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

Overview

In recent years, leading technology companies such as OpenAI, Anthropic, Google, and Meta have continuously driven the rapid development of large language models (LLMs). LLMs have shown unprecedented capabilities across various industries, greatly expanding the realm of human imagination, and even exhibiting potential to replace human labor in certain scenarios. However, the core of these technologies is firmly grasped by a few centralized tech giants. With strong capital and control over expensive computing resources, these companies have established formidable 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 the 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 societal acceptance. If not properly addressed, the debate over whether AI will be "for good" or "for evil" will become increasingly prominent, while centralized giants, driven by profit motives, often lack sufficient motivation 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 multiple 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 processes 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 shows 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 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 the 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 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 take on more complex tasks. They must not only provide computing power and complete training and inference of AI models but also contribute diversified resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants in AI infrastructure. This imposes 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 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 cost effectively reduced.

2. Outstanding 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, on-chain AI ecosystems often need to support diverse and heterogeneous types of tasks, including various model structures, 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, and preset native support for heterogeneous computing resources to ensure that various AI tasks can run efficiently, achieving smooth expansion 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 malfeasance 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 Proofs (ZK), and Multi-Party Computation (MPC), the platform enables each 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" 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 networking, data privacy protection is particularly critical. AI Layer 1 should ensure data security throughout the entire process of inference, training, and storage by adopting encrypted data processing technologies, privacy computing protocols, and data rights management, while maintaining verifiability. This effectively prevents data leakage and misuse, alleviating users' concerns about data security.

5. Powerful ecological support and development capabilities As an AI-native Layer 1 infrastructure, the platform must not only possess technological leadership but also 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 and achieves 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 progress in the field, analyzing the current state 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 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 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 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 professor Himanshu Tyagi from the Indian Institute of Science, who are responsible for AI safety and privacy protection, while co-founder of a trading platform Sandeep Nailwal leads the blockchain strategy and ecological layout. Team members have backgrounds from well-known companies such as Meta and Coinbase, 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 drive the project's implementation.

As a secondary entrepreneurial project of Sandeep Nailwal, co-founder of a trading platform, Sentient came with an aura from its inception, possessing 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 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 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 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: Authorized contract controls the entry point for model calls;
• Access Layer: Verifies whether the user is authorized through proof of permission;
• Incentive Layer: The profit routing contract will allocate 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 incentive mechanisms for open-source AI models. By integrating 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 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 determined by the DAO, and its use and modification are controlled by cryptographic mechanisms.

AI-native Cryptography

AI-native encryption is a lightweight security mechanism developed using the continuity of AI models, low-dimensional manifold structures, and the differentiable nature of models, resulting in "verifiable but non-removable" features. 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: Verifying whether the fingerprint is retained through a third-party detector (Prover) in the form of a query.
• 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 the correct answer.

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

Model Rights Confirmation and Secure Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint rights confirmation, TEE execution, and on-chain contract revenue sharing. Among them, the fingerprint method is implemented with OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which means default compliance, with violations detectable and punishable.

The fingerprinting mechanism is a key implementation of OML. It generates unique signatures 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 certain platform Nitro Enclaves) to ensure that the model only responds to authorized requests, preventing unauthorized access and usage. Although TEE relies on hardware and has certain security vulnerabilities, 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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