Ten Thousand Words Analysis Match: Integrating elements like memes and AI to spark a new round of Web 3.0 social revolution

"Match has established an efficient value social network by deeply integrating narratives from SocialFi, meme effects, AI technology, and more, addressing the precise matching problem between users and projects, achieving a combination of social interaction and wealth, and continuously providing momentum for breaking traditional boundaries in the Web3.0 social track.

The Intensifying Monopoly Effect in the Web2.0 World

In the Web2.0 world, internet platforms represented by Twitter, Google, and Facebook are becoming an indispensable part of most users' lives.

Of course, while these platforms provide long-term services to users, they not only gather enormous attention but also continuously engage in data monopolization.

On one hand, companies like Google or Facebook can retain our data for up to ten years until it can be monetized for their benefit, after all, storing data has almost no marginal cost. Web2.0 platforms that control vast amounts of data and attention can not only gain significant commercial benefits but also, as monopolies intensify, lack the willingness to share data or provide open APIs, leading to the exploitation of users and developers' interests.

Typically, Web2.0 platforms are fragmented and non-interoperable, because in the Web2.0 network, users' data and social relationship networks are usually the platform's moat. If users are allowed to interact with their social relationship networks through third-party applications, the opportunities for platforms to capture user data will diminish. After all, users will no longer use products controlled by the platform. If users can easily transfer their networks of friends and family to another application, they will have little incentive to return to your application.

So in the current internet world, it is likely that your parents are Facebook users, you are an Instagram and Snapchat user, while your sister is a TikTok user. But we cannot watch TikTok videos on Facebook and simultaneously share them with my Facebook friends, nor can I transfer my friend list from Twitter to Threads. Essentially, users do not have the initiative and ownership of their data, making it difficult to manage their social graphs outside of the platform.

Currently, there is still no shared protocol for social applications of the scale of Facebook or Twitter, which is related to the incentive structures of the internet giants themselves. In contrast, a Web2.0 product with an open social relationship network would face competition and declining revenue, neither of which are ideal outcomes.

The original intention of the internet was to help people quickly access information and communicate, but it is clear that the current development trend is contrary to this.

Web3.0 represents a brand new ideology where everyone, while becoming a contributor, user, and participant in the network, also owns a part of the network. It emphasizes human value more and aims to achieve value redistribution through bottom-up approaches using institutional technologies like blockchain, returning data ownership to users. Ideologically, Web3.0 is advanced and has the potential to improve various monopolistic phenomena present in the Web2.0 world. However, the actual development situation in the Web3.0 social field has not been ideal.

The Growth Dilemma of Web3.0 Social

In the SocialFi field, there have indeed been some social ecosystems that have caught our attention.

One example is the SocialFi application Friend.tech, which exploded in popularity last year. This protocol, based on Key's equity, the social fission mechanism of joint curves, and the FOMO effect, gained significant attention in a short period, generating over 130,000 transactions on its launch day. As a financial product based on Twitter's social user network, it created a strong interdependence between SocialFi applications and Web2.0 social products. Although it did not create new social relationships or introduce any new communication models, it effectively utilized the social graph of Web2.0 products and promoted the financialization and tokenization of influence and social relationships.

Although it surged in the early stages of development, in fact, after passing through the initial explosion period, as market enthusiasm shifted towards inscriptions and faced competition from similar products, Friend.tech's user activity rapidly declined, with its daily transaction volume currently below 500.

Data Source: https://dune.com/cryptokoryo/friendtech

Another example is Cyberconnect, which, as a leading protocol layer application in the SocialFi field, has a relatively considerable user base, previously maintaining around 1 million users. However, after it distributed airdrops last August, we saw a cliff-like decline in its growth, and a significant portion of the existing user base used multiple accounts to obtain airdrop rewards, indicating that airdrop-based Web3.0 marketing strategies are backfiring. From another perspective, the number of users of leading protocols like Cyberconnect is relatively small in the social track.

Image Source: https://dune.com/buildoncyber/cyberconnect-link3-metrics

In fact, Web3.0 social itself lacks applications capable of creating breakout effects.

On one hand, most users have become accustomed to using mainstream Web2 social products for free, while the payment model for Web3.0 social products requires purchasing Profile NFTs and paying gas fees for interactions, which usually incurs costs of several dollars without visible returns (especially on higher-cost platforms like Ethereum), leading to a lack of motivation among users. At the same time, most Web3.0 social applications merely mimic the models of Web2.0 social platforms. Without significant innovation, the pain points they attempt to address have limited appeal to users, and these needs are not important to most users or creators.

On the other hand, for non-Web3.0 users, this group has already established mature relationship circles within Web2.0 social products, and few users are willing to abandon their existing social capital to enter unfamiliar products. It is challenging for Web3.0 social to siphon off this group of users. For native Web3.0 users, in the absence of other significant needs, using Web2.0 social applications like Twitter, Discord, and Telegram can meet their requirements. However, for social products, the scarcity of users and creators will ultimately lead to insufficient blood generation capacity.

Moreover, relying solely on SocialFi's X To Earn may not be the ultimate answer.

In fact, the long-term success of Web2.0 social products does not solely rely on the WRITE TO EARN model to attract users, but rather on users finding a sense of belonging on a social platform and discovering things that are suitable and valuable to them. An example is platforms like Twitter and TikTok, where the products do not rely on rewards but genuinely resonate with users and retain them, and this process also requires a sustained supply of high-quality content.

While the X TO EARN model can incentivize user activity for a certain period, like most GameFi applications, relying solely on the X TO EARN model is unlikely to generate long-term sustained appeal for users. We see that under the occurrence of one-time incentive events and the decline of long-term incentive expectations, the activity and growth of Web3.0 social protocols can hit a bottleneck. For example, after CyberConnect issued the CYBER token, its poor performance led to the overall performance of the ecosystem not meeting expectations.

The lack of a moat of high-quality content leads to the short life cycle of most SocialFi projects, ultimately leaving them unattended. Therefore, attracting high-quality content creators and users, whether in Web2.0 or Web3.0, requires a long-term accumulation of both the quantity and quality of users and creators.

Although the overall development trend of the crypto industry is positive, SocialFi has reached an awkward stage of transition. In fact, only by better achieving qualitative changes in SocialFi products can the field further promote better upward development.

Currently, with the emergence of the new Web3.0 social project Match in the market, the development of the Web3.0 social track is ushering in a new turning point. We see that Match innovatively combines meme narratives and AI technology with SocialFi to establish an efficient value social network with wealth effects.

As a protocol layer, Match supports projects in building scalable social graphs and innovative asset solutions based on meme economy and AI technology. This allows for precise matching between users and projects, better wealth distribution, encourages user participation in ecological construction, and promotes continuous social effects among users, breaking the growth dilemma of the track.

Its AI solutions can further provide users with a range of personalized services and accumulate high-quality content while matching supply and demand on the content side. Based on massive social and user data, it will continuously drive the innovation of its AI models, thereby helping the Match ecosystem build a solid moat.

The Disruptor Match

Match is an innovative Web3.0 social ecosystem that, based on AI and data infrastructure, aims to break down barriers to value social interaction. Match deeply integrates meme elements, building social scenarios based on meme culture and wealth attributes, and creates a composable social graph system. On this basis, it further constructs social value using AI technology, expands the reach of high-precision effective content dissemination, and transforms from ecology to data to value through a wealth of high-quality real-time content.

From the product itself, Match has created a unique identity layer design based on data infrastructure. This identity layer serves as the container for users' unique Web3.0 identities. Upon entering the Match ecosystem, users will first generate an identity identifier, which acts as the carrier for building their Web3.0 graph. This identity layer design not only includes basic personal information and investment preferences but also integrates users' interaction history, social networks, and behavioral characteristics, thereby constructing a comprehensive and three-dimensional user profile.

The Match registration system can effectively filter out real users, making it unprofitable for bot accounts and mass exploitation accounts, while providing more incentives for high-quality content creators. Additionally, Match strives to allow every real on-chain user to complete their identity transformation here, which will help attract a large influx of genuine users.

Through this unique identity layer design, Match can provide users with a more personalized and precise social experience, aiming to build a social system based on real identities, further enhancing trust and interaction quality among users. Based on this identity layer, Match can further establish account abstraction and chain abstraction, significantly lowering the entry barriers for Web2.0 users into the ecosystem, and providing a foundation for cross-chain interoperability and global Web3.0 social interactions across ecosystems.

At the same time, Match can re-aggregate the market's fragmented data back to the application layer and conduct in-depth analysis and processing based on AI components, uncovering more valuable information. By precisely analyzing market behavioral data, users can quickly find content and interaction partners that match their interests and needs, reducing information overload and inefficient browsing time. This not only enhances user satisfaction and stickiness but also significantly boosts the overall competitiveness of the platform.

From the product's inherent characteristics, it essentially represents the features of Web3.0 social and, on this basis, supports the migration of existing mature relationship circles and the assetization of social graphs. Match is not merely a competitor to Web2.0 social products but hopes to complement the Web2.0 social ecosystem and address its shortcomings.

Taking Twitter as an example, although it is currently an important gathering place for Web3.0 native users, it lacks the ability to segment profiles for different roles (such as value coin users, meme coin investors, airdrop enthusiasts, and Alpha users), so Match is expected to further fill this gap. Meanwhile, Twitter requires users to subjectively follow many KOLs, wasting a lot of time searching for information, and does not directly integrate with on-chain data monitoring. If users want to know real-time on-chain data, they need to follow specific accounts, and this information often gets buried in the Twitter feed unless users frequently check for updates from these accounts. Match will directly output data conclusions based on the integration of AI and other technologies, and its value will continue to be highlighted.

From an ecological perspective, Match itself is also a programmable underlying protocol, including service layers and infrastructure layers, as well as a series of AI components. Based on this infrastructure layer, Match supports the establishment of upper-layer applications based on the underlying layer and interacts with its identity system, providing modular support for developers and preparing for the subsequent explosion of the social ecosystem.

User Growth Strategy

Cultural Foundation Based on Meme and Wealth Effect

Match's growth strategy does not rely on Web3.0 marketing to drive growth like Lens Protocol, CyberConnect, etc. Active marketing often suffers from a lack of scale effects and excessive promotion, which can backfire on the long-term development of the ecosystem.

One of Match's growth strategies is memes. Through meme culture, it hopes to build a more centripetal social ecosystem while further promoting ecological fission and continuously generating growth momentum through the wealth effect brought by memes and the underlying social integration.

In fact, meme culture has its unique value logic. In traditional finance, it is more about finding the true value of assets based on valuation models, thereby identifying undervalued assets. This approach can effectively help financial investors find potential investment targets and achieve returns through more rational thinking. However, in the crypto world, this valuation model seems to be "out of place." For example, assets like BTC and LTC, which do not offer dividends or cash flow, are often labeled as "Ponzi" by outsiders, while traditional investors like Warren Buffett dismiss crypto assets.

In reality, the value growth in the crypto world relies on consensus rather than just valuation. The crypto world is a new ideology that advocates decentralization, gathering a group of people with shared beliefs into a community, relying on the most basic supply and demand model, where constant supply and increasing demand lead to continuous value growth.

Of course, compared to some "classical" crypto narrative schools based on technology and applications, the emerging crypto faction driven mainly by meme culture seems to have gained popularity since the explosive rise of Dogecoin in 2020. For the crypto community, memes, in addition to their fun and precise communication, also bring viral dissemination effects that enhance community cohesion and "wealth effects." Moreover, a well-crafted meme cultural narrative often spreads more effectively than lengthy articles.

Crypto meme culture typically achieves rapid dissemination within a short period, quickly increasing demand for meme tokens under constant supply, attracting more people to chase meme assets, creating FOMO (fear of missing out), and continuously drawing in more "believers." In fact, meme crypto culture possesses a set of predictive logic and can achieve "instant precise communication."

During the last meme craze, the meme effects of Doge and Shib were sustained. Doge and Shib are also being accepted as payment methods by some traditional businesses, such as AMC Theatres, Uber Eats, and fresh food company meatmeCA. The new development narrative gained through memes is backed by a strong community scale.

Therefore, from Match's perspective, the growth drive of the ecosystem does not need to rely solely on X To Earn and Web3.0 marketing. The growth theory of memes has been practically validated. Match aims to resonate with crypto users through new gameplay, leveraging meme culture, and the resulting strong community consensus to drive ecological development and create wealth effects.

The popularity of memes can lay the foundation for building a large-scale traffic pool. Based on their simple and understandable characteristics and high return potential, they can quickly attract a large number of retail investors to Match. This broad market participation will also provide a sufficient user base for building the SocialFi platform. Through the popularity of meme culture, the platform can rapidly accumulate a wealth of user data, laying a solid foundation for subsequent AI analysis and social interactions.

The accumulation and sedimentation of high-quality content establish a moat for ecological development.

As mentioned above, high-quality content is usually an important foundation for establishing a moat in a social ecosystem. In the Match ecosystem, it leverages AI technology to help the platform build a content system and provide users with a series of functions to meet their investment and trading needs in the Web3.0 world with maximum efficiency and minimum barriers. While optimizing content matching, it further promotes the growth of wealth effects.

AI is a crucial factor in driving content and matching in the Match ecosystem. In Match's ecosystem, KOLs (Key Opinion Leaders) serve as an important bridge connecting the platform and users, playing a dual role as interpreters and user retention hooks. Through their professional insights and strong influence, KOLs can help users better understand various information and opportunities on the platform, effectively driving fan economy and enhancing user activity and stickiness.

KOLs can provide users with in-depth and valuable content interpretations, helping them make informed decisions in a complex information environment. This precise content output not only enhances users' sense of trust and reliance but also strengthens the platform's professional image and brand influence. This role plays a key part in information interpretation and user retention, and under the drive of fan economy, it provides strong momentum for the platform's continuous development. Match will fully leverage the potential of KOLs to create an efficient, transparent, and win-win social finance ecosystem, generating more value for users and achieving sustainable development and innovation for the platform.

KOLs are the foundational elements for establishing the platform's content system, and on this basis, the AI system will help users match with high-quality KOL content, bringing users a better content experience and continuously generating income for KOLs, thereby encouraging them to continue contributing high-quality content.

Thus, based on AI, it can better help users discover clues to high-quality content or early value discovery, providing critical timely information. Value projects can subsequently ferment through industry and KOL in-depth analysis, potentially forming discussions and trends.

Thanks to the effects brought by AI, personalized investment strategies and information matching can attract KOLs to Match. This is similar to how TikTok initially expanded into overseas markets when platforms like Twitter, Instagram, and Snapchat were already popular. However, TikTok relied on its algorithmic capabilities to solve user retention, gaining seed users, attracting KOLs, and having the internal capacity to incubate KOLs. Through the accumulation of KOL content, it further retained users and gradually became a unicorn.

At the same time, AI itself is also a tool for producing high-quality content. In the rapidly changing cryptocurrency market, Match leverages its extensive social data combined with cutting-edge AI analysis to provide users with unparalleled insights into social activities and market sentiment. This advanced integration of AI technology not only enhances the ability to track and analyze social interactions but also provides key market sentiment indicators, which are crucial for making informed decisions. This forward-looking approach enables investors and traders to capitalize on emerging trends and narratives, positioning themselves advantageously in the market and accelerating the trend of wealth effects for users.

The vast user scale and high-frequency AI invocation rates provide feedback for training AI models through large datasets, bringing users more scenarios and applications, achieving precise matching, and benefiting users, thus forming a positive cycle known as the "AI flywheel."

Therefore, we see that AI technology is becoming a key driving force for market development in the Match social ecosystem. Similar to how the iPhone led the smartphone era, AI will bring revolutionary changes to the cryptocurrency field. Through intelligent data monitoring, precise user profiling, and AI recommendation engines, the platform can quickly identify key investment opportunities, helping users find the most valuable investment opportunities amidst a sea of information. AI technology not only significantly improves users' investment decision-making efficiency but also greatly reduces the platform's operating costs, achieving true cost reduction and efficiency enhancement. In the long run, the drive of AI will not only ensure that Match ecosystem users remain loyal due to the high-quality services and precise investment advice provided by the platform but also possess sustainable commercialization capabilities.

The vast majority of Web3.0 social ecosystems have certain shortcomings in the long-term accumulation of high-quality content, while Match can not only achieve supply and demand matching for high-quality content based on AI technology but also allow high-quality content producers and consumers to continuously benefit from it. At the same time, AI is also a tool for producing high-quality content, and through continuous training and learning, it will significantly enhance the scale and quality of the Match ecosystem's content pool, strengthening the sustainability of its development. On the other hand, AI, as a highly promising narrative direction, also represents Match's high development potential. With interactions with more cutting-edge technology fields, the future development valuation of the Match ecosystem will continue to rise.

Overall, based on the aforementioned system, Match is building a value flywheel system based on the four elements of wealth, traffic, social interaction, and information, namely, wealth effects leading to traffic aggregation, traffic aggregation giving rise to social scenarios, social scenarios constructing information dissemination, and information dissemination amplifying wealth effects.

Match's Asset System

To enhance social interaction effects and promote closer connections between users, the Match ecosystem has designed an economic model to support the development of its social system.

In the Match ecosystem, there are two important assets: Match NFT and RFG tokens. Match NFT serves as an incentive tool for users on the platform. By holding and using NFTs, users can earn RFG tokens, unlock more platform features, and gain higher social value.

RFG Token

RFG is the project's meme token, abbreviated as Refugee, symbolizing blockchain digital nomads, with a self-deprecating and humorous connotation rooted in meme culture. The RFG token is entirely owned by the community, with no private placements, low circulation, and low market value. As a community-driven meme asset, it is fully linked to community consensus and has a high appreciation potential. Users can stake RFG tokens and earn income from the Match network. Currently, users can choose to stake RFG tokens for different periods, including flexible, 60 days, 90 days, 180 days, and 360 days, with longer staking periods yielding higher returns.

At the same time, users can also accelerate the income obtained from staking Match NFT assets by staking RFG tokens. The longer the staking period and the more tokens staked, the higher the yield bonus provided. This bonus coefficient can be as high as 2 times. This also means that the RFG token has maintained a tight supply since its market introduction.

NFT Assets

Match defines its NFT assets as social catalysts, aiming to play an important role in social interactions through joint staking mining. In the Match system, NFT staking is the only way to earn RFG tokens early and serves as a reward and incentive for NFT holders. Users can stake to become natives of the Match ecosystem and enjoy a series of rights.

Staked NFTs not only increase users' mining earnings but also optimize the processes of staking, yield distribution, and exit settlement through dynamic allocation algorithms. Additionally, the Match Square function derived from the AI large model can intelligently and accurately recommend matching relationships, ensuring that the assets users stake can maximize their potential returns, thereby enhancing users' investment returns and increasing the actual value and functionality of NFTs.

Match's NFT assets total 45,000 (90% auctioned to the community, 10% for team operations), and there are three types, each representing different mining weights, numbered α, β, and γ. Among them, Match γ NFT has the highest mining weight, thus its value is relatively higher. (Weight categories: α 1.1--β 1.2--γ 1.3)

In fact, both acquiring and staking NFTs have a certain level of fun and promote user interaction based on returns.

In terms of acquisition, 90% of Match NFTs (40,500) will be auctioned in a decentralized, non-fixed pricing manner. During the auction process, sales will be conducted based on bid amounts, and the funds from unsuccessful bids and amounts exceeding the winning bid will be returned to the bidders. Each address can participate in two auction rounds at most, with a maximum of two NFTs per round, and the specific number of users obtaining two NFTs will be determined by an algorithm. Additionally, the type of NFT obtained by users is also random, and the open-source algorithm ensures the fairness and impartiality of the auction process. This means that NFTs can be distributed to users in a fairer manner, avoiding the negative impacts of malicious users and high-spending users on the ecological economic model.

In terms of staking, the novel staking model makes NFTs important tools for participating in Match social activities.

From the perspective of traditional staking models, users typically operate independently with little interaction. Match has created the SMS mechanism and Match NFT staking ecosystem, comprehensively building a highly engaging community.

To this end, Match has set up three types of NFT staking pools: single NFT pool, double NFT pool, and triple NFT pool, aiming to encourage users to interact actively and enhance the social activity of the product.

Single NFT Pool: Users stake a single NFT for mining, yielding stable returns with a lower annualized return rate.

Double NFT Pool: Users stake two NFTs in pairs, in any combination (e.g., α and β, α and γ, β and γ), with an annualized return rate higher than that of the single pool.

Triple NFT Pool: Users stake three NFTs (α, β, γ) simultaneously, yielding the highest annualized return rate.

Due to the differences in yield rates among different NFT pools, the Match platform encourages users to find teammates on the Match Square page to team up for staking, building a networked social relationship. In the Match Square, users can find suitable teammates and communicate through social tools to reach a consensus on jointly staking NFTs to increase yield rates. On the square page, if a user holds an α NFT, they can find a teammate holding a β or γ NFT. Through negotiation, one person can stake an α NFT while the other stakes a γ NFT, jointly entering the double NFT pool for mining. Users can also form teams to build a triple NFT pool for mining.

The Value Growth Logic of RFG Tokens and NFT Assets

RFG Token

The RFG token is the main asset of the Match ecosystem. As a meme asset, it has no pre-mining or early market share, which means it does not face the risk of early market sell pressure. Its output method is limited to RFG single-token staking mining or NFT mining.

From the demand side of RFG tokens, the first is the staking demand. As mentioned earlier, the staking returns of RFG in Match are related to the staking period and the amount of staked funds, and staking RFG tokens will enhance the yield of NFT staking. Many pure RFG token holders and NFT holders will emerge in the network, and in pursuit of higher returns, they will have a strong demand for RFG tokens, leading to many choosing medium to long-term staking periods, which is an important factor limiting the liquidity of RFG tokens and forming early demand.

The RFG token is a meme asset, and as the ecosystem develops, it will derive various application scenarios and uses within the expansion of the social system. From a consensus perspective, the value social graph constructed by Match provides a vast user base, paving the way for RFG to consolidate consensus.

Overall, the RFG token will always be in a deflationary state, and its price will continue to rise. The increase in price will further promote more users to hold and stake long-term, forming a virtuous cycle of value growth.

NFT Assets

In terms of acquiring NFTs, the purchase of NFT assets is random, meaning that whether users can obtain an NFT each time they participate in an auction is uncertain. As the number of bidders increases, the probability of obtaining 2 NFTs gradually decreases. The total issuance of NFTs is limited to 40,500. This also means that due to their randomness and fixed total quantity, NFTs possess significant scarcity and difficulty in acquisition.

From the output side, the value of RFG tokens is currently on the rise. The current FDV is only $4.5 million, compared to the market caps of DOGE, SHIB, and others at tens of billions, indicating that RFG tokens have over 100 times potential for appreciation. Staking NFTs is an important way to produce RFG tokens. Therefore, staking NFTs and joint staking to enhance yield rates will become a primary demand. This will further increase the demand for NFT assets and enhance their value.

The rise in RFG tokens and the strong demand for joint staking will further strengthen the close ties within the ecosystem. This will accelerate the cohesion of the Match social ecosystem in the social direction, continuously driving new growth and fission. At the same time, it also promotes the continuous innovation of AI components, benefiting the Match ecosystem in the long term. Ultimately, it accelerates the flywheel of "wealth effects leading to traffic aggregation---traffic aggregation giving rise to social scenarios---social scenarios constructing information dissemination---information dissemination amplifying wealth effects."

Ecological Development Potential

Previously, Xin, a co-founder of Old Fashion Research (OFR), stated in his article that he views product strength, subcultural creativity, and good token models as a triangle, and a successful project typically needs to possess at least two of these three points. If a project wants to attract users and survive long-term, it needs to have all three elements.

As an innovative Web3.0 social ecosystem, Match can better balance the aforementioned three factors. It is built on a strong social protocol foundation, an expandable social graph, and continuously improving AI components, meeting users' needs in social interaction, investment, and capturing high-quality content, while continuously accumulating users and high-quality content. Moreover, by leveraging the cultural attributes of memes to create subcultures, it continuously expands and extends culture, making it an important cornerstone for community cohesion and wealth growth capture. Based on the meme token RFG and Match NFT assets, it can deeply empower its social system and continuously accelerate the ecological value flywheel based on economic factors. Compared to the vast majority of existing Web3.0 social ecosystems, Match has greater potential for customer acquisition and long-term survival.

From the perspective of the Web2.0 social field, social interaction is one of the most important foundations of traditional internet and has enormous market potential in the capital market, backed by over 4.62 billion users, equivalent to 58.4% of the global population. Whether it is Facebook, which peaked at a market value of over $1 trillion and has nearly 3 billion daily active users across its global series of products, or the valuations and market values of social operating companies like Twitter and Snapchat, they all indicate that the ceiling for the social track is extremely high.

On the other hand, according to data from Triple-A, there are about 420 million cryptocurrency users, accounting for less than 5% of the global population. The proportion of Web3.0 social users is even smaller, indicating that Web3.0 social is still in its very early stages of development. As a potential killer application, Match is expected to quickly capture the market during this window period and become a long-term leader in the field.

Furthermore, Match's narrative encompasses potential fields such as SocialFi, memes, and AI. With the support of multiple narrative directions, it is expected to significantly enhance the valuation of the Match ecosystem, with development potential far exceeding that of currently leading SocialFi applications. As it develops over the long term, the ecological value attributes will further be reflected in RFG and Match NFT."