Exploring the Fertile Ground of On-chain DeAI: Current Status and Future Prospects of AI Layer1

Overview

In recent years, leading tech companies such as OpenAI, Anthropic, Google, and Meta have been driving 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 held by a few centralized tech giants. With their 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 evolution in AI, public opinion often focuses on the breakthroughs and conveniences brought by the technology, while the core issues of privacy protection, transparency, and security receive relatively insufficient attention. In the long run, these issues will profoundly impact the healthy development of the AI industry and its social 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 incentive to proactively tackle 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, key links and infrastructure still rely on centralized cloud services, and the meme attributes are too heavy, 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, 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 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 accounting, the nodes of AI Layer 1 need to undertake more complex tasks, which include not only providing computing power and completing AI model training and inference but also contributing a variety of resources such as storage, data, and bandwidth, thus 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 such as AI inference and training, achieving the network's security and efficient allocation of resources. Only in this way can the network's stability and prosperity be ensured, while effectively reducing the overall computing power costs.

2. Exceptional high performance and support for heterogeneous tasks AI tasks, especially the training and inference of LLMs, place extremely high demands on 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 multi-faceted scenarios. AI Layer 1 must undergo deep optimization in its underlying architecture to meet demands for high throughput, low latency, and elastic parallelism, while also presetting native support for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve a smooth expansion from "single-type tasks" to "complex and diverse ecosystems."

3. Verifiability and Trustworthy Output Assurance AI Layer 1 not only needs to prevent model malfeasance, data tampering, and other 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 independent verification of each model inference, training, and data processing process, 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 you see is what you get" 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 adopt encryption-based data processing technologies, privacy computing protocols, and data permission management measures while ensuring verifiability, to ensure the security of data throughout the entire process of inference, training, and storage, effectively preventing data leakage and misuse, and eliminating users' concerns about data security.

5. Powerful ecological support and development capabilities As an AI-native Layer 1 infrastructure, the platform must not only possess technological leadership but also 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 realizes 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 status 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 later migrating 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 model ownership, call tracking, and value distribution issues in the centralized LLM market through the "OML" framework (Open, Monetizable, Loyal), enabling AI models to achieve on-chain ownership structure, call 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 safety and privacy protection, respectively, while the blockchain strategy and ecosystem layout are led by Sandeep Nailwal, co-founder of a trading platform. Team members come from renowned companies such as Meta and Coinbase, 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 promote the project's implementation.

As a secondary entrepreneurship project of Sandeep Nailwal, co-founder of a trading platform, Sentient was born with a halo, possessing rich resources, connections, and market recognition, providing strong endorsement 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 for model alignment.
• Loyalty Training: Ensuring that the model maintains a training process 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: Authorization contract controls the model call entry;
• Access Layer: Verifies whether the user is authorized through permission proof;
• Incentive Layer: The yield routing contract will allocate payments for each call to trainers, deployers, and validators.

OML Model Framework

The OML framework (Open, Monetizable, Loyal) is a core concept proposed by Sentient, aimed at providing clear ownership protection and economic incentives 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, with the direction of upgrades and governance determined by the DAO, and its use and modification are controlled by cryptographic mechanisms.

AI-native Cryptography

AI native encryption utilizes 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 a third-party detector (Prover) in the form of a query.
• Permission calling mechanism: Before calling, it is necessary to obtain the "authorization credential" issued by the model owner, and the system will then authorize the model to decode the input and return the accurate answer.

This method can achieve "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. Among them, the fingerprint method is implemented as the main line of OML 1.0, emphasizing the idea of "Optimistic Security", which means default compliance can be detected and punished 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. 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.

In addition, Sentient has launched the Enclave TEE computing framework, which uses 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 strong contender.