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

Overview

In recent years, the rapid development of large language models (LLM) has greatly expanded the realm of human imagination, even showing the potential to replace human labor in certain fields. However, the core of these technologies is controlled by a few centralized tech giants. With substantial capital and control over expensive computing resources, these companies have established insurmountable barriers, making it difficult for most developers and innovation teams to compete with them.

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 relatively insufficient attention is paid to core issues such as privacy protection, transparency, and security. In the long run, these issues will profoundly impact the healthy development of the AI industry and social acceptance. If not properly addressed, the controversy 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 address 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 some 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 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 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 tailored for AI. This will provide a solid foundation for open innovation, democratic governance, and data security in AI, 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 centered around 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 shared network of computing power, storage, and other resources. Unlike traditional blockchain nodes that primarily focus on ledger bookkeeping, the nodes in AI Layer 1 need to undertake more complex tasks, not only providing computing power, completing AI model training and inference, but also contributing diverse resources like storage, data, and bandwidth, thereby breaking the monopoly of centralized giants on AI infrastructure. This raises higher demands 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 network security and efficient resource allocation. Only in this way can the stability and prosperity of the network be guaranteed, effectively reducing the overall computing power costs.

2. Outstanding high performance and heterogeneous task support capabilities

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 various model architectures, data processing, inference, storage, and other multi-faceted scenarios. AI Layer 1 must perform deep optimization at the underlying architecture to meet the demands for high throughput, low latency, and elastic parallelism, while also presetting the native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can operate efficiently and smoothly scale from "single-type tasks" to "complex and diverse ecosystems."

3. Verifiability and Trustworthy Output Guarantee

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 allows each model inference, training, and data processing procedure 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 received 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 verifiability while employing cryptography-based data processing technologies, privacy computing protocols, and data permission management methods to ensure the safety of data throughout the entire process of inference, training, and storage, effectively preventing data leakage and misuse, and 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 have 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 landing of a rich and diverse range of AI-native applications, achieving the sustainable 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 track, 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, 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 model ownership, invocation tracking, and value distribution issues in the centralized LLM market through the "OML" framework ), 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. Key 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. The team members come 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 advance 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 funding, 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 the on-chain system.

The AI pipeline is the foundation for developing and training "loyal AI" artifacts, comprising two core processes:

• 数据策划###Data Curation(: A community-driven data selection process used for model alignment.
• 忠诚度训练)Loyalty Training(: Ensure that the model maintains a training process consistent with community intent.

The blockchain system provides transparency and decentralized control for protocols, ensuring the ownership, usage tracking, revenue distribution, and fair governance of AI artifacts. The specific architecture is divided into four layers:

• Storage layer: storing model weights and fingerprint registration information;
• Distribution Layer: Authorized contract control model call entry;
• Access Layer: Verify whether the user is authorized through permission proof;
• Incentive Layer: The revenue routing contract will allocate payments to trainers, deployers, and verifiers with each call.

)## OML Model Framework

The OML framework ( is open, monetizable, and loyal. It is a core concept proposed by Sentient, aiming to provide clear ownership protection and economic incentives 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 structures, facilitating community reproduction, auditing, and improvement.
• Monetization: Each model invocation 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, the direction of upgrades and governance is determined by the DAO, and its use and modification are controlled by cryptographic mechanisms.

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(## AI 原生加密学)AI-native Cryptography(

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

• Fingerprint embedding: Insert a set of hidden 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 the 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 the accurate answer.

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

(## Model Rights Confirmation and Security Execution Framework

Sentient currently adopts Melange mixed security: combining fingerprint verification, TEE execution, and on-chain contract profit sharing. The fingerprint method is implemented by OML 1.0 as the mainline, emphasizing the "Optimistic Security)Optimistic Security(" concept, which assumes compliance by default and allows for detection and punishment in case of violations.

The fingerprint 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, 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, utilizing trusted execution environments ) such as AWS Nitro Enclaves ### to ensure that models only respond 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, providing more mature solutions for the decentralized deployment of AI models.

( application layer

Currently, Sentient's products mainly include the decentralized chat platform Sentient Chat and the open-source model Dobby.