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

As the AI narrative continues to heat up, more and more attention is focused on this track. In-depth analysis has been conducted on the technical logic, application scenarios, and representative projects of the Web3-AI track, providing you with a comprehensive overview of the landscape 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 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 themselves provide AI products while also utilizing Web3 economic models as tools for production relationships, 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 introduce 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 various 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. 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, either using a public dataset 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 test sets.

2. Model Selection and Tuning: Select the appropriate model, such as Convolutional Neural Networks (CNN), which are generally suitable for image classification tasks. Tune the model parameters or architecture according to different requirements; typically, 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 model complexity and computing power.

4. Model Inference: The files of a trained model are usually referred to as model weights. The inference process refers to the use of an already trained model to predict or classify new data. In this process, a test set or new data can be used to test the classification performance of the model, which is typically evaluated using metrics such as accuracy, recall, and F1-score.

As shown in the figure, after data collection and 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 indicates the probability that the model infers is a cat or a dog.

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

Model selection and tuning: For small teams, it is difficult to acquire domain-specific 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 receive income that matches their efforts, while AI developers find it challenging to match their research results with buyers in demand.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3, which, as a new type of productive relationship, naturally adapts to AI that represents new productive forces, 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 them from AI users of 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 ways to play.

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 crowdfunding model promotes the advancement of AI models, and numerous open-source AI resources are available for users to use, while shared computing power can be obtained at a lower cost. With the help of a decentralized collaborative crowdfunding mechanism and an open AI market, a fair income distribution system can be realized, 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 role of "artist" by using AI technology to create their own NFTs, but also creates diverse gaming scenarios 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.

2. Web3-AI Ecosystem Project Map and Architecture Interpretation

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, middle layer, and application layer, with each layer 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 entire AI lifecycle, while the middle layer includes data management, model development, and validation inference services that connect the infrastructure and applications. The application layer focuses on various applications and solutions that are directly aimed at 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 the infrastructure layer. It is the support of these infrastructures that enables the training and inference of AI models and presents powerful and practical AI applications to users.

• Decentralized computing network: It 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 earn returns, 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 to earn returns 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 industry ecosystems. The decentralized AI market on-chain can trade AI assets such as data, models, agents, etc., and provide AI development frameworks and supporting development tools, represented by projects like Sahara AI. AI Chain can also promote the advancement of AI technologies in different fields, such as Bittensor promoting competition among different AI types through an innovative subnet incentive mechanism.

• Development Platform: Some projects offer AI agent development platforms, and can also facilitate the trading of 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. These infrastructures promote the widespread application of AI technology in the Web3 ecosystem.

Middleware:

This layer involves AI data, models, as well as inference and validation, and using Web3 technology can achieve higher work efficiency.

• Data: The quality and quantity of data are key factors affecting the performance of model training. In the Web3 world, resource utilization can be optimized and data costs can be reduced through crowdsourced data and collaborative data processing. Users can have autonomy over their data and sell their data under privacy protection to avoid it being stolen by unscrupulous merchants for high profits. 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, 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 marketplace like Sahara AI has data tasks from different domains, covering multi-domain data scenarios; while AIT Protocol uses a human-machine collaboration approach for data annotation.

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

Some projects support users to provide different types of models or collaborate in training 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 modular design, and 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 whether the source of the inference model is correct and whether there are malicious behaviors, etc. Inference in Web3 can typically be integrated into smart contracts by calling the model for inference. Common validation methods include technologies such as ZKML, OPML, and TEE. Representative projects like 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 more interesting and innovative gameplay. This article mainly organizes projects in several areas: AIGC (AI Generated Content), AI Agents, and Data Analysis.

• AIGC: Through AIGC