AI Layer1 Research Report: Finding the On-Chain DeAI Fertile Ground

Artificial intelligence is changing our world at an unprecedented speed. However, the current development of the AI industry faces numerous challenges: high concentration of technology and resources, prominent data privacy and security issues, expensive model training costs, and a lack of credibility and transparency in AI systems. Blockchain technology, with its decentralized, transparent, and censorship-resistant characteristics, offers new possibilities for the sustainable development of the AI industry.

To truly realize the vision of decentralized AI, enabling blockchain to securely, efficiently, and democratically support large-scale AI applications, and to perform on par with centralized solutions, we need to design a Layer 1 blockchain specifically tailored for AI. This will provide a solid foundation for open innovation in AI, democratic governance, and data security, promoting the prosperous development of a decentralized AI ecosystem.

This article will provide a detailed introduction to six representative AI Layer 1 projects, including Sentient, Sahara AI, Ritual, Gensyn, Bittensor, and 0G. It will systematically outline the latest developments in the field, analyze the current status of the projects, and discuss future trends.

Sentient: Building a Loyal Open Source Decentralized AI Model

Project Overview

Sentient is an open-source protocol platform that is building an AI Layer 1 blockchain (. The initial phase is Layer 2, which will later migrate to Layer 1). By combining AI Pipeline and blockchain technology, it aims to construct a decentralized artificial intelligence economy. Its core objective is to address the model ownership, invocation tracking, and value distribution issues in the centralized LLM market through the "OML" framework (, making AI models realize on-chain ownership structures, invocation transparency, and value sharing. Sentient's vision is to enable anyone to build, collaborate, own, and monetize AI products, thereby promoting a fair and open AI Agent network ecosystem.

The Sentient Foundation team brings together top academic experts, blockchain entrepreneurs, and engineers from around the world, dedicated to building a community-driven, open-source, and verifiable AGI platform. Core members include Pramod Viswanath, a professor at Princeton University, and Himanshu Tyagi, a professor at the Indian Institute of Science, who are responsible for AI safety and privacy protection, respectively, while Sandeep Nailwal, co-founder of Polygon, leads the blockchain strategy and ecological layout. Team members come from well-known companies such as Meta, Coinbase, and Polygon, as well as top universities like Princeton University and the Indian Institute of Technology, covering fields such as AI/ML, NLP, and computer vision, working together to promote the project.

As a second entrepreneurial project of Polygon co-founder Sandeep Nailwal, Sentient was born with an aura, possessing rich resources, connections, and market recognition, providing strong backing for the project's development. In mid-2024, Sentient completed a $85 million seed round financing, led by Founders Fund, Pantera, and Framework Ventures, with other investment institutions including Delphi, Hashkey, and dozens of well-known VCs such as Spartan.

) Design Architecture and Application Layer

Infrastructure Layer

Core Architecture

The core architecture of Sentient consists of two parts: the AI pipeline ### AI Pipeline ( and the blockchain system.

The AI pipeline is the foundation for developing and training "Loyal AI" artifacts, containing two core processes:

• 数据策划)Data Curation(: A community-driven data selection process used for model alignment.
• 忠诚度训练)Loyalty Training(: Ensure that the model maintains a training process aligned with the community's intentions.

The blockchain system provides transparency and decentralized control for protocols, ensuring ownership, usage tracking, revenue distribution, and fair governance of AI artifacts. The specific architecture is divided into four layers:

• Storage Layer: Stores model weights and fingerprint registration information;
• Distribution Layer: The entry point for model calls controlled by authorization contracts.
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The revenue routing contract will allocate payments to trainers, deployers, and validators with each call.

![Biteye and PANews jointly released AI Layer1 research report: Searching for fertile ground for on-chain DeAI])https://img-cdn.gateio.im/webp-social/moments-b9ef53f618283b15e3575581f4daeb0b.webp(

)## OML Model Framework

The OML Framework ### is an open, monetizable, and loyal core concept proposed by Sentient, aimed at providing clear ownership protection and economic incentive mechanisms for open-source AI models. By combining on-chain technology and AI-native cryptography, it has the following features:

• Openness: The model must be open source, with transparent code and data structures, facilitating community reproduction, auditing, and improvement.
• Monetization: Each model invocation triggers a revenue stream, and the on-chain contract distributes the revenue to the trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, with the direction of upgrades and governance determined by the DAO, and usage and modifications are controlled by cryptographic mechanisms.

(## AI原生加密学)AI-native Cryptography###

AI-native encryption leverages the continuity of AI models, low-dimensional manifold structures, and the differentiable properties of models to develop a "verifiable but non-removable" lightweight security mechanism. Its core technology is:

• Fingerprint embedding: Insert a set of concealed query-response key-value pairs during training to form a unique signature for the model;
• Ownership Verification Protocol: Verifies whether the fingerprint is retained through a third-party detector (Prover) in the form of a query question;
• Permission calling mechanism: A "permission certificate" issued by the model owner must be obtained before calling, and the system will then authorize the model to decode the input and return the accurate answer.

This approach enables "behavior-based authorization calls + ownership verification" without the cost of re-encryption.

(## Model Rights Confirmation and Secure Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint rights confirmation, TEE execution, and on-chain contract revenue sharing. Among them, the fingerprint method is implemented as OML 1.0 mainline, emphasizing the "Optimistic Security )" philosophy, which assumes compliance by default and allows for detection and punishment after violations.

The fingerprint mechanism is a key implementation of OML. It generates a unique signature during the training phase by embedding specific "question-answer" pairs. With these signatures, the model owner can verify ownership, preventing unauthorized copying and commercialization. This mechanism not only protects the rights of model developers but also provides a traceable on-chain record of the model's usage behavior.

In addition, Sentient has launched the Enclave TEE computing framework, utilizing trusted execution environments ### such as AWS Nitro Enclaves ( to ensure that models only respond to authorized requests, preventing unauthorized access and use. Although TEE relies on hardware and has certain security vulnerabilities, its high performance and real-time advantages make it a core technology for current model deployment.

In the future, Sentient plans to introduce zero-knowledge proofs )ZK( and fully homomorphic encryption )FHE( technology to further enhance privacy protection and verifiability, providing more mature solutions for the decentralized deployment of AI models.

![Biteye and PANews Jointly Release AI Layer1 Research Report: Searching for the On-chain DeAI Fertile Ground])https://img-cdn.gateio.im/webp-social/moments-f4a64f13105f67371db1a93a52948756.webp(

) application layer

Currently, Sentient's products mainly include the decentralized chat platform Sentient Chat, the open-source model Dobby series, and the AI Agent framework.

(# Dobby series model

SentientAGI has released multiple "Dobby" series models, primarily based on the Llama model, focusing on values of freedom, decentralization, and cryptocurrency support. Among them, the leashed version has a more constrained and rational style, suitable for stable output scenarios; the unhinged version leans towards freedom and boldness, featuring a richer dialogue style. The Dobby model has been integrated into several Web3 native projects, such as Firework AI and Olas, and users can also directly interact with these models in Sentient Chat. Dobby 70B is the most decentralized model ever, with over 600,000 owners ) holding Dobby fingerprint NFTs also being co-owners of this model ###.

Sentient also plans to launch Open Deep Search, which is a search agent system that attempts to go beyond ChatGPT and Perplexity Pro. This system combines Sensient's search capabilities ### such as query rephrasing, document processing (, and reasoning agents, enhancing search quality through open-source LLM ) like Llama 3.1 and DeepSeek (. Its performance on the Frames Benchmark has surpassed other open-source models and even approached some closed-source models, demonstrating strong potential.

)# Sentient Chat: Decentralized Chat and on-chain AI Agent Integration

Sentient Chat is a decentralized chat platform that combines open-source large language models ( such as the Dobby series ) with an advanced reasoning agent framework, supporting multi-agent integration and complex task execution. The embedded reasoning agents on the platform can perform complex tasks such as search, computation, and code execution, providing users with an efficient interactive experience. Additionally, Sentient Chat supports direct integration of on-chain agents, currently including astrology Agent Astro247, crypto analysis Agent QuillCheck, wallet analysis Agent Pond Base Wallet Summary, and spiritual guidance Agent ChiefRaiin. Users can choose different intelligent agents for interaction based on their needs. Sentient Chat will serve as a distribution and coordination platform for agents. Users' questions can be routed to any integrated model or agent to provide optimal response results.

AI Agent framework

Sentient offers two major AI Agent frameworks:

• Sentient Agent Framework: A lightweight open-source framework focused on automating Web tasks through natural language commands ( such as searching, playing videos ). The framework supports the construction of agents with perception, planning, execution, and feedback loops, suitable for lightweight development of off-chain Web tasks.
• Sentient Social Agent: An AI system developed for social platforms ### such as Twitter, Discord, and Telegram ( that supports automated interaction and content generation. Through multi-agent collaboration, this framework can understand the social environment and provide users with a more intelligent social experience, while also being able to integrate with the Sentient Agent Framework to further expand its application scenarios.

) Ecosystem and Participation Methods

The Sentient Builder Program currently has a funding plan of $1 million, aimed at encouraging developers to use its development kit to build AI Agents that can connect via the Sentient Agent API and operate within the Sentient Chat ecosystem. The ecosystem partners announced on the Sentient official website cover project teams from various fields of Crypto AI.

In addition, Sentient Chat is currently in the testing phase and requires an invitation code to enter the whitelist before it can be accessed. Regular users can submit a waitlist. According to official information, there are already over 50,000 users and 1,000,000 query records. There are 2,000,000 users waiting to join the waitlist for Sentient Chat.

( Challenges and Prospects

Sentient focuses on the model side, aiming to address the core issues of misalignment and untrustworthiness faced by large-scale language models )LLM###. Through the OML framework and blockchain technology, it provides a clear ownership structure, usage tracking, and behavioral constraints for the model, greatly promoting the development of decentralized open-source models.

With the support of Polygon co-founder Sandeep Nailwal's resources, as well as endorsements from top VCs and industry partners, Sentient is in a leading position in resource integration and market attention. However, against the backdrop of the current market gradually demystifying high-valuation projects, whether Sentient can deliver truly impactful decentralized AI products will be a crucial test of its ability to become the standard for decentralized AI ownership. These efforts are not only related to Sentient's own success but also have far-reaching implications for the entire industry's trust rebuilding and decentralized development.

![Biteye and PANews jointly released AI Layer1 research report: Searching for the fertile ground of on-chain DeAI]###https://img-cdn.gateio.im/webp-social/moments-a70b0aca9250ab65193d0094fa9b5641.webp(

Sahara AI: Creating a Decentralized AI World for Everyone's Participation

) Project Overview

Sahara AI is a decentralized infrastructure born for the AI × Web3 new paradigm, dedicated to building an open, fair, and collaborative artificial intelligence economy. The project achieves on-chain management and trading of datasets, models, and intelligent agents through decentralized ledger technology, ensuring the sovereignty and traceability of data and models. At the same time, Sahara AI introduces a transparent and fair incentive mechanism, allowing all contributors, including data providers, annotators, and model developers, to receive tamper-proof income returns during the collaboration process. The platform also protects contributors' rights to AI resources through a permissionless "copyright" system.