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 sector. This article conducts an in-depth analysis of the technical logic, application scenarios, and representative projects in the Web3-AI sector, providing a comprehensive view of 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 springing up 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 problems and AI to solve productivity problems. These projects themselves provide AI products while serving as tools for production relationships based on the Web3 economic model, with both aspects complementing each other. We categorize these projects as the Web3-AI track. To help readers better understand the Web3-AI track, we will next introduce the development process and challenges of AI, as well as how the combination of Web3 and AI perfectly addresses 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 enables computers to simulate, extend, and enhance human intelligence. It allows 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 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 its 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 testing sets.

2. Model selection and tuning: Choose the appropriate model, such as Convolutional Neural Networks (CNN), which are more suitable for image classification tasks. Tune the model parameters or architecture according to different needs. Generally speaking, the network depth of the model can be adjusted based on 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 using the already trained model to make predictions or classifications on new data. During this process, a test set or new data can be used to evaluate the classification performance of the model, typically assessed using metrics such as accuracy, recall, and F1-score to evaluate the model's effectiveness.

After data collection and data preprocessing, model selection and tuning, and training, the trained model will infer on the test set to produce the predicted values P (probability) for cats and dogs, which is the probability that the model infers to be a cat or a dog.

Trained AI models can be further integrated into various applications to perform different tasks. In this example, the AI model for cat and dog classification can be integrated into a mobile application where users upload pictures of cats or dogs to obtain 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 in obtaining data from specific fields (such as medical data) due to a lack of open-source availability.

Model selection and tuning: It is difficult for small teams to access specific domain model resources or spend a lot of costs on model tuning.

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

AI Asset Income: Data annotators often struggle to earn an income that matches their efforts, and the research results of AI developers are also difficult to match with buyers who have demand.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3. As a new type of production relationship, Web3 naturally adapts to AI, which represents a new productive force, thereby promoting simultaneous progress in technology and production capacity.

1.3 The Synergistic Effects 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 that transforms them from AI users of the Web2 era into participants, creating AI that everyone can own. 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 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 realized, 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 creates rich and diverse game scenarios and interesting interactive experiences in GameFi. Abundant infrastructure provides a smooth development experience, allowing both AI experts and newcomers looking to enter the AI field to find an appropriate entry point in this world.

2. Interpretation of the Web3-AI Ecosystem Project Map and Architecture

We mainly studied 41 projects in the Web3-AI track and categorized these projects into different tiers. The classification logic for each tier is shown in the figure below, including the infrastructure layer, intermediate layer, and application layer, each of which is 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 reasoning services that connect the infrastructure to applications. The application layer focuses on various applications and solutions that are directly oriented towards users.

Infrastructure Layer:

The infrastructure layer is the foundation of the AI lifecycle, and this article categorizes 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 use 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 such as IO.NET and Hyperbolic. Additionally, some projects have derived new gameplay, such as Compute Labs, which proposed a tokenization protocol, allowing users to participate in computing power leasing in different ways by purchasing NFTs that represent physical GPUs.

• AI Chain: Utilizing blockchain as the foundation for the AI lifecycle, achieving 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 provide AI development frameworks and supporting development tools, with representative projects like Sahara AI. AI Chain can also promote technological advancements in AI across different fields, such as Bittensor fostering competition among various AI type subnetworks through an innovative subnet incentive mechanism.

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

Middleware:

This layer involves AI data, models, as well as reasoning and validation, and utilizing 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, crowd-sourced data and collaborative data processing can optimize resource utilization and reduce data costs. Users can have autonomy over their data and sell it under privacy protection to avoid their data being stolen by malicious merchants for high profits. For data demanders, these platforms offer a wide range of choices and extremely low costs. Representative projects like 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 annotation and data classification, which may require specialized knowledge in financial and legal data processing. Users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. For example, the AI market represented by Sahara AI offers data tasks across different fields and can cover multi-domain data scenarios; while AIT Protocol labels data through a human-machine collaboration approach.

• 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 for object detection tasks, the Yolo series can be chosen. For text tasks, common models include RNN, Transformer, etc., and there are also some specific or general large models. The depth of models required for different levels of task complexity also varies, and sometimes model tuning is necessary.

Some projects support users in providing different types of models or collaboratively training models through crowdsourcing, such as Sentient which, through 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 have the capability for collaborative training.

• Inference and Validation: 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 called inference. The inference process is usually accompanied by a validation mechanism to verify the correctness of the inference model's source and to check for malicious behavior, etc. Inference in Web3 can typically be integrated into smart contracts, allowing for model inference through calls. Common validation 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. 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 primarily organizes the projects in several areas, including AIGC (AI Generated Content), AI agents, and data analysis.

• AIGC: Through AIGC, it can be extended to NFT, games, and other tracks in Web3. Users can directly generate text, images, and audio through Prompts (the cues provided by users) and even generate their own content in games based on their preferences.