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

With the rapid development of artificial intelligence technology, leading tech companies such as OpenAI, Anthropic, Google, and Meta are continuously advancing large language models (LLM). LLMs are demonstrating 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 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 stages of rapid AI evolution, 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 tends to be relatively insufficient. In the long run, these issues will profoundly impact the healthy development of the AI industry and social acceptance. If not properly addressed, the debate over whether AI should be "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 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 like Solana and Base. However, a deeper analysis reveals that these projects still face many issues: on one hand, the degree of decentralization is limited, with key links and infrastructure still relying on centralized cloud services, and an excessive meme attribute makes 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, with the depth and breadth of innovation needing improvement.

To truly realize the vision of decentralized AI, enabling the blockchain to securely, 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 crafted for AI. This will provide a solid foundation for open innovation in AI, democratic governance, and data security, fostering 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 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 mainly focus on ledger keeping, AI Layer 1 nodes need to undertake more complex tasks. They must not only provide computing power and complete training and inference of AI models but also contribute diverse resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants in AI infrastructure. This poses higher requirements for 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, ensuring the security of the network and the efficient allocation of resources. 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 support for heterogeneous task capabilities AI tasks, especially the training and inference of LLMs, place extremely high demands on computational performance and parallel processing capabilities. Furthermore, on-chain AI ecosystems often need to support diverse and heterogeneous task types, including various model architectures, data processing, inference, storage, and other multifaceted scenarios. AI Layer 1 must deeply optimize the underlying architecture for high throughput, low latency, and elastic parallelism, and preset native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve 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 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 Environments (TEE), Zero-Knowledge Proofs (ZK), and Multi-Party Computation (MPC), the platform allows 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 earned is what is desired", and enhancing user trust and satisfaction in AI products.

4. Data Privacy Protection AI applications often involve sensitive user data, and data privacy protection is particularly critical in fields such as finance, healthcare, and social networking. AI Layer 1 should adopt data processing technologies based on encryption, privacy computing protocols, and data permission management 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 ecosystem support and development capabilities As an AI-native Layer 1 infrastructure, the platform not only needs to have technological leadership 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 landing 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 through the latest developments 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 will be Layer 2, which 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, thus driving a fair and open ecosystem of AI Agent networks.

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 have backgrounds from 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 project implementation.

As a second venture of Sandeep Nailwal, co-founder of Polygon, 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 seed round financing of 85 million USD, 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 the model maintains a training process consistent with community intentions.

The blockchain system provides transparency and decentralized control for the protocol, 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 authorization 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 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 incentives for open-source AI models. It features the following characteristics by combining on-chain technology and AI-native cryptography:

• 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 distributes the revenue to the trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, and the direction of upgrades and governance is determined by the DAO, with use and modifications controlled by cryptographic mechanisms.

AI-native Cryptography

AI native encryption leverages the continuity of AI models, the low-dimensional manifold structure, and the differentiable characteristics of models to develop a "verifiable but non-removable" lightweight security mechanism. 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: A "permission credential" issued by the model owner must be obtained before calling, and the system will authorize the model to decode the input and return the accurate answer based on this.

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

Model Authorization 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 by OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which assumes compliance by default, with the ability to detect and punish violations.

The fingerprint mechanism is a key implementation of OML, which generates a unique signature during the training phase by embedding specific "question-answer" pairs. With these signatures, the model owner can verify ownership, preventing unauthorized duplication and commercialization. This mechanism not only protects the rights of model developers but also provides a traceable on-chain record of model usage.

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 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 proof (ZK) and fully homomorphic encryption (FHE) technologies to further enhance privacy protection and verifiability, providing a more mature solution for the decentralized deployment of AI models.

application layer

Currently, Sentient's products mainly include a decentralized chat platform.