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

Overview

In recent years, leading technology companies such as OpenAI, Anthropic, Google, and Meta have continuously propelled 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 showing potential to replace human labor in certain scenarios. However, the core of these technologies is firmly controlled by 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 with them.

At the same time, in the early stages of the rapid evolution of AI, public opinion often focuses on the breakthroughs and conveniences brought by technology, while there is relatively insufficient attention to core issues such as privacy protection, transparency, and security. In the long run, these issues will profoundly affect the healthy development of the AI industry and societal acceptance. If not properly addressed, the debate over whether AI is "good" or "evil" will become increasingly prominent, and centralized giants, driven by profit motives, often lack sufficient incentive 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, 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, 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 has limitations in model capabilities, data utilization, and application scenarios, with room for improvement in both the depth and breadth of innovation.

To truly realize the vision of decentralized AI, enabling the blockchain to safely, 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 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 a shared network of open resources such as computing power and storage. Unlike traditional blockchain nodes that primarily focus on ledger accounting, AI Layer 1 nodes need to perform 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 in AI infrastructure. This raises higher requirements for 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 security of the network and 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. Excellent high performance and heterogeneous task support capability 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 structures, data processing, inference, storage, and other multifaceted scenarios. AI Layer 1 must deeply optimize its underlying architecture to meet the demands for 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 and achieve smooth scaling from "single-type tasks" to "complex diversified ecosystems."

3. Verifiability and Trustworthy Output Assurance AI Layer 1 not only needs to prevent security risks such as model maliciousness and data tampering but also must ensure the verifiability and alignment of AI output results from the underlying mechanism. By integrating cutting-edge technologies such as Trusted Execution Environment (TEE), Zero-Knowledge Proof (ZK), and Multi-Party Computation (MPC), the platform enables each 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 can help users clarify the logic and basis of AI output, 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 users' sensitive data, and in fields such as finance, healthcare, and social media, data privacy protection is particularly critical. AI Layer 1 should ensure security throughout the entire process of inference, training, and storage by adopting encrypted data processing technologies, privacy computing protocols, and data permission management, while also ensuring verifiability. This will effectively prevent data leakage and misuse, alleviating users' concerns about data security.

5. Strong ecological support and development capability As an AI-native Layer 1 infrastructure, the platform not only needs to have technological superiority 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 implementation 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 developments in the field, analyzing the current status of project development, 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 will be Layer 2, which will later migrate to Layer 1. By combining AI Pipeline and blockchain technology, it aims to create a decentralized artificial intelligence economy. Its core goal is to address model ownership, call tracking, and value distribution issues in the centralized LLM market through the "OML" framework (Open, Profitable, Loyal), enabling AI models to achieve on-chain ownership structure, call transparency, and value sharing. Sentient's vision is to allow 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 responsible for AI safety and privacy protection, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecosystem layout. Team members come from well-known companies such as Meta, Coinbase, and Polygon, as well as top institutions like Princeton University and the Indian Institutes of Technology, covering fields such as AI/ML, NLP, and computer vision, working together to advance the project.

As the second entrepreneurial project of Polygon co-founder Sandeep Nailwal, Sentient came with a halo from the very beginning, possessing abundant resources, connections, and market awareness, providing 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: the AI Pipeline and the 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 process of data selection for model alignment.
• Loyalty Training: Ensure that the model maintains a training process that is consistent with the community's 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 authorized contract controls the entry point for model calls;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The yield routing contract will allocate payment for each call to trainers, deployers, and validators.

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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 characteristics:

• Openness: The model must be open source, with transparent code and data structures for the community to replicate, audit, and improve.
• Monetization: Each model invocation triggers a revenue stream, and the on-chain contract will distribute the revenue to the trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, with the direction of upgrades and governance decided by the DAO, while usage and modifications are controlled by cryptographic mechanisms.

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 hidden 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 the query form by a third-party detector (Prover);
• Permission call mechanism: Before calling, you need to obtain the "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 approach can achieve "behavior-based authorization calls + affiliation verification" without incurring re-encryption costs.

Model Rights Confirmation and Security Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint rights confirmation, TEE execution, and on-chain contract profit sharing. 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 of violations.

The fingerprint mechanism is a key implementation of OML. It generates unique signatures during the training phase by embedding specific "question-answer" pairs. With these signatures, model owners 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.

In addition, Sentient has launched the Enclave TEE computing framework, which utilizes 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 better support for the decentralized deployment of AI models.