AI Layer1 Research Report: Finding Fertile Ground for On-chain DeAI

In recent years, leading tech companies such as OpenAI, Anthropic, Google, and Meta have been driving the rapid development of large language models (LLMs). LLMs have demonstrated unprecedented capabilities across various industries, greatly expanding the realm of human imagination and even showing 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 with them.

In the early stages of the rapid evolution of AI, public opinion often focuses on the breakthroughs and conveniences brought by the technology, while the core issues of privacy protection, transparency, and security receive relatively insufficient attention. 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 "good" or "evil" will become increasingly prominent, and centralized giants, driven by their profit-seeking instincts, often lack sufficient motivation to actively tackle these challenges.

Blockchain technology, with its characteristics of decentralization, transparency, and resistance to censorship, provides new possibilities for the sustainable development of the AI industry. Currently, numerous "Web3 AI" applications have emerged on some mainstream blockchains. However, a deeper analysis reveals that these projects still face many issues: on one hand, the degree of decentralization is limited, as key links and infrastructure still rely on centralized cloud services, 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, and the depth and breadth of innovation need to be improved.

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

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 centered around the needs 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 accounting, the nodes of AI Layer 1 need to undertake more complex tasks, not only providing computing power and completing AI model training and inference but also contributing diverse resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants in AI infrastructure. This places higher demands on the underlying consensus and incentive mechanisms: AI Layer 1 must be able to accurately assess, incentivize, and verify the actual contributions of nodes in tasks such as AI inference and training, achieving network security and efficient resource allocation. Only in this way can the stability and prosperity of the network be ensured while effectively reducing the overall computing power costs.

2. Excellent high performance and support for heterogeneous task capabilities

AI tasks, especially the training and inference of LLMs, place extremely high demands on computational performance and parallel processing capabilities. Furthermore, the on-chain AI ecosystem often needs to support diverse and heterogeneous task types, including different model architectures, data processing, inference, storage, and other multifaceted scenarios. AI Layer 1 must conduct deep optimization at the underlying architecture to meet the demands for high throughput, low latency, and elastic parallelism, while also presuming native support for heterogeneous computing resources, ensuring that various AI tasks can run efficiently, achieving smooth scaling from "single-type tasks" to "complex diverse ecosystems."

3. Verifiability and Trustworthy 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 a fundamental mechanism level. By integrating cutting-edge technologies such as Trusted Execution Environment (TEE), Zero-Knowledge Proof (ZK), and Multi-Party Computation (MPC), the platform enables every model inference, training, and data processing process to be independently verified, ensuring the fairness and transparency of the AI system. At the same time, this verifiability can help users clarify the logic and basis of AI outputs, achieving "what is obtained is what is desired" 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 ensure verifiability while adopting encrypted data processing technologies, privacy computing protocols, and data permissions management to ensure the security of data throughout the entire process of inference, training, and storage, effectively preventing data leakage and misuse, 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 technical leadership but also must provide comprehensive development tools, integrated SDKs, operational support, and incentive mechanisms for ecosystem 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. It will systematically outline the latest developments in the field, analyze the current state of project development, and explore 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 (, initially as Layer 2, and will later migrate to Layer 1). By integrating AI Pipeline and blockchain technology, it aims to create a decentralized artificial intelligence economy. Its core goal is to address issues of model ownership, call tracing, and value distribution in the centralized LLM market through the "OML" framework (Open, Monetizable, Loyal), enabling AI models to achieve on-chain ownership structure, transparency in calls, and value sharing. Sentient's vision is to allow anyone to build, collaborate, own, and monetize AI products, thus 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 responsible for AI safety and privacy protection, while a co-founder of a well-known public blockchain leads the blockchain strategy and ecosystem layout. Team members have backgrounds from renowned companies such as Meta and Coinbase, as well as top universities like Princeton University and the Indian Institutes of Technology, covering fields like AI/ML, NLP, and computer vision, working together to promote the project's implementation.

As a second entrepreneurship project of a well-known public chain co-founder, Sentient was born with a halo, possessing rich resources, connections, and market recognition, providing a strong endorsement for project 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.

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:

• Data Curation: A community-driven data selection process for model alignment.
• Loyalty Training: Ensure that the model maintains a training process consistent with community intentions.

The blockchain system provides transparency and decentralized control for the protocol, 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 access point for model calls controlled by the authorization contract;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The yield routing contract will allocate payments to trainers, deployers, and validators with each call.

OML Model Framework

The OML framework (Open, Monetizable, Loyal) is the core concept proposed by Sentient, aimed at providing clear ownership protection and economic incentive mechanisms for open-source AI models. By integrating on-chain technology and AI-native cryptography, it has the following characteristics:

• Openness: The model must be open-source, with transparent code and data structure, facilitating community reproduction, auditing, and improvement.
• Monetization: Every model call triggers a revenue stream, and the on-chain contract distributes the earnings to the trainer, deployer, and validator.
• Loyalty: The model belongs to the contributor community, with upgrade direction and governance determined by the DAO, and usage and modifications controlled by cryptographic mechanisms.

AI-native Cryptography

AI-native encryption is the development of a "verifiable but non-removable" lightweight security mechanism using the continuity, low-dimensional manifold structure, and differentiable properties of AI models. 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: Verifying whether the fingerprint is retained through a third-party detector (Prover) in the form of a query.
• Permission calling mechanism: A "permission certificate" issued by the model owner must be obtained before invoking, and the system will then authorize the model to decode the input and return the accurate answer.

This method can achieve "behavior-based authorization calls + ownership verification" without the cost of re-encryption.

Model Confirmation of Rights and Security Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint authentication, TEE execution, and on-chain contract revenue sharing. Among them, the fingerprint method is implemented in OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which assumes compliance by default and allows for detection and punishment in case of violations.

The fingerprint mechanism is a key implementation of OML, which generates unique signatures during the training phase by embedding specific "question-answer" pairs. With these signatures, the model owner can verify ownership and prevent 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, which utilizes trusted execution environments (such as AWS Nitro Enclaves) to ensure that the model only responds to authorized requests, preventing unauthorized access and use. Although TEE relies on hardware and has certain security risks, its advantages in high performance and real-time capability 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 more mature solutions for the decentralized deployment of AI models.

application layer

Currently, Sentient