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

Overview

In recent years, leading technology companies such as OpenAI, Anthropic, Google, and Meta have been 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 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 that make 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 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 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 motivation to actively 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 some 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, making it difficult to support a truly open ecosystem; on the other hand, compared to AI products in the Web2 world, on-chain AI is still limited in terms of model capabilities, data utilization, and application scenarios, and the depth and breadth of innovation need to be improved.

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.

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. 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 in AI infrastructure. This raises higher demands 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 network security and efficient allocation of resources. Only in this way can the stability and prosperity of the network be ensured, effectively reducing the overall computing power costs.

2. Excellent high performance and heterogeneous task support capabilities AI tasks, especially the training and inference of LLMs, place extremely high demands on 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 conduct deep optimization at the underlying architecture to meet the demands of high throughput, low latency, and elastic parallelism, while also presetting native support capabilities for heterogeneous computing resources, ensuring that various AI tasks can run efficiently and achieve a smooth transition from "single-type tasks" to "complex and 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 allows every instance 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 can help 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 employing cryptographic data processing technologies, privacy computing protocols, and data permission management methods 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 regarding data security.

5. Powerful ecosystem support and development capabilities As an AI-native Layer 1 infrastructure, the platform not only needs to possess technological leadership but also provide comprehensive development tools, integrated SDKs, operation and maintenance support, and incentive mechanisms for ecosystem participants such as developers, node operators, and AI service providers. By continuously optimizing platform usability and developer experience, it promotes the implementation of rich and diverse AI-native applications, achieving 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 (. The initial phase is Layer 2, which 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 structures, invocation transparency, and value sharing. Sentient's vision is to enable 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 respectively responsible for AI safety and privacy protection, 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 Institutes of Technology, covering fields such as AI/ML, NLP, and computer vision, working together to drive the project's implementation.

As a secondary startup project by Sandeep Nailwal, the co-founder of Polygon, Sentient came with an aura from its inception, possessing abundant resources, connections, and market recognition, providing strong backing 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 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: the AI Pipeline and the blockchain 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: Ensure that the model maintains a training process consistent with community intentions.

The blockchain system provides transparency and decentralized control for protocols, 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: Authorization contract controls the entry point of model calls;
• Access layer: Verifies if 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 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, with upgrade direction and governance determined by the DAO, and usage and modification 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 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 calling, you need 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 correct answer.

This method enables "behavior-based authorization calls + ownership validation" 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 distribution. The fingerprint method is implemented through OML 1.0 as the main line, emphasizing the "Optimistic Security" concept, which assumes compliance by default and can detect and punish violations.

The fingerprint mechanism is a key implementation of OML. It generates unique signatures 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 traceable on-chain records of the model's usage behavior.

In addition, Sentient has launched the Enclave TEE computing framework, utilizing 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 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 a more mature solution for the decentralized deployment of AI models.

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