veDAO Research Institute: How Does AI Impact Web3?

Source: veDAO Research Institute

In the context of AI, the only certainty is uncertainty. People prefer certainty, but the uncertainty brought by AI is irreversible under the tide of technological development. Optimists believe that the emergence of AI will bring unimaginable cost reduction and efficiency improvement to the entire world. Pessimists, on the other hand, believe that AI will profoundly impact the current rules of the game in various industries, leading to massive unemployment.

Nevertheless, since the emergence of ChatGPT, people's views on AI have gradually shifted from surprise and concern to acceptance. It seems that people realize that whether welcomed or rejected, AI will undoubtedly penetrate various fields and disrupt industries with its mechanisms and potential.

Now, AI is beginning to enter Web3 and exert influence across the entire industry.

Wang Yishi, former founder of OneKey, stated on Twitter: The narrative of Web3 has shifted from cryptocurrency to AI. Wang Yishi's viewpoint is not an isolated case; many in the Web3 industry believe that AI's impact on Web3 is enormous, especially in the NFT and GameFi sectors. The emergence of the AIGC concept signifies a new paradigm in content creation. From PGC (Professionally Generated Content) to UGC (User Generated Content), and now to AIGC, content creation work is being handed over to programs.

In addition to the impact of AIGC on Web3 content, in fact, AI's influence on Web3 is more profound than we imagine.

AI is "Regulating" Web3

AI's "regulation" of Web3 comes from two aspects: on one hand, the emergence of AI technology has diverted capital's attention from Web3.

Before AI emerged, Web3 had become a darling for VCs and institutions, with various industries launching different Web3 concepts (such as digital collectibles, metaverse) as gimmicks. However, this situation changed after the advent of AI.

In the eyes of institutions, AIGC seems at least more reliable than Web3; it is a practical thing rather than a concept that requires foresight. Institutional interest is shifting, compounded by the bear market and regulatory reasons. According to statistics from the Gyroscope Research Institute, there were 86 global financing events in the Web3 sector in March this year, amounting to 5.676 billion yuan, a decrease of 47.98% compared to last year.

Funds are leaving the Web3 sector and moving into AI.

The other aspect of "regulation" is that the emergence of AI is changing the mechanisms and logic within the Web3 sector. Web3 projects are beginning to focus on adding AI elements to their ecosystems. Some projects are evolving to at least have an AI concept or at least a GPT interface to be marketable. We can view this phenomenon as AI's "regulation" of the Web3 world, or as a self-defense mechanism of the Web3 world against the strong "invasion" of AI.

Thus, the concept of AI Web3 has emerged. In the process of integrating AI and Web3, many different products have emerged in the market, which can roughly be divided into two categories: one category is based on the project's direction, adding AI elements. These products often integrate some AI tool interfaces into their existing products and emphasize AI's empowering and driving role in external PR. For example, AIGOGE.

Another combination of AI and Web3 is aimed at cost reduction and efficiency improvement, featuring AI + trading strategies like Pionex; AI + infrastructure development like Getch, Cortex, SingularityNET; and AI + financial forecasting like Numerai, among others.

The emergence of various AI concept Web3 products reflects the market and capital's preference for this type of product. For instance, the AIDOGE token launched on April 18 rose by 218.50% within two days. Tokens from projects like Fetch.ai (FET), SingularityNET (AGIX), and Ocean Protocol (Ocean) grew by 110%, 61.53%, and 66.67% respectively within 90 days.

While the secondary market for AI Web3 concepts is booming, the primary market is performing even better. This year, AI Web3 concept products have continuously secured financing, with Fetch.ai receiving a $40 million investment from SWF Labs on March 29.

Currently, the AI + Web3 concept seems poised to become a major trend in the future. Therefore, the veDAO Research Institute has organized different tracks where AI may bring changes to Web3 for reference.

AI Empowering Different Tracks in Web3

AI-Based Trading Strategies

The general idea of liquidity mining strategies based on ChatGPT is to use the ChatGPT model to predict market conditions to decide whether to participate in liquidity mining and choose the best timing.

The role of AI in trading strategies includes:

• Data Collection: Using APIs to obtain data required for liquidity mining from exchanges, such as price, trading volume, liquidity provision, and attraction.
• Data Preprocessing: Cleaning, transforming, and standardizing the collected data for subsequent analysis and modeling.
• Building ChatGPT Model: Using a trained ChatGPT model to analyze historical data and predict current and future liquidity mining trends and returns.
• Risk Control: Based on ChatGPT's predictions, formulating risk control strategies, such as setting stop-loss and take-profit conditions, controlling trading volume, etc., to protect investors' interests.
• Implementing Trading Strategies: Based on ChatGPT model predictions, formulating trading strategies, such as selecting trading pairs, deciding trading timing, setting trading prices, etc.
• Trade Execution: Executing trades according to the trading strategy, with the AI system automatically investing funds into mining and achieving expected returns.
• Monitoring and Optimization: Regularly monitoring trading results and model performance, optimizing and adjusting strategies to maintain good investment returns and risk control effects.

AI-Based Sentiment Analysis Strategies

This strategy leverages ChatGPT's natural language processing capabilities to analyze text data from news reports, social media posts, etc., to conduct sentiment analysis on market emotions. When the sentiment in most texts leans towards "positive" or "buy," the trading strategy may choose to buy; conversely, it may sell.

Implementing this strategy requires collecting market-related text data and cleaning, analyzing, and modeling this data. Sentiment analysis model building can use supervised learning algorithms, training with labeled data to predict the sentiment of the text. The trading strategy can be adjusted based on the model's predictions combined with market trends.

AI-Based Trading Strategy Analysis

This strategy utilizes ChatGPT's understanding of textual descriptions of trading strategies to analyze and evaluate them. For example, analyzing backtesting results and historical returns of trading strategies to assess their effectiveness and reliability, and formulating trading strategies accordingly. The analysis and evaluation of trading strategies can employ machine learning algorithms to predict the returns and risks of strategies through model training and optimization. Trading strategies can be adjusted based on the model's predictions combined with trial production trends.

AI-Based Portfolio Management

AI-based portfolio management tools using ChatGPT can help users better manage their asset portfolios, optimize asset allocation and risk control, while providing more accurate predictions and suggestions for investment decision-making. This can include:

Automated Asset Analysis and Coin Selection: Utilizing ChatGPT's natural language processing capabilities to analyze and evaluate the fundamentals, market conditions, and macroeconomic factors of various assets, thus automatically selecting suitable investment targets and reducing the risk of erroneous decisions.

Portfolio Optimization: Providing users with portfolio optimization suggestions based on ChatGPT's predictions of market trends and risks, achieving risk diversification and maximizing returns.

Automated Trade Execution: Based on ChatGPT's trading decision model, automating buy and sell trades to achieve real-time adjustments and optimizations of assets while reducing the risk of human intervention.

AI-Based Simulation Trading Tools (AI Demo Account)

AI-based simulated cryptocurrency trading tools are virtual trading platforms that use AI algorithms to simulate real cryptocurrency market environments and provide virtual funds for users to engage in simulated trading. Users can learn cryptocurrency trading on the platform, formulate trading strategies, and conduct simulated trades without bearing the risks of real trading, allowing more users to experience AI functionalities while advancing their investment skills.

Feasible Directions for DEX + AI:

Assisted Decision-Making: Analyzing and mining trading data to provide more accurate and comprehensive market analysis and predictions, helping traders make wiser investment decisions.

• Optimizing Portfolio Management: AI technology can analyze users' investment preferences, risk tolerance, historical trading data, and other information to provide more personalized and efficient portfolio management services.
• Improving User Experience: AI technology can offer smarter, faster, and more considerate trading service experiences through intelligent customer service, smart recommendations, and intelligent Q&A, enhancing user satisfaction and loyalty.
• Investment Information Gathering: AI can help provide sentiment, emotion, and risk information.
• Price Prediction: AI can use big data and machine learning technologies to analyze market data to predict cryptocurrency price trends, helping users make more informed investment decisions.
• Trading Decisions: Artificial intelligence can use automated trading systems to execute trading decisions based on preset rules and strategies, thereby reducing the impact of human factors on trading.

AI Security:

• Fraud Analysis: AI technology can monitor and analyze network traffic to identify and prevent cyberattacks and fraudulent activities, enhancing the security and credibility of DEX.
• Contract Auditing: AI technology can help optimize the writing and deployment of smart contracts, improving the quality and reliability of their code; it can also help monitor and prevent malicious behavior, reducing risks and vulnerabilities in DEX.
• Credit Analysis: Utilizing big data and machine learning technologies, AI can analyze customers' credit histories, financial conditions, social networks, and behavioral data to assess their credit risk levels. AI can use big data and machine learning algorithms to analyze customers' credit histories, financial conditions, and other relevant data to assess their risk levels and predict default risks.
• Fraud Detection: AI can use natural language processing and image recognition technologies to analyze customers' trading records and other behavioral data to detect potential fraudulent activities.
• Trade Monitoring: AI can use real-time data analysis technologies to monitor trading activities to identify potential abnormal trading behaviors.
• Risk Management: A risk management system based on ChatGPT utilizes natural language processing technology to analyze and assess financial market risks. By analyzing financial data and real-time market news, it generates predictions and alerts regarding market risks, helping investors manage risks better.
• Increasing Trading Speed and Efficiency: By optimizing trading processes (such as best routing choices) with AI technology, trading congestion can be reduced, trading costs lowered, and transaction completion times accelerated.

Addressing Major Issues in Current DEX:

• Insufficient Liquidity: DEX has lower trading volumes compared to CEX, leading to insufficient liquidity and making transaction prices susceptible to market fluctuations. Utilizing AI technology can enhance the intelligence of trading bots, thereby improving trading efficiency and profitability, increasing trading volume and liquidity.
• Security Issues: Due to the decentralized nature of DEX, there are security risks during transactions, such as asset theft and contract vulnerabilities. AI technology can enhance risk control capabilities, achieving intelligent risk management and security monitoring to prevent risk events.
• Poor User Experience: The user interface of DEX is relatively simplistic compared to CEX, resulting in a subpar user experience. AI technology can enhance personalized service capabilities, implementing intelligent customer relationship and recommendation systems to improve user experience.
• High Trading Costs: Compared to the low-cost fees of CEX, DEX currently has relatively high trading costs due to miner fees and other reasons. AI technology can optimize trading bots' trading strategies, reducing trading costs and risks, and improving profitability.

Conclusion:

Overall, the emergence of AI is not merely a new technology but a new concept and field that will bring a series of iterations and even disruptions to the underlying operational logic of society as a whole. The same goes for the Web3 world. The relationship between AI and Web3 will not be limited to the fusion of concepts or the simple addition of AI tools to a specific project. Instead, it will directly penetrate the underlying logic of Web3, endowing all actions within Web3 with the significance of AI existence, making Web3 more efficient and intelligent.

Just like the philosophical connection between production tools and production relations. The two cannot be viewed independently. The type of production tools available determines the level of productivity, and the nature of productivity provides the necessary conditions for the emergence and proliferation of corresponding production relations. If we say that Web3, based on blockchain, represents a newer production relationship, then AI is undoubtedly the most advanced production tool of this era. Therefore, we have reason to believe that the emergence, popularization, and integration of AI technology as a production tool will undoubtedly play a decisive role in the subsequent popularization and promotion of the Web3 concept.