Original text: https://xangle.io/research/detail/2246
1. New conditions in the AI era: trusted infrastructure
Today, AI has rapidly broken through the scope of daily tasks such as searching, writing, and painting, and has expanded into high-level decision-making areas such as disease diagnosis, accounting, and investment judgment. However, as AI deeply penetrates into daily life, its operation mode and data processing still lack reliable guarantees. Most users have no way of knowing what data AI learns and what logic it uses to make decisions - it is almost a "black box", and it is also opaque who controls the exposure boundary of sensitive information.
Based on this problem awareness, people's expectations for the combination of AI and blockchain technology are also rising. In terms of reward mechanisms for data provision, authenticity verification of learning data, and distributed processing of computing resources, blockchain has begun to attract attention as an infrastructure that supports AI to become a trusted technology. In particular, there have been attempts to record AI's learning process and reasoning results on the chain, or to control model execution conditions through smart contracts.
Although these attempts have made some contributions to solving the problems of Web3 and AI, most of them remain at the level of tracking results and execution conditions or providing computing infrastructure. The core question of how to safely calculate and protect sensitive data has not yet been fully answered.
Mind Network is committed to solving the unresolved data privacy issues in the field of AI through the key technology of FHE (Fully Homomorphic Encryption). The platform is building an infrastructure based on FHE technology, which can keep data fully encrypted throughout the calculation process, achieving true "data available but invisible".
FHE goes beyond simple privacy protection and provides a technical foundation for AI systems to sustain trust by practicing the principle of "using data but never viewing it". This FHE-centric design fundamentally reconstructs the structural conditions for AI to gain trust, and has attracted much attention as a technical path to achieve the ideal of "personal data sovereignty" that Web3 has long advocated.
Mind uses FHE technology to create a new paradigm of "encrypted AI computing" for securely processing sensitive data in an open environment like blockchain. This technology is particularly suitable for fields such as finance, medical care, and agent-based AI that require both accuracy and privacy, and will become an important technical cornerstone for promoting the scale of practical applications of Web3.
The market also recognizes this technology vision and long-term demand potential. In fact, Mind has raised more than $12.5 million in investment from major institutions such as Binance Labs, HashKey, Animoca Brands, Chainlink, and has twice received research funding from the Ethereum Foundation. With the rapid development of AI technology, today's era requires not only presenting results, but also proving the entire process of "how to process and verify data."
Mind is using FHE technology to build a new AI infrastructure model that ensures data consensus and execution transparency while protecting the privacy of the computing process.
2. ZK is just the beginning, FHE is the ultimate answer to privacy
ZK (zero-knowledge proof), a privacy technology that has emerged in the Web3 ecosystem, is hailed as a breakthrough technology for its feature of "verifying authenticity without exposing information." However, ZK technology mainly focuses on the "verification" function and has limitations in AI application scenarios that require complex calculations.
AI systems based on data prediction and decision-making not only need to verify rationality, but also need to have the ability to perform direct operations in an encrypted state. It is at this point that FHE (fully homomorphic encryption) has attracted much attention as a new generation of privacy technology paradigm after ZK.
In short, FHE is a technology that can directly operate on encrypted data without decrypting the data. With FHE technology, mathematical operations such as addition, subtraction, multiplication and division can be performed without viewing sensitive information, and the entire process from input to output remains encrypted. In the end, the data processor only provides the result of the operation without knowing the content of the information, and only the data owner can decrypt and view the result.
This architecture provides an alternative to traditional privacy protection solutions for scenarios such as AI reasoning, machine learning training, and high-risk operations on public chains. For example, when medical institutions perform AI diagnosis based on patient data, the AI model can complete the calculation without directly viewing the patient's information, and the diagnosis results can only be decrypted and viewed by the patient himself. This solution can achieve dual protection of data privacy and AI credibility in a decentralized environment.
Fully homomorphic encryption (FHE) is not so much a new concept as it is a technology that has existed in the field of cryptography and has been studied for a long time, just like zero-knowledge proof (ZK) or hash functions. In 1978, Rivest, Adleman and Dertouzos proposed the concept of "performing operations in an encrypted state", but due to the high computing cost caused by the limitations of the technology at the time, this technology remained in the theoretical stage for a long time.
It was not until 2009 that Craig Gentry proposed a feasible FHE scheme for the first time using lattice structure and bootstrapping technology, opening a breakthrough for practical application. Since then, various schemes such as BGV, BFV, and CKKS have been introduced, and the computing efficiency and accuracy have continued to improve. In particular, with the emergence of new schemes based on fast bootstrapping such as TFHE, FHE has now reached the level of being actually deployed in a variety of application scenarios, just like ZK technology.
Mind is applying FHE technology to blockchain and AI environments based on its development achievements. The platform is the first in the industry to implement the Rust language high-performance library TFHE-rs v1.0.0 developed by Zama. With the help of the TFHE solution that excels in bit operations and bootstrap encryption, FHE technology deployment is implemented in AI reasoning scenarios that require fast processing and precise control.
For machine learning scenarios that require real number approximation operations, Mind simultaneously uses the HEAAN library based on CKKS; for general computing needs, OpenFHE is used in parallel to build the most optimized FHE tool chain according to the computing environment and goals. This multi-library parallel strategy not only achieves technology integration, but also demonstrates excellent scalability and practicality by flexibly responding to users' actual computing needs. Mind is working with industry-leading partners to accelerate the commercialization of FHE technology, and this practice is of great significance.
Mind Network does not simply use the FHE solution as an independent computing module, but deeply integrates it into all levels of the system architecture. Based on the self-developed HTTPZ communication protocol, it realizes encryption processing of the entire process of data storage, transmission, and computing, and is currently developing a series of privacy AI product matrix based on this.
For example, AgenticWorld allows users to create AI agents and perform various calculations by staking tokens; FHE Bridge enables fully encrypted transmission of assets and data between chains; and MindX, an AI assistant that uses FHE encryption technology to securely manage user conversation content and setting information.
Of particular note is that these systems allow users to fully control their own data when interacting with AI, and this environment that protects digital sovereignty is conceptualized as "digital citizenship (CitizenZ)." In other words, Mind provides a new type of participation architecture that protects personal privacy and sovereignty for a zero-trust-based digital society.
Mind Network innovatively integrates the technical features of FHE with the blockchain business model, and designs a complete system including a re-staking architecture, an encryption verification process for consensus participants on the chain, and an open contribution reward mechanism, so that FHE can actually operate in a public chain environment. For example, through cooperation with Phala Network, a governance system combining TEE and FHE was built. Under the premise of ensuring the absolute privacy of individual voting records, only the overall statistical results are disclosed to the chain, achieving the dual protection of privacy protection and system transparency.
The network also designed the FHE Bridge solution, which enables large transactions between institutions to hide transaction content and addresses while meeting legal regulatory requirements and achieving compliant privacy transaction processing.
Through the Mind platform, FHE is evolving from a simple encryption technology to a core infrastructure technology in the era of AI and Web3 integration. If ZK is "technology that can be proved without showing", then FHE is "technology that can be executed without seeing" - it realizes a new paradigm of privacy protection that can complete calculations and applications without touching the original data.
Especially in a society where AI is gradually replacing more human judgment, the ability to output credible results while performing calculations in a fully encrypted state lays the foundation for reconstructing the human-computer interaction model.
Just as ZK technology has gone through a long process from concept to practical application, FHE also faces technical challenges such as high computational complexity and performance bottlenecks. To achieve deployment in large-scale AI applications that require sub-second real-time response, it is necessary to advance technical breakthroughs in multiple aspects such as computational optimization, hardware acceleration, and standardized tool chains in parallel. These are not challenging problems that can be easily solved in the short term.
Nevertheless, FHE has gone beyond the scope of simple technology because of its dual breakthroughs in achieving complete privacy protection and trusted computing at the same time. It is regarded as a transformative technology with the potential to reshape the norms and infrastructure of the AI era and has attracted much attention.
3. Three Mind Engines Driving a Decentralized AI Society
Mind Network is not only a concept, but also accelerates the realization of a decentralized AI society by implementing FHE technology into actual products. Its core relies on three engines: the first is AgenticWorld, an autonomous AI environment that supports users to create and operate AI agents; the second is MindX, a conversational AI assistant based on complete privacy protection; and the third is FHE Bridge, which realizes cross-chain data and asset encrypted transmission.
These three engines, each performing unique functions while interconnected, together form the foundation of the privacy-centric AI economy that Mind is committed to building. In addition, the newly launched Mind Chain natively implements these FHE technologies, and its design architecture directly guarantees the credibility and privacy of calculations at the blockchain level. As a result, the system is evolving into the next-generation infrastructure that securely connects Web3 and AI.
3-1. AgenticWorld: An AI economy that enables agents to learn autonomously and receive rewards
AgenticWorld is a decentralized AI platform. Its AI agents are not only tools for performing tasks, but also independent entities that can learn, judge and obtain rewards autonomously. After users activate the agent by staking $FHE, they can allocate computing and collaboration tasks in various hubs, and the agent will receive rewards in real time based on the task results. All calculations are processed based on FHE technology to ensure that privacy is fully protected.
AgenticWorld adopts a multi-hub architecture to continuously expand the space for agents to engage in activities. This design not only supports richer interactions and collaborations between agents, but also builds an ecological structure similar to how humans act and grow in a multi-dimensional environment.
AgenticWorld's vision is not only to provide a space for AI model operation, but also to become the core pillar of the "learning economic system". After the agent has mastered basic skills in the Basic Hub, he will perform more complex tasks in advanced hubs such as DeepSeek and World AI Health Hub to achieve progressive growth. In this process, the agent is no longer a simple consumer object, but an asset that users can invest in and cultivate, which constitutes an essential difference from most Web3-based AI platforms.
In addition, all activity records in the hub are transparently recorded on-chain through smart contracts, and a differentiated reward mechanism is implemented based on the agent's performance, which enables autonomous AI to develop into an ecosystem of actual economic activity subjects.
The practical utility of AgenticWorld has been verified through real use cases. For example, DeepSeek, which once attracted attention as an alternative to ChatGPT, has implemented a full-process encryption processing architecture from query to response by integrating Mind Network's FHE Rust SDK. This attempts to solve the long-standing opacity problem of "not being able to know the basis for model decisions" faced by traditional LLM-based AI, as well as the risk of sensitive information leakage.
Taking the example of a user querying the price of Bitcoin, the relevant requests and responses are processed in an encrypted state throughout the process without being decrypted and recorded directly to the blockchain. This not only eliminates the possibility of external tampering, but also ensures the credibility and transparency of the AI computing process and results. DeepSeek is accelerating the expansion of applications in high-trust fields such as medical care, education, and finance based on this FHE architecture.
Continuing this trend, Mind Network launched the World AI Health Hub, a high-end medical hub focusing on high-trust fields, in the AgenticWorld ecosystem. The hub is designed for privacy-sensitive health data processing. All information is encrypted on the user terminal and ciphertext operations are performed through the FHE framework.
Agents can predict health conditions or build personalized health profiles based on encrypted symptom data, and gradually learn the functions required for actual medical diagnosis and research. World AI Health Hub is effectively expanding the boundaries of privacy protection and AI applications, aiming to verify the possibility of applying sensitive information on the chain.
Mind Network not only continues to optimize independent hubs, but also strives to upgrade the collaborative architecture among multiple agents. Through cooperation with ElizaOS, Virtuals, etc., FHE technology is deeply integrated into the AI framework system to ensure that sensitive data is not exposed during the entire computing process.
Especially in the "swarm" mode where multiple agents operate in parallel, consensus can be reached only through encrypted voting, and trusted collaborative decision-making can be achieved without central control. It is expected to be widely used in Web3 governance, DeFi strategy formulation, AI joint research and other fields. AgenticWorld has achieved remarkable results: more than 111,000 FHE-protected agents have been deployed, more than 2 million active wallets, more than 20 FHE hubs, and more than 80 million encrypted transactions.
However, AgenticWorld still needs to overcome several technical challenges to become a truly autonomous AI economic system. First, it is necessary to continuously optimize the model architecture to improve the learning accuracy and response speed of the intelligent agent, while strengthening the interconnectivity between hubs and building a system design that supports the organic collaboration of intelligent agents. What is particularly critical is that fully homomorphic encryption (FHE) operations naturally have high computational complexity and resource consumption characteristics, which has become the main bottleneck restricting the expansion of actual application scenarios.
To overcome these limitations, it is necessary to introduce algorithm optimization solutions and hardware acceleration technologies that can improve computing efficiency. In addition, reducing the single computing cost of FHE infrastructure and ensuring network scalability will become the decisive factors for future popularization. Therefore, Mind's technology roadmap and partner strategy also need to continue to review and evolve to meet market demand.
3-2. FHE Bridge: A next-generation cross-chain bridging facility with privacy-preserving transactions at its core
FHE Bridge is a new generation of cross-chain infrastructure that can handle asset and data transfers between Ethereum, BNB chain and MindChain in a completely private state. This bridge developed by Mind Network uses a structure that combines fully homomorphic encryption (FHE) and stealth address protocol (SAP) to solve the structural privacy defects of traditional cross-chain bridges, such as transaction tracking and address exposure.
This solution encrypts the entire message transmission process in a quantum-resistant environment, allowing users to transfer assets on any chain with the same level of privacy protection as the FHE native chain.
On top of the basic security architecture, the recent integration with Chainlink CCIP is driving the development of FHE Bridge into a cross-chain data/asset transfer bridge that can be used by financial institutions. Through CCIP integration, the solution can achieve secure value transfer from the central bank digital currency (CBDC) chain to the public chain, as well as the secure sharing and protection of high-frequency sensitive information in the DeFi environment.
In particular, the transmission architecture based on stealth addresses, by concealing all transaction records, meets the dual requirements of regulatory compliance and privacy protection, which is not only suitable for regulated institutions, but also a practical choice for private enterprises with strict security requirements. For users, through the interconnection with AgenticWorld, the scope of activities and asset operation flexibility of AI agents will be greatly expanded; for enterprises/institutions, this provides an adoptable core infrastructure possibility for privacy-preserving data transmission and cross-chain business automation.
In addition to privacy functions, FHE Bridge is gradually meeting the diverse needs of various enterprises/institutions by expanding application scenarios. Recently, Mind Network deployed the FHE privacy layer for Circle's USDC Cross-Chain Transfer Protocol (CCTP), realizing a technical architecture for processing wallet addresses and transfer amount information in a fully encrypted state.
The solution uses Chainlink CCIP to achieve secure transmission of encrypted information while retaining Circle's original infrastructure and CCTP framework. Institutional users can conduct multi-chain USDC transfers under privacy protection. This function currently covers mainstream public chain networks such as Ethereum, Arbitrum, and Polygon.
However, FHE Bridge still needs to break through several bottlenecks to truly become an institutional-level infrastructure, such as improving transaction processing speed, reducing the cost of generating stealth addresses, and formulating compliance plans to deal with complete transaction invisibility. In particular, when it comes to on-chain operations of sensitive financial assets such as CBDC or RWA, a compliance framework that takes into account both privacy protection and transparent supervision must be established.
Despite this, FHE Bridge is still seen as a breakthrough solution that uses the latest encryption technology to solve the long-standing problem of "cross-chain interconnection".
3-3. MindX: An AI assistant based on FHE technology that fully protects sensitive interactions
MindX is a conversational AI platform built with cutting-edge fully homomorphic encryption (FHE) technology, which fully protects user conversations and data while ensuring complete privacy. Unlike traditional chatbot services that store conversation records and personal information in plain text on a central server, MindX encrypts all conversation content, usage records, personal settings and other data into a form that cannot be decrypted without the user's private key.
Its revolutionary nature lies in the mechanism that users independently encrypt data and AI operates in a ciphertext state, fundamentally eliminating the possibility of any third party, including service providers, accessing the original data, thus forming an essential difference from existing AI services.
Although MindX is designed based on Web3, it has carefully crafted a user experience that makes it easy for traditional Web2 users to adapt. Users can access the MindX platform in a familiar Web2 way by linking their email accounts and wallets, and can naturally transition to the Web3 environment without crypto-native knowledge. This architecture maintains the core values of Web3 privacy protection and digital asset ownership without compromising user experience.
In terms of functions, MindX is going beyond the scope of simple security and focusing on enhancing practicality and scalability. Through the "context persistence interface", it has begun to support short-term/long-term memory functions, and provides customized feedback to users, making it evolve from a simple chatbot to a personal AI assistant that can maintain long-term relationships.
In addition, the upcoming "BYOD (Bring Your Own Data)" feature allows users to securely connect personal data to personalize or customize AI responses, while the "Prompt Word Market" is planned to expand into a platform for community members to share and trade high-quality prompt words.
However, MindX still needs to overcome several key issues to achieve full adoption and establish itself as an autonomous AI platform. In particular, the expansion of the user base and the verification of the actual effectiveness of intelligent agents are core challenges. Given that community-driven features such as BYOD (bring your own device) and the prompt word market are still in the development stage, it is necessary to simultaneously promote refined UX design and continuous user engagement strategies.
In addition, in order to maintain the privacy protection characteristics of FHE computing while ensuring that AI response speed and interaction quality are above the baseline, it is necessary to promote model lightweighting, computing optimization and strategic technical cooperation in parallel. If these conditions can be gradually met, MindX is expected to become a new standard for privacy-first AI services and challenge as a next-generation interactive interface representing the Web3 era.
4. Privacy-oriented AI economic architecture based on $FHE and autonomous chain
Mind Network was initially launched on the BNB chain, but there are limitations in ensuring the complete privacy of on-chain AI agents when running. This is due to the extremely high demand for computing power for fully homomorphic encryption (FHE) calculations, which conflicts with the existing L1 chain architecture at the technical and cost levels. To this end, Mind has specially designed a self-developed EVM-compatible chain "MindChain" that optimizes FHE calculations.
Like the application chain, this chain is independently built for the specific goal of AI and computing, becoming a dedicated infrastructure that takes into account both AI computing and privacy protection.
The core pillar of the Mind Network ecosystem is the $FHE token. $FHE is designed as a utility token that runs through the Mind Network governance function, AI agent execution fuel, calculation result reward settlement, and the entire token economic system. Users can activate AgenticWorld's AI agents by staking $FHE and obtain $FHE rewards in real time based on the calculations and tasks performed by these agents in each hub.
Ultimately, $FHE plays the role of a core asset in the privacy-first AI economy by building a circular system of "AI computing → data protection → reward distribution → governance participation".
$FHE first entered the market through the Token Generation Event (TGE) based on the strategic cooperation between Binance Wallet and PancakeSwap on April 10, 2025. After the end of TGE, $FHE showed a short-term surge, but due to the relatively high initial circulation ratio of 24.9%, cash selling emerged in advance, causing the price to experience a correction.
Despite this, 41.7% of the total supply is still allocated to community rewards and long-term airdrops. The team and early investors' shares also follow the conditions of 12-month lock-up and 48-month batch unlocking. The medium- and long-term circulation stability has been fully designed.
As of June 4, 2025, although $FHE has been launched on Binance Futures and some mainstream trading platforms (such as Binance Alpha), some centralized exchanges (CEX) including Binance have not yet completed the listing. About 80% of the trading volume still occurs on PancakeSwap V3. While maintaining the current DeFi liquidity strategy, Mind is seeking a diversified layout of liquidity foundation through cross-chain expansion to multiple chains such as Ethereum.
This move aims to break the structural limitations of a single chain and enhance the practical application possibility and accessibility of $FHE by expanding service touchpoints and connecting with users across multiple chains and exchanges.
Whether the FHE token economy can succeed is still in the early stages of TGE. It is too early to evaluate now. The key lies in whether substantial demand inflow can be achieved. At present, some use cases have been put into operation within AgenticWorld, but there has not been a significant increase in demand that can be frequently observed in reality. To this end, the Mind team is actively expanding practical application channels, including expanding the hub network, promoting cross-chain collaboration, and introducing Agent-as-a-Service models. At the same time, it is reported that it is also consolidating the circulation infrastructure through CEX listing.
The circulation structure of FHE has been carefully designed, and the future challenge is whether the practical application base can be quickly expanded to promote spontaneous consumption within the ecosystem. In particular, when the task economy in AgenticWorld is fully launched and begins to actually consume $FHE, the growth of circulation and the expansion of demand will form a virtuous circle, and it is expected that there will be a positive change in both price and demand.
5. Conclusion: Mind will set a new standard for "trusted AI"
Mind Network has brought fully homomorphic encryption (FHE) technology to a practical level, providing a new technical reconciliation possibility for resolving the conflicting needs of AI computing and privacy protection. Its core products, AgenticWorld, MindX, and FHE Bridge, all have clear application scenarios and functional characteristics, and have entered the actual service implementation stage.
Cooperation cases with projects such as DeepSeek, Swarms, and Allora also show that the technology is connecting with diversified real-world needs, proving that Mind has gained a considerable degree of recognition of its technical strength and realization possibilities in the industry.
However, in order to continue to expand the Mind Network ecosystem, several core issues still need to be resolved. First, the high computing cost and low processing speed that are inevitably caused by the characteristics of FHE technology are still bottlenecks for the promotion of practical applications. How to minimize resource investment while ensuring complete privacy protection will determine the actual performance of this technology. Secondly, to cover general users and corporate environments beyond Web3 users, the key is to design an intuitive UX interface that can hide the complex technical architecture.
It is crucial that users can use the experience directly without having to understand Web3 or crypto technology.
Despite this, Mind Network seems to be the most substantial "privacy-first AI infrastructure" construction project at present. FHE-based computing not only solves the credibility and accuracy issues of AI, but also provides architectural support for the privacy needs of future enterprises and institutions. Under the trend of increasingly strengthened global privacy supervision, the technical direction proposed by Mind can not only serve the Web3 ecosystem, but its solutions also show sufficient scalability and substitutability in the Web2 environment.
“Trustworthy AI” is now a technologically achievable reality, and Mind promises to be the project closest to that turning point.