Huobi Research Institute's latest research report: Exploring investment directions in the AI+Crypto track

In recent years, with the development of artificial intelligence (AI)andBlockchain技术的迅猛发展，AI+Crypto 赛道成为了投资者关注的热点领域。BlockchainIts characteristics of decentralization, high transparency, low energy consumption and anti-monopoly make up for the strong centralization and opaque processing of AI systems. The combination of the two has brought us unprecedented opportunities.

According to Vitalik, AI andBlockchainThe combined applications of AI can be divided into four categories: as an application participant, as an application interface, as an application rule, and as an application target. He proposed that the role of AI in Crypto should be considered more from the perspective of "application", including optimizing computing power, optimizing algorithms, and optimizing data.

Huobi Research Institute distinguishes the direction of Crypto technology participation according to the application level of AI, which can be divided into the basic layer, execution layer and application layer. There are opportunities worth exploring at each level. For example, zkML technology combined withZero knowledge proofandBlockchaintechnology, providing AI agent behaviorSafety, verifiable and transparent solutions. In addition, AI is also very useful in data processing, automated dApp development, and on-chain transactions.SafetyAt the application level, AI-driven trading robots, predictive analysis tools, and AMM liquidity management play an important role in the DeFi field.Xiaobai Navigationeffect.

本文将详细探讨 AI+Crypto 赛道的投资方向，重点关注基础架构层面和应用层面的创新与发展，从中期和长期投资策略的角度，分析 AI 与区块链结合的前景与挑战。

This article was written by Huobi Research, a team currently affiliated with HTX Ventures. HTX Ventures is the global investment arm of Huobi HTX, integrating investment, incubation and research to identify the best and most promising teams in the world. Currently, HTX Ventures has supported more than 200 projects across multiple blockchain tracks, some of which have been listed on Huobi HTX trading.

Key areas of AI track

Blockchain is completely opposite to artificial intelligence in terms of centralization, low transparency, energy consumption, and monopoly. According to the above criteria and his own thinking, Vitalik divides the applications of combining artificial intelligence and blockchain into four categories:

• AI as a player in a game

• AI as an interface to the game

• AI as the rules of the game

• AI as the objective of the game

Vitalik Buterin considers the role of AI in Crypto more from the perspective of "application". If we consider it from the perspective of productivity VS production relations, Crypto actually provides more production relations. From this point of view, we can consider it from three main directions:

- Optimize computing power: Provide decentralized and efficient computing resources, reduce the risk of single point failure, and improve overall computing efficiency.

- Optimization algorithms: Promote open source, sharing and innovation of algorithms or models.

- Optimizing data: decentralized storage, contribution, use andSafetymanage

HTX Research believes that the overall direction of AI can be divided into the basic layer, execution layer and application layer according to the general architecture. Correspondingly, we can also explore AI+Web3 projects from these three major directions. At the basic layer, including model training, data, decentralized computing power and hardware at the infrastructure level, focus on the combination of zk technology and artificial intelligence ML technology; at the execution layer, data processing and data transmission, AI agent, zkML, FHE (fully homomorphic encryption) at the model level; at the application layer, it mainly focuses on AI+DeFi, AI+GameFi, Metaverse, AIGC and Meme, as well as RAAS (Robotics as a Service), oracles, coprocessors, UBI (universal basic income) at the blockchain level.

Among them, projects at the infrastructure and application levels are developing rapidly, such as Io.net at the computing power level, Flock at the basic model level, ZeroGravity, the blockchain infrastructure, Myshell, the AI agent, and 0x Scope at the application level.

The following directions can be explored:

1. zkML Direction

zkML technology combinesZero knowledge proofand blockchain technology, providing a way to monitor and constrain AI agent behaviorSafetyFor example, the Modulus Labs project uses zkML technology to prove to stakeholders that its AI has performed a specific task while protecting personal privacy and business secrets.

zkML, as an intermediary between artificial intelligence and blockchain, proposes a set of solutions to solve the privacy protection issues of AI models and inputs, and ensure the verifiability of the reasoning process. It creates a new way to use public models when verifying private data, or use public data when verifying private models. By integrating the power of machine learning, intelligentcontractIt can achieve more autonomy and dynamism, and can operate based on real-time on-chain data rather than just static rules. This innovation enables smartcontractMore flexible and able to adapt to more different application scenarios, even those that were not foreseen when the contract was originally set up.

Introduction to typical zkML technology projects

The first table is a brief introduction of some potential ZKML projects. The second picture shows some other ZKML projects.

2. Data processing direction

It mainly refers to various breakthroughs in AI at the execution level, especially some breakthroughs in the data transmission level and development level of some blockchains. The specific analysis is as follows:

a. AI and on-chain data analysis

This direction mainly refers to using AI technology to deeply mine these data, and using LLM large models and deep learning algorithms to gain more insights. For example, the Web3 Analytics project uses AI to analyze on-chain data to reveal market trends and user behaviors. It can help users gain insights into on-chain transactions and market trends.

b. AI and automated dApp development

This direction is mainly aimed at some infrastructure projects of DevOps. Some AI projects that use automated development can take on more developers, making the ecosystem more prosperous. Some development tools that use AI can also help developers write smart contracts more quickly and automatically correct errors, and some can also realize drag-and-drop DAPP programming functions.

c. AI and on-chain transactionsSafety

This direction mainly refers to AI agents. It involves deploying AI agents on the blockchain to improve the security and credibility of AI applications. These AI agents can automatically perform tasks such as transactions, data analysis, automatic decision-making, etc., and being deployed on the blockchain makes their operations not only transparent and traceable, but also difficult to be tampered with, which improves the security of the entire system. AI technology can identify and defend against malicious attacks and data leaks through real-time monitoring and intelligent analysis, ensuring the security of transactions and the integrity of data.

• Project cases:
SeQure is a security platform that uses AI for real-time monitoring and analysis to promptly detect and defend against various malicious attacks and data leaks, ensuring the stability and security of on-chain transactions.

3. AI+DEFI direction

The most important combination of AI and the application layer is AI+DEFI. The following are some AI+DEFI directions that need attention:

1. AI-driven trading robots

These robots are able to execute trades quickly and accurately, analyzing market data, news sentiment and price trends to make split-second trading decisions, often outperforming human traders.

2. Predictive Analytics

While predicting crypto market volatility has always been a challenge, AI-powered analytics tools are emerging as an important tool that can provide reliable predictions of market trends and potential price movements.

3. AMM Liquidity Management

For example, when adjusting the liquidity range of Uniswap V3, through the integration of AI, the protocol can adjust the liquidity range more intelligently, thereby optimizing the efficiency and benefits of the automated market maker (AMM).

4. Liquidation protection and debt position management

Combining on-chain and off-chain data allows for more intelligent implementation of liquidation protection strategies, ensuring that debt positions are protected during market fluctuations.

5. Complex DeFi structured product design

When designing treasury mechanisms, you can rely on financial AI models rather than fixed strategies. Such strategies may include AI-managed transactions, loans, or options, which increase the intelligence and flexibility of the product.

4. AI+GameFi direction

The application of AI in the GameFi project is mainly to enrich the gaming experience and increase the possibility of innovation. Its main directions are as follows:

1. Game strategy optimization:

AI can learn from players' gaming habits and strategies, adjust the difficulty and strategy of the game in real time, and provide a more personalized and challenging gaming experience. Through deep learning and reinforcement learning, AI can evolve itself and better adapt to players' needs and preferences.

2. Game asset utilization management:

AI technology can help players manage and trade virtual assets in games more efficiently. Using smart contracts and automated trading strategies, players can maximize the use of assets, for example, automatically buy, sell, lease and lend game assets, thereby optimizing returns on investment.

3. Enhance game interaction:

AI can create more intelligent and responsive non-player characters (NPCs), and through natural language processing (NLP) and machine learning (ML) technologies, achieve more natural and smooth interactions with players, thereby improving game immersion and player satisfaction.

Possible investment strategies from a time perspective

- In the short term, we should pay attention to the areas where AI first landed in Crypto, such as some conceptual AI applications and memes. Logic: This year, the mainstream AI circle will continue to output new hot spots. Every time a large model of web2 companies such as NVIDIA and OpenAI is upgraded, it will ignite the hot spots of the AI track, and it will also bring in new funds. These are all the emotional aspects of this track.

- In the medium term, the combination of Ai Agent and Intent, and the combination with smart contracts is a highlight. Once AI is successful, it will provide a solution for the expansion of smart contracts, thus forming a new blockchain of ledger + contract + AI, breaking through the ledger + contract narrative of the ETH era.

– AI agent is a subdivision that Vitalik Buterin has mentioned. AI Agent refers to an AI intelligent entity that can autonomously obtain information from the environment, process information, make decisions, execute, and change the environment. AI Agent is currently a cutting-edge subdivision in the field of AI, and is the application layer closest to Mass Adoption.

– From a narrative perspective. AI Agent is a sexy beauty, GPU cloud computing power is a steady and mature middle-aged entrepreneur, and the AI model combined with the DA layer is a scientist with messy hair.

– In the long run, the combination of AI and zkML technology (although the ML bosses of web2 AI companies scoff at Crypto’s use of AI) will ultimately affect the Crypto field.

references

– Twitter: https://twitter.com/FinanceYF5/status/1772434625387717055

-Web3 Caff: https://twitter.com/Web3 Caff_Res

– Twitter Vitalik: https://twitter.com/VitalikButerin

appendix:

List of decentralized computing and AI reasoning platform projects

It mainly refers to the use of Crypto as an incentive to share and utilize idle computing resources on a global scale.

List of AI data and model source projects

This part is mainly based on the authenticity, transparency and traceability of data, and uses the Crypto economic model to provide data incentives (for C-end users) and model incentives (Dev, B-end).

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