AI Layer1 Track Analysis: Finding On-Chain DeAI's Fertile Ground

Overview

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 the potential to replace human labor in certain scenarios. However, the core of these technologies is firmly 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 the vast majority of developers and innovation teams to compete.

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 attention to core issues such as privacy protection, transparency, and security is relatively insufficient. In the long run, these issues will profoundly impact the healthy development of the AI industry and societal acceptance. If not properly addressed, the debate over whether AI will be 'for good' or 'for evil' will become increasingly prominent, while centralized giants, driven by profit motives, often lack sufficient motivation to proactively tackle these challenges.

Blockchain technology, with its characteristics of decentralization, transparency, and censorship resistance, provides 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 shows limitations in terms of model capability, data utilization, and application scenarios, with room for enhancement in both the depth and breadth of innovation.

To truly realize the vision of decentralized AI, enabling blockchain to securely, efficiently, and democratically support large-scale AI applications while competing in performance with centralized solutions, we need to design a Layer 1 blockchain specifically tailored for AI. This will provide a solid foundation for the open innovation of 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, 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 have 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, 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 diverse resources such as 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 accurately assess, incentivize, and verify the actual contributions of nodes in tasks like AI inference and training, achieving both network security and efficient resource allocation. Only in this way can the stability and prosperity of the network be ensured, while effectively reducing overall computing costs.

2. Excellent high performance and heterogeneous task support capability AI tasks, especially the training and inference of LLMs, place extremely high demands on computing 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 be deeply optimized at 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, enabling smooth expansion from "single-type tasks" to "complex diverse ecosystems."

3. Verifiability and Assurance of Trustworthy Outputs AI Layer 1 not only needs to prevent malicious model behavior and data tampering security risks, 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 outputs, 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 sectors such as finance, healthcare, and social networking, data privacy protection is particularly critical. AI Layer 1 should ensure verifiability while employing data processing technologies based on encryption, privacy computing protocols, and data rights management, to guarantee 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 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, operation and maintenance 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 implementation of diverse AI-native applications and realizes the continuous 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. It will systematically sort out the latest developments in the track, analyze the current development status of the projects, 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 objective is to solve the 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, 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, while Polygon co-founder Sandeep Nailwal leads the blockchain strategy and ecosystem layout. Team members have backgrounds spanning 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 drive the project's implementation.

As the second entrepreneurial project of Polygon co-founder Sandeep Nailwal, Sentient was born with an aura, possessing rich resources, connections, and market recognition, providing a strong endorsement for the project's 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 other VCs such as 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 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 entry point for model calls controlled by the authorization contract;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The revenue routing contract allocates payments to trainers, deployers, and validators for 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 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 allocates the profits to trainers, deployers, and validators.
• Loyalty: The model belongs to the contributor community, with upgrade direction and governance decided by the DAO, and usage and modification controlled by cryptographic mechanisms.

AI-native Cryptography

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

• Fingerprint embedding: Inserting a set of concealed 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 a third-party detector (Prover) in the form of a query.
• Permission calling mechanism: Before making a call, 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 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 authentication, TEE execution, and on-chain contract revenue sharing. The fingerprint method is implemented by OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which assumes compliance by default and allows for detection and punishment of violations.

The fingerprint mechanism is a key implementation of OML. It generates unique signatures during the training phase by embedding specific "question-answer" pairs. With these signatures, the model owner can verify ownership, preventing 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.

Additionally, 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 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 a more mature solution for the decentralized deployment of AI models.