Can AI that Hitchhikes Lead A New Trend in Bull Market Narratives?

[TL；DR]

The AI+Web3 track can be roughly divided into three layers: infrastructure layer, intermediate layer, and application layer. The infrastructure layer focuses on providing computing power and storage, which is currently the most popular and popular field.

In addition to application layer cases in gaming, social networking, and trading, AI can also be used in fields such as data analysis, information monitoring and tracking, and bidding betting.

Projects closely related to the concept of AI often quickly gain market favor, but attention should be paid to filtering out projects that do not live up to their name and are purely focused on hot topics.

Introduction

Recently, a series of AI+Web3 projects have sparked market enthusiasm. In order to explore this potential market opportunity in depth, Gate.io Research will combine various hot projects to conduct in-depth analysis of various links in the AI+Web3 industry chain, in order to provide readers with a comprehensive and in-depth understanding.

AI+Web3: New Technology, New Direction, New Speculation

Last year, with the emergence of large-scale generative AI models such as ChatGPT, AI has become a hot investment topic pursued by the world capital market. At the same time, the Web3 market has also ushered in a new round of prosperity.

The organic combination of AI and Web3 undoubtedly becomes the intersection of the two hot topics in the current technology field. Recently, we have observed a large number of new and old projects around this theme receiving market attention, highlighting the strong interest and high expectations of investors for this combination.

According to the definition of Wanxiang blockchain, the combination of AI and Web3 is mainly reflected in two aspects: how Web3 promotes the development of AI, and how Web3 applications combine AI technology. Currently, most projects tend to utilize Web3 technology and concepts to drive the advancement of AI. To analyze this combination, we can start with the entire process of AI from model training to application.

The production process of AI roughly includes: data acquisition, providing a foundation for model training; Data preprocessing and feature/hint engineering, involving data cleaning, annotation, and structured queries; Model training and optimization, improving model performance through iteration; Model review and governance to ensure model quality and transparency; Model inference, predicting new data; Model deployment and monitoring to ensure optimal model performance in practical applications.

In this process, Web3 has many integration points. For example, the distributed network and incentive mechanism of Web3 can build more open and open-source AI networks and communities, meeting the needs of AI applications for low-cost, open infrastructure and data networks. Meanwhile, combining Web3 with crypto technologies such as ZK can improve the trust issues of AI, address challenges such as model transparency, bias, and ethical applications.

Source: Wanxiang blockchain

As shown in the above figure, the AI+Web3 track can be roughly divided into three layers: infrastructure layer, intermediate layer, and application layer.

The infrastructure layer focuses on providing computing power and storage, and the addition of Web3 can reduce its costs and serve more AI applications.

The middle layer utilizes Web3 technology to optimize AI production processes, such as data acquisition, preprocessing, and model validation, resulting in numerous innovative projects.

The application layer demonstrates the widespread application of AI in Web3, such as content generation, analysis, and prediction. Based on the author’s observation, the deion of the application layer in the above figure is still quite conservative. We will discuss it in detail in the afternoon. Although there have not been any leading projects yet, the potential is enormous, and future competition will focus on product and technological capabilities.

We will provide specific case studies for these three tier projects in the following sections.

AI+Web3 Trending Projects Emerge One After Another

AI+DePIN

The entire workflow of AI relies on the support of computing and storage infrastructure. These facilities are not only responsible for providing powerful computing power for model training and prediction but also for storing, managing, and parsing data throughout the entire data model and lifecycle.

Presently, the rapid growth of AI applications has led to a huge demand for infrastructure, especially high-performance computing power. Therefore, developing more efficient, cost-effective, and resource rich computing and storage infrastructure has become a key trend in the early stages of AI development, which is currently the most popular field.

Source: Render Network

In this field, several representative projects have emerged, such as the rendering network that was born in the previous bull market and mainly provides rendering services, Akash that focuses on cloud computing, Filecoin and Arweave that focus on cloud storage, IO.NET and Aethir that are newly launched in this bull market and mainly provide computing power support for AI. In our recent article “BOME Creates A Record for Skyrocketing Prices, Analyze the Trending Projects in the SOL Eco“ it introduced cutting-edge projects such as IO.NET, which will not be further elaborated here.

AI+Data

The middle layer is a key LINK in the AI production process, which utilizes Web3 technology to optimize and improve specific workflows.

Firstly, in the data acquisition stage, the middle layer introduces decentralized data identity management, which not only protects user data security but also ensures clear ownership of the data. At the same time, through incentive mechanisms, users can be encouraged to share high-quality data to obtain monetization, thereby expanding the sources of data.

Due to the limitations of the industry’s development stage, there were almost no relatively well-known projects in this field in the previous round of bull and bear markets. In this bull market, there have been AI identity projects such as Worldcoin (which we have written about multiple times), Aspecta invested by Gate.io, Ocean Protocol, a data trading platform, and Grass, a data network for broadband mining.

Source: Aspecta

Secondly, in the data preprocessing stage, the middle layer is committed to building a distributed AI data annotation and processing platform, providing strong support for subsequent model training. In this regard, projects such as Public AI have achieved significant results.

Finally, in the model validation and inference stage, the middle layer fully utilizes the combination of Web3 technology and cryptography techniques, such as ZK and homomorphic encryption, to verify whether the inference process of the model uses the correct data and parameters. This not only ensures the accuracy of the model but also protects the privacy of input data. The typical application scenarios are ZKML, such as Bittensor, Privasea, Modulus, and Privasea invested by Gate Labs.

AI+intent-centric

Intent centric, translated as “intention centered,” refers directly to “what you want to do,” focusing on the outcome rather than the process. Intent centric aims to optimize protocols and infrastructure to enable tedious on chain operations to be done in one step. More precisely, by hiding the complex operational processes of the past, users can achieve their goals without feeling or directly, reflecting the essence of chain abstraction.

The common intention scenarios for using AI currently include cross chain, airdrop, governance, high-value transactions, and batch operations. The Telegram Bot we previously discussed in our article can also be classified under this category.

For example, Delysium (AGI) is committed to using AI to create an AI Agent Network centered on user intent for Web3, which has gained high attention in markets such as South Korea.

As shown in the figure, due to market speculation and value discovery, the token of this project has seen an astonishing increase in recent times.

Source: Gate.io

Delysium has launched an AI Agent called Lucy. As an AI driven Web3 operating , Lucy is able to intelligently plan and automatically ute workflows that can solve user needs based on understanding the intentions and goals contained in natural language, simplifying the complex operational processes of current Web3 applications and protocols.

AI+Game

AI+Games also have extremely high imagination space. AI technology not only accelerates the game production process, but also runs through every aspect of game production, from exploring user habits to customizing personalized interaction scenes, demonstrating enormous potential. Nowadays, major game manufacturers are actively embracing AI and restructuring the game industry chain eco.

Regarding game production, AI provides strong support for art, planning, and operations. Whether it’s creative inspiration, level generation, copywriting and operational analysis, AI is providing acceleration for the production of game content. In terms of gaming experience, the natural language generation and image generation capabilities brought by AI make the gameplay more innovative and diverse, and the interaction between NPCs more intelligent and vivid.

For example, the Jue Wu AI of “Honor of Kings” has been widely applied in level uation and testing; In “Mount & Blade II: Bannerlord” ChatGPT enables NPCs to dynamically respond to players, enhancing the game’s interactivity; In “Naraka：Bladepoint” players can even use AI painting to generate fashion models and vote for the most popular works, showcasing the enormous potential of AI in game innovation.

Source: sleeplessAI

In addition to traditional Web2 games embracing AI, Web3 games are no exception. For example, Ultriverse provides users with AI deep feature analysis and customized social, gaming, metaverse and other multiple experiences through its powerful AI engine, as well as sleepless AI’s virtual companion game that focuses on AI.

AI+Analysis

In addition to application layer cases in gaming, social networking, and trading, AI can also be used in fields such as data analysis, information monitoring and tracking, and bidding betting. Representative projects such as Kaito and Dune have emerged, setting a benchmark for the industry.

We often cite Dune’s data graphs in our blog posts, so there is no need to elaborate on them here.

To Sum Up

In the past year, the integration of Web3 and AI has not only led a new trend in technology, but also spawned a new consensus in the industry: blockchain has changed production relations, and AI has changed productivity. This concept has now deeply rooted in people’s hearts and become a powerful driving force for industry development.

With game developers, DeFi protocols, and other Web3 infrastructure projects increasing their investment in AI, the combination of AI and Web3 is becoming an important direction for industry innovation. In fact, projects closely related to the concept of AI often quickly gain market favor, and we have already noticed this amazing growth earlier.

However, beneath the surface prosperity and hype, we cannot ignore the practical obstacles in the AI+Web3 industry. Especially for practitioners, it is necessary to deeply explore their practical and feasible application scenarios, uate their ability to create value and build industry narratives. In the long run, how will the ecological pattern of the AI+Web3 industry be formed, which fields will show huge development potential, and whether it will face ethical and moral dilemmas need to be continuously explored and answered in practice.

Therefore, in the face of the wave of AI+Web3, we should not only see the opportunities it brings, but also maintain a clear mind and rationally view its challenges and shortcomings. Only in this way can we better grasp the development trajectory of the AI+Web3 industry, promote its healthy and sustainable development, and seize the profit opportunities brought by the trend.