After general large models, the next stop for AI is vertical intelligence: How does Match reshape the Web3.0 investment landscape?

Today, AI is deeply reconstructing quantitative trading, risk control, and even regulatory compliance in the traditional financial sector, while Web3.0 remains a battleground of human senses and luck, 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 completely contradictory information, providing almost no help for investment judgment. Once investors blindly buy in large amounts, project teams may even dump tokens directly, leaving users with a pile of worthless coins.

The birth of Match AI, a vertical large model, is tearing open a crack and 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, driven entirely by AI from research, signals to investment target selection; "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, avoiding 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 proactively 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 win rate of up to 90%. Match will launch the AI section's On-chain Tianyan and investment strategies 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 pioneer 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 an inevitability.

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 but marks an important milestone as general AI fully enters the "multimodal operating system with reasoning capabilities" stage. 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, and 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 multimodal capabilities—general large models are tearing down the barriers of technological monopoly at an astonishing speed, passing the torch of AI to billions of ordinary people. Today's AI has fully entered the "tools as infrastructure" era, with every industry pondering how to integrate AI into their 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 summarized PPT with charts," and AI can instantly sort emails, transcribe records, distill logic, and generate beautiful slides. The mechanical and time-consuming information transfer and formatting in traditional office work are quietly evaporating under the influence of automation and intelligence.

In the education sector, general large models are triggering a new educational revolution. Khan Academy's "Khanmigo," powered by GPT-4, has transformed into a Socratic 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 for the public. 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 "do not understand deeply enough" in specialized scenarios, and their flaws are becoming increasingly evident in real industrial applications.

For example, Singapore's DBS Bank tested using GPT-4 to automatically review loan contracts, and the AI misinterpreted "floating interest rate cap" in key clauses as "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 harbors hidden dangers in compliance scenarios that require millimeter-level precision. Additionally, some manufacturing companies have reported attempting to use large models to analyze turbine vibration sensor data. Faced with subtle anomalies in the frequency spectrum, general models can only provide vague conclusions like "there may be misalignment issues." Lawyers have also reported that when using Claude to sort through local regulations for cross-border mergers and acquisitions, the model would overlook the latest anti-monopoly implementation details, as its training data had not deeply integrated the dynamic database of local legislation in that niche field.

Fundamentally, the "depth anemia" of general large models in vertical scenarios stems from three structural contradictions: first, although the pre-training corpus is vast, it is difficult to cover the highly specialized "knowledge dark matter"; second, the model lacks a true internalized understanding of the implicit logic and contextual rules of the industry; 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 gaze at 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 as super information interfaces continuously lower the barriers to use; while specialized models rooted in fields such as healthcare, financial investment, and industry need to deeply integrate industry knowledge graphs, real-time data streams, and feedback from domain experts to build an impenetrable professional depth moat.

The ultimate form of AI will inevitably move towards a dialectical unity of "breadth" and "depth." When the light 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 Failure

While 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 useless.

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 discusses Bitcoin's historical price increases 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 issue, failing to answer the question directly: whether the current price of Bitcoin is a buy. This question requires a comprehensive 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 more comprehensively exposed 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 further list community sentiment, giving an evaluation like "the project's white paper has a complete technical description, community enthusiasm is high, and it has growth potential." After users go all in, 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 are never mentioned by general large models.

Why does this limitation exist? Mainly 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 more "slick."

First is the proliferation of unstructured data. Information in traditional financial markets is relatively standardized, primarily sourced from financial statements, official announcements, news reports, and other structured channels. 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 may become popular overnight due to a new technological trend or plunge into despair due to changes in regulatory policies. Changes in market sentiment often influence price movements more 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 see value growth due to increased capital inflow; conversely, negative expectations can accelerate a project's decline. This reflexive 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 challenging to establish effective investment models, and investment decisions remain at a relatively 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 the fluctuations of community sentiment. This high reliance on human judgment and emotion-driven investment methods not only carries high risks but also involves strong gaming elements, forming a stark contrast to 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 support from real-time on-chain data, 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

Web3.0 investment requires not an AI that understands language, but one 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 field of "AI x Web3.0 investment intelligence," dedicated to solving the cognitive and efficiency bottlenecks users face in real trading scenarios with vertical models, filling the gaps left by general AI in this area. Unlike general AI, which pursues "correct answers," Match AI's goal is "accurate decision-making"—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 need not another AI assistant that can chat, but a truly knowledgeable partner in investment and Web3.0 that 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 scan contracts, identify potential risks and opportunities in smart contracts; analyze KOLs, track the statements and behavioral patterns of opinion leaders; monitor exchange anomalies, and promptly detect large capital flows; 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, impact level, and response strategies. This output method allows ordinary users to quickly understand complex market information and make informed investment decisions accordingly.

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.), capital indicators (large holder movements, capital inflows and outflows, position distribution, etc.), and news indicators (project progress, partnership announcements, regulatory dynamics, etc.) across four dimensions, calculating a comprehensive score through complex weighting algorithms and inferring 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 capital; fundamental analysis, while considering the intrinsic value of projects, 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 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 tokens for explosive growth or collapse. This proactive service model allows users to receive key information in real-time, preventing them from missing investment opportunities or suffering 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, conducting comprehensive deep perception to infer signals and achieve precise signal interpretation in conjunction with agents, thus completing more intelligent and personalized deep pushes.

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

In the field of Web3.0 investment, 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 layer a Web3.0 shell over general large models but achieves multiple key breakthroughs in its technical architecture, forming unique technical 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 for both on-chain and off-chain data. In terms of on-chain data, the system collects key information in real-time from the transaction records of major public chains, the execution status of smart contracts, the capital flow of DeFi protocols, and the transfer trajectories of tokens. This data provides the most direct and authentic market foundation for investment analysis.

More importantly, Match AI also integrates a wealth of off-chain intelligence sources, including sentiment changes on Twitter, project activity on GitHub, depth data from major CEXs, official updates from project teams, reports from Medium and news media, 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 serves as 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 at the moment it is generated. This technical capability provides a high-quality data foundation for subsequent intelligent analysis. Additionally, the system has 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 possesses traceability and self-correction capabilities, allowing it to display a complete chain of thought when answering questions and proactively correct itself when logical errors are detected.

The Multi-Agent parallel collaboration mechanism is another technical highlight. The system constructs specialized agent groups, each focusing on specific analytical 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 their analysis results through a collaborative mechanism.

Most notably, the introduction of causal reasoning cores is crucial. 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 behaviors and token performance. Match AI, through its causal reasoning model, can identify and establish the real 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 monitoring social sentiment, some for chart analysis, and some for analyzing code, etc. By complementing the strengths of different models, the overall analytical capability is enhanced while reducing reliance 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, linking real-time news to how many tokens' growth it can drive or how many sectors it can boost.

The Web3 pre-training model is also a feature of Match AI, deeply integrating Web3.0 industry knowledge, akin to a "Web3.0 PhD," rather than being a general large model that is broad but not deep, often answering questions off-topic. What Match AI aims to do is to provide substantial answers that meet users' needs and help them gain.

GRPO (Group Robust Policy Optimization) group strategy optimization is another technical 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 true "personalized service for everyone."

Match's technical head, Lucas, made a vivid analogy. "For example, after Match AI integrates various analyses and finds that a user's profile is aggressive and prefers high-leverage operations; when that user interacts with Match AI, the large model will recommend broader data within the user's risk tolerance to pursue higher returns."

The domain-specific fine-tuning function 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 based on the characteristics of different sectors, providing users with more precise investment advice. This granular level of professional adjustment is something general AI models cannot achieve.

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

Match AI has built a persistent knowledge graph system capable of storing and linking vast amounts 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 about new patterns and changes in the market in real-time. The dynamic knowledge updating mechanism enables the system to continuously learn and adapt to market changes. The rapid development of the Web3 market, with new projects, new play styles, and new risks constantly emerging, often leaves traditional static models lagging behind market developments. Match AI, through its continuous learning mechanism, can promptly capture and understand these new changes.

The retrieval-augmented generation (RAG) mechanism is a key technology for reducing AI hallucination rates, ensuring that AI large models do not make misjudgments and can accurately identify the direction and sub-sector of the questions posed by users, providing a core answer that users seek. The system employs a "retrieve first, then generate" process, retrieving relevant real data from the knowledge graph before generating 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 AI will "summarize," reducing the likelihood of AI spouting nonsense, ensuring that outputs are "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 suggestions." The environmental perception module can identify various abnormal signals in the market through multi-dimensional anomaly detection algorithms, including trading volume anomalies, changes in capital 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 suggestions output executable trading strategies, including specific operational advice, risk control measures, and expected return analyses. This end-to-end service capability allows ordinary investors to receive professional-level investment guidance.

For example, if the Federal Reserve suddenly announces a rate cut, causing Bitcoin and altcoins to surge, AI can promptly recognize changes in the market environment, such as increased activity in traditional financial markets, and 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 meet 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 selection 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 investment. 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 vantage point of 2025, we are witnessing a significant technological turning point. General large models have proven 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 only a product of technological innovation but also an inevitable result driven by market demand. As the Web3.0 market continues to mature, investors' demand for specialized tools will grow stronger. Those who still rely on traditional methods for investment decisions will find themselves at an increasingly disadvantageous position 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 truly meet user needs. In the high-risk, high-reward, fast-paced field of Web3.0 investment, this deeply specialized AI service will become an important competitive advantage for investors.

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

For those who wish to achieve investment success in the Web3.0 world, embracing this technological transformation is not a choice but an inevitability. 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.

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