How Does AI Impact Web3?

06/13/2023·1Y

In the context of AI, the only certainty is uncertain. People like certainty, but the uncertainty brought by AI is irreversible in 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 believe that AI will profoundly impact the rules of the current industries and thus lead to a large amount of unemployment.

But no matter what, from the appearance of ChatGPT to now, people's views on AI have gradually been accepted from surprise and concern. It seems that people realize that, whether welcoming or rejecting, AI will undoubtedly penetrate into various fields of people and bring about a revolution to various industries with its mechanisms and potential.

Now, AI is beginning to enter Web3 and exerting influence on the entire industry.

Wang Yishi, the former founder of OneKey, stated on Twitter: The narrative of Web3 has shifted from cryptocurrency to AI. Wang Yishi's view is not unique. Many people in the Web3 industry believe that the impact of AI on Web3 is significant, especially in the NFT and GameFi fields. The emergence of AIGC concept implies a new paradigm in content creation. From PGC (Professionally Generated Content) to UGC (User Generated Content), and now to AIGC, the work of content creation is handed over to programs.

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

AI is "Rectifying" Web3

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

Before the emergence of AI, Web3 had become a hot cake in the eyes of VCs and institutions, and various industries had also introduced various Web3 concepts (such as digital collectibles, metaverse, etc.) as gimmicks. However, this situation changed after the emergence of AI.

In the eyes of institutions, AIGC at least looks more reliable than Web3, at least it is something practical, not a concept that needs to be anticipated. The interest of institutions is shifting, coupled with the bear market and regulatory reasons. According to statistics from the Gyroscope Research Institute, there were 86 global financing events in the Web3 field in March this year, with an amount of 5.676 billion yuan, a year-on-year decrease of 47.98%.

Funds are leaving the Web3 field and entering AI.

The other aspect of "rectification" is that the emergence of AI is causing changes in the mechanisms and logic of the Web3 field. Web3 projects are beginning to focus on adding AI elements to their own ecosystems. Some projects are evolving to have at least an AI concept or at least a GPT interface to be presentable. We can view this phenomenon as AI's "rectification" of the Web3 world, or as a means of self-defense for the Web3 world against the strong "invasion" of AI.

Thus, the concept of AI Web3 has emerged. In the process of the integration of AI and Web3, many different products have emerged in the market. These products can be roughly divided into two categories: one is to add AI elements based on the direction of the project itself. These products often intervene in some AI tool interfaces on their own products and emphasize the empowerment and driving role of AI on the products when promoting them, such as AIGOGE.

Another type of combination of AI and Web3 is based on the idea of cost reduction and efficiency improvement, such as Pionex, which focuses on AI+ trading strategies; Getch, Cortex, SingularityNET, which focus on AI+ infrastructure construction; and Numerai, which focuses on AI+ financial forecasting, and so on.

The emergence of different AI concept Web3 products reflects the favor of the market and capital for this type of product. For example, the AIDOGE currency launched on April 18th rose by 218.50% within 2 days. (Fetch.ai) FET, SingularityNET (AGIX), Ocean Protocol (Ocean) and other project tokens increased by 110%, 61.53%, and 66.67% respectively within 90 days.

While the secondary market for AI Web3 concepts is hot, the primary market performance is even more impressive. Since the beginning of this year, AI Web3 concept products have also been continuously receiving financing. On March 29th this year, Fetch.ai received a $40 million investment from SWF Labs.

At present, the AI+Web3 concept seems to be a major trend in the future. Therefore, veDAO Research Institute has organized different tracks of AI's potential impact on Web3 for reference.

AI Empowering Different Tracks of Web3

AI-based Trading Strategies

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

The role of AI in trading strategies:

Data collection: Use APIs to obtain the data required for liquidity mining, such as the price of trading pairs, trading volume, liquidity provision, and attraction.
Data preprocessing: Clean, transform, and standardize the collected data for subsequent analysis and modeling.
Building ChatGPT model: Use the trained ChatGPT model to analyze historical data, predict current and future trends and returns of liquidity mining.
Risk control: Based on the prediction results of ChatGPT, formulate risk control strategies, such as setting stop-loss and take-profit conditions, controlling trading volume, etc., to protect the interests of investors.
Implementation of trading strategies: Based on the prediction results of ChatGPT model, formulate trading strategies, such as selecting trading pairs, determining trading timing, setting trading prices, etc.
Trading execution: Execute trades according to the trading strategy, the AI system automatically invests funds in mining and obtains expected returns.
Monitoring and optimization: Regularly monitor trading results and model performance, optimize and adjust strategies to maintain good investment returns and risk control effects.

AI-based Sentiment Analysis Strategies

This strategy is based on the natural language processing ability of ChatGPT to analyze text data such as news reports and social media posts to conduct sentiment analysis of the market. When the sentiment tendency in most texts is "positive" or "buy", the trading strategy may choose to buy; and vice versa.

The implementation of this strategy requires the collection of market-related text data, and the cleaning, analysis, and modeling of this data. Supervised learning algorithms can be used for modeling sentiment analysis, using labeled training data for prediction of text sentiment tendency. Trading strategy formulation can be adjusted based on the model's prediction results, combined with market trends and other factors.

AI-based Trading Strategy Analysis

This strategy is based on ChatGPT's understanding of the text description of trading strategies, to analyze and evaluate trading strategies. For example, analyzing the backtesting results of trading strategies, historical returns, etc., to evaluate the effectiveness and reliability of the strategy, and formulate trading strategies based on this analysis. Machine learning algorithms can be used for the analysis and evaluation of trading strategies, to predict the return and risk of the strategy through model training and optimization. Trading strategy formulation can be adjusted based on the model's prediction results, combined with trial production trends and other factors.

AI-based Asset Portfolio Management

Asset portfolio management tools based on ChatGPT can use natural language processing technology to help users better manage asset portfolios, optimize asset allocation and risk control, and provide more accurate predictions and recommendations for investment decision-making. It can achieve:

Automated asset analysis and selection: Using natural language processing of ChatGPT, analyze and evaluate the fundamentals, market conditions, and macroeconomic factors of various assets, and automatically select suitable investment targets to reduce the risk of wrong decisions.

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

Automated trading execution: Automatically execute buy and sell trades based on ChatGPT's trading decision model, realizing real-time adjustment and optimization of assets, while reducing the risk of human intervention.

AI-based Simulated Trading Tools (AI Demo Account)

AI-based simulated cryptocurrency trading tools are virtual trading platforms that simulate real cryptocurrency market environments based on AI algorithms, providing virtual funds for users to conduct simulated trading. Users can learn cryptocurrency trading on the platform, formulate trading strategies, and conduct simulated trading without the risk of real trading, allowing more users to experience AI functions and advance their investment skills.

Feasible Directions for DEX+AI:

Decision support: Analyze and mine trading data to provide more accurate and comprehensive market analysis and predictions, helping traders make wiser investment decisions.

Optimized asset portfolio management: AI technology can analyze user investment preferences, risk tolerance, historical trading data, etc., to provide more personalized and efficient asset portfolio management services.
Improved user experience: AI technology can provide users with more intelligent, efficient, and intimate trading service experiences through intelligent customer service, recommendations, and Q&A, improving user satisfaction and loyalty.
Investment information collection: AI can help provide public opinion, sentiment, 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 wiser investment decisions.
Trading decisions: Artificial intelligence can use automated trading systems to execute trading decisions, such as trading 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 through artificial intelligence to identify and prevent network attacks and fraudulent behavior, improving the security and credibility of DEX.
Contract audit: AI technology can help optimize the writing and deployment of smart contracts, improve the quality and reliability of their code; it can also help monitor and prevent malicious behavior, reducing the risk and vulnerabilities of DEX.
Credit analysis: Using big data and machine learning technologies, artificial intelligence can analyze multidimensional information such as customer credit history, financial status, social networks, and behavioral data to evaluate customer credit risk levels. Artificial intelligence can use big data and machine learning algorithms to analyze customer credit history, financial status, and other relevant data to evaluate customer risk levels, predicting customer default risk.
Fraud detection: Artificial intelligence can use natural language processing and image recognition technologies to analyze customer transaction records and other behavioral data to detect potential fraudulent behavior.
Transaction monitoring: Artificial intelligence can use real-time data analysis technology to monitor trading activities to identify potential abnormal trading behavior.
Risk management: The risk management system based on ChatGPT is a system that uses natural language processing technology to analyze and evaluate financial market risks. It can generate predictions and warnings about market risks through the analysis of financial data and real-time market news, helping investors better manage risks.

Improving trading speed and efficiency: By optimizing the trading process through AI technology (such as selecting the best route), it can reduce trading congestion, lower trading costs, and accelerate transaction completion time.

Solving the major problems of current DEX:

Insufficient liquidity: DEX has a smaller trading volume compared to CEX, leading to insufficient liquidity and easily affected transaction prices due to market fluctuations. Using AI technology can improve the intelligence of trading robots, thereby improving trading efficiency and profitability, increasing trading volume and liquidity.
Security issues: Due to the decentralized nature of DEX, there are security risks in the trading process, such as asset theft and contract vulnerabilities. Using AI technology can enhance risk control capabilities, achieve intelligent risk control and security monitoring, and prevent risk events.
Poor user experience: The user interface of DEX is relatively rudimentary compared to CEX, resulting in a poor user experience. Using AI technology can enhance the capability of personalized services, achieve intelligent customer relations and recommendation systems, and 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, etc. Using AI technology can optimize the trading strategies of trading robots, reduce trading costs and risks, and improve profitability.

Conclusion:

Overall, the emergence of AI is not just a new technology, but a new concept and field that will bring a series of iterations and even subversion to the underlying operating logic of the entire society. The same goes for the Web3 world. The relationship between AI and Web3 will not be limited to the integration of concepts, or the simple addition of AI tools to a project. Instead, it will directly penetrate into the underlying logic of Web3, giving meaning to all behaviors in Web3 with the existence of AI, making Web3 more efficient and intelligent.

Just as the philosophy of production tools and production relations are related. The two cannot be viewed independently. The type of production tools determines the type of productivity, and the type of productivity provides the necessary conditions for the emergence and popularization of corresponding production relations. If Web3, based on blockchain, represents updated production relations, 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 inevitably play a decisive role in the popularization and promotion of the Web3 concept that follows.

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