The Development of the AI Track in the Crypto Industry

Forward the Original Title：加密行业的AI赛道的发展，会像几年前的元宇宙吗？

Recently, due to the release of OpenAI’s Sora and NVIDIA’s financial report, NVIDIA’s market value approached $2 trillion, leading to a surge in interest in the AI track in the Crypto industry. Projects like WLD, AGIX, FET, among others, have shown impressive performance. With the overall market trending upward and the NVIDIA AI conference, the AI track is experiencing another surge.

Currently, there are over hundreds of AI + Web3 concept projects covering various directions such as infrastructure, data, computing power, etc. Top institutions like a16z, Binance, among others, have begun to lay out related tracks. AI + Web3, as a new narrative, is bound to run through this bull market cycle. Today, let’s take a simple look at the AI track.

Similar Trajectory of AI + Web3 and the Metaverse

Although new narratives have been emerging in the Crypto industry, and each bull market cycle is different, there are some similarities in the development process. For example, both the AI track and the metaverse track are products extending from Web2 to Web3.

The metaverse initially gained popularity because the first metaverse stock, Roblox, was on fire, and then this trend spread to the Crypto industry, giving rise to the concept of a crypto version of the metaverse. Additionally, with Facebook renaming itself Meta, the related concepts received comprehensive publicity, and the metaverse-related concepts in the Crypto industry also peaked in this environment.

Recently, there hasn’t been much discussion about metaverse-related concepts for a long time. Checking it out, reports about “the world’s first metaverse stock Roblox” have turned mostly negative, with consecutive declines after listing, 70% of revenue coming from marketing, and a market value decline of over 70%. As the first metaverse stock is not popular any more, the concept of the metaverse in the Crypto industry has also come to an end.

Whether the subsequent development of metaverse-related concepts will pick up again remains to be seen, but the development of the AI track in the Crypto industry is remarkably similar. The first wave of excitement began with the release of ChatGPT, coupled with extensive media coverage, which propelled BN’s only two AI projects to skyrocket. Although criticized for their inadequate technology, whenever AI is mentioned, FET and AGIX are invariably included. The second wave is due to the recent release of OpenAI’s Sora, leading to another wave of AI-related concepts.

It can be seen that the current development of the AI track in the Crypto industry is still heavily reliant on OpenAI, with most projects in the conceptual stage or riding on the coattails, yet to achieve mature implementation. Imagine, if OpenAI were to stagnate or collapse in the future, would the AI track in the Crypto industry still be as hot?

AI Track Classification

Currently, the integration of AI + Web3 mainly focuses on algorithm models, data, and other directions, as follows:

• Decentralized Computing Power (GPU)

As AI models become increasingly complex, the demand for computing resources, especially high-performance hardware like GPUs, is rapidly increasing. From the perspective of AI, the application of computing power can be divided into two directions: inference and training. Currently, the focus is mainly on the inference direction, as it is relatively simple and has lower hardware requirements. However, the development of the training direction still faces many challenges because it needs to integrate with neural network design and requires high hardware requirements, making implementation very difficult.

The core idea of decentralized computing systems is to allow owners of computing resources to rent out their idle resources and trade them on a secondary market through competitive pricing and ease of access. This means that teams can rent computing resources on demand, while resource owners can rent out their resources at competitive prices, thereby improving market efficiency and avoiding resource idleness issues. In addition to the economic and efficiency benefits, decentralized computing systems also have significant value in terms of censorship resistance.

Currently, most computing power projects are in the stage of following the trend, loudly advocating decentralization, but ignoring the inefficiency of decentralized networks, with many projects suffering from severe homogeneity in design.

• zkML (Zero-Knowledge Machine Learning)

Since smart contracts can only perform pre-set events, slightly more complex operations incur prohibitively high costs or are impractical. Integrating AI (such as machine learning models) into smart contracts can increase functionality, security, and efficiency, and improve user experience. However, this integration also brings additional risks, as it cannot guarantee that models supporting smart contracts will not be attacked, and it is difficult to handle model training issues with insufficient data.

Machine learning requires a large amount of computing resources to run complex models, making it costly to run AI models directly within smart contracts. To reduce the high cost of running AI models within smart contracts, some projects are researching the use of zkML. zkML can perform calculations off-chain and only need to submit proofs of verification on-chain. This can greatly reduce the need for on-chain computation, reduce costs, and maintain the property of trustlessness. This approach is particularly suitable for scenarios that require a large amount of computing resources, such as yield optimization models in DeFi protocols.

• AI Agents

AI agents are robots that can independently receive, understand, and execute tasks, using AI models to make decisions and take actions. In the combination of AI and cryptocurrency, agents play an important role. Cryptocurrency provides a permissionless and trustless payment infrastructure, while agents can leverage this feature.

Once trained, agents can have their own digital wallets, enabling them to interact directly with smart contracts. For example, a simple agent can use AI models to search for information on the internet and trade based on predicted market conditions. Agents can make decisions and execute transactions autonomously through smart contracts without relying on third-party trust.

• AI Empowered DApps

AI-empowered DApps are a very common direction. The widespread applicability of generative AI enables it to be accessed via APIs, thereby simplifying and intelligentizing various applications, including data analysis platforms, trading bots, blockchain encyclopedias, and more.

In addition, generative AI can also act as chatbots (such as Myshell) or AI companions (such as Sleepless AI), and can even create virtual characters (NPCs) in blockchain games. However, due to the low technical barriers, many applications simply access an API and claim to be AI projects after minor adjustments.

Representative project

There are currently several strong projects under review. After all, in this industry, in an environment where the strong are always strong, it is still too difficult for most investors to obtain alpha returns. It is not bad to steadily capture beta. In addition, the AI ​​​​track has been relatively hot in the short term recently, so be careful about the risk of a correction.

• Worldcoin

Worldcoin is a project founded by OpenAI co-founder Sam Altman to build a global identity and financial network to achieve widespread adoption around the world. At the heart of the project is World ID, which enables users to verify their identity online while protecting their privacy.

Worldcoin’s goal is to solve some of the problems existing in the existing financial system and provide financial services and identity verification to one billion people around the world. By building a global identity network, it aims to provide a safe, reliable and privacy-preserving way to verify a person’s identity.

For a detailed introduction to Worldcoin, you can read our previous article “The founder of OpenAI’s WorldCoin goes live, and Vitalik Buterin publishes the full article on his views on its iris recognition proof”.

With the recent surge in the AI track, various data related to Worldcoin has also seen an increase. According to official reports, as of February 21st, the number of verified World ID users has exceeded 3.5 million, while the daily user count of its flagship World App has surpassed one million. The market value of WLD is close to $1.2 billion, with a fully diluted market capitalization of $88 billion. Worldcoin, from various aspects including its background, can be considered a perfect target for the AI track, but due to its terrifying fully diluted market capitalization, it may deter the majority of investors.

• Render

Render focuses on meeting the growing computational demands in media production, particularly in areas such as augmented reality, virtual reality, and AI-enhanced media. By connecting decentralized graphics processing units (GPUs) globally and sharing computing power, Render enables AI video rendering and contributes AI computing power. Render’s core product is integration with AI, supporting AI-related tasks to help artists generate assets, optimize digital art pieces, and extend management capabilities for large-scale art collections and rendering workflows.

Render’s vision is to build a decentralized computing power pool that connects created computing power with applications that require it. Render’s backers include companies like Microsoft and Apple, with full support from its parent company, OTOY. OTOY is a reputable company in the video rendering field, providing video rendering technology services to top tech companies like Microsoft, Apple, and Google.

• Fetch.AI

Fetch.AI is an open platform designed to build AI applications and services. By combining AI and blockchain technology, it offers users an innovative way to leverage AI agents for various tasks and transactions.

AI agents are Fetch.AI’s core components; they are modular modules capable of performing specific tasks. These agents can autonomously connect, search, and transact, interact with other agents, and create dynamic markets. By integrating with these agents, traditional products can access AI, optimize business processes, and create new business models. Its goal is to drive the development of the intelligent economy, achieve a decentralized digital economy, and enhance the automation and efficiency of various processes.

• SingularityNET

SingularityNET is a decentralized AI platform and marketplace designed to connect various AI tools and services and facilitate their cooperation and transactions. It provides an open platform for AI agents to communicate and collaborate with each other, while also offering users a convenient way to access and use AI technology.

On the SingularityNET platform, users can delegate tasks to different AI agents through transactions, negotiation of payments, and ratings. This decentralized structure allows agents to freely collaborate and compete with each other, providing users with more choices and high-quality AI services.

SingularityNET aims to promote the development and application of AI technology and foster its widespread use in various fields. By establishing an open platform and ecosystem, it is committed to strengthening the interoperability of AI tools and services, bringing more innovation and convenience to users.

FET and AGIX are often discussed together as they have been consistently rising and falling together. When mentioning the AI track, BN will come to mind, despite being criticized by professionals for their technical shortcomings. However, their early advantage does not prevent them from benefiting from each wave of the AI boom. Both projects’ market values are approaching the $1 billion mark.

• Bittensor

Bittensor is an open-source protocol and a blockchain-based decentralized machine learning network aimed at democratizing AI by creating a platform for multiple decentralized commodity markets, also known as “subnetworks,” unified under a single token system. The mission of Bittensor is to establish a network comparable to large AI super-companies like OpenAI through unique incentive mechanisms and advanced subnetwork architecture.

The Bittensor system can be viewed as a machine that efficiently transfers AI capabilities onto the blockchain. The network is managed by two key participants: miners and validators. Miners submit pre-trained AI models to the network and receive rewards for their contributions. Validators ensure the validity and accuracy of the model outputs. This setup creates a competitive environment that incentivizes miners to continually improve their models to achieve better performance and more rewards (in the form of the native token TAO). Users interact with the network by sending queries to validators, who then distribute the queries to miners. Validators rank the responses from miners and return the highest-ranked reply to the user.

Bittensor does not train models because model training is complex and costly. Instead, the network relies on decentralized training mechanisms. The task of validators is to evaluate the models generated by miners using specific datasets and score each model based on specific criteria such as accuracy and loss function. This decentralized evaluation ensures the continuous improvement of model performance.

According to Coingecko data, Bittensor currently has a market capitalization of around $3.6 billion. It performed well last year due to its presence in both the PoW and AI tracks, but overall liquidity is relatively low.

• Arkham

Arkham is a crypto intelligence analytics platform that provides detailed data and analysis by connecting blockchain addresses with real-world entities. It also utilizes the concept of token economics to create an intelligence trading platform, allowing users to buy and sell information about the owners of blockchain addresses. It uses an AI algorithm engine called Ultra to connect blockchain addresses with real-world entities to reveal individuals and companies behind blockchain activities and provide relevant data and analysis.

Its main product, Profiler, offers a comprehensive view of entity or address activities, including transaction history, holdings, balance history, profit and loss situation, trading platform usage, and top counterparties. By using Profiler, users can access detailed information about entity activities and conduct deeper analysis.

Its goal is to systematically analyze and de-anonymize blockchain transactions, establish a decentralized intelligence earning economy, and promote transparency and efficiency in the crypto market. It is worth mentioning that Sam Altman, co-founder of OpenAI, is one of the investors.

Summary

The above is a summary of the AI track. As a new narrative of this round, coupled with assistance from Web2, it is foreseeable that the AI track will undoubtedly occupy a place in this bull market. Additionally, it is advisable to focus on the progress of AI in the Web2 industry, as significant events typically involve linkage between Web2 and Web3.

Disclaimer：

1. This article is reprinted from [baihua blockchain]. *Forward the Original Title‘加密行业的AI赛道的发展，会像几年前的元宇宙吗？’.All copyrights belong to the original author [Day]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
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