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

With the continuous rise of AI narratives, more and more attention is focused on this track. An in-depth analysis of the technical logic, application scenarios, and representative projects of the Web3-AI track has been conducted to present a comprehensive overview 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 substantively 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 while utilizing Web3 economic models as tools for production relationships, with both aspects complementing each other. We categorize these types of 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 resolves issues 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 allows computers to simulate, extend, and enhance human intelligence. It enables computers to perform a variety of complex tasks, from language translation, image classification to applications such as facial recognition and autonomous driving. AI is changing the way we live and work.

The process of developing artificial intelligence models usually 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 that 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 the appropriate model, such as Convolutional Neural Networks (CNN), which are well-suited for image classification tasks. Fine-tune the model parameters or architecture based on different needs; 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 a trained model are usually referred to as model weights, and the inference process refers to the process of using the already trained model to predict or classify new data. During 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 and preprocessing, model selection and tuning, and training, the trained model will perform inference on the test set to obtain the prediction values P (probability) for cats and dogs, which indicates the probability that the model infers whether 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 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 restrictions on obtaining data in specific fields (such as medical data) when the data is not open source.

Model selection and tuning: It is difficult for small teams to obtain model resources in specific domains 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 annotators often struggle to earn an income that matches their efforts, while 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 integrating with Web3. Web3, as a new type of production relationship, is inherently compatible with AI, which represents a new form of productive forces, thus promoting simultaneous progress in technology and production capabilities.

1.3 The Synergistic Effect of 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 them from AI users of the Web2 era into participants, creating AI that belongs to everyone. At the same time, the integration of the Web3 world and AI technology can also 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 ensured, 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 more. Generative AI not only allows users to experience the "artist" role, such as creating their own NFTs using AI technology, but also to create rich and diverse game scenarios and interesting interactive experiences in games. Abundant 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. Web3-AI Ecosystem Project Landscape and Architecture Interpretation

We primarily studied 41 projects in the Web3-AI sector and categorized them into different tiers. The logic for dividing each tier is shown in the figure below, which includes the infrastructure layer, intermediate layer, and 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 covers the computing resources and technology architecture that support the entire AI lifecycle, the middle layer includes data management, model development, and verification inference services that connect the infrastructure with applications, while 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 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 Network: It 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, with representative projects like IO.NET and Hyperbolic. In addition, some projects have derived new gameplay, such as Compute Labs, which proposed a tokenized protocol. Users can participate in computing power leasing to earn profits in different ways by purchasing NFTs that represent GPU entities.

• AI Chain: Utilizing blockchain as the foundation for the AI lifecycle, achieving seamless interaction of AI resources on and off the chain, and promoting the development of industry ecosystems. The decentralized AI market on the chain allows for the trading of AI assets such as data, models, agents, etc., and provides AI development frameworks and supporting development tools, with representative projects like Sahara AI. AI Chain can also facilitate advancements in AI technologies across different fields, such as Bittensor promoting competition among subnet types through innovative subnet incentive mechanisms.

• Development Platforms: Some projects provide AI agent development platforms and also enable trading of AI agents, such as Fetch.ai and ChainML. All-in-one tools help developers more conveniently create, train, and deploy AI models, with representative projects like Nimble. This infrastructure facilitates 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 that affect the effectiveness of model training. In the Web3 world, data can be optimized for resource utilization and cost reduction through crowdsourced data and collaborative data processing. Users can have autonomy over their data, selling it under privacy protection to avoid it being stolen and profited from by malicious merchants. For data demanders, these platforms provide 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, which may require specialized knowledge for financial and legal data processing tasks. 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 from different domains and can cover data scenarios across multiple fields; while AIT Protocol labels data through human-machine collaboration.

• Model: In the AI development process mentioned earlier, different types of requirements need to match suitable models. Commonly used models for image tasks include CNN and GAN, while for object detection tasks, one can choose from the Yolo series. 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 varies with the complexity of the tasks, and sometimes model tuning is necessary.

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

• Inference and Verification: After training, the model generates weight files that can be used for classification, prediction, or other specific tasks; this process is called inference. The inference process is usually accompanied by a verification mechanism to validate whether the origin of the inference model is correct and whether there are malicious behaviors, etc. 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 such as the ORA on-chain AI oracle (OAO) have introduced OPML as a verifiable layer for the AI oracle, and their official website also mentions their research on ZKML and opp/ai (ZKML combined with OPML).

Application Layer:

This layer is mainly user-facing applications that combine AI with Web3 to create more interesting and innovative solutions.