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

With the continued rise of AI narratives, more and more attention is focused on this track. This article provides 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 AI products. Therefore, such projects are not included in the discussion of Web3-AI projects in this article.

This article focuses on projects that use blockchain to solve production relationship issues and AI to solve productivity problems. These projects themselves provide AI products while using the Web3 economic model as a tool for production relationships, making the two complementary. We categorize these projects as the Web3-AI track. To help readers better understand the Web3-AI track, we will now 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 allows computers to simulate, extend, and enhance human intelligence. It enables computers to perform various 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 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 to classify images of cats and dogs, you need:

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 the 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 Network ( CNN ), which is more suitable for image classification tasks. Tune the model parameters or architecture based on different requirements. 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: Models can be trained using GPU, TPU, or high-performance computing clusters, with training time affected by model complexity and computational power.

4. Model Inference: The file of a trained model is usually referred to as model weights. 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 test the classification performance of the model, and the model's effectiveness 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 will perform inference on the test set to obtain the predicted values for cats and dogs P(probability), that is, the probability that the model infers is a cat or a dog.

Trained AI models can further be 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 on data being non-open source when obtaining specific domain data ( such as medical data ).

Model selection and tuning: For small teams, it is difficult to obtain domain-specific model resources or spend a large amount of cost 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 income that matches their efforts, while the research outcomes of AI developers also find it difficult to match with buyers who have demand.

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 productive force, thus promoting the simultaneous advancement of 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 AI users in the Web2 era to participants, creating AI that everyone can own. At the same time, the integration of the Web3 world and AI technology can spark even 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 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 incentivizing more people to drive the progress 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 using AI technology to create their own NFTs, but also creates rich and diverse game scenes and interesting interactive experiences in GameFi. A 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. Interpretation of the Web3-AI Ecological Project Landscape and Architecture

We mainly studied 41 projects in the Web3-AI sector and categorized these projects into different levels. The classification logic for each level is illustrated in the figure below, including the infrastructure layer, middle layer, and application layer, with each layer further divided into different sectors. 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 verification inference services that connect the infrastructure to 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 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 utilization of computing resources. Some projects offer decentralized computing power markets where users can rent computing power at low costs or share computing power for profit, represented by projects like IO.NET and Hyperbolic. Additionally, some projects have derived new gameplay, such as Compute Labs, which propose tokenized protocols where users can participate in computing power leasing to earn profits 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 marketplace on the 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 facilitate advancements in AI technology across different fields, such as Bittensor promoting competition among different AI-type subnets through innovative subnet incentive mechanisms.

• Development Platforms: Some projects offer AI agent development platforms, and can also facilitate AI agent trading, such as Fetch.ai and ChainML. All-in-one 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.

Middle Layer:

This layer involves AI data, models, as well as reasoning and verification, and the use of 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, through crowdsourced data and collaborative data processing, resource utilization can be optimized and data costs reduced. Users can have autonomy over their data and sell it under privacy protection to avoid their data being stolen and profited off by unscrupulous merchants. For data demanders, these platforms provide a wide range of choices at extremely low costs. Representative projects include Grass, which uses 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 ordinary users to perform data preprocessing tasks such as image labeling and data classification. These tasks may require professional knowledge for financial and legal data processing. Users can tokenize their skills to enable collaborative crowdsourcing of data preprocessing. For example, AI markets like Sahara AI encompass data tasks from different fields, covering multi-domain data scenarios; while AIT Protocol labels data through human-machine collaboration.

• 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-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 in providing different types of models or collaboratively training models through crowdfunding. For example, Sentient, 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 has been 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 source of the inference model, checking for correctness and malicious behavior, etc. Inference in Web3 can typically be integrated into smart contracts, allowing for inference through model invocation. Common validation methods include technologies such as ZKML, OPML, and TEE. Representative projects like the ORA blockchain AI oracle (OAO) introduced OPML as a verifiable layer for AI oracles, and their official website also mentions their research on the combination of ZKML and opp/ai(ZKML with OPML).

Application Layer:

This layer is primarily aimed at user-facing applications, combining AI with Web3 to create more interesting and innovative gameplay. This article mainly outlines the projects in the areas of AIGC( AI-generated content), AI agents, and data analysis.

• AIGC: Through AIGC, it can be extended to NFT, gaming, and other tracks in Web3. Users can directly generate text, images, and audio through the prompts provided by Prompt(, and even create custom gameplay according to their preferences in games. NFT projects like NFPrompt allow users to generate NFTs through AI for trading in the market; games like Sleepless enable users to shape the personality of virtual companions through dialogue to match their preferences.

• AI Agent: Refers to artificial intelligence systems that can autonomously perform tasks and make decisions. AI agents usually possess the capabilities of perception, reasoning, learning, and action, allowing them to execute complex tasks in various environments. Common AI agents include language translation,