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

Overview

Background

In recent years, leading tech companies such as OpenAI, Anthropic, Google, and Meta have been continuously driving the rapid development of large language models (LLMs). LLMs have demonstrated unprecedented capabilities across various industries, significantly expanding the realm of human imagination, and even showing 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 computational 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 stage of the rapid evolution of AI, public opinion often focuses on the breakthroughs and conveniences brought by technology, while 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 acceptance by society. If not properly addressed, the debate over whether AI will be used "for good" or "for evil" will become increasingly prominent, and centralized giants, driven by profit motives, often lack sufficient incentive to proactively address 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 mainstream blockchains such as Solana and Base. However, a deeper analysis reveals that these projects still face many 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 overly 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 has limitations in terms of 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 with centralized solutions in terms of performance, we need to design a Layer 1 blockchain tailored specifically 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 tailored specifically 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 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 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 validate 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 guaranteed and the overall computing power costs effectively reduced.

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 different model architectures, data processing, inference, storage, and other diverse scenarios. AI Layer 1 must deeply optimize the underlying architecture for high throughput, low latency, and elastic parallelism, while also providing native support for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve smooth scaling from "single-task" to "complex and diverse ecosystem."

3. Verifiability and Assurance of Trustworthy Outputs 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 Environments (TEE), Zero-Knowledge Proofs (ZK), and Secure Multi-Party Computation (MPC), the platform allows 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 helps users clarify the logic and basis of AI outputs, achieving "what is obtained is what is desired," thereby enhancing user trust and satisfaction with AI products.

4. Data Privacy Protection AI applications often involve sensitive user data, making data privacy protection particularly critical in finance, healthcare, social networking, and other fields. AI Layer 1 should ensure data security throughout the entire process of inference, training, and storage by employing encryption-based data processing technologies, privacy computing protocols, and data access management, while also maintaining verifiability. This effectively prevents data leakage and misuse, alleviating users' concerns regarding 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, the platform promotes the landing of diverse AI-native applications, achieving 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 summarize 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 ( initially as Layer 2, and will later migrate to Layer 1). By combining AI Pipeline and blockchain technology, it aims to construct a decentralized artificial intelligence economy. Its core goal is to address issues of model ownership, invocation tracking, and value distribution in the centralized LLM market through the "OML" framework (Open, Monetizable, Loyal), enabling AI models to achieve on-chain ownership structure, invocation transparency, and value sharing. Sentient's vision is to empower 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 universities like Princeton University and the Indian Institutes of Technology, covering fields such as AI/ML, NLP, and computer vision, working together to promote the project's implementation.

As a second venture project of Polygon co-founder Sandeep Nailwal, Sentient was born with a halo, 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: 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: Ensuring that the model maintains a training process that aligns with the community's intentions.

The blockchain system provides transparency and decentralized control for the protocol, ensuring the 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 allocates payment to trainers, deployers, and validators with each invocation.

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 with AI-native cryptography, it has the following characteristics:

• 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 earnings to the trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, and the upgrade direction and governance are determined by the DAO, with usage and modifications controlled by cryptographic mechanisms.

AI-native Cryptography

AI-native encryption leverages 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: Inserting a set of hidden 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.
• Permission calling mechanism: Before calling, it is necessary to obtain the "authorization credential" issued by the model owner, and the system will then authorize the model to decode the input and return the accurate answer.

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

Model Authorization and Secure 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 as OML 1.0, emphasizing the "Optimistic Security" concept, which assumes compliance by default and can detect and punish violations.

The fingerprint mechanism is a key implementation of OML, which generates a unique signature for the model during the training phase by embedding specific "question-answer" pairs. 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 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.