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

As AI narratives continue to gain traction, 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, providing you with a comprehensive presentation of the panorama 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 narrative has been exceptionally popular in the Web3 industry, with AI projects emerging like mushrooms after rain. Although many projects involve AI technology, some 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 issues of production relations and AI to address productivity problems. These projects themselves provide AI products while also utilizing Web3 economic models as tools for production relations, with both complementing 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 integration 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, 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 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 to classify images of cats and dogs, you need to:

1. Data collection and data preprocessing: Collect a dataset of images containing cats and dogs, which can be done using public datasets or by collecting real data yourself. Then label each image with a 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 testing sets.

2. Model Selection and Tuning: Choose an appropriate model, such as Convolutional Neural Networks (CNN), which are well-suited for image classification tasks. Tune model parameters or architectures based on different requirements; generally speaking, 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: 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 trained file of a model is usually referred to as model weights. The inference process refers to 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 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 and preprocessing, model selection and tuning, and training, the trained model is applied to infer on the test set to obtain the prediction values P (probability) for cats and dogs, which indicates the model's inferred probability of being 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 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 limitations with non-open-source data when obtaining data in specific fields (such as medical data).

Model selection and tuning: It can be difficult for small teams to access specific domain model resources or spend large amounts on model tuning.

Acquiring computing power: For individual developers and small teams, the high costs of purchasing GPUs and renting cloud computing power can pose a significant financial 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 in need.

The challenges existing in centralized AI scenarios can be addressed by integrating with Web3. Web3, as a new form of productive relationship, is naturally suited to represent the new productive forces of AI, thereby promoting simultaneous progress in technology and production capabilities.

1.3 The Synergistic Effect 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, allowing them to transform from AI users in the Web2 era to 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 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 accessed at a lower cost. With the help of decentralized collaborative crowdsourcing mechanisms and an open AI market, a fair income distribution system can be achieved, 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" by using AI technology to create their own NFTs, but also creates diverse game scenarios and interesting interactive experiences in GameFi. 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 Web3-AI Ecosystem Project Layout and Architecture

We mainly studied 41 projects in the Web3-AI sector and classified 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, 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 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 with applications. The application layer focuses on various applications and solutions that are directly user-facing.

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, 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 economical utilization of computing resources. Some projects offer decentralized computing power markets where users can rent computing power at a low cost or share computing power to earn profits, 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. Users can participate in computing power leasing in different ways by purchasing NFTs that represent physical GPUs to earn profits.

• 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 advancements in AI technologies across different fields, such as Bittensor, which fosters competition among different AI types through an innovative subnet incentive mechanism.

• Development Platforms: Some projects offer AI agent development platforms, which can also facilitate the trading of AI agents, such as Fetch.ai and ChainML. All-in-one tools help developers to 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.

Middle Layer:

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

• Data: The quality and quantity of data are key factors affecting the model training results. 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, selling their own data under privacy protection to avoid it being stolen and profited from by unscrupulous merchants. For data demanders, these platforms offer a wide range of choices at very low costs. Representative projects such as Grass utilize user bandwidth to scrape web data, and xData collects media information through user-friendly plugins, supporting users in uploading tweet information.

In addition, some platforms allow domain experts or regular users to perform data preprocessing tasks, such as image labeling and data classification. These tasks may require specialized knowledge in finance and legal data processing. Users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. For example, the AI marketplace represented by Sahara AI has data tasks from different fields, covering multi-domain data scenarios; while AIT Protocol labels data through a human-machine collaborative 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, one can choose from the Yolo series. 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 different complexities varies, and sometimes model tuning is necessary.

Some projects support users to provide different types of models or collaboratively 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 modular design. The development tools provided by Sahara AI come with advanced AI algorithms and computing frameworks, and they have collaborative training capabilities.

• 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. This process is called 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 malicious behaviors, etc. Web3 inference can typically be integrated into smart contracts, allowing for inference by calling the model, with common verification methods including ZKML, OPML, and TEE technologies. Representative projects such as the AI oracle on the ORA chain (OAO) have introduced OPML as a verifiable layer for AI oracles. The ORA official website also mentions their research on ZKML and opp/ai (combining ZKML with OPML).

Application Layer:

This layer mainly consists of user-facing applications that combine AI with Web3 to create more interesting and innovative gameplay. This article mainly organizes the projects in several areas, including AIGC (AI-generated content), AI agents, and data analysis.

• AIGC: Through AIGC, it can