In-depth Analysis of AI Layer 1 Projects: The Cutting Edge of Blockchain and Artificial Intelligence Integration

With the rapid development of AI technology, traditional Blockchain architectures have struggled to meet the high-performance computing and complex data processing demands of AI applications. This has led to the rise of Layer 1 Blockchain platforms specifically optimized for AI, which exhibit diverse characteristics in terms of technological architecture, application scenarios, and business models.

This article provides an in-depth analysis of five leading AI Layer 1 projects: Bittensor, Vana, Kite AI, Nillion, and Sahara.

1. Bittensor: Decentralized AI Network Infrastructure

Bittensor, as an early explorer in the Blockchain AI field, is committed to building an open decentralized artificial intelligence collaboration network. Its goal is to break down the centralized barriers in traditional AI research and development, allowing more participants to contribute and benefit together.

Bittensor's technical architecture adopts a dual-layer structure design:

• Root Network (Mainnet): Responsible for the coordination, verification, and issuance management of TAO tokens in the entire system, serving as the central hub for resource allocation across the network.
• Subnet Ecosystem: Each subnet is like an independent AI laboratory, developing specialized solutions for specific AI application scenarios and proving its value in market competition.

This design allows Bittensor to simultaneously balance the overall stability of the network and the expertise in various fields, providing a flexible infrastructure for the development of decentralized AI.

Ecological Development Progress

• The number of subnets has expanded from the initial 32 to over 64, covering various AI application scenarios such as text generation, trading signals, and data labeling.
• The number of active users has reached 140,000, doubling compared to the previous year.
• The total market valuation of subnet exceeds 100 million USD, with a daily trading volume maintained at around 45 million USD.
• Institutional participation has significantly increased, with well-known funds incorporating TAO into their decentralized AI fund, adjusting the weight to 29.55%.

The recent dTAO (Dynamic TAO) system upgrade completed by Bittensor is an important innovation in its economic model. The core of this upgrade lies in optimizing the allocation mechanism of the TAO token, shifting from a resource allocation method that relies on the subjective judgment of validators to a more market-oriented allocation mechanism, allowing resources to flow more precisely to those subnetworks that are truly competitive.

To address these issues, the dTAO upgrade has introduced a dynamic resource allocation system based on market mechanisms. This system transforms each subnet into an independent economic unit, driving resource allocation through users' actual needs. Its core innovation is the subnet token (Alpha token) mechanism:

• Operation principle: Users can obtain Alpha tokens issued by various subnets by staking TAO, which represent the user's support for a specific subnet.
• Resource Allocation Logic: The market price of Alpha tokens serves as a signal for measuring the demand intensity of the subnet. Initially, the price of alpha tokens is the same, with only 1 TAO and 1 alpha token in each pool. As liquidity of the two types of tokens in the subnet increases, the price of alpha tokens will also change accordingly. The emission of TAO is proportionally allocated based on the price of subnet tokens among all tokens. Higher-priced subnets will receive more TAO allocation, thereby achieving automatic optimization of resource allocation.

The currently most active subnets include:

• Subnet 4 Targon: Focused on AI inference services for text generation, characterized by fast response speed and low cost.
• Subnet 64 Chutes: Provides various LLM API interfaces, allowing developers to build and deploy AI applications on the Bittensor network.
• Subnetwork No. 8 PTN: Focused on the financial sector, incentivizing miners to generate accurate trading signals through a reward mechanism, covering various financial markets such as foreign exchange and cryptocurrency.
• Subnet No. 52 Dojo: Perform data annotation and encourage users to earn tokens through data annotation.

2. Vana: Data Sovereignty and Value Reconstruction Platform

The Vana project focuses on addressing a core issue in today's digital economy: the ownership of personal data and value distribution. The innovation of Vana lies in creating an ecosystem where users truly own and control their own data while being able to derive economic benefits from it.

As an EVM-compatible Layer 1 Blockchain network, Vana's technical architecture includes five core components:

1. Data Liquidity Layer: This is the core of the Vana network, achieving the incentivization, aggregation, and verification of data assets through the Data Liquidity Pool (DLP).

2. Data Portability Layer: Ensure that user data can be easily transferred between different applications and AI models, enhancing the flexibility of data usage.

3. General Connection Group: Track the real-time data flow throughout the entire ecosystem, forming a data ecology map to ensure the transparency of the system.

4. Non-custodial data storage: Users' original data will not be placed on the blockchain but will be stored in a location chosen by the users themselves, ensuring complete control over their own data.

5. Application Ecosystem: Developers can leverage the data accumulated by DLP to build various innovative applications, including AI applications, while data contributors can receive dividend rewards from these applications.

Latest developments

• In February 2025, a certain institution announced a strategic investment in Vana.
• In terms of ecological construction, Vana has built data projects covering multiple fields from social media data to financial forecasting data.
• Recently, Vana organized a hackathon during Eth Denver, offering a high prize pool to incentivize developers to build DataDAO and AI applications based on Vana data, further expanding its ecosystem.

3. Kite AI: Technical Breakthrough of AI-Native Blockchain

Kite AI is a native Layer 1 Blockchain project focused on the AI field, built on the Avalanche framework. It aims to address various challenges faced by traditional blockchains when handling AI assets, particularly how to achieve transparency in rights and incentives for AI data, models, and agent contributions. Kite AI proposes four core technological innovations:

1. PoAI consensus mechanism: A system of on-chain verifiable contribution records that precisely tracks the value contributions of data, models, and AI agents.

2. Composable AI Subnet: Supports developers to build industry-specific AI collaboration ecosystems as needed.

3. AI Native Execution Layer: Specifically handles AI computation tasks such as inference, embedding, and fine-tuning/training.

4. Decentralized Data Engine: Ensures that data creators receive fair compensation in the AI workflow.

Development status

• Kite AI launched the incentive testnet on February 6, 2025, which is the first AI-native Layer 1 sovereign Blockchain testnet.
• Less than 70 hours after the online testing went live, the number of connected wallets exceeded 100,000, and as of now, a total of 1.95 million wallets have joined the incentive testnet V1.
• The project background is strong, built by an experienced Silicon Valley team, with core team members from top tech companies and academic institutions.
• In terms of capital support, the project has received investments from several top institutions and has established technical cooperation relationships with multiple well-known projects.

4. Nillion: Frontier Exploration of Privacy Computing

Nillion is redefining the way sensitive data is processed through its unique "blind computation" technology, paving new avenues for digital privacy protection in the future.

Nillion's core advantage lies in its "blind computation" capability - a process that allows data to remain encrypted throughout its storage, transmission, and processing lifecycle. Its technical architecture integrates various cutting-edge privacy protection technologies:

• Multi-Party Computation ( MPC )
• Fully Homomorphic Encryption ( FHE )
• Zero-Knowledge Proof ( ZKP )
• Nada Language

The network architecture of Nillion consists of three main layers: the processing layer, the coordination layer, and the connection layer.

Latest development process

• The Nillion mainnet is scheduled to launch in March 2025.
• In terms of financing, Nillion completed a $25 million funding round led by an institution on October 30, 2024.
• In terms of ecological expansion, Nillion has established integration relationships with multiple mainstream public blockchains.
• In the AI ecosystem, Nillion has established partnerships with multiple AI-related projects.

5. Sahara AI: A platform for building a new economy of AI assets

The core concept of Sahara AI is to build a "Human-AI Collaboration Network" that allows ordinary users, developers, and enterprises to participate in the creation, deployment, and monetization of AI assets.

The technical architecture of the platform consists of three key components:

1. Sahara Blockchain
2. AI Infrastructure
3. Sahara AI Marketplace

Latest development progress

• In December 2024, Sahara AI launched the Beta version test network of the first phase data service platform.
• In February 2025, Sahara AI launched the second phase of its testnet.
• Sahara AI announced that it will launch a public testnet called "SIWA" on March 10, 2025.

Sahara AI has announced its roadmap for the 2024-2025 fiscal year, which includes several key milestones. On March 1, 2025, Sahara AI will launch an incubator program aimed at discovering and supporting the most promising AI x Web3 innovative projects globally.

Summary

AI Layer 1 is currently in a critical stage of rapid evolution. This emerging track is reconstructing the underlying architecture of AI technology through decentralized infrastructure. From data rights confirmation to computing resource allocation, from model training to application deployment, these platforms are breaking through the limitations of traditional centralized AI systems, building a more open, transparent, and efficient technological ecosystem. In the future, this track will continue to drive technological innovation, advancing artificial intelligence towards a more decentralized and collaborative development direction.