According to Vitalik Buterin, Ethereum’s co-founder, the applications of combining AI and blockchain can be categorized into four types: AI as a player, AI as an interface, AI as the rules, and AI as the objective. He suggests that AI’s role in crypto should be considered more from an “application” perspective, including optimizing computing power, algorithms, and data.
HTX Research distinguishes crypto technology participation based on AI’s application levels, which can be divided into foundational, execution, and application layers. Each layer presents opportunities worth exploring. For example, zero-knowledge machine learning (zkML) technology, which combines zero-knowledge proofs and blockchain technology, offers a secure, verifiable, and transparent solution for AI agent behavior. Additionally, AI shows great potential in data processing, automated decentralized application (dApp) development, and on-chain transaction security within the execution layer. In the application layer, AI-driven trading bots, predictive analytics tools, and automated market maker (AMM) liquidity management are playing crucial roles in the decentralized finance (DeFi) sector.
This article will delve into the investment opportunities where AI meets crypto, focusing on innovations and developments in the foundational and application layers. It will also analyze the prospects and challenges of combining AI and blockchain from mid-term and long-term perspectives.
Key Opportunities in the AI Sector
Blockchain and AI differ fundamentally in centralization, transparency, energy consumption, and monopolization. Based on this principle and his analysis, Vitalik has classified applications that combine AI 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 approaches AI’s role in the crypto world from an “application” perspective. Additionally, we can consider crypto as a means of facilitating technologies and resources, including three perspectives:
– Optimizing Computing Power: Providing decentralized and efficient computational resources to reduce the risk of single points of failure and enhance overall computing efficiency.
– Optimizing Algorithms: Advancing the open-source sharing and innovation of algorithms or models.
– Optimizing Data: Enabling decentralized storage, contribution, usage, and secure management of data.
HTX Research suggests that AI’s development can be categorized into the foundational layer, execution layer, and application layer based on a general architecture. Correspondingly, we can also explore AI + Web3 projects from these dimensions. The foundational layer includes model training, data, decentralized computing power, and hardware, where particular attention should be given to combining zk (zero-knowledge) with AI/ML (machine learning) technologies. The execution layer involves data processing and transmission, AI agents, zkML, and FHE (fully homomorphic encryption). The application layer features AI + DeFi, AI + GameFi, the metaverse, AIGC (AI-generated content), memes, and blockchain-related technologies like RAAS (Robotics as a Service), oracles, coprocessors, and UBI (universal basic income).
Rapid progress has been made in foundational and application projects. These include Io.net, a computing power project; Flock, a foundational model; ZeroGravity, a blockchain infrastructure; Myshell as an AI agent; and 0xScope in the application layer.
We suggest exploring these opportunities:
I. zkML
zkML technology combines zero-knowledge proofs with blockchain to provide a secure, verifiable, and transparent solution for monitoring and constraining AI agent behavior. For example, the Modulus Labs project uses zkML to prove to stakeholders that their AI has performed specific tasks while protecting personal privacy and commercial secrets.
As an intermediary between AI and blockchain, zkML offers a solution to address privacy protection for AI models and inputs and ensures the verifiability of the inference process. It has pioneered a method that allows using public models to verify private data or using public data to verify private models. Integrating machine learning capabilities enhances the autonomy and dynamism of smart contracts, enabling operation based on real-time on-chain data rather than merely static rules. This innovation makes smart contracts more flexible, allowing them to adapt to a wider range of applications, even those unforeseen at the contract’s inception.
Overview of Typical zkML Projects
The first table presents promising zkML projects while the second lists other zkML projects.
| Project | Description |
| Modulus Labs | Modulus Labs is one of the most diverse projects in the zkML space, conducting related research while actively building on-chain AI application examples. The team has showcased zkML use cases through RockyBot (an on-chain trading bot) and Leela vs. the World (a chess game). They also perform benchmark tests on the speed and efficiency of various verification systems for models of different sizes. |
| Giza | Giza is a protocol for deploying AI models on-chain. Its tech stack includes the ONNX format for machine learning models, the Giza Transpiler for converting these models into the Cairo program format, the ONNX Cairo Runtime for executing models in a verifiable and deterministic way, and the Giza Model smart contract for deploying and executing models on-chain. Giza serves as an on-chain compiler from machine learning models to proofs, offering an alternative path for the development of on-chain AI. |
| Zkaptcha | Zkaptcha focuses on addressing the bot problem in Web3 by providing captcha services for smart contracts and using zero-knowledge proofs to create Sybil-resistant smart contracts. The project allows end users to prove human activity by completing captchas, which are verified on-chain. The team plan to integrate zkML to offer services similar to existing Web2 captchas, including analyzing mouse movements to determine if the user is human. |
II. Data Processing
This area includes breakthroughs in the execution layer of AI, particularly in blockchain data transmission and development layers.
AI and On-chain Data Analysis
This area involves using AI technology to deeply mine blockchain data and leveraging large language models (LLMs) and deep learning algorithms to gain more insights. The Web3 Analytics project, for example, uses AI for on-chain data analysis to discover market trends and user behavior. It helps users understand on-chain transactions and market dynamics more effectively.
AI and Automated dApp Development
This area focuses on infrastructure projects related to DevOps. AI projects using automated development can attract more developers, thereby making the ecosystem more prosperous. Some AI-powered development tools can help developers quickly write smart contracts and automatically correct errors. Others even offer drag-and-drop dApp programming capabilities.
AI and On-chain Transaction Security
This area involves AI agents. It entails deploying AI agents on the blockchain to enhance the security and reliability of AI applications. These AI agents can automatically perform tasks such as trading, data analysis, and automated decision-making. Deploying them on the blockchain ensures their operations are transparent, traceable, and tamper-resistant, thereby increasing the system’s overall security. Through real-time monitoring and smart analysis, AI technology can identify and defend against malicious attacks and data breaches, ensuring transaction security and data integrity.
Project Example:
SeQure is a security platform that uses AI for real-time monitoring and analysis. It detects and defends against malicious attacks and data breaches, ensuring the stability and security of on-chain transactions.
III. AI + DeFi
The most significant aspect of integrating AI with the application layer is AI + DeFi. Here are some key areas to focus on:
AI-driven Trading Bots
These bots can execute trades swiftly and accurately by analyzing market data, news sentiment, and price trends to make instant decisions. They often outperform human traders.
Predictive Analytics
While predicting the volatility of the crypto market has always been challenging, AI-driven analysis tools are becoming increasingly important. They can provide reliable forecasts of market trends and potential price movements.
AMM Liquidity Management
For instance, when adjusting the liquidity range for Uniswap V3, integrating AI allows the protocol to more intelligently set the liquidity range. This optimizes the efficiency and profitability of AMMs.
Liquidation Protection and Debt Position Management
Through a combination of on-chain and off-chain data, smarter liquidation protection strategies can be implemented. This ensures that debt positions are safeguarded during market volatility.
Design of Complex DeFi Structured Products
In designing vault mechanisms, financial AI models can be used instead of fixed strategies. These models may include AI-managed trading, lending, or options, enhancing product intelligence and flexibility.
IV. AI + GameFi
AI in GameFi projects enhances the gaming experience and fosters innovation. Here are some primary focuses:
Game Strategy Optimization
AI can learn players’ habits and strategies to adjust game difficulty and tactics in real time, providing a more personalized and challenging experience. Through deep learning and reinforcement learning, AI can evolve to better suit players’ needs and preferences.
Game Asset Management
AI can help players manage and trade in-game virtual assets more effectively. Leveraging smart contracts and automated trading strategies, players can maximize the use of their assets, such as automatic buying, selling, renting, and lending game assets, thus optimizing investment returns.
Enhanced Game Interaction
AI can create smarter and more responsive non-player characters (NPCs). Through natural language processing (NLP) and machine learning (ML) technologies, AI enables more natural and seamless interactions with players, enhancing immersion and satisfaction.
Investment Strategies in Different Timeframes
– In the short term, we should focus on areas where AI is first applied in crypto, such as conceptual AI applications and memes. Mainstream AI communities will continue to generate new hot topics, and major upgrades by companies like Nvidia and OpenAI will spark attention in the AI sector, attracting capital inflows. These can create excitement and drive momentum.
– In the mid-term, the combination of AI agents with Intents and with smart contracts will garner traction. If successful, AI could extend smart contracts to create a new type of blockchain that integrates ledgers, contracts, and AI, moving beyond Ethereum’s “ledger + contract”.
– Vitalik has highlighted AI agents as a promising area. AI agents are intelligent entities capable of autonomously acquiring and processing information, making decisions, executing actions, and altering the environment. This is a cutting-edge field within AI, closest to mass adoption at the application layer.
– Metaphorically speaking, AI agents are like attractive and dynamic individuals, GPU cloud computing is a stable and mature entrepreneur, and AI models in the DA layer are disheveled scientists.
– In the long term, the combination of AI and zkML technology, despite skepticism from ML experts in Web2 AI companies, will ultimately exert a significant impact on the crypto world.
References:
– Twitter: https://twitter.com/FinanceYF5/status/1772434625387717055
– Web3Caff: https://twitter.com/Web3Caff_Res
– Twitter Vitalik: https://twitter.com/VitalikButerin
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Appendix:
Decentralized Computing and AI Inference Platform Projects
These projects leverage crypto incentives to globally share and utilize idle computing resources.
| Project | Description |
| Ritual | It aims to create an incentive network powering distributed computing devices and providing services for machine learning inference workloads. |
| Akash Network | Integrated with Cosmos, it offers a distributed peer-to-peer marketplace for cloud computing, providing a secure platform for users to exchange data and develop applications. |
| The Render Network | It allows users to connect their GPUs to a rendering network to receive and complete rendering tasks for RNDR rewards. |
| bittensor | It utilizes crypto incentives to encourage participants to share their computing resources, data, and AI models, enabling global machine learning models and algorithms to learn and improve from each other. |
| io.net | A decentralized computing network that supports the development, execution, and scaling of machine learning applications on the Solana blockchain. |
| Hyperbolic | It aims to build a computing power platform where everyone can share and access computing resources. |
| gensyn | It connects idle, machine learning-capable computing devices worldwide into a global supercluster, increasing available computing power for machine learning. |
| Prime Intellect | A decentralized AI platform that commodifies computing and intelligence, providing developers with affordable distributed computing and a sustainable business model for open-source models. |
AI Data and Model Source Projects
These projects focus on data authenticity, transparency, and traceability, using crypto economic models to incentivize data contributions (for end users) and model enhancements (for developers and businesses).
| Project | Description |
| Rainfall | A privacy-protecting smart platform that uses Edge-AI and Web3 technologies to unlock economic value from user data while safeguarding privacy. |
| Numbers Protocol | It ensures that all digital media created by humans and AI have accurate data provenance. |
| Grass | It provides applications that enable data contribution in the background on phones or computers, allowing AI labs to directly obtain internet data to train their AI models. |
| Koii | A distributed cloud computing platform where anyone with a computer can become a node and earn passive income on Koli. |
| FLock.io | An integrated on-chain decentralized machine learning platform that provides secure and efficient solutions for fine-tuning and inferring AI models. |
| Hyperspace | It aims to establish a world with millions of community-based large language models, available for billions of people to use for free every day. |
| Ocean Protocol | A privacy-preserving data sharing protocol for AI and the new data economy, enabling people to buy and sell private data while protecting privacy. |
| Syntropy | It allows anyone to become a provider in the Web3 data open market. Token holders determine reliable providers based on the quality of data provided and overall performance. |
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