AI Layer1 Research Report: Seeking On-Chain DeAI's Fertile Ground

Overview

In recent years, leading technology 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, 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 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 the rapid evolution of AI, 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 affect 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 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 links and infrastructure still rely on centralized cloud services, and there is an excessive meme attribute that makes it difficult to support a truly open ecosystem; on the other hand, compared to AI products in the Web2 world, on-chain AI is still limited in terms of 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 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 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, the nodes of AI Layer 1 are required 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 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 like 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 overall computing power costs.

2. Excellent high performance and heterogeneous task support capabilities AI tasks, especially LLM training and inference, 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 structures, data processing, inference, storage, and other diverse scenarios. AI Layer 1 must deeply optimize its underlying architecture for high throughput, low latency, and elastic parallelism, and pre-set native support capabilities for heterogeneous computing resources to ensure that various AI tasks can run efficiently, achieving a smooth expansion from "single-type tasks" to "complex diverse ecosystems."

3. Verifiability and Trustworthy Output Assurance AI Layer 1 not only needs to prevent model malfeasance, data tampering, and other security risks 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 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 output, achieving "what is obtained is what is desired" and enhancing users' trust and satisfaction with AI products.

4. Data Privacy Protection AI applications often involve sensitive user data, and in fields such as finance, healthcare, and social interaction, data privacy protection is particularly critical. AI Layer 1 should adopt encryption-based data processing technologies, privacy computing protocols, and data permission management methods while ensuring verifiability, 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 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 implementation 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, 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 Layer1 blockchain. The initial phase is Layer 2, and it 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 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 structures, 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, responsible for AI safety and privacy protection, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecosystem layout. Team members have backgrounds from renowned companies such as Meta, Coinbase, and Polygon, as well as top universities like Princeton University and the Indian Institute of Technology, covering fields such as AI/ML, NLP, and computer vision, working together to promote project implementation.

As a secondary venture of Polygon co-founder Sandeep Nailwal, Sentient was born with a halo, equipped with abundant 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 firms including Delphi, Hashkey, and dozens of well-known VCs such as 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:​

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

Blockchain systems provide transparency and decentralized control for protocols, ensuring ownership of AI artifacts, usage tracking, revenue distribution, and fair governance. 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 on each call.

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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 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 structure, facilitating community reproduction, auditing, and improvement.
• Monetization: Each model call will trigger 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 determined by the DAO, and its use and modification are controlled by cryptographic mechanisms.

AI-native Cryptography

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

• Fingerprint embedding: Inserting 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 third-party detector (Prover) in the form of a query question;
• Permission calling mechanism: Before 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 accurate answers.

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

Model Authentication and Secure Execution Framework

Sentient currently adopts the Melange mixed security: combining fingerprint rights confirmation, TEE execution, and on-chain contract profit distribution. The fingerprint method is implemented in OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which means default compliance, and detection and punishment in case of violations.

The fingerprinting mechanism is a key implementation of OML. It generates a unique signature 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 models only respond to authorized requests, preventing unauthorized access and usage. 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 for AI models.