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

With the continued rise of AI narratives, more and more attention is being 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 provide you with a comprehensive view of the landscape and development trends in this field.

1. Web3-AI: Analyzing 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 provide AI products while utilizing Web3 economic models as tools for production relationships, and the two complement 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 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 facial recognition, autonomous driving, and other application scenarios. AI is changing the way we live and work.

The process of developing an artificial intelligence model 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 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 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 is well-suited 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 may be sufficient.

3. Model Training: You can use GPUs, TPUs, 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. The inference process refers to the process of using a 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 model's effectiveness.

As shown in the figure, after data collection, data preprocessing, model selection and tuning, and training, the trained model is used for inference on the test set to obtain the predicted values P (probability) for cats and dogs, which is the probability that the model infers it is 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 images 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 accessing non-open source data in specific fields (such as medical data).

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

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

AI Asset Income: Data annotators often struggle to earn an 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. As a new type of production relationship, Web3 naturally adapts to AI, which represents a new type of productive force, 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, allowing them to transition from being AI users in the Web2 era to participants, creating AI that can be owned by everyone. Meanwhile, the integration of the Web3 world with 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 guaranteed, 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 acquired 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 motivating more people to drive the progress of AI technology.

In the Web3 scenario, AI can have a positive impact across multiple domains. 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" by using AI technology to create their own NFTs but also creates a rich variety of game scenarios and interesting interactive experiences in GameFi. Abundant infrastructure provides a seamless development experience, making it possible for both AI experts and newcomers wanting to enter the AI field to find suitable entry points in this world.

2. Web3-AI Ecosystem Project Map and Architecture Interpretation

We primarily studied 41 projects in the Web3-AI track and categorized these projects into different levels. The classification logic for each level is shown in the figure below, including the Infrastructure layer, Intermediate layer, and Application layer, with each level further divided into different sections. 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 operation of the entire AI lifecycle, while the middle layer includes data management, model development, and verification inference services that connect the infrastructure to the 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 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, with representative projects such as IO.NET and Hyperbolic. In addition, some projects have derived new gameplay, such as Compute Labs, which proposed a tokenization protocol where users can participate in computing power leasing to earn profits 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 on and off the chain, and promoting the development of the industry ecosystem. 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 types of AI subnets through its innovative subnet incentive mechanism.

• Development Platform: Some projects offer AI agent development platforms that also enable trading of AI agents, such as Fetch.ai and ChainML. One-stop tools assist developers in more conveniently creating, training, and deploying AI models, represented by projects like Nimble. This infrastructure facilitates the widespread application of AI technology in the Web3 ecosystem.

Middle Layer:

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

• Data: The quality and quantity of data are key factors affecting model training effectiveness. 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 their data being stolen and exploited for high profits by unscrupulous merchants. For data demanders, these platforms offer a wide range of choices at 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. 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. An example is the AI marketplace represented by Sahara AI, which has data tasks from different fields and can cover multi-domain data scenarios; 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. Common models for image tasks include CNN and GAN, while for object detection tasks, the Yolo series can be selected. Common models for text tasks include RNN and Transformer, and of course, there are some specific or general large models as well. 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. For example, Sentient allows users to place trusted model data in the storage layer and distribution layer for model optimization through its modular design. Sahara AI provides development tools that are equipped 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, a process 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 are any malicious behaviors, etc. Inference in Web3 can typically be integrated into smart contracts, where models are called for inference. Common verification methods include technologies like ZKML, OPML, and TEE. Representative projects such as the ORA on-chain 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 is mainly user-facing applications that combine AI with Web3 to create.