In recent years, with the rapid development of Artificial Intelligence (AI) and blockchain technology, the AI+Crypto track has become a hot area of interest for investors. Blockchain, with its characteristics of decentralization, high transparency, low energy consumption, and anti-monopoly, complements the strong centralization and opaque processing of AI systems. The combination of the two has brought unprecedented opportunities.
According to Vitalik, the integration of AI and blockchain applications can be mainly divided into four categories: as application participants, as application interfaces, as application rules, and as application objectives. He suggests that the role of AI in Crypto should be considered more from an “application” perspective, including optimizing computing power, algorithms, and data.
Huobi Research distinguishes the directions of Crypto technology participation based on the application levels of AI, which can be divided into basic, execution, and application layers, each offering exploration opportunities. For example, zkML technology combines zero-knowledge proofs and blockchain technology to provide a secure, verifiable, and transparent solution for AI agent behavior. Additionally, AI has shown great potential in data processing, automated dApp development, and on-chain transaction security at the execution level. At the application level, AI-driven trading bots, predictive analysis tools, and AMM liquidity management play significant roles in the DeFi space.
This article will delve into the investment directions of the AI+Crypto track, focusing on innovation and development at the infrastructure and application levels. From the perspectives of medium and long-term investment strategies, it will analyze the prospects and challenges of combining AI with blockchain. The article is authored by Huobi Research, a team currently under HTX Ventures. HTX Ventures is the global investment arm of Huobi HTX, integrating investment, incubation, and research to identify the most outstanding and promising teams globally. Currently, HTX Ventures has supported over 200 projects spanning multiple blockchain tracks, with some high-quality projects already listed on the Huobi HTX exchange.
Key Directions in AI Track
Blockchain is completely opposite to artificial intelligence in terms of centralization, low transparency, energy consumption, and monopolization. Following the above criteria and his own reflections, Vitalik categorizes applications that combine artificial intelligence and blockchain into 4 main classes:
– 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’s perspective on the role of AI in Crypto is more focused on the “application” angle. When considering it from a productivity vs. production relations perspective, Crypto actually provides more on the production relations side. From this standpoint, it can mainly be considered from three directions:
– Optimizing computing power: providing decentralized and efficient computing resources, reducing the risk of single points of failure, and enhancing overall computing efficiency.
– Optimizing algorithms: promoting the open-source, sharing, and innovation of algorithms or models.
– Optimizing data: decentralized storage, contribution, usage, and secure management of data.
HTX Research believes that the overall direction of AI can be categorized into basic, execution, and application layers according to a general architecture. Correspondingly, AI+Web3 projects can also be explored from these three major directions. At the basic level, focusing on model training, data, decentralized computing power, and hardware at the infrastructure level, with a strong emphasis on the integration of zk technology with AI ML technology; at the execution level, focusing on data processing, data transmission, AI agent at the model level, zkML, FHE (Fully Homomorphic Encryption), etc.; at the application level, the main focus is on AI+DeFi, AI+GameFi, Metaverse, AIGC, Meme, and blockchain-level RAAS (Robotics as a Service), oracles, coprocessors, UBI (Universal Basic Income), etc.
Among these, projects developing at the infrastructure and application levels are progressing rapidly, such as Io.net in the computing power aspect, Flock in the basic model aspect, ZeroGravity in blockchain infrastructure, Myshell for AI agents, and 0x Scope at the application level.
Key Exploratory Directions
1. zkML Direction
zkML technology provides a secure, verifiable, and transparent solution for monitoring and constraining AI agent behavior by combining zero-knowledge proofs and blockchain technology. For example, the Modulus Labs project utilizes zkML technology to protect personal privacy and business secrets while proving to stakeholders that its AI has performed specific tasks.
zkML, as an intermediary between artificial intelligence and blockchain, offers a solution aimed at addressing privacy protection issues between AI models and inputs, ensuring the verifiability of the inference process. It introduces a new method that uses public models to verify private data or vice versa. By integrating the capabilities of machine learning, smart contracts are able to achieve more autonomy and dynamism, operating based on real-time on-chain data rather than static rules. This innovation makes smart contracts more flexible, adaptable to a wider range of application scenarios, including those unforeseen at the contract’s initial setting.
2. Data Processing Direction
This primarily refers to breakthroughs in various AI executions at the execution level, especially targeting blockchain data transmission and development layers. Specific analyses include:
a. AI and on-chain data analysis: utilizing AI technology to delve deeper into this data, using LLM large models and deep learning algorithms to gain more insights. For example, the Web3 Analytics project applies AI for on-chain data analysis, revealing market trends and user behavior. It helps users gain insights into on-chain transactions and market trends.
b. AI and automated dApp development: focusing on infrastructure projects for Devops. Some AI projects for automated development can attract more developers to enhance the ecosystem. Some AI-based development tools can help developers write smart contracts more quickly and automatically correct errors, and some can even enable drag-and-drop DApp programming features.
c. AI and on-chain transaction security: involving AI agents deployed on the blockchain to enhance the security and trustworthiness of AI applications. These AI agents can automatically perform tasks such as transactions, data analysis, and automated decision-making, and deploying them on the blockchain ensures their operations are transparent, traceable, and resistant to tampering, enhancing the overall system’s security. AI technology can identify and defend against malicious attacks and data breaches through real-time monitoring and intelligent analysis, ensuring transaction security and data integrity.
– Project Example: SeQure is a security platform that utilizes AI for real-time monitoring and analysis, promptly detecting and defending against various malicious attacks and data breaches, ensuring the stability and security of on-chain transactions.
3. AI+DeFi Direction
The most important aspect of the combination of AI with the application layer is AI+DeFi. Here are some AI+DeFi directions worth noting:
1. AI-driven trading bots: these bots can execute trades quickly and accurately, analyze market data, news sentiment, and price trends to make instant trading decisions, often outperforming human traders.
2. Predictive analysis: while predicting fluctuations in the crypto market has always been a challenge, AI-driven analytical tools are gradually becoming an important tool, providing reliable forecasts of market trends and potential price movements.
3. AMM liquidity management: for example, when adjusting the liquidity range of Uniswap V3, through AI integration, the protocol can intelligently adjust liquidity ranges to optimize the efficiency and returns of automated market makers (AMM).
4. Liquidation protection and debt position management: by combining on-chain and off-chain data, smart liquidation protection strategies can be intelligently implemented to ensure debt positions are protected during market fluctuations.
5. Complex DeFi structured product design: when designing treasury mechanisms, one can rely on financial AI models rather than fixed strategies. These strategies may include AI-managed trading, lending, or options, increasing the intelligence and flexibility of products.
4. AI+GameFi Direction
The application of AI in GameFi projects mainly enriches the gaming experience while increasing innovation possibilities. The primary directions are as follows:
1. Game strategy optimization: AI can adjust game difficulty and strategies in real-time based on player habits and strategies, providing a more personalized and challenging gaming experience. Through deep learning and reinforcement learning, AI can self-evolve better to adapt to player needs and preferences.
2. Game asset utilization management: AI technology can help players manage and trade in-game virtual assets more effectively. By using smart contracts and automated trading strategies, players can maximize the utilization of assets, such as automatic buying and selling, leasing, and borrowing game assets, optimizing investment returns.
3. Enhanced game interaction: AI can create more intelligent and responsive non-player characters (NPCs), enabling more natural and fluid interactions between players through natural language processing (NLP) and machine learning (ML) technologies, enhancing game immersion and player satisfaction.
Potential Investment Strategies over Time
– In the short term, attention should be focused on the areas where AI first lands in Crypto, such as conceptual AI applications and memes. The logic behind this is that mainstream AI circles will continue to generate new hotspots whenever companies like NVIDIA and OpenAI upgrade their large models, igniting emotions in the AI track and attracting new funds into the space.
– In the medium term, the combination of AI Agent and Intent, as well as with smart contracts, is a highlight. Once AI successfully provides solutions for smart contract extensions, it will form a new blockchain model of ledger + contract + AI, breaking the narrative of the ledger + contract era.
– AI Agent is a niche direction that Vitalik has milked. AI Agent refers to an AI entity that can autonomously acquire information from the environment, process information, make decisions, execute, and autonomously change the environment. AI Agent currently belongs to the forefront of the AI field’s niche track, closest to the Mass Adoption application layer.
– Narratively, AI Agent is the sexy and hot lady, GPU cloud computing power is the stable and mature middle-aged entrepreneur, and the integration of AI large models at the DA level is the messy-haired scientist.
– In the long term, the integration of AI with zkML technology (despite the disdain of ML experts from web2 AI companies towards Crypto) will ultimately impact the Crypto field.
References:
– Twitter: https://twitter.com/FinanceYF5/status/1772434625387717055
– Web3 Caff: https://twitter.com/Web3Caff_Res
– Twitter Vitalik: https://twitter.com/VitalikButerin
Appendix:
Decentralized Computing and AI Reasoning Platform Project List
Primarily using Crypto for incentives to share and utilize idle computing resources globally.
AI Data and Model Sourcing Project List
Based on data authenticity, transparency, and traceability, using Crypto economic models for data incentives (for C-end users) and model incentives (Dev, B-end).
