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 pushing 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 showing 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 computing resources, these companies have established insurmountable barriers, making it difficult for the vast majority of developers and innovation teams to compete with them.

At the same time, during 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 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-seeking instincts, 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 several mainstream blockchains. However, a deeper analysis reveals that these projects still face many issues: on one hand, the degree of decentralization is limited, as critical components and infrastructure still rely on centralized cloud services, and the meme attribute is overly pronounced, 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 terms of model capabilities, data utilization, and application scenarios, with the depth and breadth of innovation needing improvement.

To truly realize the vision of decentralized AI, and enable blockchain to securely, efficiently, and democratically support large-scale AI applications, while competing 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, 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 primarily 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 diversified resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants on AI infrastructure. This places 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 AI inference, training, and other tasks to achieve 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. Outstanding high performance and heterogeneous task support capabilities AI tasks, especially the training and inference of LLMs, require extremely high computational performance and parallel processing capabilities. Furthermore, on-chain AI ecosystems often need to support diverse and heterogeneous task types, including different model architectures, data processing, inference, storage, and other diverse scenarios. AI Layer 1 must deeply optimize for high throughput, low latency, and elastic parallelism at the underlying architecture level, and preset native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve smooth expansion from "single-type tasks" to "complex and diverse ecosystems."

3. Verifiability and Assurance of Trustworthy Output 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 an underlying mechanism perspective. By integrating cutting-edge technologies such as Trusted Execution Environment (TEE), Zero-Knowledge Proof (ZK), and Multi-Party Computation (MPC), the platform can allow 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," 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 encryption-based data processing technologies, privacy computing protocols, and data permission management, among other means, to ensure the security of data throughout the entire process of inference, training, and storage, effectively preventing data leakage and abuse, and eliminating users' concerns regarding data security.

5. Powerful ecosystem 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 ecosystem participants such as developers, node operators, and AI service providers. By continuously optimizing platform usability and developer experience, we promote the implementation of diverse AI-native applications and achieve 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, systematically sorting out the latest developments in the field, analyzing the current status of the projects, and exploring 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 (. By integrating AI Pipeline and blockchain technology, it aims to create 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, Profitable, 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 Indian Institute of Science professor Himanshu Tyagi, who are responsible for AI safety and privacy protection, respectively, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecosystem layout. Team members have backgrounds spanning well-known 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 drive the project forward.

As a second entrepreneurial project of Sandeep Nailwal, co-founder of Polygon, 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 Delphi, Hashkey, and dozens of well-known VCs such as Spartan.

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)# Design Architecture and Application Layer

Infrastructure Layer

Core Architecture

The core architecture of Sentient consists of two parts: the AI Pipeline and the blockchain system.

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: Ensures 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: The authorized contract controls the entry point for model calls;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The revenue 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 incentive mechanisms 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 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, the direction of upgrades and governance is determined by the DAO, and its use and modification are controlled by cryptographic mechanisms.

AI-native Cryptography

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

• Fingerprint embedding: Insert a set of concealed query-response key-value pairs during training to form a unique model signature;
• Ownership Verification Protocol: Verify whether the fingerprint is retained through a third-party detector (Prover) in the form of a query.
• Permission calling mechanism: Before calling, you need to obtain a "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 approach 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 profit sharing. 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 of violations.

The fingerprinting mechanism is a key implementation of OML, which generates a unique signature for the model 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 traceable on-chain records for the usage behavior of the model.

In addition, Sentient has launched the Enclave TEE computing framework, which utilizes trusted execution environments (such as AWS Nitro Enclaves) to ensure that models only respond 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 proofs (ZK) and fully homomorphic encryption (FHE) technology to further enhance privacy protection and verifiability for AI models.