Nesa Network is a blockchain platform designed to integrate artificial intelligence (AI) into decentralized environments. Its core function is to enable secure AI execution directly on-chain, eliminating the need for centralized AI processing, often raising concerns about data privacy, trust, and security.
The integration of AI with blockchain is becoming a major area of interest due to the increasing demand for decentralized machine learning models. Traditional AI systems are controlled by centralized entities, making them prone to manipulation, single points of failure, and limited transparency. Nesa aims to solve these issues by creating a blockchain infrastructure that allows AI models to operate securely, efficiently, and in a decentralized manner.
Unlike conventional smart contract platforms focusing on financial transactions and automation, Nesa is specifically built to handle AI inference processes. This allows developers to build AI-powered applications that can run directly on the blockchain, ensuring transparency and reducing reliance on third-party service providers.
Source: Website
Nesa Network is a Layer-1 blockchain platform designed to integrate artificial intelligence (AI) inference directly on-chain. It provides a secure, decentralized, and efficient infrastructure for executing AI models without relying on centralized service providers. The network enables AI applications to operate with improved privacy, security, and transparency, making it suitable for industries that require sensitive data processing.
Unlike traditional blockchain platforms, which primarily handle financial transactions and smart contracts, Nesa is built specifically for AI workloads. This means that instead of just storing AI models or relying on off-chain execution, Nesa allows AI computations to happen natively on the blockchain while preserving user data confidentiality.
The main purpose of Nesa Network is to provide a trustless AI environment where developers, businesses, and individuals can deploy and use AI models without concerns about data security or model integrity. The platform ensures that no single entity controls the AI execution process, reducing the risks associated with centralized AI systems, such as data breaches, biased decision-making, and single points of failure.
Additionally, Nesa Network introduces a token-based economic model that supports executing AI tasks, incentivizes participants, and ensures fair resource allocation within the network. The $NES token is used for transactions, staking, governance, and AI model access, ensuring the sustainability of the ecosystem.
Nesa Network was established in 2023 by a team of experts in artificial intelligence (AI) and blockchain technology. The founding team includes Dr. Harry Yang, Dr. Yue Zhao, Dr. Claudio Angione, and Dr. Marco Di Maggio.
Dr. Harry Yang (Founder)
Dr. Yang previously worked at Facebook AI, where he developed ‘Sora,’ also known as ‘Make-a-Video.’ He contributed to the early development of large language model (LLM) at Meta, specifically for ‘Llama.’ His expertise lies in computer vision and generative AI for video.
Dr. Yue Zhao (Chief Scientist)
An Assistant Professor of AI at the University of Southern California, Dr. Zhao is the creator of PyOD, a widely-used anomaly detection AI tool with over 22 million downloads, utilized by organizations like Tesla and NASA. He has received recognition as an AAAI New Faculty Highlight and has been awarded research grants from Google and Meta.
Dr. Claudio Angione (Chief of Research)
Recipient of the Alan Turing Development Award in AI, Dr. Angione holds a PhD in Computer Science from the University of Cambridge. He has served as a Microsoft Research Scientist and is a Professor of AI at Teesside University.
Dr. Marco Di Maggio
While specific details about Dr. Di Maggio’s role are limited, he is listed among the key personnel involved in the Nesa Network.
The inception of Nesa Network was driven by the founders’ experiences with centralized large language models (LLMs). They observed inconsistent performance and a lack of transparency in these models, which motivated them to develop a decentralized, open, and trustless AI network. This initiative led to the creation of Nesa, aiming to integrate AI functionalities directly into the blockchain to enhance performance tracking, verification, and control.
Source: Documentation
Nesa Network operates as a Layer-1 blockchain platform specifically designed to execute AI inference tasks with high privacy, security, and trust. Its architecture integrates advanced methods to ensure secure and efficient AI computations on-chain.
The AIVM is a unified execution environment that bridges on-chain assets with off-chain computation, enabling trustless inference of large-scale AI models. It provides consistent execution across the Nesa network, addressing challenges related to execution inconsistencies that can hinder consensus on AI inference results.
Nesa employs Equivariant Encryption to perform inference on neural networks without exposing the underlying input and output data to external parties. This method ensures that data remains encrypted throughout the computation process, maintaining confidentiality without introducing significant latency.
Nesa introduces a model-agnostic hybrid sharding approach to manage computational loads efficiently. This technique distributes AI inference tasks across multiple nodes in the network, enhancing scalability and performance.
Nesa integrates both hardware and software-based security protocols. Trusted Execution Environments (TEEs) provide secure zones for data processing, while software-based approaches like Zero-Knowledge Machine Learning (ZKML) and Split Learning (SL) ensure data privacy and model integrity during AI inference.
Developers upload AI models to the Nesa platform, containerized and stored in decentralized storage solutions such as IPFS and Arweave. When a query is submitted, the AIVM processes the request by executing the relevant AI model. Equivariant Encryption ensures that data remains encrypted during processing, maintaining confidentiality.
The network employs consensus-based verification to ensure consistent outputs across nodes. This process confirms the integrity of the model and the accuracy of the inference results before they are recorded on-chain. By integrating these components and processes, Nesa Network provides a decentralized platform for secure and efficient AI inference, addressing challenges associated with centralized AI systems.
Source: Website
Nesa enables AI models to run directly on its blockchain, eliminating the need for centralized servers. This decentralization enhances trust and reduces reliance on single points of control.
Source: Website
The platform employs advanced methods such as zero-knowledge machine learning (ZKML) and split learning (SL) to ensure that data used in AI inference remains confidential. These techniques allow computations without exposing underlying data, maintaining user privacy.
Nesa integrates TEEs to provide secure areas within nodes where sensitive computations occur. This hardware-based security measure ensures data and AI model integrity are maintained during processing.
Nesa introduces a model-agnostic hybrid sharding approach to manage computational loads efficiently. This technique distributes AI inference tasks across multiple nodes, enhancing scalability and performance.
Nesa implements a dynamic pricing model for AI model inference queries. This adaptable fee structure aligns resource allocation with market demand, enabling users to prioritize their requests by paying higher fees. It ensures efficient system utilization and optimizes network throughput by managing the queuing of inference tasks based on variable pricing.
Nesa’s AI Link™ protocol enables interoperability with different blockchain networks, facilitating the transfer of models, data, parameters, and computational tasks across chains. This design allows for seamless integration with various blockchain ecosystems.
Nesa lowers the barrier to entry for participating nodes by accommodating various computational capabilities. Even nodes with limited resources can contribute as validators, democratizing AI inference and making network participation accessible to a broader range of participants.
Investing in $NES, the native token of the Nesa Network, requires a thorough understanding of its utility, market potential, and associated risks. $NES serves as the gas token for all transactions within the Nesa ecosystem, including payments for AI inference queries.
Participants can stake $NES tokens to become miners or validators, securing the network and earning rewards. The token compensates model developers and miners’ operating nodes for AI orchestration.
Nesa’s approach to integrating AI with blockchain technology is innovative, potentially offering a competitive edge in the market. The success of $NES is closely tied to the adoption and growth of the Nesa Network. Increased participation from developers and users can enhance the token’s utility and value.
As with most cryptocurrencies, $NES may experience significant price fluctuations, which can impact investment value. Changes in regulations concerning cryptocurrencies and AI technologies could affect the Nesa Network’s operations and the value of $NES. The platform’s success depends on its ability to execute its roadmap and achieve widespread adoption.
Source: Website
Participating in official token sales or airdrop events is a primary method to obtain $NES tokens. Monitoring Nesa’s official channels for announcements regarding these opportunities is essential.
Users can receive a portion of staking rewards by staking $NES tokens to support the Nesa mining network. This process involves locking up a certain amount of $NES tokens to help secure the network and, in return, earning additional tokens as rewards.
Individuals can set up a miner node on the Nesa Network to contribute to AI model processing and, in return, earn $NES tokens. This requires technical expertise and adherence to the network’s hardware and software requirements. Detailed instructions for deploying a miner node are available in the Nesa miner repository.
Users can purchase tokens directly from these platforms once $NES is listed on cryptocurrency exchanges. Using reputable exchanges and following standard security practices when trading cryptocurrencies is important.
Source: Documentation
The $NES token is integral to the Nesa Network’s ecosystem, serving multiple roles that facilitate the platform’s operations and governance.
Public Allocation (20% total)
R&D and Ecosystem Development: 27.2%
This allocation is designated for research, development, and growth of the ecosystem to ensure long-term sustainability.
Initial Core Contributors: 18.1%
Reserved for the team and early contributors who played a key role in developing the Nesa Network.
Early Backers:
Source: Documentation
Source: Website
The distribution model for $NES tokens is designed to promote the network’s growth and sustainability. A portion of the tokens is allocated to a community pool, which receives 2% of all block rewards. This pool funds ecosystem initiatives, with $NES stakers voting on how these funds are allocated.
Nesa employs a standard gas-price prioritized mempool, where validators prioritize transactions with higher fees. Fees consist of a flat fee per transaction and a variable fee based on the size of each query. This structure ensures that the network remains efficient and resources are used effectively.
The tokenomics of $NES are designed to create a balanced economic model that incentivizes various participants within the ecosystem. Miners must stake $NES tokens, ensuring they have a vested interest in processing AI queries honestly and efficiently. This approach aligns miners’ interests with the overall health and security of the network.
Source: Documentation
As the Nesa platform evolves, its vision for the future includes a strong focus on interoperability and expanded capabilities. Interoperability is essential for the platform to seamlessly integrate with the broader ecosystem of decentralized technologies and address a growing range of use cases. This focus ensures that Nesa remains vital to the decentralized AI and blockchain landscape.
The NESBridge protocol is pivotal in facilitating cross-chain interactions on the Nesa Network. NESBridge enables the transfer of models, data, and computational tasks across different blockchain platforms.
By doing so, it integrates Nesa with other networks and contributes to creating a more robust and powerful decentralized AI ecosystem. This protocol can potentially set industry standards for AI model execution and storage on distributed ledgers as the platform brings in more partner networks.
Layer-2 (L2) connections enhance Nesa’s interoperability by allowing blockchain-based AI services to connect to the network through adapters. L2 platforms post data to Nesa, which validates the data’s format and quality before sending it to the Consensus and Execution Layer for processing. The results are then rolled up for settlement, ensuring efficient communication and seamless execution across systems.
As Nesa moves toward the mainnet, it plans to expand the AI Kernel Market to accommodate a wider range of AI models and inference scenarios. This includes scaling up to handle larger and more complex models, integrating new machine learning frameworks, and incorporating the latest advancements in AI research. These updates aim to provide users with a cutting-edge execution environment that meets diverse requirements.
The AI Kernel’s modular architecture is designed for long-term adaptability. Its flexibility allows components to be added, removed, or upgraded without disrupting the overall system. This ensures that Nesa can quickly adapt to emerging technologies and evolving user needs, fostering sustainability and continuous growth.
Nesa Network is a blockchain platform specifically designed to enable decentralized AI inference. Integrating AI models on-chain provides a secure, efficient, and transparent way to execute AI queries while preserving data privacy. Unlike traditional AI systems that rely on centralized servers, Nesa ensures that AI computations are verifiable, reducing trust dependency on third parties.
The platform’s architecture includes privacy-preserving techniques such as Zero-Knowledge Machine Learning (ZKML), Trusted Execution Environments (TEEs), and Equivariant Encryption (EE) to secure AI inference processes. These features make Nesa suitable for industries where data security and integrity are critical.
The $NES token is central to Nesa’s ecosystem, facilitating transactions, governance, and network security through staking. Tokenomics are structured to incentivize participation while maintaining long-term sustainability. The roadmap focuses on scaling the platform, attracting developers, forming industry partnerships, and expanding its use cases.
Nesa Network is positioned at the intersection of blockchain and AI, two of the fastest-growing technological fields. Its success will depend on adoption, network security, and its ability to attract AI developers. While the project presents potential investment opportunities, it also carries risks related to market volatility and regulatory challenges.
Nesa Network is a blockchain platform designed to integrate artificial intelligence (AI) into decentralized environments. Its core function is to enable secure AI execution directly on-chain, eliminating the need for centralized AI processing, often raising concerns about data privacy, trust, and security.
The integration of AI with blockchain is becoming a major area of interest due to the increasing demand for decentralized machine learning models. Traditional AI systems are controlled by centralized entities, making them prone to manipulation, single points of failure, and limited transparency. Nesa aims to solve these issues by creating a blockchain infrastructure that allows AI models to operate securely, efficiently, and in a decentralized manner.
Unlike conventional smart contract platforms focusing on financial transactions and automation, Nesa is specifically built to handle AI inference processes. This allows developers to build AI-powered applications that can run directly on the blockchain, ensuring transparency and reducing reliance on third-party service providers.
Source: Website
Nesa Network is a Layer-1 blockchain platform designed to integrate artificial intelligence (AI) inference directly on-chain. It provides a secure, decentralized, and efficient infrastructure for executing AI models without relying on centralized service providers. The network enables AI applications to operate with improved privacy, security, and transparency, making it suitable for industries that require sensitive data processing.
Unlike traditional blockchain platforms, which primarily handle financial transactions and smart contracts, Nesa is built specifically for AI workloads. This means that instead of just storing AI models or relying on off-chain execution, Nesa allows AI computations to happen natively on the blockchain while preserving user data confidentiality.
The main purpose of Nesa Network is to provide a trustless AI environment where developers, businesses, and individuals can deploy and use AI models without concerns about data security or model integrity. The platform ensures that no single entity controls the AI execution process, reducing the risks associated with centralized AI systems, such as data breaches, biased decision-making, and single points of failure.
Additionally, Nesa Network introduces a token-based economic model that supports executing AI tasks, incentivizes participants, and ensures fair resource allocation within the network. The $NES token is used for transactions, staking, governance, and AI model access, ensuring the sustainability of the ecosystem.
Nesa Network was established in 2023 by a team of experts in artificial intelligence (AI) and blockchain technology. The founding team includes Dr. Harry Yang, Dr. Yue Zhao, Dr. Claudio Angione, and Dr. Marco Di Maggio.
Dr. Harry Yang (Founder)
Dr. Yang previously worked at Facebook AI, where he developed ‘Sora,’ also known as ‘Make-a-Video.’ He contributed to the early development of large language model (LLM) at Meta, specifically for ‘Llama.’ His expertise lies in computer vision and generative AI for video.
Dr. Yue Zhao (Chief Scientist)
An Assistant Professor of AI at the University of Southern California, Dr. Zhao is the creator of PyOD, a widely-used anomaly detection AI tool with over 22 million downloads, utilized by organizations like Tesla and NASA. He has received recognition as an AAAI New Faculty Highlight and has been awarded research grants from Google and Meta.
Dr. Claudio Angione (Chief of Research)
Recipient of the Alan Turing Development Award in AI, Dr. Angione holds a PhD in Computer Science from the University of Cambridge. He has served as a Microsoft Research Scientist and is a Professor of AI at Teesside University.
Dr. Marco Di Maggio
While specific details about Dr. Di Maggio’s role are limited, he is listed among the key personnel involved in the Nesa Network.
The inception of Nesa Network was driven by the founders’ experiences with centralized large language models (LLMs). They observed inconsistent performance and a lack of transparency in these models, which motivated them to develop a decentralized, open, and trustless AI network. This initiative led to the creation of Nesa, aiming to integrate AI functionalities directly into the blockchain to enhance performance tracking, verification, and control.
Source: Documentation
Nesa Network operates as a Layer-1 blockchain platform specifically designed to execute AI inference tasks with high privacy, security, and trust. Its architecture integrates advanced methods to ensure secure and efficient AI computations on-chain.
The AIVM is a unified execution environment that bridges on-chain assets with off-chain computation, enabling trustless inference of large-scale AI models. It provides consistent execution across the Nesa network, addressing challenges related to execution inconsistencies that can hinder consensus on AI inference results.
Nesa employs Equivariant Encryption to perform inference on neural networks without exposing the underlying input and output data to external parties. This method ensures that data remains encrypted throughout the computation process, maintaining confidentiality without introducing significant latency.
Nesa introduces a model-agnostic hybrid sharding approach to manage computational loads efficiently. This technique distributes AI inference tasks across multiple nodes in the network, enhancing scalability and performance.
Nesa integrates both hardware and software-based security protocols. Trusted Execution Environments (TEEs) provide secure zones for data processing, while software-based approaches like Zero-Knowledge Machine Learning (ZKML) and Split Learning (SL) ensure data privacy and model integrity during AI inference.
Developers upload AI models to the Nesa platform, containerized and stored in decentralized storage solutions such as IPFS and Arweave. When a query is submitted, the AIVM processes the request by executing the relevant AI model. Equivariant Encryption ensures that data remains encrypted during processing, maintaining confidentiality.
The network employs consensus-based verification to ensure consistent outputs across nodes. This process confirms the integrity of the model and the accuracy of the inference results before they are recorded on-chain. By integrating these components and processes, Nesa Network provides a decentralized platform for secure and efficient AI inference, addressing challenges associated with centralized AI systems.
Source: Website
Nesa enables AI models to run directly on its blockchain, eliminating the need for centralized servers. This decentralization enhances trust and reduces reliance on single points of control.
Source: Website
The platform employs advanced methods such as zero-knowledge machine learning (ZKML) and split learning (SL) to ensure that data used in AI inference remains confidential. These techniques allow computations without exposing underlying data, maintaining user privacy.
Nesa integrates TEEs to provide secure areas within nodes where sensitive computations occur. This hardware-based security measure ensures data and AI model integrity are maintained during processing.
Nesa introduces a model-agnostic hybrid sharding approach to manage computational loads efficiently. This technique distributes AI inference tasks across multiple nodes, enhancing scalability and performance.
Nesa implements a dynamic pricing model for AI model inference queries. This adaptable fee structure aligns resource allocation with market demand, enabling users to prioritize their requests by paying higher fees. It ensures efficient system utilization and optimizes network throughput by managing the queuing of inference tasks based on variable pricing.
Nesa’s AI Link™ protocol enables interoperability with different blockchain networks, facilitating the transfer of models, data, parameters, and computational tasks across chains. This design allows for seamless integration with various blockchain ecosystems.
Nesa lowers the barrier to entry for participating nodes by accommodating various computational capabilities. Even nodes with limited resources can contribute as validators, democratizing AI inference and making network participation accessible to a broader range of participants.
Investing in $NES, the native token of the Nesa Network, requires a thorough understanding of its utility, market potential, and associated risks. $NES serves as the gas token for all transactions within the Nesa ecosystem, including payments for AI inference queries.
Participants can stake $NES tokens to become miners or validators, securing the network and earning rewards. The token compensates model developers and miners’ operating nodes for AI orchestration.
Nesa’s approach to integrating AI with blockchain technology is innovative, potentially offering a competitive edge in the market. The success of $NES is closely tied to the adoption and growth of the Nesa Network. Increased participation from developers and users can enhance the token’s utility and value.
As with most cryptocurrencies, $NES may experience significant price fluctuations, which can impact investment value. Changes in regulations concerning cryptocurrencies and AI technologies could affect the Nesa Network’s operations and the value of $NES. The platform’s success depends on its ability to execute its roadmap and achieve widespread adoption.
Source: Website
Participating in official token sales or airdrop events is a primary method to obtain $NES tokens. Monitoring Nesa’s official channels for announcements regarding these opportunities is essential.
Users can receive a portion of staking rewards by staking $NES tokens to support the Nesa mining network. This process involves locking up a certain amount of $NES tokens to help secure the network and, in return, earning additional tokens as rewards.
Individuals can set up a miner node on the Nesa Network to contribute to AI model processing and, in return, earn $NES tokens. This requires technical expertise and adherence to the network’s hardware and software requirements. Detailed instructions for deploying a miner node are available in the Nesa miner repository.
Users can purchase tokens directly from these platforms once $NES is listed on cryptocurrency exchanges. Using reputable exchanges and following standard security practices when trading cryptocurrencies is important.
Source: Documentation
The $NES token is integral to the Nesa Network’s ecosystem, serving multiple roles that facilitate the platform’s operations and governance.
Public Allocation (20% total)
R&D and Ecosystem Development: 27.2%
This allocation is designated for research, development, and growth of the ecosystem to ensure long-term sustainability.
Initial Core Contributors: 18.1%
Reserved for the team and early contributors who played a key role in developing the Nesa Network.
Early Backers:
Source: Documentation
Source: Website
The distribution model for $NES tokens is designed to promote the network’s growth and sustainability. A portion of the tokens is allocated to a community pool, which receives 2% of all block rewards. This pool funds ecosystem initiatives, with $NES stakers voting on how these funds are allocated.
Nesa employs a standard gas-price prioritized mempool, where validators prioritize transactions with higher fees. Fees consist of a flat fee per transaction and a variable fee based on the size of each query. This structure ensures that the network remains efficient and resources are used effectively.
The tokenomics of $NES are designed to create a balanced economic model that incentivizes various participants within the ecosystem. Miners must stake $NES tokens, ensuring they have a vested interest in processing AI queries honestly and efficiently. This approach aligns miners’ interests with the overall health and security of the network.
Source: Documentation
As the Nesa platform evolves, its vision for the future includes a strong focus on interoperability and expanded capabilities. Interoperability is essential for the platform to seamlessly integrate with the broader ecosystem of decentralized technologies and address a growing range of use cases. This focus ensures that Nesa remains vital to the decentralized AI and blockchain landscape.
The NESBridge protocol is pivotal in facilitating cross-chain interactions on the Nesa Network. NESBridge enables the transfer of models, data, and computational tasks across different blockchain platforms.
By doing so, it integrates Nesa with other networks and contributes to creating a more robust and powerful decentralized AI ecosystem. This protocol can potentially set industry standards for AI model execution and storage on distributed ledgers as the platform brings in more partner networks.
Layer-2 (L2) connections enhance Nesa’s interoperability by allowing blockchain-based AI services to connect to the network through adapters. L2 platforms post data to Nesa, which validates the data’s format and quality before sending it to the Consensus and Execution Layer for processing. The results are then rolled up for settlement, ensuring efficient communication and seamless execution across systems.
As Nesa moves toward the mainnet, it plans to expand the AI Kernel Market to accommodate a wider range of AI models and inference scenarios. This includes scaling up to handle larger and more complex models, integrating new machine learning frameworks, and incorporating the latest advancements in AI research. These updates aim to provide users with a cutting-edge execution environment that meets diverse requirements.
The AI Kernel’s modular architecture is designed for long-term adaptability. Its flexibility allows components to be added, removed, or upgraded without disrupting the overall system. This ensures that Nesa can quickly adapt to emerging technologies and evolving user needs, fostering sustainability and continuous growth.
Nesa Network is a blockchain platform specifically designed to enable decentralized AI inference. Integrating AI models on-chain provides a secure, efficient, and transparent way to execute AI queries while preserving data privacy. Unlike traditional AI systems that rely on centralized servers, Nesa ensures that AI computations are verifiable, reducing trust dependency on third parties.
The platform’s architecture includes privacy-preserving techniques such as Zero-Knowledge Machine Learning (ZKML), Trusted Execution Environments (TEEs), and Equivariant Encryption (EE) to secure AI inference processes. These features make Nesa suitable for industries where data security and integrity are critical.
The $NES token is central to Nesa’s ecosystem, facilitating transactions, governance, and network security through staking. Tokenomics are structured to incentivize participation while maintaining long-term sustainability. The roadmap focuses on scaling the platform, attracting developers, forming industry partnerships, and expanding its use cases.
Nesa Network is positioned at the intersection of blockchain and AI, two of the fastest-growing technological fields. Its success will depend on adoption, network security, and its ability to attract AI developers. While the project presents potential investment opportunities, it also carries risks related to market volatility and regulatory challenges.