AI Layer1 Research Report: Searching for On-Chain DeAI Fertile Ground

Overview

In recent years, leading technology companies such as OpenAI, Anthropic, Google, and Meta have driven the rapid development of large language models (LLM). LLMs have demonstrated unprecedented capabilities across various industries, significantly expanding the realm of human imagination and even showcasing the potential to replace human labor in certain scenarios. However, the core of these technologies is firmly held by a few centralized tech giants. With substantial capital and control over expensive computing resources, these companies have established insurmountable barriers, making it difficult for the vast majority of developers and innovation teams to compete.

At the same time, during the early stages of the rapid evolution of AI, public opinion often focuses on the breakthroughs and conveniences brought about by the technology, while the attention to core issues such as privacy protection, transparency, and security is relatively insufficient. In the long run, these issues will profoundly affect the healthy development of the AI industry and its social acceptance. If not properly addressed, the debate over whether AI will be "for good" or "for evil" will become increasingly prominent, and centralized giants, driven by profit motives, often lack sufficient motivation to proactively tackle these challenges.

Blockchain technology, with its decentralized, transparent, and censorship-resistant characteristics, offers new possibilities for the sustainable development of the AI industry. Currently, many "Web3 AI" applications have emerged on mainstream blockchains such as Solana and Base. However, a deeper analysis reveals that these projects still face numerous issues: on one hand, the degree of decentralization is limited, as key processes and infrastructure still rely on centralized cloud services, and the meme attributes are excessively heavy, making it difficult to support a truly open ecosystem; on the other hand, compared to AI products in the Web2 world, on-chain AI still shows limitations in model capabilities, data utilization, and application scenarios, with the depth and breadth of innovation needing improvement.

To truly realize the vision of decentralized AI, enabling the blockchain to securely, efficiently, and democratically support large-scale AI applications while competing with centralized solutions in performance, 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.

Core Features of AI Layer 1

AI Layer 1, as a blockchain specifically tailored for AI applications, has its underlying architecture and performance design closely aligned with the demands of AI tasks, aiming to efficiently support the sustainable development and prosperity of the on-chain AI ecosystem. Specifically, AI Layer 1 should possess the following core capabilities:

1. Efficient incentives and decentralized consensus mechanism The core of AI Layer 1 lies in building an open network for sharing resources such as computing power and storage. Unlike traditional blockchain nodes that mainly focus on ledger bookkeeping, the nodes in AI Layer 1 need to undertake more complex tasks. They must not only provide computing power and complete AI model training and inference but also contribute diverse resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants on AI infrastructure. This raises higher requirements for the underlying consensus and incentive mechanisms: AI Layer 1 must be able to accurately evaluate, incentivize, and verify the actual contributions of nodes in tasks such as AI inference and training, ensuring the security of the network and the efficient allocation of resources. Only in this way can the stability and prosperity of the network be guaranteed, and the overall computing power costs effectively reduced.

2. Outstanding high performance and heterogeneous task support capabilities AI tasks, especially the training and inference of LLMs, place extremely high demands on computing performance and parallel processing capabilities. Furthermore, the on-chain AI ecosystem often needs to support diverse and heterogeneous task types, including various model architectures, data processing, inference, storage, and other multifaceted scenarios. AI Layer 1 must deeply optimize its underlying architecture for high throughput, low latency, and elastic parallelism requirements, while also pre-setting native support capabilities for heterogeneous computing resources to ensure that all AI tasks can run efficiently, achieving a smooth transition from "single-type tasks" to "complex and diverse ecosystems."

3. Verifiability and Reliable Output Assurance AI Layer 1 not only needs to prevent security risks such as model malfeasance and data tampering, but also must ensure the verifiability and alignment of AI output results from the underlying mechanisms. By integrating cutting-edge technologies such as Trusted Execution Environment ( TEE ), Zero-Knowledge Proof ( ZK ), and Multi-Party Computation ( MPC ), the platform enables every instance of model inference, training, and data processing to be independently verified, ensuring the fairness and transparency of the AI system. At the same time, this verifiability helps users clarify the logic and basis of AI outputs, achieving "what you get is what you want", and enhancing user trust and satisfaction with AI products.

4. Data Privacy Protection AI applications often involve sensitive user data, and data privacy protection is particularly critical in fields such as finance, healthcare, and social networking. AI Layer 1 should adopt cryptographic data processing technologies, privacy computing protocols, and data permission management methods while ensuring verifiability, to guarantee the security of data throughout the entire process of reasoning, training, and storage, effectively preventing data leakage and abuse, and alleviating users' concerns about data security.

5. Powerful ecological support and development capabilities As an AI-native Layer 1 infrastructure, the platform not only needs to possess technological leadership but also must provide comprehensive development tools, integrated SDKs, operational support, and incentive mechanisms for ecological participants such as developers, node operators, and AI service providers. By continuously optimizing platform usability and developer experience, it promotes the landing of diverse AI-native applications and achieves the sustained prosperity of a decentralized AI ecosystem.

Based on the above background and expectations, this article will provide a detailed introduction to six representative AI Layer 1 projects, including Sentient, Sahara AI, Ritual, Gensyn, Bittensor, and 0G, systematically sorting out the latest progress in the field, analyzing the current development status of the projects, and discussing future trends.

Sentient: Building Loyal Open Source Decentralized AI Models

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 solve the issues of model ownership, invocation tracking, and value distribution in the centralized LLM market through the "OML" framework (, making AI models achieve on-chain ownership structure, 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 Princeton University professor Pramod Viswanath and Indian Institute of Science professor Himanshu Tyagi, who are respectively responsible for AI safety and privacy protection, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecosystem layout. Team members have backgrounds in well-known companies such as Meta, Coinbase, and Polygon, as well as top universities like Princeton University and the Indian Institutes of Technology, covering fields such as AI/ML, NLP, and computer vision, working together to promote project implementation.

As a second venture of Polygon co-founder Sandeep Nailwal, Sentient was born with a halo, possessing rich resources, connections, and market recognition, providing a strong endorsement 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 dozens of well-known VCs such as Delphi, Hashkey, and Spartan.

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) design architecture and application layer

Infrastructure Layer

Core Architecture

The core architecture of Sentient consists of two parts: AI Pipeline ### and on-chain system.

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

• 数据策划(Data Curation): A community-driven data selection process for model alignment.
• 忠诚度训练(Loyalty Training): Ensure that the model maintains a training process consistent with community 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: Store model weights and fingerprint registration information;
• Distribution Layer: Authorized contract controls the model call entry;
• 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 for each call.

(## OML Model Framework

The OML framework ) is open, monetizable, and loyal, which is the 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 will distribute the earnings to the trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, with upgrade direction and governance determined by the DAO, and the use and modification controlled by cryptographic mechanisms.

AI 原生加密学(AI-native Cryptography)

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

• Fingerprint embedding: Insert a set of covert query-response key-value pairs during training to form a unique signature for the model;
• Ownership Verification Protocol: Verify whether the fingerprint is retained by querying through a third-party detector ###Prover(;
• Permission Call Mechanism: Prior to calling, a "permission certificate" issued by the model owner must be obtained, 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 Security Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint confirmation, TEE execution, and on-chain contract profit distribution. 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 in case of 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. Through these signatures, model owners 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.

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 risks, 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) technologies to further enhance privacy protection and verifiability, providing a more mature solution for the decentralized deployment of AI models.

(# 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