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

With the continuous rise of AI narrative, more and more attention is focused on this track. This article conducts an in-depth analysis of the technical logic, application scenarios, and representative projects in the Web3-AI track, providing you with a comprehensive view of the panorama 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, with the underlying token economics having 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 using Web3 economic models as tools for production relationships, complementing each other. We categorize such projects as the Web3-AI track. To help readers better understand the Web3-AI track, the following sections will elaborate on 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 various complex tasks, from language translation, image classification to applications such as facial recognition and autonomous driving. AI is changing the way we live and work.

The process of developing artificial intelligence models usually includes 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 image datasets containing cats and dogs, which can use public datasets or collect 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 test 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 the model parameters or architecture based on different needs; generally, the network layers of the model can be adjusted according to the complexity of the AI task. In this simple classification example, a shallower network 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 the trained model are usually referred to as model weights. The inference process refers to the process of using a pre-trained model to make predictions or classifications on new data. During this process, a test set or new data can be used to assess the classification performance of the model, and the effectiveness of the model 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 derive the predicted values P (probability) for cats and dogs, which indicates 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 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 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 acquire model resources specific to a particular domain or to spend a lot of costs on model tuning.

Acquiring computing power: For individual developers and small teams, the high purchase costs of GPUs and cloud computing rental fees can pose a significant economic burden.

AI Asset Income: Data labeling workers often struggle to earn an income that matches their efforts, while the research results 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. As a new type of productive relationship, Web3 is naturally compatible with AI, which represents a new 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 users of AI 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 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 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 "artist" by using AI technology to create their own NFTs but also creates a rich variety of 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 a suitable entry point in this world.

2. Interpretation of the Web3-AI Ecological Project Landscape and Architecture

We mainly studied 41 projects in the Web3-AI track and classified these projects into different tiers. The classification logic for each tier is shown in the following diagram, which includes the infrastructure layer, intermediate 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 technology architecture that support the entire AI lifecycle, the middle layer includes data management, model development, and verification inference 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, and 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 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 low costs or share computing power to earn returns, represented by projects like IO.NET and Hyperbolic. In addition, some projects have derived new play styles, such as Compute Labs, which proposed a tokenization 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 AI resources both on-chain and off-chain, 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, represented by projects like Sahara AI. AI Chain can also promote technological advancements in AI 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 that also enable trading by AI agents, such as Fetch.ai and ChainML. One-stop tools help developers create, train, and deploy AI models more conveniently, with representative projects like Nimble. These infrastructures facilitate the widespread application of AI technology in the Web3 ecosystem.

Middleware:

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 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 their data under privacy protection to avoid it being stolen and exploited for high profits by malicious merchants. For data demanders, these platforms offer a wide range of choices at very 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 labeling and data classification, which may require specialized knowledge for 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 offers data tasks across different domains, covering multi-domain data scenarios; while AIT Protocol labels data through a human-machine collaboration approach.

• 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. For text-related tasks, common models include RNN and Transformer, among others, as well as some specific or general large models. The depth of the models required varies depending on 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 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. The development tools provided by Sahara AI come with advanced AI algorithms and computing frameworks, and have the capability for collaborative training.

• 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 check whether the source of the inference model is correct and whether there are malicious activities, etc. Inference in Web3 can usually be integrated into smart contracts, allowing model inference through calls. Common verification 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 mainly consists of applications directly aimed at users, combining AI with Web3.