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

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 exhibiting the potential to replace human labor in certain scenarios. However, the core of these technologies remains firmly in the hands of a few centralized tech giants. With substantial capital and control over high-cost 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 rapid AI evolution, public opinion often focuses on the breakthroughs and conveniences brought about 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 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 motivation to proactively tackle these challenges.

Blockchain technology, with its decentralized, transparent, and censorship-resistant characteristics, has provided new possibilities for the sustainable development of the AI industry. Currently, numerous "Web3 AI" applications have emerged on 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 exhibits 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 with centralized solutions in 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, 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 undertake more complex tasks. They must not only 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 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 like AI inference and training, achieving the security of the network and efficient allocation of resources. Only in this way can the stability and prosperity of the network be guaranteed, while effectively reducing the overall computing power costs.

2. Excellent high performance and support for heterogeneous tasks AI tasks, especially the training and inference of LLMs, demand extremely high requirements for 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 multifaceted scenarios. AI Layer 1 must deeply optimize the underlying architecture for high throughput, low latency, and elastic parallelism, while also pre-setting native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve smooth scaling 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 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 user trust and satisfaction with AI products.

4. Data Privacy Protection AI applications often involve sensitive user data, particularly in finance, healthcare, social networking, and other fields where data privacy protection is crucial. AI Layer 1 should adopt encrypted data processing technologies, privacy computing protocols, and data permission management methods while ensuring verifiability, to guarantee the security of data throughout the entire process of inference, training, and storage, effectively preventing data leaks 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 have 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 fosters the implementation 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 detail six representative AI Layer 1 projects, including Sentient, Sahara AI, Ritual, Gensyn, Bittensor, and 0G. It will systematically sort out the latest developments in the field, analyze the current state of project development, and discuss 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, 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 structures, invocation transparency, and value sharing. Sentient's vision is to allow anyone to build, collaborate, own, and monetize AI products, thereby fostering 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 safety and privacy protection, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecological layout. Team members have backgrounds from well-known companies such as Meta, Coinbase, Polygon, and top universities like Princeton University and the Indian Institutes of Technology, covering fields like AI/ML, NLP, and computer vision, working together to drive the project forward.

As a second startup project of Polygon co-founder Sandeep Nailwal, Sentient was born with a halo, possessing rich resources, connections, and market recognition, providing strong backing for project 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, which includes two core processes:

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

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: Stores model weights and fingerprint registration information;
• Distribution Layer: The entry point for model calls controlled by the authorization contract;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The profit routing contract distributes payments to trainers, deployers, and validators with each call.

OML Model Framework

The OML framework (Open, Monetizable, Loyal) is the 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 structures, facilitating community reproduction, auditing, and improvement.
• Monetization: Each model invocation triggers a revenue stream, and the on-chain contract distributes the earnings 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 usage and modifications controlled by cryptographic mechanisms.

AI-native Cryptography

AI-native encryption leverages the continuity of AI models, low-dimensional manifold structures, 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 third-party detectors (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 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 rights confirmation, TEE execution, and on-chain contract profit-sharing. Among them, the fingerprint method is implemented as OML 1.0 mainline, emphasizing the "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, 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 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 suitable for the current model deployment.