As crypto assets are gaining acceptance in the traditional financial industry, market dynamics are progressively shifting towards a more retail-oriented approach. Perhaps influenced by the GameStop incident, the meme coin sector has caught the attention of Gen Z, gradually transitioning from being considered alternative assets to essential components within the mainstream investment landscape.

According to CoinMarketCap, meme coins account for about 2% of the entire crypto market. The underlying reason for the meme craze is that institution-dominated value projects have become less attractive due to high valuations and the selling pressure from continuous unlocking, leading to decreased retail participation. As institutions and retail investors avoid buying from each other, meme projects have become the market’s focal point.

The key issue is the weakening wealth effect rather than a conflict between institutions and retail investors. The crucial problem is matching projects with users.

Match is a social platform based on AI and big data technology, aiming to establish an efficient value-based social network enabled by the wealth effect of meme coins. By constructing a sustainable value-based social ecosystem, Match deeply understands how to integrate user needs and economic mechanisms to promote both social interaction and economic returns. Match provides privacy-protected social functions and powerful AI capabilities, enabling users to securely manage their social data assets across various Web 3.0 applications, thereby achieving true value-based social networking.

The social networking market has always been in high growth, making it one of the most competitive fields. Recent data shows that there are 5.07 billion social media users globally, accounting for 62.6% of the world’s population. According to Ecwid, the number of social media users grew from 1.72 billion in 2013 to 4.76 billion in 2023, with a compound annual growth rate of 10.72%.

Despite its vast potential, the social networking market has high entry barriers. Walter Isaacson, a renowned author who has written biographies for Einstein, Jobs, and Kissinger, has a viewpoint on social networking: “Almost every digital tool, whether designed for it or not, was commandeered by humans for a social purpose”. But they must first be effective tools to create social opportunities. Therefore, recent social networking applications like Clubhouse and Damus quickly rose and fell due to the lack of unique tool attributes.

Match’s design is different from previous ones; it is a SocialFi application based on meme coins. Similar to social networking, meme culture is rooted in fundamental human needs. Any type of viral cultural transmission can form a meme culture. When meme culture combines with cryptocurrency, it creates meme coins.

Meme coins inherently have social spreading value. Match introduces new gameplay, creating strong community consensus through the resonance of Meme culture to drive ecosystem development. Match is about to issue its exclusive meme coin, RFG. RFG not only creates wealth effects but also attracts a large number of users, laying a solid foundation for social interaction on the platform. By leveraging its meme coin, RFG, Match will accumulate substantial user data, providing a solid foundation for subsequent AI analysis and social interaction.

In its development into an innovative value-based social ecosystem, Match follows four stages: Departure, Sailing, Breaking Waves, and Voyage. In the “Departure” stage, Match focuses on decentralized auction, social matching and mining, and the development of backend services. In the “Sailing” stage, Match enters a period of rapid expansion and application innovation. After launching basic functions, the platform promotes community building and promotional activities.

In the “Breaking Waves” stage, Match will further expand RFG’s application scenarios and deeply integrate AI technology into its infrastructure. RFG’s application covers multiple fields, including finance and social networking, meeting users’ diverse needs with innovative financial products and services. Through rich application scenarios, Match achieves both user scale and platform value growth. Match also introduces an on-chain DID system and social accessories to activate and amplify personal social value. By bringing in more SocialFi services and partners, Match continuously expands its ecosystem and drives global development.

In the “Voyage” stage, Match will complete the construction of a comprehensive service ecosystem, advancing to higher development levels. The platform offers diverse AI services, including intelligent advisory, personalized recommendations, and risk assessments, helping users make more informed decisions in complex market environments.

Match also utilizes ZK technology for privacy capabilities, addressing trust issues with off-chain data and computations. Users can share data confidentially within a trustless settlement layer, ensuring data privacy. ZK applications allow users to create confidential on-chain identities in a modular manner, surpassing traditional data obfuscation methods. Off-chain security guarantees provided by ZK technology ensure the integrity and expected state of vast off-chain data and computations.

Meme coins currently hold a market cap of around USD 49 billion. Match combines SocialFi with meme coins, forging a new value-based social ecosystem. Building upon the wealth effects of meme coins, Match attracts users and encourages them to engage in social activities, enabling value exchange and wealth amplification among users with SocialFi mechanisms. This innovative model not only attracts a large user base but also enhances long-term user retention and activity.

However, meme coins are community-driven and rely on community enthusiasm and hype, lacking sustainable development mechanisms for the long haul. How can Match sustain itself when interest in meme coins fades?

After accumulating data in the first stage, Match will use AI technology as its core driving force. Through intelligent data monitoring, precise user profiling, and AI-driven recommendation engines, Match helps users identify lucrative investment opportunities amidst the ocean of information, improving investment decision efficiency and reducing operational costs.

The success of meme coins often involves inevitability and chance. In the second stage, Match aims to help users seize early opportunities through AI. For example, when CCTV featured the meme coin SAMO, its community hype led to a 500% price surge. Similarly, the pre-sale of Darkfarm’s BOME surpassed expectations. By leveraging AI, such events can be identified, analyzed, and capitalized on.

Currently, Match is still in the early stages of AI training with limited data sets. As the volume and quality of data improve, Match may experience a breakthrough moment for SocialFi similar to the iPhone’s launch.

Additionally, Match plans to introduce a modular social layer to nurture a sustainable ecosystem. In the future, Match will be compatible with various mainstream public chains, allowing seamless integration of data and functionalities. This means that users can enjoy a unified, seamless social experience on the Match platform, regardless of the public chain they use. Furthermore, Match aims to evolve as the ideal platform for social IDOs. Through intelligent recommendations and precise matching, Match provides project teams with high-quality authentic users, facilitating swift traffic and user aggregation for projects on public chains. This not only improves the efficiency and success rate of token issuance but also enhances user engagement via social interactions, forming a sustainable ecosystem.

By harmonizing the four core pillars of wealth, traffic, social interaction, and information, Match forges a powerful value flywheel.

In the rapidly evolving and cacophonous blockchain domain, AI is often more of a gimmick rather than a practical tool. Match seeks to integrate AI into meme coins and SocialFi to deliver tangible applications, potentially heralding a fresh wave of transformation.

By utilizing AI for intelligent data monitoring and analysis, Match creates precise user profiles and offers tailored strategies to guide users toward profitable investments amidst vast information streams.

By analyzing social media trends, discussion scales, and on-chain data, Match’s AI delineates authentic project potential across multifaceted dimensions, distinguishing between retail and project-oriented buyers, empowering users to make more informed trading decisions. Besides, Match’s AI enables real-time access to project insights, saving users research time and helping them seize investment opportunities. The integration of AI not only improves the platform’s service quality but also provides users with personalized social experiences.

Moreover, Match’s AI can craft tailored strategies based on user profiles. Whether users are looking for quick profits, interested in memes, or are seasoned investors, Match’s AI provides bespoken recommendations and helps project teams devise more effective marketing strategies. In a cacophony of information, Match’s AI filters out valuable insights, providing value to users and addressing user retention issues. This not only increases user engagement but also enhances user loyalty.

Through AI-driven recommendations and precise matching, Match provides project teams with high-quality authentic users, making it the ideal platform for social IDOs. The deep integration of social interaction and finance allows users to enjoy the fun of socializing while earning considerable investment returns. Through in-depth AI-based analysis over social interaction data of users, Match generates precise user profiles and personalized recommendation strategies, offering more targeted services to meet different user needs. Moreover, Match uses social interaction data to create soul identities and on-chain credit certificates, further enhancing user trust and security on the platform. This approach not only improves user experience but also reduces costs and increases efficiency.

As Match accumulates more data and users, AI will provide more assistance to both users and project teams. For example, AI can aid in identifying product strengths and weaknesses for targeted optimization and improvements. It can also design more intelligent and personalized financial products to meet diverse user needs. Just like OpenAI, by increasing parameter training data sets, Match aims to trigger an AI revolution through qualitative changes brought by quantitative accumulation.

Surveying the industry’s social applications, from the early ONO to BM’s Voice, the common goal was to attract users to these new platforms for token rewards. However, this narrative has been debunked. The hurdle lies not in the veracity of Web 3.0 + social interaction but in the lofty costs associated with user migration from giants like Sina Weibo and Twitter. Even Sina Weibo’s own pictorial social platform, Oasis, faded quickly, illustrating the challenge.

An alternative approach entails leveraging niche appeal, akin to Douban or the present-day Farcaster, catering to specific user groups without attempting to supplant other social platforms.

In Web 3.0’s landscape, where trading takes a central stage, social applications can explore this facet, as exemplified by Match. Preceding Match, Friend.tech emerged with a viable proposition akin to exclusive circles where users believed prominent influencers could unlock wealth. However, Friend.tech merely attracted users through wealth effects without resolving the core query of user retention. Match aims to fill this gap.

Imagine such an offering: AI streamlines redundant social information for you, identifies investment projects that align with your flair, and offers you with passive SocialFi income while you are trading. Would you embrace this offering? This epitomizes the essence of most users’ demands in Web 3.0, a pain point Match solves.

At the heart of any application lies user retention; users are the linchpin for all endeavors, especially in the realm of social platforms. Presently, valuations of most prevalent social applications stay under USD 1 billion. To sustain these valuations, project teams must incessantly helm operations to enhance user retention. Conversely, Match, possessing an innate ability to naturally draw and retain a substantial user base, could potentially command valuations surpassing these figures by a wide margin, possibly even breaching the hundred-billion-dollar mark.