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

With the continuous warming of AI narrative, more and more attention is focused on this track. This article conducts an in-depth analysis of the technical logic, application scenarios, and representative projects of the Web3-AI track, providing you with a comprehensive presentation of the panorama and development trends in this field.

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

1.1 The Integration 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 are not substantially related 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 provide AI products themselves while serving as tools for production relationships based on Web3 economic models, complementing each other. We categorize such projects as the Web3-AI track. To help readers better understand the Web3-AI track, this article 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 a variety of 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 an artificial intelligence model 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 a dataset of images containing cats and dogs, which can be done using public datasets or by collecting real data yourself. Then label each image with its 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 test sets.

2. Model Selection and Tuning: Choose an appropriate model, such as Convolutional Neural Networks (CNN), which are well-suited for image classification tasks. Tune the model parameters or architecture based on different requirements. Generally, 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 procedure 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, typically using metrics such as accuracy, recall, and F1-score to assess the effectiveness of the model.

As shown in the figure, after data collection, data preprocessing, model selection and tuning, and training, performing inference on the test set with the trained model will yield the predicted values P (probability) for cats and dogs, which is the probability that the model infers it is a cat or a dog.

Trained AI models can be further integrated into various applications to perform different tasks. In this example, the cat-dog classification AI model can be integrated into a mobile application where users upload images of cats or dogs and 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 data not being open source when obtaining data in specific fields (such as medical data).

Model selection and tuning: For small teams, it is difficult to obtain model resources in specific fields or to spend a lot of cost on model tuning.

Acquiring 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 struggle to earn income that matches their efforts, while AI developers find it difficult to match their research results with buyers in need.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3. Web3, as a new type of production relationship, is inherently compatible with AI, which represents a new type of productive force, thereby promoting simultaneous advancements 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, providing users with an open AI collaboration platform, transforming users from AI users in the Web2 era into participants, creating AI that can be owned by everyone. At the same time, the integration of the Web3 world and 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 guaranteed, 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 promote 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 more. Generative AI not only allows users to experience the "artist" role, such as creating their own NFTs using AI technology, but also creates diverse game scenarios and interesting interactive experiences in GameFi. Rich infrastructure provides a smooth development experience, allowing both AI experts and newcomers wanting to enter the AI field to find suitable entry points in this world.

2. Interpretation of Web3-AI Ecological Project Layout and Architecture

We primarily studied 41 projects in the Web3-AI track and categorized these projects into different tiers. The logic for categorizing each tier is shown in the diagram below, which includes the infrastructure layer, the intermediate layer, and the application layer, with each layer further divided into different segments. In the next chapter, we will conduct a Depth analysis of some representative projects.

The infrastructure layer encompasses the computing resources and technological architecture that support the entire AI lifecycle, while the middle layer includes data management, model development, and verification inference services that connect the infrastructure to applications. The application layer focuses on various applications and solutions that are directly facing users.

Infrastructure Layer:

The infrastructure layer is the foundation of the AI lifecycle. This article classifies computing power, AI Chain, and development platforms as part of 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: can provide distributed computing power for AI model training, ensuring efficient and cost-effective 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 gain profits, represented by projects such as IO.NET and Hyperbolic. In addition, some projects have derived new gameplay, such as Compute Labs, which proposed a tokenized protocol where users can participate in computing power leasing in various ways by purchasing NFTs representing GPU entities.

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

• Development Platforms: Some projects offer AI agent development platforms, which can also enable the trading of AI agents, such as Fetch.ai and ChainML. All-in-one tools help developers more easily create, train, and deploy AI models, represented by projects like Nimble. This infrastructure facilitates the widespread application of AI technology in the Web3 ecosystem.

Intermediate Layer:

This layer involves AI data, models, as well as reasoning and verification, achieving higher work efficiency through Web3 technology.

• 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 data being stolen by unscrupulous merchants for high profits. For data demanders, these platforms offer a wide range of choices and very low costs. Representative projects such as Grass utilize user bandwidth to scrape web data, and 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 professional knowledge in financial and legal data processing. Users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. For example, the AI marketplace represented by Sahara AI has data tasks from different fields that can cover multi-domain data scenarios; while AIT Protocol labels data through human-machine collaboration.

• Models: In the AI development process mentioned earlier, different types of requirements need to match suitable models. Common models for image tasks include CNN and GAN, while the Yolo series can be chosen for object detection tasks. For text-related tasks, common models include RNN and Transformer, as well as some specific or general large models. The depth of the models required for tasks of varying complexity also differs, and sometimes model tuning is necessary.

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

• Inference and Verification: After the model is 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 validate whether the source of the inference model is correct and whether there is any malicious behavior. In Web3, inference can typically be integrated into smart contracts by calling the model for inference. Common verification methods include technologies such as ZKML, OPML, and TEE. Representative projects include the ORA on-chain AI oracle (OAO), which introduced OPML as a verifiable layer for the AI oracle. Their official website also mentions their research on ZKML and opp/ai (combining ZKML with OPML).

Application Layer:

This layer is mainly user-facing applications that combine AI with Web3, creating more interesting and innovative gameplay. This article mainly summarizes projects in several areas including AIGC (AI Generated Content), AI agents, and data analysis.

• AIGC: Through AIGC