AI is currently deeply reconstructing quantitative trading, risk control, and even regulatory compliance in the traditional financial sector. However, Web3.0 remains a battleground of human senses and luck, as well as a "dark forest" of information games.

Players can only spam in Telegram groups, scroll through KOL tweets one by one, and bet on luck amidst fragmented information. Even when using general large models like ChatGPT to query Web3.0 projects, users only receive various official marketing texts or even contradictory information, providing almost no help for investment judgment. Once investors blindly buy in large amounts, project teams may even directly dump tokens, leaving users with a pile of worthless coins.

The birth of Match AI's vertical large model is tearing open a crack, lighting a beacon in this forest. On July 28, Match officially held the "Match AI Large Model NexAlpha Launch Conference" in Bangkok, Thailand, marking the entry of Web3.0 investment into an AI-driven new era.

At the conference, the head of the Match ecosystem stated that Match AI is an on-chain investment advisor and Web3.0 financial operating system designed specifically for ordinary people. From research and signals to the selection of investment targets, everything is driven by AI; "Its mission is to help users grow their wealth and build a fair and win-win ecosystem."

In addition, Match AI is about to launch two major features: on-chain "Tianyan" and investment strategy recommendation system, helping Web3.0 users comprehensively analyze the technical, financial, informational, and emotional aspects of projects, providing clear investment directions and the ability to issue risk warnings in advance, preventing users from blindly chasing prices at high points. "Match AI is not just a Q&A robot; it is an AI investment advisor that understands chains, opportunities, and risks. It can actively push real-time opportunities or entry points for investment, rather than waiting for others to tell you."

The technical head of Match showcased the real trading data results of Match AI: from December last year to now, $13 million in real trading capital has generated a profit of $1.7 million, with professional traders using Match AI achieving a trading success rate of up to 90%. Match will launch the on-chain "Tianyan" and investment strategy in the AI section on August 1, open source the core wallet code of the Match APP on August 8, and begin to complete the open source of some core AI large models on August 18.

Vertical intelligence is reshaping Web3.0, and Match AI is the vanguard of this transformation. For those who wish to achieve investment success in the Web3.0 world, embracing this technological change is not a choice but a necessity.

General AI Large Models: Opening the Door to Popularization, Yet Difficult to Conceal Concerns of Vertical Depth

The release of ChatGPT in 2022 brought the fantasy of AGI (Artificial General Intelligence) into focus. After years of development, today's general AI large models are reshaping the development landscape across various industries.

Especially with the launch of Grok 4 by Elon Musk's xAI, this is not just another large model release; it marks an important milestone as general AI fully enters the stage of "a multimodal operating system with reasoning capabilities." Grok, with its deep integration of real-time data from the X platform and multimodal understanding capabilities, allows global users to experience the disruptive reconstruction of information acquisition patterns by AI with almost zero barriers to entry. The world once again witnesses the surge of the general AI wave.

This is just the tip of the iceberg. From the nationwide celebration of ChatGPT to Claude 3's stunning performance in long text processing, and Gemini's seamless mastery of multimodality—general large models are rapidly tearing down the barriers of technological monopoly, passing the torch of AI to billions of ordinary people. Today's AI has fully entered the "tools as infrastructure" era, with every industry contemplating how to integrate AI into its business processes.

In the traditional financial sector, this transformation has penetrated deeply. Wall Street's quantitative trading systems have long incorporated AI algorithms, and intelligent advisory services provide personalized asset allocation advice for ordinary investors, while risk control systems monitor market anomalies in real-time through machine learning. Investment banking giants like Goldman Sachs and JPMorgan Chase invest billions of dollars annually in AI technology, fully aware that in the competition of financial markets, whoever masters more advanced AI tools holds the initiative.

In the job market, Microsoft Copilot is deeply integrated into the Office ecosystem, allowing users to issue vague natural language commands, such as "turn last week's meeting notes into a PowerPoint summary with charts," and AI can instantly sort emails, transcribe records, extract logic, and generate beautiful slides. The mechanical and time-consuming tasks of information handling and formatting in traditional office work are quietly evaporating under the influence of automation and intelligence.

In the education sector, general large models are sparking a new educational revolution. Khan Academy's "Khanmigo," powered by GPT-4, has transformed into a Socratic-style tutor available 24/7 for countless students. It can generate personalized guidance paths for unique errors in a math problem, making the age-old educational ideal of "teaching according to aptitude" finally within reach on a large scale.

The powerful generalization capabilities exhibited by general large models are like the fire of Prometheus, illuminating the path of AI inclusivity. However, when the brilliance of technology shines into the deep valleys of vertical industries, its limitations of being "broad but not deep" emerge like shadows. These models often "lack depth of understanding" in specialized scenarios, and their flaws are becoming increasingly evident in real industrial applications.

For instance, Singapore's DBS Bank tested using GPT-4 to automatically review loan contracts, and the AI misinterpreted the "floating interest rate cap" in key terms as a "fixed rate commitment," nearly leading to a risk exposure of tens of millions of dollars. The "shallow cognition" of general models regarding financial terminology and industry regulatory logic conceals dangers in compliance scenarios that require millimeter-level precision. Additionally, some manufacturing companies have reported that attempts to use large models to analyze turbine vibration sensor data resulted in vague conclusions like "there may be misalignment issues" when faced with subtle anomalies in the spectrogram. Lawyers have also noted that when using Claude to sort through local regulations for cross-border mergers and acquisitions, the model overlooked the latest antitrust implementation details because its training data did not deeply integrate the dynamic database of local legislation in that niche field.

At its core, the "depth anemia" of general large models in vertical scenarios stems from three structural contradictions: first, while the pre-training corpus is vast, it fails to cover the highly specialized "knowledge dark matter"; second, the model lacks a true internalized understanding of industry implicit logic and contextual rules; third, the data barriers and privacy constraints in specialized fields make it difficult for general models to access the most sensitive data sources in the industry.

As general large models push open the door to AI popularization with overwhelming momentum, we must also soberly observe the fault lines of their vertical depth. The future AI ecosystem will present a dual-track evolution of "general foundation + vertical deep brain": general models will serve as super information interfaces, continuously lowering the barriers to use; while specialized models rooted in fields like healthcare, financial investment, and industry will need to deeply integrate industry knowledge graphs, real-time data streams, and expert feedback to build an impenetrable moat of professional depth.

The ultimate form of AI will inevitably move towards a dialectical unity of "breadth" and "depth." When the brilliance of general large models illuminates every corner of the world, those specialized intelligent agents deeply engaged in vertical scenarios are quietly accumulating the power to change the underlying logic of industries, which is the most profound and exciting undercurrent in the wave of AI transformation.

Web3.0 Investment: The "Dark Forest" of General AI's Ineffectiveness

Although general AI excels in language understanding and generation, it still has significant capability shortcomings in vertical scenarios that require deep professional knowledge and real-time data analysis. Especially in a rapidly changing and information-dense field like Web3.0, the "jack-of-all-trades" nature of general models becomes a disadvantage, rendering them quite ineffective.

For example, Bitcoin is currently at a high of $118,000. When you ask various AI large models, "Is the current price of Bitcoin worth investing in?" the responses vary widely: Grok 4 tells you from a policy perspective that Bitcoin has been legalized and that U.S. listed companies are investing in Bitcoin; DeepSeek informs you that domestic investment in virtual currencies is prohibited and that Bitcoin carries significant volatility risks; ChatGPT responds with historical price increases of Bitcoin and lists analysts' opinions; other AI large models even analyze the development prospects of distributed ledger technology from a technical perspective to argue for Bitcoin's potential…

These explanations seem comprehensive but actually avoid the core question: whether the current price of Bitcoin is a good buy. This question requires a thorough analysis of the buy and sell orders that may affect the price, combined with technical indicators, which is currently lacking in major general AI large models.

The disadvantages of general AI large models in the Web3.0 world are even more pronounced in another scenario. When users ask whether a certain meme project is worth investing in, these large models will provide various official marketing materials, and may even list community sentiment, giving an evaluation like "the project's white paper has a complete technical description, and community enthusiasm is high, indicating growth potential." After users invest heavily, a few days later, the project team starts rug-pulling and selling tokens to run away. A post-mortem reveals that on-chain data shows: the initial liquidity pool has long been withdrawn, and 99% of the tokens are concentrated in five associated addresses—these fatal signals were not mentioned by the general large models.

Why does this limitation exist? It is primarily because Web3.0 itself is a "behavioral maze" within a complex artificial system. Compared to traditional financial markets, the Web3.0 market exhibits three significant characteristics that make traditional investment analysis methods difficult to apply effectively, and AI is becoming increasingly "slick."

First is the proliferation of unstructured data. Information in traditional financial markets is relatively standardized, mainly sourced from structured channels like financial statements, official announcements, and news reports. In the Web3.0 world, key information is often scattered across tweets on Twitter, code submissions on GitHub, discussions on Discord, and messages in various announcement channels. This information not only comes in various formats but is also difficult to verify, making it challenging for ordinary investors to extract valuable investment signals.

Second is the high degree of uncertainty. The narrative in the Web3.0 market switches rapidly; a project can become a sensation overnight due to a new technological trend, or it can plunge into despair due to changes in regulatory policies. Changes in market sentiment often have a greater impact on price movements than fundamental analysis, and this high degree of uncertainty renders traditional valuation models ineffective.

Third is the extreme reflexivity characteristic. In the Web3.0 market, market expectations often become self-fulfilling; if a project is widely regarded positively, it may indeed realize value growth due to an influx of more funds. Conversely, negative expectations can accelerate a project's decline. This reflexivity mechanism means that investment decisions must consider not only the intrinsic value of the project but also changes in market sentiment and expectations.

Faced with such a complex system, ordinary users find it difficult to establish effective investment models, and investment decisions remain at a rather primitive stage. Retail investors rely on KOL recommendations, searching for "insider information" in various Telegram groups and Discord channels, deciding when to buy or sell based on fluctuations in community sentiment. This highly dependent approach on human judgment and emotion-driven investment is not only extremely risky but also highly competitive, contrasting sharply with the scientific and systematic nature of traditional financial markets.

In this context, general AI large models are destined to be ineffective in solving investment problems in Web3.0, which has become a consensus in the industry. When models lack real-time on-chain data support, they may provide seemingly reasonable but actually incorrect judgments, which can be fatal in a high-risk investment environment.

More importantly, the design goal of general models is "language generation," not "risk identification." They can fluently explain what liquidity mining is and analyze the operational mechanisms of DeFi protocols, but when faced with specific investment decisions, they often can only provide conservative risk warnings without offering actionable investment advice.

Match AI: A Vertical AI Large Model Born for Web3.0 Investment

What Web3.0 investment needs is not an AI that understands language, but an AI that understands game structures and behavioral judgments. This AI needs to comprehend the unique rules of the Web3.0 market, extract key information from vast amounts of unstructured data, recognize trends in market sentiment changes, and make relatively reasonable predictions in a highly uncertain environment. This is precisely where the value of vertical AI lies.

It is against this backdrop that Match has emerged. Match focuses on the niche of "AI x Web3.0 Investment Intelligence," dedicated to using vertical models to solve the cognitive and efficiency bottlenecks users face in real trading scenarios, filling the gaps left by general AI in this field. Unlike general AI, which pursues "correct answers," Match AI's goal is "decision accuracy"—not only to correctly understand users' questions but also to provide accurate and reliable investment decision support. The emergence of Match AI undoubtedly sets an important milestone for this trend.

Match's value positioning is clear and unique. In a Web3.0 investment environment filled with noise and traps, ordinary investors do not need another AI assistant that can chat; they need a smart partner that truly understands investment, knows Web3.0, and can provide reliable advice at critical moments. Match AI is designed to meet this demand as a professional investment assistant.

Match's core capabilities are reflected in three key systems, each deeply optimized for specific pain points in Web3.0 investment.

First is Match's "On-Chain Tianyan" system. The value of this system lies in unifying the collection and analysis of information scattered across various platforms. It can conduct contract scanning to identify potential risks and opportunities in smart contracts; perform KOL analysis to track the statements and behavioral patterns of opinion leaders; monitor exchange anomalies to detect large fund flows in a timely manner; and analyze project growth indicators to assess the development potential of projects.

More importantly, the output of the "Tianyan" system is not a cold pile of data but executable information with clear suggestions, traceable sources, and reasonable explanations. When the system identifies potential risks associated with a certain token, it not only informs users of the risk's existence but also explains the source of the risk, its impact, and response strategies. This output method allows ordinary users to quickly understand complex market information and make informed investment decisions based on it.

Second is the investment strategy recommendation system. Web3.0 investors have vastly different risk preferences and investment goals; some pursue stable returns, while others are keen on high-risk, high-reward speculative opportunities. Match AI can provide personalized investment portfolio strategies based on users' risk preferences.

The core of this system is a multi-dimensional factor weighting analysis mechanism. It simultaneously considers technical indicators (price trends, trading volume changes, technical patterns, etc.), sentiment indicators (social media discussion heat, community activity, public opinion trends, etc.), funding indicators (large holder movements, fund inflows and outflows, position distribution, etc.), and news indicators (project progress, partnership announcements, regulatory dynamics, etc.) across four dimensions of information, calculating a comprehensive score through complex weighting algorithms and deducing possible price paths based on this.

The advantage of this multi-dimensional analysis method lies in its ability to grasp market dynamics more comprehensively. Traditional technical analysis often focuses only on price and trading volume while neglecting changes in sentiment and funding; fundamental analysis, while considering a project's intrinsic value, often reacts slowly to changes in market sentiment. By integrating information from multiple dimensions, Match AI can more accurately predict market trends.

Third is the proactive signal push system. The design philosophy of this system is to eliminate the need for investors to "ask questions." In traditional AI interaction modes, users must actively pose questions to the AI for it to provide answers. However, in the rapidly changing Web3.0 market, by the time users realize what questions to ask, the best investment opportunities may have already been missed.

Match AI's proactive push system can monitor market dynamics 24/7, automatically sending risk or opportunity alerts to users when it identifies potential surges or crashes in tokens. This proactive service model allows users to receive key information in real-time, preventing them from missing investment opportunities or incurring unnecessary losses due to delayed information.

The intelligence of the push system is high; it does not simply push all market changes but filters the most relevant information based on users' investment preferences and historical behavior. This ensures both the timeliness of information and avoids information overload. The push system can combine data from both on-chain and off-chain sources for comprehensive deep perception, enabling signal inference and interpretation in conjunction with agents for precise signal interpretation, thus achieving smarter and more personalized deep pushes.

Match AI's Technological Breakthroughs: Building Core Competitiveness for Native Web3.0 Investment Large Models

In the Web3.0 investment field, the technical barriers and market complexity are unprecedented, and traditional investment analysis tools often fall short. Match AI, as a large asset management model specifically designed for Web3.0, does not simply overlay a Web3.0 shell on a general large model but achieves multiple key breakthroughs in its technical architecture, forming unique technological highlights and building a truly "from 0 to 1" native investment intelligence system.

(1) Multi-source Data Fusion: Constructing a Panoramic View of Web3 Investment

Match AI's data layer breaks through the limitations of traditional financial analysis, establishing a comprehensive multi-source data fusion system that integrates on-chain and off-chain data. In terms of on-chain data, the system collects key information in real-time from various mainstream public chains, including transaction records, smart contract execution statuses, DeFi protocol fund flows, and token transfer trajectories. This data provides the most direct and authentic market foundation for investment analysis.

More importantly, Match AI also integrates rich off-chain intelligence sources, including sentiment changes on Twitter, project activity on GitHub, depth data from major CEXs, official updates from project teams, Medium and news media reports, as well as community discussions. The multi-source data combines deep vertical models within the Web3 industry, allowing for noise reduction of irrelevant content and identifying unique industry signals, features, and data results that conventional models cannot uncover.

This multi-dimensional data collection capability is a key advantage for Web3 investment analysis, as price fluctuations in the crypto market are often strongly influenced by off-chain public opinion and sentiment. The comprehensive data collection capability forms the foundation for Match AI's accurate analysis.

The value of data lies not only in quantity but also in quality and timeliness. The Match AI system employs heterogeneous data standardization processing, allowing data from different platforms and formats to be uniformly converted into analyzable standard formats; it also establishes a real-time data update mechanism that can collect and process data as soon as it is generated. This technical capability provides a high-quality data foundation for subsequent intelligent analysis. Additionally, the system possesses data cleaning and noise reduction capabilities, filtering out false information and irrelevant noise to extract genuinely valuable investment signals.

(2) Intelligent Scheduling Engine: Collaborative Intelligence of Agent Groups

Match AI employs a scheduling collaborative engine based on COT (Chain of Thought) reflection at the engine layer, which is one of its core innovations in technical architecture. Unlike traditional AI systems, Match AI's reasoning process features traceability and self-correction capabilities, allowing it to present a complete chain of thought when answering questions and proactively correct itself upon discovering logical errors.

The Multi-Agent parallel collaboration mechanism is another technological highlight. The system constructs specialized agent groups, with each agent focusing on specific analysis tasks, such as technical analysis agents, fundamental analysis agents, sentiment analysis agents, etc. These agents can work in parallel, leveraging their professional advantages, and then integrate analysis results through a collaborative mechanism.

Most notably, the introduction of a causal reasoning core is significant. The Web3 market is filled with complex causal relationships, such as the correlation between Federal Reserve policy changes and cryptocurrency prices, and the causal chain between project team behavior and token performance. Match AI, through causal reasoning models, can identify and establish the true causal relationships between events rather than merely conducting correlation analysis, providing a more reliable logical foundation for investment decisions.

(3) AI Model Fusion Layer: Specialized Multi-Model Collaboration

At the AI model level, Match AI adopts a multi-model ensemble architecture, which allows for selecting the most suitable model based on different task characteristics. For example, some large models are suitable for text output, some for social sentiment monitoring, some for chart analysis, and some for code analysis, etc. By leveraging the complementary advantages of different models, the overall analytical capability is enhanced while reducing dependence on a single model provider.

Additionally, Match AI integrates planner models and causal reasoning models. For instance, if Ethereum surges, a human's real reaction might be to think that the altcoin season is coming, based on causal reasoning. However, machines do not make such associations. Through causal strategy models, AI can mimic human reasoning based on historical data, using causal reasoning to connect how much a specific real-time news event can drive the growth of multiple tokens or how many sectors it can boost.

Web3 pre-trained models are also a feature of Match AI, deeply integrating Web3.0 industry knowledge, functioning like a "Web3.0 PhD," rather than a general large model that is broad but not precise, often providing irrelevant answers. What Match AI aims to do is to provide substantial responses that meet user expectations, ensuring users gain value.

GRPO (Group Robust Policy Optimization) is another technological breakthrough of Match AI. Traditional AI large models often provide a single "optimal solution," while Match AI, through group decision optimization, can offer diverse strategy choices for different types of investors, achieving a truly "personalized" service for everyone.

Match's technical leader, Lucas, made a vivid analogy. "For example, if Match AI analyzes various factors and finds that a certain user profile is aggressive and prefers high-leverage operations; when that user interacts with Match AI, the large model will recommend a broader range of data within the user's risk tolerance based on their investment style, thus pursuing higher returns."

The domain-specific fine-tuning feature is specially optimized for different Web3.0 vertical scenarios such as DeFi, NFT, and GameFi, allowing the large model to better understand Web3.0. The system can adjust the analysis model according to the characteristics of different sectors, providing users with more precise investment advice. This fine-grained professional adjustment is something general AI models cannot achieve.

(4) Enhanced Generation System: Deep Integration of Knowledge Graph and RAG

Match AI has built a persistent knowledge graph system that can store and associate a vast array of knowledge points in the Web3.0 domain, forming a complete knowledge network, with each user interaction being recorded.

Moreover, unlike traditional static knowledge bases, this system has dynamic updating capabilities, allowing it to learn in real-time about new patterns and changes in the market. The dynamic knowledge updating mechanism enables the system to continuously learn and adapt to market changes. The Web3 market is rapidly evolving, with new projects, new play styles, and new risks constantly emerging; traditional static models often lag behind market developments. Match AI can timely capture and understand these new changes through its continuous learning mechanism.

The Retrieval-Augmented Generation (RAG) mechanism is a key technology for reducing AI hallucination rates, enabling the AI large model to avoid misjudgments and accurately identify the direction and sub-sector of the issues raised by users, providing a core answer that users seek. The system employs a "retrieve first, then generate" process, where it first retrieves relevant real data from the knowledge graph when answering user questions, and then generates answers based on that data, rather than relying on historical data from training for speculation. This mechanism is particularly important in high-risk investment environments, ensuring the reliability of analytical results. Compared to traditional LLMs that rely on training data for static reasoning, RAG can dynamically introduce the latest facts, enhancing the timeliness, accuracy, and interpretability of generated content, and effectively avoiding false answers caused by outdated or missing training data.

For example, when a user inquires about a certain project, the RAG module will retrieve information from on-chain data, APIs, and DEX news, and then the AI will "summarize," reducing the likelihood of AI nonsense, ensuring that the output is "evidence-based and traceable."

(5) Closed-Loop Signal System: A Complete Chain from Perception to Execution

Match AI's signal system establishes a closed-loop detection system of "environmental perception → intelligent judgment → precise recommendations." The environmental perception module can identify various abnormal signals in the market through multi-dimensional anomaly detection algorithms, including trading volume anomalies, changes in fund flows, and surges in social media discussion heat.

The intelligent judgment phase conducts risk assessments based on causal reasoning, capable of not only identifying the existence of anomalies but also analyzing the reasons behind them and their potential impacts. This deep analytical capability helps investors understand the essence of market changes rather than being misled by superficial phenomena.

The precise recommendations output executable trading strategies, including specific operational suggestions, risk control measures, and expected return analyses. This end-to-end service capability allows ordinary investors to receive professional-level investment guidance.

For instance, if the Federal Reserve suddenly announces an interest rate cut, causing Bitcoin and altcoins to surge, the AI can promptly recognize changes in the market environment, such as increased activity in traditional financial markets, and thus provide crypto users with suitable recommendations for increasing their positions.

In summary, Match AI's greatest advantage lies in its end-to-end native design philosophy. Unlike many AI products that simply transplant traditional financial models, Match AI is entirely designed to cater to the characteristics of the Web3.0 market, free from the historical burdens of traditional financial models.

This native design is reflected in multiple aspects: the choice of data sources is entirely based on the characteristics of the Web3.0 ecosystem, the analysis models are optimized for the volatility characteristics of the crypto market, and the decision-making framework considers the unique risk factors of Web3.0 investments. This specialized design enables Match AI to better understand and respond to the complex challenges of the Web3.0 market.

AI Vertical Large Models Reshaping the Web3.0 Investment Landscape

Looking back from the perspective of 2025, we are witnessing a significant technological turning point. General large models have demonstrated the immense potential of AI technology, but true commercial value often emerges in the deep application of vertical niches. The Web3.0 investment field is such an important scenario waiting to be reshaped by vertical AI.

The emergence of Match AI's vertical large model is not just a product of technological innovation but also an inevitable result driven by market demand. As the Web3.0 market continues to mature, the demand for specialized tools among investors will become increasingly strong. Those who still rely on traditional methods for investment decisions will find themselves at a growing disadvantage in the competition. Match AI, through its unique technical architecture and native design philosophy, provides Web3.0 investors with a truly professional and reliable intelligent investment assistant, marking the official arrival of the AI-driven Web3.0 investment era.

The value of AI vertical large models lies in their ability to deeply understand the rules and characteristics of specific fields, providing solutions that genuinely meet user needs. In the high-risk, high-reward, fast-paced domain of Web3.0 investment, this deeply specialized AI service will become a significant competitive advantage for investors.

Of course, the development of vertical AI also faces challenges. Issues such as the difficulty of data acquisition, the cost of model training, and the cultivation of user acceptance all need to be addressed. But just as the mobile internet changed people's lifestyles, AI technology will undoubtedly profoundly change the patterns of investment decision-making.

For those participants hoping to achieve investment success in the Web3.0 world, embracing this technological transformation is not a choice but a necessity. In this new era filled with opportunities and challenges, the ultimate winners will be those investors who can best leverage the advantages of AI technology.

Notice: This article is a submission and does not represent the views of ChainCatcher, nor does it constitute investment advice.

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