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

In recent years, leading tech companies such as OpenAI, Anthropic, Google, and Meta have continuously driven the rapid development of large language models (LLM). LLMs have demonstrated unprecedented capabilities across various industries, greatly expanding the boundaries of human imagination and even showing potential to replace human labor in certain scenarios. However, the core of these technologies remains firmly in the hands of a few centralized tech giants. With strong 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, in the early stages of rapid AI evolution, public opinion often focuses on the breakthroughs and conveniences brought by the technology, while attention to core issues such as privacy protection, transparency, and security is relatively insufficient. In the long run, these issues will profoundly impact the healthy development of the AI industry and social acceptance. If not properly addressed, the debate over whether AI is "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, 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, and key links and infrastructure still rely on centralized cloud services, with an excessive emphasis on meme attributes, 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 capability, 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, 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, is designed with its underlying architecture and performance closely aligned with 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 mechanisms 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 primarily focus on ledger bookkeeping, AI Layer 1 nodes 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 raises higher requirements for the underlying consensus and incentive mechanisms: AI Layer 1 must be able to accurately assess, incentivize, and verify the actual contributions of nodes in AI inference, training, and other tasks, achieving the security of the network and the efficient allocation of resources. Only in this way can the stability and prosperity of the network be ensured, while effectively reducing the overall computing power costs.

2. Outstanding high performance and heterogeneous task support 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 a diverse range of heterogeneous task types, including various model architectures, data processing, inference, storage, and other diverse scenarios. AI Layer 1 must deeply optimize its underlying architecture to meet the demands of high throughput, low latency, and elastic parallelism, while also pre-setting native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can run efficiently, achieving a smooth transition 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 misuse 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 ensures that every model inference, training, and data processing process can be independently verified, maintaining 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 data security throughout the entire process of inference, training, and storage by adopting cryptographic data processing technologies, privacy computing protocols, and data permission management measures while ensuring verifiability. This effectively prevents data leakage and abuse, alleviating users' concerns about data security.

5. Powerful ecosystem support and development capabilities As an AI-native Layer 1 infrastructure, the platform not only needs to have leading technology 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 the developer experience, it promotes the landing of diverse AI-native applications, achieving 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 sort out the latest progress in the field, analyze the current development status of the projects, and discuss 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 starting as Layer 2 and later migrating 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 issues of model ownership, call tracking, and value distribution in the centralized LLM market through the "OML" framework (Open, Profitable, Loyal), allowing AI models to achieve on-chain ownership structure, call 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 blockchain strategy and ecosystem development. Team members have backgrounds from 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 drive the project forward.

As a second venture of Polygon co-founder Sandeep Nailwal, Sentient was born with a halo, possessing abundant resources, connections, and market recognition, providing strong backing 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 used 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 authorization contract controls the entry point for model calls;
• Access Layer: Verifies whether users are authorized through permission proofs;
• Incentive Layer: The profit routing contract allocates payments to trainers, deployers, and validators with each call.

OML Model Framework

The OML framework (Open, Monetizable, Loyal) is a core concept proposed by Sentient, aimed at providing clear ownership protection and economic incentives 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 call triggers a revenue stream, and the on-chain contract distributes the earnings to the trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, and the direction of upgrades and governance is determined by the DAO, with usage and modifications controlled by cryptographic mechanisms.

AI-native Cryptography

AI-native encryption utilizes the continuity of AI models, low-dimensional manifold structures, and the differentiable nature of 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 through a query in the form of a third-party detector (Prover);
• Permission calling mechanism: Before calling, it is necessary to obtain a "permission certificate" issued by the model owner, and the system will then authorize the model to decode the input and return the accurate answer.

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

Model Confirmation and Security Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint verification, TEE execution, and on-chain contract revenue sharing. Among them, the fingerprint method is implemented by OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which means default compliance, with the ability to detect and punish violations afterwards.

The fingerprinting mechanism is a key implementation of OML, which embeds specific "question-answer" pairs to generate a unique signature during the training phase. Through 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 high performance and real-time advantages make it a current model.