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

Overview

Background

In recent years, leading tech companies such as OpenAI, Anthropic, Google, and Meta have continuously driven the rapid development of large language models (LLM). 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 this technology is firmly held by a few 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.

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 attention to core issues such as privacy protection, transparency, and security is relatively insufficient. In the long run, these issues will profoundly affect the healthy development of the AI industry and social acceptance. If not properly addressed, the debate over whether AI will be "good" or "evil" will become increasingly prominent, and centralized giants, driven by profit-seeking instincts, often lack sufficient motivation to actively respond to 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 such as 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 critical components and infrastructure still relying on centralized cloud services, and there is an overemphasis on meme attributes, 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, enabling the blockchain to securely, efficiently, and democratically support large-scale AI applications, and to compete 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 the decentralized AI ecosystem.

The 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 in AI Layer 1 need to undertake more complex tasks. They must not only provide computing power and complete AI model training and inference but also contribute diversified resources such as storage, data, and bandwidth, thereby breaking the monopoly of centralized giants on 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 efficient allocation of resources. Only in this way can the stability and prosperity of the network be guaranteed, while effectively reducing overall computing power costs.

2. Excellent high performance and heterogeneous task support capability AI tasks, especially the training and inference of LLMs, have extremely high requirements for computing performance and parallel processing capabilities. Furthermore, the on-chain AI ecosystem often needs to support diverse and heterogeneous task types, including various model structures, data processing, inference, storage, and other multifaceted scenarios. AI Layer 1 must deeply optimize the underlying architecture for requirements such as 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 smooth scaling from "single-type tasks" to "complex and diverse ecosystems."

3. Verifiability and Trustworthy Output Assurance AI Layer 1 not only needs to prevent malicious model behavior, data tampering, and other security risks, but also must ensure the verifiability and alignment of AI output results from the underlying mechanisms. By integrating cutting-edge technologies such as Trusted Execution Environments (TEE), Zero-Knowledge Proofs (ZK), and Multi-Party Computation (MPC), the platform allows the entire process of model inference, training, and data processing to be independently verified, ensuring the fairness and transparency of the AI system. At the same time, this verifiability helps users clarify the logic and basis of AI outputs, 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, and data privacy protection is particularly critical in fields such as finance, healthcare, and social networking. AI Layer 1 should ensure verifiability while adopting encrypted data processing technologies, privacy computing protocols, and data rights 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 ecological carrying and development support capabilities As an AI-native Layer 1 infrastructure, the platform not only needs to have technological leadership but also needs to 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 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 out 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 (, initially starting as Layer 2, and will later migrate to Layer 1). By combining AI Pipeline and blockchain technology, it aims to create a decentralized artificial intelligence economy. Its core objective 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. The vision of Sentient is to empower 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 security and privacy protection, respectively, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecosystem layout. Team members come from well-known companies such as Meta, Coinbase, and Polygon, as well as top universities like Princeton University and the Indian Institute of Technology, covering fields like AI/ML, NLP, and computer vision, working together to promote the project's 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 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, consisting of two core processes:

• Data Curation: A community-driven data selection process used for model alignment.
• Loyalty Training: Ensuring the model maintains a training process that aligns with community 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 entry point for model calls controlled by the authorization contract;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The yield routing contract distributes payments to trainers, deployers, and validators on 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 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 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 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 of the model;
• Ownership Verification Protocol: Verify whether the fingerprint is retained through a third-party detector (Prover) in the form of a query.
• Permission calling mechanism: A "permission certificate" issued by the model owner must be obtained before calling, and the system will then authorize the model to decode the input and return the accurate answer.

This approach enables "behavior-based authorized 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 with OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which assumes compliance by default, with violations being detectable and punishable.

The fingerprint mechanism is a key implementation of OML, which generates a unique signature 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 leverages 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) technologies to further enhance privacy protection and verifiability, providing more mature solutions for the decentralized deployment of AI models.

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