectly solves problems and creates new application scenarios.

1.2 The Development Process and Challenges of AI: From Data Collection to Model Inference

AI technology is a technology that enables computers to simulate, extend, and enhance human intelligence. It allows computers to perform various complex tasks, from language translation and image classification to facial recognition and autonomous driving applications. AI is changing the way we live and work.

The process of developing artificial intelligence models typically includes the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. For a simple example, to develop a model for classifying images of cats and dogs, you need to:

1. Data collection and data preprocessing: Collect an image dataset containing cats and dogs, which can be done using public datasets or by collecting real data yourself. Then label each image with a category (cat or dog), ensuring the labels are accurate. Convert the images into a format that the model can recognize, and divide the dataset into training, validation, and testing sets.

2. Model Selection and Tuning: Choose an appropriate model, such as Convolutional Neural Networks (CNN), which is well-suited for image classification tasks. Tune the model parameters or architecture based on different requirements. Generally speaking, the network depth of the model can be adjusted according to the complexity of the AI task. In this simple classification example, a shallower network depth may be sufficient.

3. Model Training: You can use GPU, TPU, or high-performance computing clusters to train the model, and the training time is affected by the complexity of the model and the computing power.

4. Model Inference: The files of the trained model are usually referred to as model weights. The inference process refers to the process of using the already trained model to make predictions or classifications on new data. In this process, a test set or new data can be used to evaluate the classification performance of the model, which is typically assessed using metrics such as accuracy, recall, and F1-score.

As shown in the figure, after data collection and preprocessing, model selection and tuning, and training, the trained model will infer on the test set to yield the predicted values P (probability) for cats and dogs, which indicates the model's inferred probability of being a cat or a dog.

The trained AI model can be further integrated into various applications to perform different tasks. In this example, the cat and dog classification AI model can be integrated into a mobile application where users upload pictures of cats or dogs to receive classification results.

However, the centralized AI development process has some issues in the following scenarios:

User Privacy: In centralized scenarios, the development process of AI is often opaque. User data may be stolen and used for AI training without their knowledge.

Data source acquisition: Small teams or individuals may face limitations on non-open source data when obtaining data in specific fields (such as medical data).

Model selection and tuning: For small teams, it is difficult to access specific domain model resources or spend a lot of money on model tuning.

Acquisition of computing power: For individual developers and small teams, the high costs of purchasing GPUs and renting cloud computing power can pose a significant financial burden.

AI Asset Income: Data labeling workers often cannot obtain income that matches their efforts, and the research results of AI developers are also difficult to match with buyers in need.

The challenges existing in centralized AI scenarios can be addressed by combining with Web3. Web3, as a new type of production relationship, is inherently compatible with AI, which represents a new productive force, thus promoting simultaneous progress in technology and production capacity.

1.3 The Synergy between Web3 and AI: Role Transformation and Innovative Applications

The combination of Web3 and AI can enhance user sovereignty by providing an open AI collaboration platform, transforming users from AI consumers in the Web2 era into participants, creating AI that everyone can own. At the same time, the integration of the Web3 world with AI technology can spark more innovative application scenarios and gameplay.

Based on Web3 technology, the development and application of AI will usher in a brand new collaborative economic system. People’s data privacy can be protected, and the data crowdsourcing model promotes the advancement of AI models. Numerous open-source AI resources are available for users, and shared computing power can be obtained at a lower cost. With the help of a decentralized collaborative crowdsourcing mechanism and an open AI market, a fair income distribution system can be achieved, thereby encouraging more people to drive the advancement of AI technology.

In the Web3 scenario, AI can have a positive impact across multiple tracks. For example, AI models can be integrated into smart contracts to enhance work efficiency in various application scenarios, such as market analysis, security detection, social clustering, and many other functions. Generative AI not only allows users to experience the "artist" role, such as using AI technology to create their own NFTs, but also creates rich and diverse game scenarios and interesting interactive experiences in GameFi. The rich infrastructure provides a smooth development experience, where both AI experts and newcomers looking to enter the AI field can find suitable entry points in this world.

2. Interpretation of the Web3-AI Ecological Project Landscape and Architecture

We mainly studied 41 projects in the Web3-AI track and classified these projects into different tiers. The classification logic for each tier is shown in the figure below, including the infrastructure layer, middle layer, and application layer, with each layer further divided into different sectors. In the next chapter, we will conduct a depth analysis of some representative projects.

The infrastructure layer encompasses the computing resources and technical architecture that support the entire AI lifecycle. The middle layer includes data management, model development, and verification reasoning services that connect the infrastructure with applications. The application layer focuses on various applications and solutions directly aimed at users.

Infrastructure Layer:

The infrastructure layer is the foundation of the AI lifecycle. This article categorizes computing power, AI Chain, and development platforms as the infrastructure layer. It is the support of these infrastructures that enables the training and inference of AI models, presenting powerful and practical AI applications to users.

• Decentralized computing networks: They can provide distributed computing power for AI model training, ensuring efficient and economical utilization of computing resources. Some projects offer decentralized computing power markets where users can rent computing power at low costs or share computing power to earn profits, represented by projects such as IO.NET and Hyperbolic. In addition, some projects have derived new play styles, such as Compute Labs, which proposed a tokenized protocol where users can purchase NFTs representing physical GPUs to participate in computing power leasing in various ways to earn profits.

• AI Chain: Utilizing blockchain as the foundation for the AI lifecycle, enabling seamless interaction of on-chain and off-chain AI resources to promote the development of the industry ecosystem. The decentralized AI market on the chain can trade AI assets such as data, models, agents, etc., and provide AI development frameworks and supporting development tools, represented by projects like Sahara AI. AI Chain can also promote technological advancements in AI across different fields, such as Bittensor, which fosters competition among different types of AI subnetworks through innovative subnet incentive mechanisms.

• Development Platforms: Some projects offer AI agent development platforms that can also facilitate trading of AI agents, such as Fetch.ai and ChainML. One-stop tools help developers create, train, and deploy AI models more conveniently, with representative projects like Nimble. These infrastructures promote the widespread application of AI technology in the Web3 ecosystem.

Middleware:

This layer involves AI data, models, as well as reasoning and verification, and adopting Web3 technology can achieve higher work efficiency.

• Data: The quality and quantity of data are key factors affecting the effectiveness of model training. In the Web3 world, crowdsourced data and collaborative data processing can optimize resource utilization and reduce data costs. Users can have autonomy over their data and sell their own data under privacy protection to avoid it being stolen and exploited for high profits by malicious vendors. For data demanders, these platforms offer a wide range of options at very low costs. Representative projects such as Grass utilize user bandwidth to scrape web data, while xData collects media information through user-friendly plugins and supports users in uploading tweet information.

In addition, some platforms allow domain experts or ordinary users to perform data preprocessing tasks, such as image labeling and data classification. These tasks may require specialized knowledge for processing financial and legal data. Users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. An example is the AI marketplace like Sahara AI, which has data tasks across different domains and can cover data scenarios in multiple fields; while AIT Protocol labels data through human-machine collaboration.

• Model: In the previously mentioned AI development process, different types of requirements need to match suitable models. Commonly used models for image tasks include CNN and GAN, while for object detection tasks, the Yolo series can be selected. For text-related tasks, common models include RNN, Transformer, and there are also some specific or general large models. The depth of the model required varies depending on the complexity of the task, and sometimes model tuning is necessary.

Some projects support users to provide different types of models or collaborate on training models through crowdsourcing. For example, Sentient allows users to place trusted model data in the storage layer and distribution layer for model optimization through modular design. The development tools provided by Sahara AI come with advanced AI algorithms and computing frameworks and have the capability for collaborative training.

• Inference and Verification: After the model has been trained, it generates model weight files that can be used for direct classification, prediction, or other specific tasks; this process is known as inference. The inference process is usually accompanied by a verification mechanism to verify the source of the inference model, whether it is correct, and whether there are malicious behaviors, etc. Inference in Web3 can typically be integrated into smart contracts, allowing for inference by calling the model. Common verification methods include technologies such as ZKML, OPML, and TEE. Representative projects like the ORA blockchain AI oracle (OAO) have introduced OPML as a verifiable layer for AI oracles, and their official website also mentions their research on ZKML and opp/ai (ZKML combined with OPML).

Application Layer:

This layer mainly consists of applications directly facing users, combining AI with Web3 to create more interesting and innovative gameplay. This article mainly outlines the projects in several areas such as AIGC (AI Generated Content), AI agents, and data analysis.

• AIGC: AIGC can be used to extend