With the rapid development of artificial intelligence technology, the global demand for high-quality AI data is increasing. According to a MarketsandMarkets report, the global AI market is expected to reach $2.1 trillion by 2030, with data as one of the core elements driving this growth. However, the acquisition and governance of AI data still face multiple industry challenges, limiting its full potential.
Currently, data acquisition largely relies on centralized platforms, where users' personal data is managed in a centralized manner, yet these platforms still lack sufficient privacy protection measures. According to a Statista survey, over 60% of internet users express concerns about the leakage and opaque handling of their personal data. This situation leads many users to be hesitant about providing data to AI platforms, hindering the progress of AI training and applications.
The quality and governance issues of AI data also represent a bottleneck for current technological development. Most existing AI models rely on non-transparent data sources and lack effective decentralized governance systems. Whether in data collection, storage, or processing stages, the absence of transparency and oversight makes AI systems susceptible to bias and unjust data. A Forbes survey indicates that over 45% of data scientists believe poor data quality is a major factor affecting AI training effectiveness.
Despite the growing demand for AI data, existing incentive mechanisms have failed to effectively attract user participation in data contributions. On traditional centralized platforms, users typically lack control over their data and do not receive fair rewards. Most users lack motivation to contribute data, leading to insufficient data supply, which in turn affects AI training and the optimization of intelligent systems. According to McKinsey data, approximately 70% of users are reluctant to contribute personal data to AI platforms, primarily due to insufficient incentives.
Additionally, large tech companies currently dominate the collection of AI training data, creating a centralized monopoly on data. According to a PwC report, the five largest companies hold over 80% of the global data market. This data monopoly not only exacerbates the difficulties smaller businesses and independent developers face in accessing data but also limits innovation in AI systems, reducing diversity and fairness.
SquadVerse aims to reshape the methods of acquiring, governing, and utilizing AI data by leveraging a Web3 social platform combined with a SocialFi model, offering users a new experience in data contribution and rewards. Through a decentralized governance system and cryptographic solutions, SquadVerse ensures user data privacy and security while promoting transparency and quality in data.
SocialFi, as our core model, empowers users to earn rewards by participating in social interactions, completing tasks, and contributing data. The Web3 social participation model can effectively motivate users to join and continuously contribute data, providing a decentralized ecosystem that breaks the monopoly and inequality of traditional AI data acquisition, ensuring that every user can contribute data and receive rewards in a fair and secure environment.
SquadVerse is an innovative social profit platform that builds a Web3 artificial intelligence data network based on SocialFi. The platform provides users with dual opportunities for social interaction and data contribution through two core modules—the SquadVerse Web3 Social Task Protocol and the SquadVerse Protocol Data Governance Network. Users can earn rewards by participating in social tasks and supporting AI model training through data contribution.
The SquadVerse Web3 Social Task Protocol is designed to incentivize users to participate and contribute data through decentralized social tasks. Each user is ensured fair rewards based on their interactions, data contributions, and task completion.
The SquadVerse Protocol Data Governance Network focuses on a decentralized data governance system that protects user data privacy and security. Through Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKP), the platform ensures data transparency and trustworthiness while supporting AI models. This protocol allows users to participate in data governance and AI ecosystem development while protecting their privacy, promoting sustainable development of the platform.
SquadVerse's vision is to promote the healthy development of Web3 communities and AI data networks through blockchain technology and the SocialFi model, empowering users to create, contribute, and benefit. Our mission is to build an open and transparent ecosystem where every user can enjoy fair rewards while contributing data and promoting the joint progress of Web3 and artificial intelligence.
The SquadVerse social task system helps users contribute valuable data while enjoying social experiences and earning rewards through a range of specific tasks and interaction tools. This system combines user-friendly social tools and task templates, making it easy for users to participate and contribute to the platform's AI data ecosystem.
Task categories cover social interaction, content creation, data contribution, and more. Users will receive platform tokens or point rewards upon task completion. These tasks not only encourage active participation but also enhance the quality and diversity of platform data.