Overview

As AI rapidly evolves, it requires more computing power, data processing, and collaboration. Traditional blockchain systems, with their performance limits, can no longer meet the growing demands of AI applications.

A new generation of Layer 1 blockchains designed for AI has emerged. These projects focus on solving key challenges like efficiency, data privacy, resource allocation, and decentralized collaboration with innovative technology.

This article reviews and analyzes key AI Layer 1 projects like Bittensor, Vana, Kite AI, Nillion, and Sahara by focusing on their core technologies, use cases, and future potential.

What is an AI Layer 1?

A Layer 1 blockchain is a basic blockchain network with its own consensus system and infrastructure. Unlike Layer 2 solutions, which depend on existing blockchains, Layer 1 blockchains create and maintain a fully decentralized ecosystem.

An AI Layer 1 is a blockchain built for artificial intelligence. Its main goal is to create a decentralized system for training AI models, running AI tasks, sharing data, and providing incentives.

AI Layer 1 projects integrate blockchain technology to enhance transparency and fairness in AI systems and tackle the persistent issue of AI being a “black box” by utilizing blockchain’s verifiability and immutability.

Significance:
The integration of AI and blockchain is a key area of technological innovation, driven by a clear supply-and-demand dynamic:
AI relies on massive data and computational power: Blockchain can provide decentralized data storage, model hosting, and computing power markets;
AI decisions are opaque: Blockchain records provide traceable and verifiable processes, which increases trustworthiness;
Lack of incentive mechanisms: Blockchain’s native token economy can effectively incentivize data contributors, computing nodes, and model developers;
Data and model ownership are hard to define: Smart contracts and on-chain verification provide a foundation for AI asset ownership and transactions.
These core needs are the driving force behind the rapid growth of AI Layer 1 projects.

Key Features
AI Layer 1 is not just a new narrative; it has a distinct technological focus compared to general-purpose blockchains:
High-performance computing power and parallel processing capabilities: Providing foundational support for model training and inference;
Native AI Agent support: Developing runtime environments and interaction mechanisms specifically designed for AI Agents;
Native data incentive mechanisms: Using tokens to incentivize data contribution and model collaboration;
On-chain verification and transaction of models and tasks: Enabling true Web3 integration of AI assets.

Four Key Trends Driving AI Layer 1
The emergence of AI Layer 1 is not coincidental; it is driven by four key trends:
The explosion of generative AI: The proliferation of large models has driven exponential demand for computing power and data;
The evolution of Web3 incentive mechanisms: Creating an economic cycle for data, models, and computing power.
The progressive establishment of the AI Agent ecosystem: Necessitating a secure foundational execution environment and a robust value network.
A discernible shift towards blockchain modularity: Transitioning Layer 1 blockchains from “general-purpose” to “specialized” architectures.

Major Projects

Overview of Projects with Native Tokens

Bittensor

Background and Vision
Bittensor aims to build a decentralized AI collaboration network that breaks down the centralized barriers in traditional AI development. It encourages global developers and compute providers to train and optimize AI models jointly. Its vision is to drive the co-evolution of the AI ecosystem through market-driven mechanisms, thus creating an open and transparent intelligent network.

Technology Architecture and Innovation
Bittensor employs a dual-layer architecture: the root network coordinates the global system, while the subnet ecosystem supports a variety of AI tasks. The core innovation lies in the dTAO market-driven resource allocation mechanism, which introduces the subnet token Alpha to compete in the market. Resources are allocated on demand, while validation power is decentralized to avoid control by a single entity. This design enhances both efficiency and the system’s resistance to censorship.

Application Scenarios and Ecosystem
Bittensor’s ecosystem includes multiple subnets with typical applications such as Targon (focused on text generation, similar to advanced language models), Chutes (providing LLM API interface services), PTN (generating financial trading signals), and Dojo (data labelling collaboration). These submodules showcase Bittensor’s broad applicability in fields like text generation, financial prediction, and data processing. They attract developers and industry users.

Token Economy and Value
The $TAO token is the core of the Bittensor ecosystem, used to pay for computing power, reward contributors, and drive subnet competition. Holders can stake $TAO to participate in network governance or earn subnet rewards. As the subnet ecosystem expands, the demand for $TAO is expected to grow, with its value closely tied to network computing power and application activity.

Source

Vana

Background and Vision
Vana’s goal is to reshape data sovereignty to allow users to truly own and benefit from their personal data, rather than being exploited by tech giants. Its vision is to reconstruct the value of data through decentralized technology, thus creating a user-driven data economy ecosystem.

Technology Architecture and Innovation
Vana’s technology is divided into the Data Liquidity Layer and the Data Portability Layer, complemented by a non-custodial storage design. The key innovation is that user data remains off-chain to ensure privacy, while still enabling data sharing and value distribution through liquidity incentives and contributor rewards. This user-centric model balances privacy protection with meaningful economic incentives.

Application Scenarios and Ecosystem
Vana’s typical applications include Finquarium (a financial data marketplace), GPT Data DAO (conversation data collection and governance), and Reddit/Twitter data projects (using social media data). These scenarios demonstrate Vana’s potential in fields like finance, AI training, and social analysis. They attract data contributors and developers to join the ecosystem.

Token Economy and Value
$VANA powers the Vana ecosystem. Users earn it by contributing data, and businesses spend it to access that data. It also enables governance participation. As data usage grows, the token’s value becomes increasingly tied to network activity.

Source: https://www.vana.org/

Nillion

Background and Vision
Nillion is building a privacy-first computing network designed to solve data privacy challenges. By leveraging “blind computing”—a method that enables computation on encrypted data—it aims to support secure, large-scale AI and Web3 applications adoption.

Technology Architecture and Innovation
At the heart of Nillion is Nil Message Compute (NMC), a “blind computing” paradigm that integrates multi-party computation (MPC) and zero-knowledge proofs (ZKPs) to enable zero-knowledge data exchange. Its core innovation lies in achieving consensus without a traditional blockchain, while preserving zero-leakage encrypted processing, unlocking new possibilities for privacy-critical applications.

Application Scenarios and Ecosystem
Nillion’s applications include Web3 authentication (protecting user identity privacy), AI privacy training (training models on encrypted data), and confidential data modeling (enterprise-level privacy computing). These scenarios demonstrate its broad applicability in identity management, AI development, and business data processing, attracting industry users with privacy needs.

Token Economy and Value
$NIL serves as the native utility token of the Nillion network. It facilitates payment for computation tasks, incentivizes node operators, and fuels overall ecosystem growth. As demand for privacy-preserving computation continues to rise, the value of $NIL is increasingly tied to network activity and contributed compute power, positioning it for strong long-term potential.

Source

HyperCycle

Background and Vision
HyperCycle is a project providing high-performance infrastructure for AI Agents. It collaborates with SingularityNET to accelerate cross-platform collaboration and efficient operation of AI agents. Its vision is to build the infrastructure to support the next generation of AI applications, thus driving the widespread adoption of AI technology.

Technology Architecture and Innovation
HyperCycle’s technical architecture focuses on a high-performance AI compute support layer that is deeply collaborating with SingularityNET’s ecosystem. Innovation highlights include supporting high-performance AI operations, cross-platform collaboration capabilities, and optimizing AI agent execution efficiency, providing foundational support for complex multi-agent systems.

Application Scenarios and Ecosystem
HyperCycle is not yet fully launched, but its typical applications may involve AI agent clusters, such as distributed task processing or cross-platform intelligent collaboration. With its collaboration with SingularityNET, its ecosystem is expected to cover AI applications in fields like education, healthcare, and finance, with promising potential.

Token Economics and Value
$HYPC is the token of HyperCycle, used to pay for computing resources, reward contributors, and participate in ecosystem governance. As part of the high-performance AI infrastructure, the value of $HYPC is closely tied to the scale of AI Agent usage and computing demand. Its long-term growth depends on the maturity of the ecosystem.

Source

OriginTrail

Project Background and Vision
OriginTrail integrates knowledge graph technology with AI to build a decentralized platform for managing knowledge assets. It aims to enhance data verifiability and foster cross-domain collaboration, with a vision to drive digital transformation in areas such as supply chains and scientific research through trusted knowledge exchange.

Technology Architecture and Innovation
OriginTrail’s core is built on knowledge graph technology and a decentralized system for storing and verifying knowledge assets. Its innovation lies in enabling verifiable knowledge sharing, ensuring data reliability, and facilitating cross-domain integration. These provide a robust infrastructure for complex data applications.

Application Scenarios and Ecosystem
OriginTrail’s typical applications include supply chain tracking (ensuring product origin transparency) and data trust verification (certifying scientific data). The ecosystem has already partnered with various industries, such as food supply chains and healthcare data management. This showcases the real-world value of knowledge graphs.

Token Economics and Value
$TRAC is OriginTrail’s native token, used to pay for data storage, verification fees, and to incentivize node operations. As knowledge graph applications expand into more domains, the demand for $TRAC is linked to ecosystem data volume and industry adoption, providing stable value support.

Source: https://origintrail.io/

Mainstream Technical Architecture

AI Layer 1 public blockchains are blockchain networks built specifically to support AI applications. They offer efficient, secure, and scalable infrastructure for tasks such as AI model training, inference, data processing, and collaborative operations.

Unlike traditional Layer 1 blockchains (such as Ethereum, Solana), AI Layer 1 must accommodate needs like computing power distribution, data privacy, model execution, and decentralized governance. These endow its technical architecture with unique innovations.

Below is an overview of the key technical architectures of AI Layer 1 blockchains, focusing on the main trends and exemplary projects such as Kite AI and Bittensor.

Future Outlook and Investment Value

1. Technological Evolution: Intelligence and Integration

Comprehensive Integration of Multimodal AI
The future AI ecosystem will go beyond single modalities (e.g., text generation) and move toward multimodal integration (text, images, audio, video, and even sensor data). Bittensor’s subnet ecosystem may expand to support multimodal model training, and HyperCycle’s AI Agent infrastructure could become the core for cross-modal collaboration.

Adaptive Consensus Mechanism
Mechanisms like PoAI (Kite AI) and dTAO (Bittensor) will be further optimized by introducing adaptive algorithms that dynamically adjust consensus rules based on task complexity, computing power requirements, and privacy needs to improve efficiency and fairness.

Standardization of Privacy Computing
Nillion’s “blind computation” and ZKP/MPC technologies may become industry standards.It will transform privacy protection from an edge function to a core component of AI Layer 1. The deep integration of TEE (Trusted Execution Environment) with blockchain will also accelerate.

Preliminary Introduction of Quantum Computing
By 2030, quantum computing may begin integrating with the AI ecosystem, especially in high-performance computing fields (e.g., HyperCycle). This will significantly boost the training speed and optimization capabilities of complex models.

Outlook: Technology will shift from decentralized innovation to integration and standardization, with the core competitiveness of the AI ecosystem lying in intelligence and cross-domain adaptability.

Source: Kite AI

2. Ecosystem Expansion: Globalization and Industry Penetration

Formation of a Global Computing Power Network
Decentralized computing power markets (e.g., Gensyn, Bittensor) will integrate global idle resources to form a computing power network similar to an “AI internet”. Low-cost computing power in developing countries may become a key supplement to the ecosystem.

Flourishing of Industry-Specific Sub-Ecosystems
Vertical sub-ecosystems such as Kite AI’s medical subnet and OriginTrail’s supply chain tracking will further segment into more industries such as education, energy, and entertainment. Each sub-ecosystem may develop independent tokens and governance mechanisms.

Cross-Chain and Cross-Ecosystem Collaboration
AI Layer 1 will establish tighter interoperability with traditional blockchains (e.g., Ethereum, Polkadot). For example, Vana’s data layer could integrate with DeFi protocols, and OriginTrail’s knowledge graph may connect with NFT markets.

User-Driven Ecosystem Growth
Vana’s data sovereignty model may extend to more individual users. Data sources from social media, IoT devices, etc., will become important inputs for the AI ecosystem, and the role of community contributors will become more prominent.
Outlook: The AI ecosystem will shift from technology-driven to application- and user-driven, thus forming a global, multi-layered network structure that permeates all aspects of social life.

3. Economic and Social Impact: Value Reshaping

Maturity of Token Economics
Tokens like $TAO, $VANA, and $NIL will not only serve as payment tools but may evolve into “smart assets,” with their value directly tied to computing power contribution, data quality, and model performance. HyperCycle’s $HYPC may become the standard currency in the AI Agent market.

New Careers and Economic Models
The AI ecosystem will spawn new professions such as “computing power miners,” “data providers,” and “model optimizers.” Vana’s profit-sharing mechanism may become widespread and allow individuals to earn stable income by contributing data or computing power.

Enhanced Social Efficiency
Applications like OriginTrail’s supply chain transparency and Bittensor’s financial signal generation will optimize resource allocation and reduce waste. AI inference markets (e.g., Ritual) may reduce businesses’ costs to acquire intelligent services.

Challenges of the Digital Divide
Developed regions may benefit first from the AI ecosystem, while underdeveloped areas risk falling behind due to technological barriers and a lack of infrastructure. This could potentially exacerbate the global digital divide.
Outlook: The AI ecosystem will reshape the economic structure. It will empower individuals to participate more while amplifying potential risks of social inequality.

Source: OriginTrail

Challenges

1. Technical Challenges

The technical complexity of the AI ecosystem presents several core issues that must be addressed:

Computing Power and Energy Bottlenecks
Challenge: AI model training and inference require massive computing power, especially for large multimodal models (e.g., video generation or cross-domain inference). Bittensor’s subnet ecosystem and Gensyn’s distributed training rely on global computing power integration, but current GPU supply is limited, and the energy consumption of high-performance computing is enormous, which may lead to cost increases or network bottlenecks.
Impact: Insufficient computing power may limit ecosystem expansion, and energy issues could spark environmental controversies, thus damaging project sustainability.
Potential Solutions: Develop low-power algorithms (e.g., more efficient Transformer variants), integrate renewable energy-driven computing nodes, or explore early quantum computing applications.

Source: TFSC

Privacy and Security Technical Implementation
Challenge: Nillion’s “blind computation” and Vana’s non-custodial storage need to ensure zero data leakage in decentralized environments, but ZKP (zero-knowledge proofs) and MPC (multi-party computation) incur high computational costs, potentially reducing efficiency. Kite AI’s TEE (Trusted Execution Environment) also faces hardware trust and supply chain security risks.
Impact: If privacy technologies cannot balance efficiency and security, their use in high-throughput scenarios (e.g., real-time inference) may be limited.
Potential Solutions: Optimize encryption algorithms (e.g., lightweight ZKP), develop specialized privacy computation chips, or introduce on-chain verifiable trust mechanisms.

Lack of Interoperability and Standardization
Challenge: AI Layer 1 projects (e.g., Bittensor, Kite AI) each build independent subnets or modules but lack unified protocol standards, making cross-ecosystem collaboration difficult. For example, HyperCycle’s AI Agent clusters may struggle to integrate directly with OriginTrail’s knowledge graph.
Impact: Ecosystem fragmentation may weaken overall competitiveness, and developers will need to adapt to different platforms, increasing development costs repeatedly.
Potential Solutions: Promote cross-chain interoperability protocols (e.g., Polkadot-like bridging mechanisms) or industry standards (e.g., unified AI model formats).

Source: Polkadot Wiki

Model Governance and Traceability
Challenge: Decentralized AI (e.g., Bittensor’s Targon subnet) lacks effective on-chain governance mechanisms to track and limit misuse and could be used to generate false information or malicious content. Although OriginTrail’s knowledge graph can verify data credibility, it is hard to monitor dynamically generated AI outputs in real time.
Impact: Misuse of technology could lead to a trust crisis and hinder widespread adoption of the ecosystem.
Potential Solutions: Introduce on-chain identity authentication, content traceability mechanisms, or develop decentralized ethical review tools.

2. Market Challenges

The market competition and user adoption within the AI ecosystem are crucial to its commercialization success. The current challenges include:

Intense Competition and Ecosystem Fragmentation
Challenge: AI Layer 1 projects (e.g., Bittensor vs. Kite AI) and related ecosystems (e.g., Vana vs. Nillion) overlap in functionalities (e.g., computing power allocation, data privacy), which leads to resource and user fragmentation. Traditional tech giants (e.g., Google, Microsoft) are also positioning themselves with centralized AI solutions. They leverage mature ecosystems and brand advantages to capture market share.
Impact: Decentralized projects may struggle to develop due to insufficient user bases or high migration costs for developers.
Potential Solutions: Attract niche markets through differentiated positioning (e.g., Bittensor focusing on subnet competition, Vana on data sovereignty), or collaborate with traditional enterprises (e.g., OriginTrail with the supply chain industry) to expand influence.

User Education and Adoption Barriers
Challenge: The decentralized nature of the AI ecosystem (e.g., token incentives, on-chain governance) is complex for average users and enterprises. For example, Vana’s data contribution model requires users to understand data sovereignty concept, and Bittensor’s subnet participation requires developers to have expertise in both blockchain and AI.
Impact: A steep learning curve may limit user participation and slow ecosystem growth.
Potential Solutions: Develop user-friendly interfaces (e.g., Vana’s simplified data upload tools), provide educational resources, or reduce developer entry barriers through SDKs.

Instability in Token Economics
Challenge: Tokens such as $TAO, $VANA, and $NIL are tied to ecosystem activity, but market volatility may lead to speculation-driven behavior (e.g., price crashes after early hype). HyperCycle’s $HYPC risks unclear value anchoring as its applications are not fully deployed.
Impact: Token price instability may undermine user trust, affecting long-term investments and ecosystem participation.
Potential Solutions: Design more robust token mechanisms (e.g., dynamic burn, long-term lock-up rewards) or enhance token intrinsic value through real-world use cases (e.g., OriginTrail’s supply chain payment scenarios).

Source

Sustainability of Business Models
Challenge: Many AI ecosystem projects use token incentives to attract early users, but long-term profitability models remain unclear. For instance, Ritual’s inference market needs to prove its cost-effectiveness over centralized cloud services, and Gensyn’s distributed training needs to validate its commercial competitiveness.
Impact: Lack of sustainable revenue streams could lead to project funding gaps, thus limiting ecosystem expansion.
Potential Solutions: Explore hybrid business models (e.g., subscription + token payments), or sign long-term contracts with enterprise clients (e.g., Nillion’s privacy computing services).

3. Regulatory Challenges

As AI and blockchain technologies become more widespread, the global regulatory landscape increasingly impacts AI ecosystems:

Data Privacy and Cross-Border Compliance
Challenge: Projects like Vana and Nillion emphasize data sovereignty and privacy, but there are significant regulatory differences between countries regarding data storage and cross-border transmission (e.g., EU’s GDPR). Decentralized storage might be seen as a way to evade regulation, which leads to legal risks.
Impact: Increased compliance costs could weaken the global competitiveness of projects and even lead to market bans in certain regions.
Potential Solutions: Develop flexible compliance frameworks (e.g., regional node deployment) or collaborate with local regulators (e.g., OriginTrail’s supply chain compliance practices in the EU).

Source

AI Ethics and Accountability
Challenge: Decentralized AI (e.g., Bittensor’s text generation subnet) may generate harmful content, but on-chain governance may struggle to assign accountability. Global regulators (e.g., EU AI Act) demand strict transparency and accountability for high-risk AI applications, which conflicts with the decentralized nature of anonymity.
Impact: Regulatory pressure could force projects to adjust their architecture or withdraw from certain markets, increasing operational complexity.
Potential Solutions: Introduce on-chain traceability (e.g., OriginTrail-based content verification), or collaborate with industry associations to develop AI ethics standards.

Source

Tokens and Financial Regulation
Challenge: Tokens like $TAO and $VANA may be classified as securities or payment instruments. They must abide by financial regulations (e.g., the SEC’s Securities Act in the US). HyperCycle’s $HYPC, if used for cross-border payments, could trigger anti-money laundering (AML) scrutiny.
Impact: Regulatory uncertainty could limit token circulation and affect the economic model of the ecosystem.
Potential Solutions: Clarify the utility attributes of tokens (instead of investment attributes), or apply for regulatory sandbox testing (e.g., blockchain project models in Singapore).

Industry-Specific Compliance Requirements
Challenge: Kite AI’s medical subnet and Nillion’s privacy computing must comply with industry-specific regulations (e.g., HIPAA for healthcare data protection). Decentralized architectures might struggle to provide the audit capabilities required by traditional systems.
Impact: The complexity of industry compliance could delay project implementation and limit market access.
Potential Solutions: Partner with compliance tech providers (e.g., on-chain audit tools), or focus on markets with lower regulatory thresholds for early deployment.

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Conclusion

As AI technology continues to advance, AI Layer 1 blockchain projects are emerging as critical infrastructure for addressing challenges in compute, data processing, and decentralized coordination. By leveraging innovative architectures, these projects show strong potential to improve AI system efficiency, enhance data privacy, and optimize the distribution and incentivization of computational resources.

While the outlook for AI Layer 1 projects is promising, they are not without hurdles. The intricacies of implementation and cross-domain integration may hinder rapid advancement. Adoption is still in its early stages, with the market’s confidence in AI-blockchain convergence yet to be fully established. Regulatory uncertainty further complicates the landscape. Above all, safeguarding privacy while enabling meaningful data sharing remains a central—and unresolved—challenge, particularly in enterprise-grade deployments.

In summary, although these projects bring technological innovation and transformative opportunities, participants should remain cautious of the associated risks. Continuous technological iteration and robust market validation will be essential to ensure long-term sustainability and resilience in this rapidly evolving field.