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

Overview

In recent years, leading tech companies such as OpenAI, Anthropic, Google, and Meta have driven the rapid development of large language models (LLM). LLMs have demonstrated unprecedented capabilities across various industries, greatly 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 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 technology, while the 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 societal 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 incentive to proactively 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 multiple 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 has 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 with centralized solutions in terms of performance, 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 demands 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 constructing an open network for sharing resources such as computing power and storage. Unlike traditional blockchain nodes that mainly focus on ledger bookkeeping, nodes in AI Layer 1 need to undertake more complex tasks. They not only must provide computing power and complete the training and inference of AI models, but also contribute diverse resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants on AI infrastructure. This imposes higher requirements 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, and the overall computing power costs effectively reduced.

2. Excellent high performance and support for heterogeneous tasks 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 varied scenarios. AI Layer 1 must perform depth optimization at the underlying architecture to meet the demands of high throughput, low latency, and elastic parallelism, and preconfigure native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve a smooth transition from "single-type tasks" to a "complex and diverse ecosystem."

3. Verifiability and Trustworthy Output Assurance AI Layer 1 not only needs to prevent security risks such as model malice 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 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," thereby enhancing user 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 networking, data privacy protection is particularly critical. AI Layer 1 should ensure verifiability while employing encrypted data processing technologies, privacy computing protocols, and data permission management methods to guarantee the security of data throughout the entire process of inference, training, and storage, effectively preventing data leakage and abuse, and alleviating user 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 technical 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 deployment of diverse AI-native applications and achieves sustained prosperity of a decentralized AI ecosystem.

Based on the above background and expectations, this article will detail 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 a Loyal Open Source Decentralized AI Model

Project Overview

Sentient is an open-source protocol platform that is building an AI Layer 1 blockchain. The initial phase is Layer 2, which will later migrate to Layer 1. Through the combination of AI Pipeline and blockchain technology, it aims to create a decentralized artificial intelligence economy. Its core objective is to address the 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 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 security and privacy protection, while Sandeep Nailwal, co-founder of a trading platform, leads the blockchain strategy and ecosystem layout. Team members come from a range of well-known companies as well as top universities like Princeton 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 entrepreneurial project of Sandeep Nailwal, co-founder of a well-known trading platform, Sentient has carried a halo since its establishment, possessing rich resources, connections, and market awareness, which provide 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 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 the community's intentions.

The blockchain system provides transparency and decentralized control for protocols, 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: Verify whether the user is authorized through permission proof;
• Incentive Layer: The yield routing contract allocates payments to trainers, deployers, and validators with 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 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, facilitating community reproduction, auditing, and improvement.
• Monetization: Each model call 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 upgrade direction and governance determined by the DAO, and its use 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: A set of covert query-response key-value pairs is inserted 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: Before calling, it is necessary 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 method can achieve "behavior-based authorization calls + ownership verification" without the cost of re-encryption.

Model Affirmation and Security Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint rights confirmation, TEE execution, and on-chain contract profit distribution. Among them, the fingerprint method is implemented in OML 1.0 as the main line, emphasizing the idea of "Optimistic Security", which means default compliance, and violations can be detected and punished afterwards.

The fingerprint mechanism is a key implementation of OML, which generates a unique signature during the training phase by embedding specific "question-answer" pairs. Through 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 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) technologies to further enhance privacy protection and verifiability, providing more mature solutions for the decentralized deployment of AI models.

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