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. We have conducted an in-depth analysis of the technical logic, application scenarios, and representative projects in the Web3-AI track, presenting a comprehensive view of the landscape 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 have no substantial connection to the 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 also utilizing Web3 economic models as tools for production relationships, with both aspects complementing each other. We classify such projects as the Web3-AI track. To help readers better understand the Web3-AI track, we will introduce the development process and challenges of AI, as well as how the combination of Web3 and AI can perfectly solve problems and create 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 artificial intelligence models typically involves the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. To give a simple example, if you want to develop a model to classify 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 is more suitable for image classification tasks. Tune the model parameters or architecture according to different needs; generally, 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 trained for the model are usually referred to as model weights. The inference process refers to the procedure of using the already trained model to predict or classify new data. In this process, a test set or new data can be used to evaluate the classification effectiveness of the model, typically assessed using metrics such as accuracy, recall, and F1-score to evaluate the model's effectiveness.

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 yield the predicted values P (probability) for cats and dogs, which is the probability that the model infers it 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 images 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 non-open source data when acquiring data in specific fields (such as medical data).

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

Acquiring computing power: For individual developers and small teams, the high cost 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, while the research outcomes 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. As a new type of production relationship, Web3 is naturally suited to represent the new productive forces of AI, thereby 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, providing users with an open AI collaboration platform that transforms users from AI users in 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 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 decentralized collaborative crowdsourcing mechanisms and open AI markets, 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 role of an "artist", such as creating their own NFTs using AI technology, but it can also create rich and diverse game scenes and interesting interactive experiences in GameFi. The rich infrastructure provides a smooth development experience, allowing both AI experts and newcomers looking to enter the AI field to find suitable entry points in this world.

II. Analysis of the Web3-AI Ecosystem Project Landscape and Architecture

We mainly researched 41 projects in the Web3-AI track and categorized these projects into different tiers. The logical division of each tier is shown in the diagram below, including 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 encompasses the computing resources and technology 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, 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 networks: They 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, represented by projects like IO.NET and Hyperbolic. Additionally, some projects have derived new gameplay, such as Compute Labs, which proposed a tokenized protocol where users can 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, enabling seamless interaction of AI resources both on-chain and off-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 technological advancements in AI across different fields, such as Bittensor promoting competition among different AI type subnetworks through its innovative subnet incentive mechanism.

• Development Platforms: Some projects offer AI agent development platforms, which can also facilitate trading with AI agents, such as Fetch.ai and ChainML. One-stop tools help developers to more conveniently create, train, and deploy AI models, represented by 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 using 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 it under privacy protection, avoiding data theft and high profits for malicious merchants. For data demanders, these platforms offer a wide range of choices and extremely low costs. Representative projects include Grass, which utilizes user bandwidth to scrape web data, and xData, which collects media information through user-friendly plugins and supports users in uploading tweet information.

In addition, some platforms allow domain experts or general users to perform data preprocessing tasks, such as image labeling and data classification. These tasks may require specialized knowledge for financial and legal data processing, and users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. Examples include AI markets like Sahara AI, which have data tasks from different fields and can cover multi-domain data scenarios; whereas 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. Common models for image tasks include CNN and GAN, while for object detection tasks, the Yolo series can be chosen. Common models for text tasks include RNN and Transformer, and there are also 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 to train models through crowdsourcing, such as Sentient, which allows users to place trusted model data in the storage layer and distribution layer for model optimization through a modular design. The development tools provided by Sahara AI are equipped with advanced AI algorithms and computing frameworks, and they have collaborative training capabilities.

• Inference and Verification: After training, the model generates a model weight file that can be used for direct classification, prediction, or other specific tasks, a process known as inference. The inference process is typically accompanied by a verification mechanism to validate the source of the inference model, checking for correctness and the presence of malicious behavior, etc. In Web3, inference can typically be integrated into smart contracts, invoking models for inference. Common verification methods include technologies such as ZKML, OPML, and TEE. Representative projects include the ORA on-chain AI oracle (OAO), which introduces OPML as a verifiable layer for AI oracles. Their official website also mentions their research on ZKML and opp/ai (ZKML combined with OPML).

Application Layer:

This layer primarily consists of user-facing applications that combine AI with Web3 to create more