Imagine a decentralised internet where intelligent agents interact, ideate, and collaborate, powered by the combined forces of AI and blockchain technology.

This is the promise of Web3 AI, where artificial intelligence flourishes in a trustless, transparent, user-centric, and decentralised environment provided by Web3.

This combination of AI and blockchain is giving rise to decentralised AI applications on layer 1 blockchain platforms. In a decentralised AI framework, data and computations are spread across multiple nodes, enhancing security and fostering trust among participants.

In this article, we'll explore four ways that layer 1 blockchains are key to building a Web3 AI ecosystem, and various challenges to be navigated along the way.

What Do Layer 1 Blockchains Do?

Layer 1 blockchains are the foundational layer or base networks of the blockchain ecosystem. Think of them as the bedrock – independent and self-sufficient chains like Bitcoin and Ethereum that provide the essential infrastructure for decentralised applications and transactions.

In the context of Web3 AI, layer 1 blockchains have unique characteristics that make them ideally suited for supporting the development and deployment of AI dApps. These include:

Security: Layer 1 blockchains provide an immutable and tamper-proof ledger, ensuring the integrity of AI models, training data, and transaction history. This is crucial for building trust and accountability in AI systems.
Scalability: AI applications often require massive computational power and vast amounts of data. Layer 1 blockchains are designed to handle these demands, with ongoing innovations in consensus mechanisms and network architecture to improve transaction throughput and reduce latency.
Decentralisation: Layer 1 blockchains are not controlled by any single entity and this fosters a collaborative and permissionless environment for AI development, where anyone can contribute and benefit from the network

Some of the most prominent layer 1 blockchains that are well-suited for AI development include Ethereum, with its robust smart contract capabilities, Solana, known for its high transaction speeds, and Polkadot, which enables cross-chain interoperability.

1. Secure and Transparent Data Storage

Decentralised AI relies on vast amounts of data to train and improve its algorithms. Layer 1 blockchains offer a secure and transparent way to store this data. Transactions on the blockchain are encrypted and distributed across a network of computers, making them tamper-proof and resistant to censorship. This ensures the integrity of the data used to train the AI, preventing bias or manipulation from any single source.

*Learn more about what aelf, AI layer 1 blockchain, does for users and developers on its website*

An example is the aelf blockchain, a cutting-edge AI layer 1 blockchain that offers parallel processing, cloud-native architecture, and AI tools, ensuring greater scalability. aelf supports the creation, integration, and deployment of smart contracts and decentralised apps (dApps) through its native C# software development kit (SDK) and additional SDKs in languages such as Java, JS, Python, and Go. Their robust AI blockchain ecosystem nurtures a wide range of dApps such as Portkey, AwakenSwap, ETransfer, Project Schrodinger, HamsterWoods, and more.

By leveraging aelf's AI blockchain infrastructure, decentralised AI systems can securely store and access the vast amounts of data needed for sophisticated algorithm development, significantly enhancing the reliability and performance of AI applications.

2. Decentralised Computing Power

Training complex AI models requires immense computational resources. Blockchains with AI capabilities can facilitate the creation of decentralised computing networks, where users can contribute their unused computing power to the network and earn rewards in return, usually in the form of cryptocurrency. This distributed approach provides the necessary processing power for training and running decentralised AI models without relying on centralised cloud providers.

Decentralised computing marketplaces like Golem allow companies to contribute their spare server capacity to the network. Golem functions as a marketplace, matching these companies with users who require that power for demanding tasks like training AI models.

Through Golem, companies can rent out their unused server capacity for a fee, usually paid in Golem's cryptocurrency, GLM. This contributes valuable resources to the network while allowing companies to earn rewards for their idle hardware. This decentralised and Web3 approach offers a cost-effective and scalable way to tackle complex computing needs.

3. Coordination and Collaboration

Decentralised AI projects often involve collaboration between stakeholders like Web3 developers and crypto investors. Blockchains offer an avenue for collaboration through Decentralised Autonomous Organisations (DAOs) where smart contracts automate tasks and govern the interactions between different components of the AI system. This fosters trust and transparency between participants, ensuring that everyone involved adheres to the agreed-upon rules.

TMRWDAO supports the creation of DAOs through AI assistance and governance tools

An example of this is Network DAO, deployed on TMRWDAO within the aelf AI blockchain ecosystem, which aims to achieve fully decentralised governance of the aelf AI blockchain. It allows communities to vote for Block Producers (BPs) and enables BPs to participate directly in decision-making processes. By leveraging smart contracts,

Network DAO ensures transparency, security, and the trustless execution of tasks. It empowers community members to submit proposals, supports establishing and managing decentralised organisations, facilitates transparent elections for BPs, simplifies smart contract management, and enables the trading of resource tokens.

This comprehensive approach enhances collaborative efforts within the Web3 AI and blockchain ecosystem, ensuring that decentralised AI projects can leverage the collective intelligence and resources of a global Web3 and crypto community.

4. Tokenisation and Incentives

Blockchains enable the creation of tokens, digital units of value, that can represent various things in a decentralised AI and blockchain ecosystem. These tokens can be used to incentivise data contribution, participation in training processes, and access to AI services. This creates a self-sustaining economic model that drives the development and growth of decentralised AI.

*Screenshot from Ocean Protocol's Website*

For instance, Ocean Protocol uses tokens to incentivise data sharing and ensure that AI models have access to diverse and high-quality datasets. Data owners can tokenise their datasets through their platform, essentially creating access rights they can sell on the Ocean Market. This incentivises data sharing while allowing owners to retain control and ensure privacy through techniques like secure multi-party computation. By facilitating high-quality and diverse data access, Ocean Protocol fuels the development of powerful AI models in a decentralised way while fostering collaboration between data providers, AI developers, and researchers.

Challenges and Considerations

While layer 1 blockchains offer a powerful foundation, there are challenges to consider. Scalability is a significant issue; many layer 1 blockchains struggle to handle a high volume of transactions, which can slow down processing and increase costs, posing a bottleneck for large-scale decentralised AI applications.

Security is another concern. Although blockchains are secure overall, vulnerabilities in specific blockchains can be exploited to compromise the AI system. Interoperability is also a challenge, as different blockchains often have their own protocols and standards, making it difficult for decentralised AI systems on different blockchains to interact with each other.

Developers are constantly working on improving layer 1 blockchains to address these challenges. New layer 1 solutions are emerging that are specifically designed for scalability and interoperability. For example, Ethereum 2.0 aims to significantly increase transaction throughput and reduce fees, making it more suitable for AI blockchain applications. Similarly, projects like Polkadot and Cosmos focus on creating interoperable blockchain networks that interact seamlessly.

Emerging Trends of Layer 1 Blockchains and AI

Today, layer 1 blockchains already serve compelling applications such as facilitating the collaboration of AI models, building of AI dApps, and decentralised governance in AI DAOs. The transparent and secure nature of the layer makes this all possible.

Given the speed of innovation in the Web3 and AI intersection, there are bubbling trends of specialised layer 1 chains, AI NFTs, and AI metaverse.

Specialised layer 1 blockchains for AI: They are specifically designed to support AI applications. These chains are optimised for the unique requirements of AI, such as high throughput for processing large datasets and low latency for real-time AI inference.
Integration with NFTs: Other than artworks, AI agents can also be tokenised these days. This creates new markets for AI assets, enable fractional ownership of AI models, and incentivise data sharing and collaboration.
AI in the Metaverse: Metaverse offers immersive 2D or 3D experiences for users, and AI can take away the human control if need be — think AI-powered NPCs (non-player characters) or virtual agents, as well as the automation of content creation.

Unlocking AI's Full Potential in the Web3 and Blockchain Space

The synergy between decentralised AI and layer 1 blockchain platforms unlocks unprecedented opportunities within the Web3 ecosystem. Blockchain's inherent transparency, security, and decentralisation are making AI applications more reliable and trustworthy. As the AI and blockchain landscape continues to evolve, addressing emerging trends and challenges will be essential to harnessing the full power of this transformative technology.

In embracing this evolution, aelf has pivoted to be a dedicated AI blockchain, incorporating AI tools and functionalities like chatbots and smart contract audits into the blockchain. On top of that, high profile partnerships have also been forged with leading AI players in the space, including Netmind.ai, Aethir, NAWS.ai, ChainGPT, Neurochain, and more.

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*Disclaimer: The information provided on this blog does not constitute investment advice, financial advice, trading advice, or any other form of professional advice. aelf makes no guarantees or warranties about the accuracy, completeness, or timeliness of the information on this blog. You should not make any investment decisions based solely on the information provided on this blog. You should always consult with a qualified financial or legal advisor before making any investment decisions.

About aelf

aelf, an AI-enhanced Layer 1 blockchain network, leverages the robust C# programming language for efficiency and scalability across its sophisticated multi-layered architecture. Founded in 2017 with its global hub in Singapore, aelf is a pioneer in the industry, leading Asia in evolving blockchain with state-of-the-art AI integration to ensure an efficient, low-cost, and highly secure platform that is both developer and end-user friendly. Aligned with its progressive vision, aelf is committed to fostering innovation within its ecosystem and advancing Web3 and AI technology adoption.

For more information about aelf, please refer to our Whitepaper V2.0.

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Tool/Platform	What's it about
ChainGPT	An AI-powered assistant for the blockchain and crypto industry, specializing in content creation, language translation, smart contract coding, and more.
aelf	A high-performance, customisable multi-chain blockchain framework with AI-powered smart contracts and a developer-friendly environment.
Quick Intel	An AI-powered crypto security platform that helps investors avoid scams, rug pulls, and honeypots by analysing smart contracts, social sentiment, and on-chain data.
Cogwise AI News Aggregator	An AI-powered news aggregator that analyses real-time news and global trends to predict market movements in the crypto market.
OpenAI Codex	An AI system that transforms natural language into code, automating code creation for smart contracts, blockchain applications, and other development tasks.
QnA3	An AI-powered Web3 knowledge hub that provides real-time information and insights on cryptocurrencies, blockchain projects, and the broader Web3 ecosystem, with unique Solana Actions integrations.
Fetch.ai	A platform that combines AI and blockchain to create autonomous economic agents (AEAs) for various applications, including supply chain optimisation and smart city management.
Octavia	A decentralised digital identity (DID) platform for creating and managing self-sovereign digital identities, empowering users to control their data and online presence.
TensorFlow	A versatile open-source machine learning framework used for building AI models that can analyse blockchain data, enhance security, and predict market trends.
Alethea	A platform that creates 'Intelligent NFTs' (iNFTs) with unique personalities and the ability to engage in conversations, powered by AI and blockchain technology.

Chain	URL	Explorer
AELF	https://aelf-test-node.aelf.io/	https://explorer-test.aelf.io/
tDVW	https://tdvw-test-node.aelf.io/	https://explorer-test-side02.aelf.io/

Feature	Description
Inheritance	ERC-404 inherits from the interfaces IERC-404, IERC721 Receiver, and IERC165, implementing functions specified in these interfaces, behaving as both ERC20 and ERC721.
Data Structures	Mappings track balances, allowances, approvals, and ownership of tokens. Double-ended queue stores ERC721 token IDs. Data for managing ownership and indices of ERC721 tokens.
Constructor	Initialises token name, symbol, decimals, and other necessary variables for ERC20 and ERC721 tokens.
Token Minting and Burning	_mintERC20 function for minting ERC20 tokens. _retrieveOrMintERC721 function for minting ERC721 tokens and retrieving from a bank. _withdrawAndStoreERC721 function for depositing ERC721 tokens into the bank.
Transfers	Functions for transferring ERC20 and ERC271 tokens between addresses. Distinguishes between token types based on ID. _transferERC20WithERC721 function handles transfers involving both types.
ERC20 and ERC721 Interactions	Approve function handles approvals for both token types. Functions like transferFrom, safeTransferFrom, and permit handle transfers and approvals.
EIP-2612 Support	Supports EIP-2612 permit functionality for ERC20 tokens.
Interface Compatibility	Implements necessary functions for ERC-404 and ERC165 interfaces, indicating compatibility with ERC20 and ERC721 standards.

Role	Max Number
BP	2N+1, N starts from 8 in 2022
Candidate node	5*(2N+1)
Voter	No limit

Benefits	Description
Makes smart contracts 'smarter'	AI-driven smart contracts can autonomously modify terms based on predefined conditions and real-time data input, ensuring that the contracts are not just automated but also intelligent and self-improving over time.
Levelled up data analysis	Data is the new oil, and AI is the machinery that refines it. It is possible for AI to reduce large-volume blockchain data analysis time by up to 90%.
Boosts security	Through machine learning models, AI can prevent frauds and detect security threats in real time. It can identify potential breaches, predict future threats, and adapt to evolving security measures.
Lowers carbon footprint	AI algorithms help optimise energy consumption in blockchain operations, given the flak drawn by activities like Bitcoin mining.

Building the Web3 AI Ecosystem: The Role of Layer 1 Blockchains