Overview and Vision of the MindNetwork Project
In the wave of rapid development of artificial intelligence, AI Agents have become the focus due to their autonomous decision-making and collaboration capabilities. However, the accompanying data privacy and security issues have become increasingly prominent: only autonomous AI built on a secure foundation is trustworthy. MindNetwork was established in 2022 against this backdrop, with the core vision of creating a 'trust operating system' that supports the secure operation of intelligent agents. The team brings together experts from cryptography, blockchain, and artificial intelligence, aiming to solve the issues of data sovereignty and trust in the AI era using fully homomorphic encryption (FHE) technology.
As the industry's first project to apply FHE to AI infrastructure, MindNetwork has received incubation and investment from Binance Labs, completing a $2.5 million seed round (2023) and a $10 million Pre-A round (2024) shortly after its establishment, with investors including renowned institutions such as Animoca Brands. The project has also been selected for Binance Labs' fifth season incubation program and Chainlink BUILD program, and has engaged in technical collaborations with Zama, Chainlink, etc., successively releasing innovative outcomes such as the HTTPZ protocol and MindV Hubs. MindNetwork officially launched its mainnet (MindChain) in November 2024 and completed its token TGE in April 2025, with subscriptions on the PancakeSwap platform being extremely enthusiastic (over 174 times oversubscribed). This series of developments reflects the community's enthusiastic participation and the industry's high attention to its vision.
MindNetwork's vision is to lead Web3 into a new era of quantum resistance (against quantum attacks) and end-to-end encryption. It aims to build a secure and efficient network platform based on FHE, providing solutions for data sovereignty protection, fair consensus, private voting, secure cross-chain transmission, and trustworthy AI applications. In simple terms, MindNetwork hopes to make **'data calculable while encrypted'** a reality, fundamentally solving the trust issues faced by AI Agents. Through this trust operating system, humans and AI will be able to coexist and collaborate in a trustworthy environment, paving the way for large-scale AI applications.
The technical background and principles of Fully Homomorphic Encryption (FHE)
Fully Homomorphic Encryption (FHE) is hailed as one of the holy grail technologies in cryptography, distinguished by its ability to perform direct computations on ciphertext without needing to decrypt during the process. This means that data remains encrypted throughout, and the results of computations are also encrypted, with only authorized private key holders able to ultimately decrypt and obtain plaintext. Under traditional encryption techniques, processing data typically requires decrypting before computation, which introduces significant security risks during the conversion from ciphertext to plaintext. FHE cleverly allows operations such as addition and multiplication to be performed on ciphertext while ensuring that these operation results correspond to the correct outputs of the original plaintext calculations.
The basic workflow of FHE includes three stages:
• Encryption: Data providers use specific algorithms and public keys to encrypt plaintext into ciphertext.
• Computation: The computation party performs allowed operations (such as addition, multiplication) directly on the ciphertext without decrypting. The homomorphic properties ensure that the results of the ciphertext operations correspond to the correct plaintext operation results.
• Decryption: Only the recipient holding the private key can decrypt the computed ciphertext results back to plaintext.
Through the above mechanism, FHE achieves **'calculating ciphertext directly to obtain ciphertext results'**, which is particularly valuable in multi-party data collaboration scenarios—each participating party can jointly complete meaningful calculations without exposing their original data, and only authorized parties can decrypt and view the results. For example, in joint medical analysis, different hospitals can submit encrypted patient data and perform statistical analyses in the cloud, with the results also being encrypted. Only those authorized to view the results can decrypt and see the analysis conclusions. Throughout this entire process, patient privacy is always protected.
It is noteworthy that FHE differs from other popular privacy computing technologies such as zero-knowledge proofs (ZK) and multi-party secure computation (MPC), each with its focus:
• ZK: Proves that a certain fact is true without revealing specific information, excelling in scenarios such as identity verification and permissions proof. Its advantages include privacy protection but does not involve actual data computation outputs. Its limitations lie in the complexity of protocol design and high mathematical and coding requirements.
• MPC: Supports multiple parties to jointly compute while maintaining the confidentiality of their respective data, applicable for cross-institutional data analysis, joint financial audits, etc. It makes multi-party collaboration safer, but as the number of participants increases, synchronization and communication overhead rise sharply, making protocol execution slow and complex.
• FHE: Allows computations to be conducted entirely under ciphertext, enabling sensitive data to be processed by third parties without concerns about leakage, making it very suitable for cloud computing and AI model training services that require 'loaning' data for computation. Its downside is relatively low computational efficiency; during large-scale complex calculations, ciphertext processing can be time-consuming and resource-intensive. However, with algorithm optimization and hardware acceleration, this issue is gradually improving.
Overall, FHE opens a new path of 'data used without being seen'. In fields such as finance, cloud computing, artificial intelligence, and the Internet of Things, FHE has shown enormous potential, with preliminary application attempts already underway. As related algorithms (such as lattice-based cryptography schemes) continue to be optimized, and industries increase investment in FHE (e.g., launching chips or libraries that support homomorphic computation), fully homomorphic encryption is transitioning from academic theory to practicality, bringing revolutionary impacts on data privacy protection and secure computing architectures.
MindNetwork's FHE applications in data security, AI model protection, and privacy computing
MindNetwork fully applies the above FHE technology in its product architecture, providing innovative solutions for data security, AI model protection, and privacy computing. Specifically, MindNetwork summarizes four core security objectives around the needs of intelligent agents and achieves them through FHE technology:
• Data Security: AI Agents handling sensitive data (such as medical records, financial transactions) leverage the MindNetwork platform to store and compute data always in encrypted form, without exposing the original content. This ensures that even when AI agents call upon external computing power or collaborate with others, user privacy data will not leak. The end-to-end encryption of data achieves true data sovereignty: only data owners or authorized individuals can decrypt and view, eliminating the leakage risks associated with plaintext processing.
• AI Model Protection: With FHE, the inference and training processes of AI models are conducted in an encrypted state, preventing the model itself and intermediate results from being exposed or stolen. This protects the intellectual property and sensitive parameters of AI models. For instance, with MindNetwork's support, AI service providers can safely deploy models in decentralized environments to accept encrypted user queries, thereby protecting model parameters from theft and keeping user inputs confidential, establishing a foundation for trustworthy computation. As a result, both AI model providers and users can interact securely under conditions of **'zero trust'**—models will not be reverse-engineered due to running in external environments, and user privacy will not be compromised when using services.
• Privacy Computing and Collaboration: MindNetwork utilizes FHE to break through the privacy barriers of multi-party collaborative computing, enabling **'each holding ciphertext, collaborating on computation'**. In traditional cases, different organizations or individuals' AI systems find it difficult to cooperate directly due to the trust gap in data sharing. However, in MindNetwork, whether among multiple AI Agents or across organizational AI systems, they can securely exchange information and compute together without exposing their respective data. For instance, an intelligent agent from Exchange A and an intelligent agent from Exchange B can collaboratively complete risk control tasks without disclosing their trading data—FHE ensures that they always exchange ciphertext while obtaining the desired statistical results. This encrypted collaborative capability provides new possibilities for privacy computing, enabling many collaborative analyses that were previously unfeasible due to privacy concerns.
• Communication and Consensus Security: In the intelligent agent network, Agents need to communicate frequently and reach a consensus. MindNetwork achieves end-to-end secure communication through FHE and zero-trust encryption protocols, preventing intermediate information from being tampered with or eavesdropped. Additionally, MindNetwork introduces an encrypted consensus mechanism, allowing multiple AI Agents to vote or verify based on encrypted data during collaborative decision-making. This ensures that even in a decentralized environment, consistency among agents remains trustworthy and results cannot be maliciously manipulated. This is particularly important in scenarios requiring multiple AI to make joint decisions (such as in autonomous driving, where perception, decision-making, and control agents must synchronize in response to emergencies): encrypted consensus can ensure that decisions made by each agent are quickly aligned while protecting privacy.
Through the above measures, MindNetwork has built an entirely encrypted AI operating environment. Whether it is data input and output, secure storage, model operation mechanisms, or multi-agent interaction and collaboration, the entire process is protected under FHE. Notably, MindNetwork, in collaboration with the well-known homomorphic encryption company Zama, launched the next-generation network communication protocol HTTPZ, expanding this end-to-end encryption concept to a broader area of internet data transmission. Compared to traditional HTTP/HTTPS, HTTPZ achieves encryption of data throughout its lifecycle during transmission, storage, and computation, employing a zero-trust architecture to eliminate reliance on centralized intermediaries. This innovation not only serves communication within the AI Agent network but also provides a new paradigm for secure communication in the entire Web3 ecosystem. Overall, MindNetwork incorporates FHE throughout every aspect of the AI system, providing unprecedented guarantees for AI data security and privacy computing.
The impact of FHE on existing AI infrastructure (data availability, scalability, security)
The introduction of FHE technology has profoundly impacted the existing AI infrastructure, which can be understood from three aspects: data availability, scalability, and security.
• Significant Improvement in Data Availability: For a long time, 'data privacy' and 'data utilization' have been a contradiction. Many high-value data cannot be fully utilized by AI models due to privacy or regulatory restrictions, leading to the widespread phenomenon of data islands. FHE breaks this constraint—it allows sensitive data to participate in computation without revealing its contents. For AI infrastructure, this means being able to access more types of data sources, enhancing the breadth and depth of model training and inference. For example, medical AI can train models using patient data from different hospitals while complying with privacy regulations, and financial AI can obtain joint statistical trends without touching plaintext customer information. The characteristic of data being usable while encrypted greatly expands the data boundaries for AI, enhancing AI systems' adaptability to real-world problems.
• Evolution of Scalability: In traditional AI architectures, data and computation are often confined to trusted local or cloud environments to prevent leakage risks. This constraint limits the flexibility and scalability of AI deployment. With FHE, AI computation can be more confidently extended to less-than-fully-trusted environments, such as multi-clouds and multi-edge nodes managed by different organizations. For example, different companies or individuals can contribute computing power to participate in the joint training of AI models without completely trusting each other, as all intermediate data is encrypted. This provides a new idea for the elastic expansion of AI computing power—a truly decentralized, globally collaborative AI infrastructure becomes possible. However, the computational overhead of FHE remains a significant challenge. Ciphertext computation is much slower than plaintext and the volume of ciphertext data is larger, which is a bottleneck for real-time requirements or large-scale data processing. Therefore, in the short term, AI infrastructures need to gradually reduce the performance loss of FHE through algorithm optimization and hardware acceleration. Fortunately, projects like MindNetwork are already optimizing FHE algorithms (such as being the first to implement Zama's TFHE-rs v1.0 library optimization), and the industry is also developing dedicated chips to rapidly improve the efficiency of homomorphic computation. Looking ahead, with these improvements, FHE is expected to significantly narrow the speed gap with traditional computation while ensuring security, clearing obstacles for large-scale deployment of AI.
• Security Transformation: The enhancement of security is arguably the most intuitive and profound impact of FHE on AI infrastructure. First, the encryption of data throughout greatly strengthens the AI system's ability to resist external attacks—even if hackers breach the servers or nodes, what they obtain is incomprehensible ciphertext. This is crucial for preventing data breaches and maintaining user trust. Secondly, FHE brings the concept of a zero-trust architecture, wherein various components within the system can work collaboratively without needing mutual trust, as trust is replaced by mathematical algorithms. Any node only needs to process or verify encrypted data according to the rules without needing to know the actual content of the data, significantly reducing the risk of security incidents caused by internal malfeasance or human error. Furthermore, as mainstream fully homomorphic encryption schemes are based on lattice cryptography and other quantum-resistant algorithms, FHE also endows AI infrastructure with quantum resistance, proactively addressing threats that may arise from future quantum computers. This means that from a long-term perspective, AI systems adopting FHE possess a longer-lasting security viability. In summary, FHE elevates the security protection level of AI infrastructure to a new height—transitioning from the traditional approach of 'trying not to be breached' to 'even if breached, it is of no consequence'. This paradigm shift provides a more solid foundation for the future development of AI.
Practical application scenarios: Web3, DePIN, AI data markets, etc.
With the aforementioned advantages, fully homomorphic encryption is injecting vitality into many emerging scenarios, and MindNetwork has already begun practices in several typical fields, demonstrating the potential of FHE to reshape AI application models:
• Web3 Privacy Applications: Introducing FHE into blockchain and decentralized applications can address many existing pain points. For example, in DAO governance, voting needs to be confidential while publicly counting results; traditional solutions often rely on intermediaries to summarize or cryptographic commitments. With MindNetwork's FHE, private voting can truly be realized: each ballot is encrypted and uploaded, smart contracts directly count the votes on ciphertext, and the final results are decrypted and made public, with no one aware of the contents of individual votes. Similarly, in cross-chain data transmission, smart contracts or AI Agents on different chains wishing to interact, but distrustful of each other's data sources, can always encrypt requests and responses through the HTTPZ zero-trust protocol provided by MindNetwork, ensuring data privacy when sharing information across chains. Additionally, the Web3 community is exploring the integration of AI into fields such as DeFi and NFTs, for instance, automated trading bots and metaverse AI assistants. In these scenarios, MindNetwork provides a privacy-protected operating base for AI Agents. On Binance's BNB Chain, MindNetwork has built the AgenticWorld platform, a multi-agent economic system that integrates staking, training, and collaboration. Users can create their own AI Agents by staking MindNetwork's token $FHE and allow them to train and grow in an encrypted environment, subsequently participating in complex tasks to earn rewards. Throughout this process, the decision consensus, task execution, and revenue distribution of intelligent agents are guaranteed by blockchain, while privacy-sensitive data exchanges are protected by FHE. This new combination model of Web3 + AI demonstrates the prototype of future decentralized intelligent applications.
• DePIN Decentralized Physical Infrastructure Network: DePIN refers to utilizing blockchain incentives to mobilize distributed physical resources (communication nodes, computing hardware, etc.) to form service networks. For example, decentralized cloud computing, edge computing networks allow global individuals or institutions to contribute idle computing power for AI use. However, the key issue is: how to establish trust between the party providing computing power and the party using data. MindNetwork provides the answer: through FHE, computing power providers can perform computations on others' encrypted AI tasks, completing the workload while not accessing the original data results, with clear mutual benefits and trust foundations secured by encryption. This is precisely the starting point for the collaboration between io.net and MindNetwork—io.net, as a distributed computing platform, introduces MindNetwork's FHE solution to enhance the security of its services, ensuring that globally distributed GPUs do not steal or leak data even when computing sensitive AI tasks. This combination not only protects task data but also enhances the utilization efficiency for computing power providers (especially important given the global GPU shortage). More broadly, in scenarios of IoT data processing, edge devices collecting large amounts of personal or environmental data can be remotely analyzed without reading the plaintext of the devices, thus building applications like smart cities and intelligent industries while respecting personal privacy and data sovereignty. These practices demonstrate that FHE injects trustworthy computing genes into DePIN, enabling decentralized entity networks to undertake more complex and sensitive AI tasks, achieving true wide-area collaboration without concerns.
• AI Data Markets and Cross-Organizational Collaboration: Data is regarded as the oil of the new century, but how to unleash the value of data while protecting privacy has always been a challenge. FHE provides a feasible path for constructing AI data markets. In an imagined scenario, different data holders (hospitals, banks, individual users, etc.) can encrypt their data and place it in the market, where AI developers or model trainers can purchase this encrypted data for model training or analysis without needing to access plaintext. Ultimately, once the model training is complete, since computations are always performed in the ciphertext domain, the privacy of the data providers is not violated, and the model owners only obtain training results without being able to reverse-engineer the original data. This 'data available but not visible' market mechanism will greatly promote data circulation and sharing. For example, in medical research, multiple pharmaceutical companies and hospitals can build an encrypted data alliance on MindNetwork, sharing clinical trial and patient data to train AI models for drug efficacy. Each participant encrypts their data, and the MindNetwork platform aggregates training in the ciphertext domain, ensuring that only the final model is usable, while all original data remains completely confidential. In the financial risk control field, different banks can jointly train fraud detection AI, but their customer data remains undisclosed to each other, making the resulting model effective for all participants. These are all early forms of AI data markets, made feasible by FHE technology. Additionally, in AI model trading, FHE holds great potential. Model owners can utilize MindNetwork to host models in an encrypted environment, allowing clients to submit encrypted inputs to call the model and receive results, while the model parameters themselves remain encrypted, protecting the model's intellectual property while providing model services to clients. This effectively creates a new business model of **'model as a service'**: a trust bridge through encrypted computation between buyers and sellers. In these scenarios, MindNetwork plays a foundational supporting role, providing a platform for the secure computation and privacy protection of data and model transactions, opening up a new paradigm for the AI economy.
In summary, whether in decentralized intelligent applications in Web3, physical network collaboration in DePIN, or secure trading markets for AI data/models, FHE technology showcases the power to reshape traditional paradigms. MindNetwork is translating this power into reality through a series of practical cooperation cases: from healthcare and finance to cross-chain and IoT, a trustworthy AI ecosystem is gradually taking shape. The successful implementation of these application scenarios also lays a solid foundation for the future vision of AI—a new era is approaching where data flows freely and privately, intelligence is ubiquitous, and security is trustworthy.
MindNetwork's technical roadmap and development potential
As the world's first FHE project to go live on the mainnet, MindNetwork has clear plans and enormous potential in its technological roadmap and ecosystem development:
• Technical Iteration and Mainnet Evolution: As previously mentioned, MindNetwork has launched the MindChain mainnet specifically designed for AI Agents by the end of 2024. MindChain can be seen as the underlying foundation of the AgenticWorld intelligent entity world, providing a cryptographic computing environment that supports large-scale intelligent agents. In April 2025, MindNetwork completed its token generation event (TGE) and introduced the market through platforms such as PancakeSwap, allowing more people to participate in the network ecosystem. To promote multi-chain integration, MindNetwork has launched an official bridging tool, supporting the cross-chain circulation of the $FHE token between Ethereum, BNB Chain, and MindChain. This multi-chain strategy enables MindNetwork to interact with mainstream public chain ecosystems and attracts users from various chains to join its privacy computing network. Meanwhile, the experimental results of AgenticWorld on the BNB Chain will gradually migrate and expand to the native MindChain network. In the future, as MindChain continues to improve, its performance and functionality will be optimized for AI scenarios, such as enhancing ciphertext transaction throughput, reducing latency, and supporting more types of homomorphic computing operations. This will create conditions for more complex AI applications to run directly on MindChain.
• Ecosystem Building and Cooperation Expansion: MindNetwork understands that it cannot rely solely on itself to pave a complete ecological landscape; therefore, it places great importance on cooperation with external partners. Over the past year, the project has established strategic partnerships with multiple AI and blockchain teams, such as integrating the Rust FHE SDK with DeepSeek to ensure the secure inference of open-source models; collaborating with Swarms to develop Swarms-rust to enhance the concurrency performance and secure consensus of multi-agent systems; partnering with InfStones and ZAMA to create the World AI Health Center in the medical field for cross-hospital AI collaborative diagnosis; and integrating with the Phala Network in the Polkadot ecosystem to merge trusted execution environments (TEE) with FHE, developing a new generation of secure AI Agent solutions. These collaborations not only validate the usability of MindNetwork technology in different scenarios but also bring valuable users and data resources to its ecosystem. In the future, MindNetwork plans to further expand its partnership landscape, such as accessing more decentralized storage networks to enrich data sources and collaborating with more AI startups to develop encrypted AI solutions in vertical fields. With the addition of these application partners, MindNetwork will form a thriving privacy computing + AI ecosystem, attracting developers to contribute code and nodes to its network, while drawing data providers and demanders to trade on its platform. This network effect will enhance the project's vitality and competitiveness.
• Technical Potential and Outlook: In the long run, MindNetwork targets a trillion-dollar AI secure computing market. As AI applications touch key areas related to privacy and security, such as finance, healthcare, and government affairs, the demand for AI infrastructure with end-to-end encryption capabilities is expected to grow exponentially. As a pioneer in this field, MindNetwork possesses valuable first-mover advantages and technological accumulation. On one hand, its continuous investment in optimizing FHE algorithms (such as being the first to implement the new generation TFHE algorithm and contribute open-source code) will ensure its technological performance remains leading; on the other hand, the concepts it advocates, such as the HTTPZ zero-trust internet and MindV Hubs, are expected to become industry standards and be widely adopted. It is foreseeable that if MindNetwork continues to steadily execute its roadmap and expand its ecosystem, it has the potential to become a provider of infrastructure for the 'trustworthy AI era', just as blockchain is for encrypted finance and HTTP/HTTPS is for internet communication. Notably, breakthroughs may also occur in the field of fully homomorphic encryption itself, such as new mathematical schemes significantly enhancing computational efficiency, the proliferation of hardware acceleration cards, and increased understanding of FHE in academia and industry. All these external factors will further expand MindNetwork's applicability. Although there are still some performance bottlenecks and challenges in market education that FHE technology needs to overcome, 'opportunity and challenge coexist' is the norm in the field of frontier technology. MindNetwork has already demonstrated its determination and capability to build infrastructure on solid ground, and we have reason to believe that with technological innovation and ecosystem improvement, MindNetwork will unleash greater energy in the future, driving the evolution of the entire AI Agent industry. Standing at the threshold of a new era of trustworthy AI, MindNetwork has laid a solid launching pad, and its development prospects are highly anticipated.
MindNetwork's Unique Advantages in the Market Competitive Landscape
In the intersection track of privacy computing and artificial intelligence, competitors include various zero-knowledge projects, federated learning platforms, and even solutions from traditional tech giants. However, due to its technological innovation and first-mover layout, MindNetwork has formed a series of unique advantages that distinguish it in the current market competition.
• Pioneering Technical Path: MindNetwork is the world's first project to use fully homomorphic encryption in blockchain consensus management and AI integration, setting an industry precedent. It has successfully implemented FHE on the mainnet and large-scale encrypted collaboration of intelligent agents, while competitors remain at the proof-of-concept or partial application stage. This first-mover advantage means it possesses valuable practical experience and market perception, occupying a leading position in the FHE + AI track.
• Generational Upgrade of Data Security: Compared to other AI privacy solutions that require 'plaintext' at certain stages, MindNetwork provides the most thorough end-to-end encryption solution currently available. In the consensus or collaboration processes of traditional AI systems, data transmission and computation often require decryption, posing leakage risks; whereas MindNetwork allows data to participate in processes in ciphertext form from start to finish, significantly reducing the possibility of sensitive information leakage. This advantage is particularly evident in scenarios such as financial transactions and medical diagnoses, where data privacy requirements are extremely high—MindNetwork can provide a level of security that competitors find hard to match.
• Large-scale Collaboration Efficiency: Many projects sacrifice efficiency when involving multiple parties, for example, multi-party secure computation (MPC) experiences significant speed declines as nodes increase. MindNetwork combines blockchain and homomorphic encryption to design an efficient multi-Agent collaboration mechanism. For example, in applications like smart city traffic management involving a massive number of nodes, MindNetwork relies on FHE to achieve rapid consensus verification on encrypted data, theoretically supporting higher concurrency and scalability than traditional consensus algorithms. Although FHE computation incurs higher costs per operation, through parallel architecture and consensus optimization, MindNetwork demonstrates the potential for both performance and scale, making it likely to meet the grand collaborative demands of future AI networks.
• Innovation of Trust Mechanisms: In cross-organizational and cross-domain AI collaboration, traditional trust-building often relies on central institutions, identity authentication, or long-term reputation, which is both inefficient and fragile. MindNetwork introduces new mechanisms of zero trust and encrypted verification: network nodes do not need to know each other's real identities or data content, they only need to verify encrypted proofs to cooperate. This method effectively avoids the space for human forgery. For example, in supply chain finance scenarios, different companies' AIs can jointly assess risks without exchanging trade secrets, as all parties only trust the mathematical guarantees provided by MindNetwork. This shift in the trust paradigm is a major highlight that distinguishes MindNetwork from its competitors, providing a feasible solution to the long-standing cross-entity collaboration challenges in the industry.
• Flexible and Scalable Architecture: MindNetwork has constructed a diverse and modular distributed node system, including high-performance mainnet nodes and supporting various levels of Agent Hubs. This architectural design allows it to scale deployment on demand in fields such as DePIN and AI Agents. Whether deployed in powerful data centers or expanded to edge devices or mobile terminals, MindNetwork's tech stack has corresponding solutions. This flexibility enables it to adjust strategies according to different application scenarios, occupying more entry points. In contrast, many competing projects are either limited to on-chain contracts or a fixed architecture, exhibiting weaker adaptability. MindNetwork possesses a broader space for development.
• Comprehensive Incentive Mechanism: A healthy blockchain network relies on well-designed economic incentives. MindNetwork showcases advantages in this area as well: its native token $FHE serves both as the network's pricing and payment method and as a deposit for consensus participation; through multiple mechanisms such as block rewards, transaction fee sharing, and honor rewards for active contributors, it encourages nodes to actively participate and perform their duties honestly. Especially in the AgenticWorld ecosystem, users can create and train agents by staking $FHE, earning rewards while contributing secure computing power to the network, forming a virtuous cycle. This design that integrates economics and technology is relatively rare among competitors, as most AI privacy projects remain at the technical implementation level with insufficient consideration of economic systems. MindNetwork's early layout of its economic model undoubtedly contributes to the prosperity and long-term stable development of its ecosystem.
In addition to the above points, MindNetwork's unique advantages also lie in its rich research and development achievements and practical landing experience. The project has open-sourced over 40 FHE-related SDK modules and built more than 20 agent centers for developers and enterprises. More importantly, even before 'AI Agents' became a buzzword, MindNetwork's FHE technology had already protected over 3,000 intelligent agents in production environments. This includes collaborations with projects like Phala Network that provide trusted hardware environments, running thousands of real business agents on encrypted networks. This achievement is unmatched by many paper-based startup competitors and proves the reliability and maturity of MindNetwork's technology. Combined with endorsements and support from top institutions like Binance Labs, Ethereum Foundation, and Chainlink, MindNetwork also possesses strong advantages in resources and ecological connections.
In summary, MindNetwork, centered on fully homomorphic encryption, has opened a new avenue for AI privacy computing and established a leading position in a short period. The end-to-end encrypted AI infrastructure it has built not only solves the trust pain points faced by today's intelligent agents but also provides a feasible path for the large-scale and secure implementation of AI in the future. As technology evolves and ecosystems expand, we have reason to believe that MindNetwork will play a key role in reshaping the future of AI, driving the entire industry toward a more secure, intelligent, and trustworthy direction.
