AI in сrypto: how AI agents and AI tokens are changing Web3

AI-driven automation and blockchain technology are shaping a new era of efficiency, intelligence and decentralization in Web3.

The intersection of artificial intelligence (AI) and blockchain is redefining decentralized finance by transforming trading, asset management and digital ownership. AI-driven trading algorithms are enhancing market efficiency, optimizing DeFi strategies and automating complex financial decisions. Meanwhile, decentralized AI agents are emerging as autonomous entities capable of executing transactions, managing digital assets and interacting with smart contract-based environments within predefined frameworks

Beyond finance, AI is also revolutionizing digital creativity. AI-generated NFTs, in-game assets and evolving metaverse environments are pushing the boundaries of what’s possible in Web3. However, challenges remain, including scalability, security risks and regulatory uncertainties surrounding AI-powered financial operations.

As AI and blockchain technologies continue to converge, they are laying the foundation for a more autonomous and intelligent decentralized economy – one where AI agents interact seamlessly, financial markets adapt dynamically and digital assets evolve in real time.

What are AI agents? 

AI agents are autonomous software programs capable of collecting and analyzing data, then executing actions based on predefined algorithms or learned patterns, often without direct human intervention. These agents can manage wallets, conduct transactions and contribute to managing decentralized physical infrastructure networks (DePIN). Their ability to adapt and learn makes them particularly valuable in areas such as automated DeFi strategies, high-frequency trading and AI-powered NFT creation.

One emerging trend is the rise of Decentralized Autonomous Agents (DAAs) and Decentralized Autonomous Corporations (DACs) - two complementary yet distinct applications of AI in blockchain.

DAAs are autonomous AI agents that interact with blockchain networks to execute transactions, manage digital assets and optimize DeFi strategies with minimal human oversight. These agents operate independently, leveraging smart contracts to perform tasks such as high-frequency execution, liquidity allocation and governance participation. While not all DAAs are self-learning, they can execute orders and interact with dApps in a decentralized and trust-minimized environment.

DACs, by contrast, are decentralized corporate structures governed by AI-driven protocols rather than individual agents. They may leverage Trusted Execution Environments (TEEs) or other cryptographic mechanisms to securely execute AI-driven operations, such as managing digital economies, distributing profits and automating business logic. Unlike DAAs, which operate independently on behalf of users or systems, DACs resemble decentralized companies, where tokenized ownership or governance models allow stakeholders to participate in decision-making and revenue-sharing structures.

What are AI tokens?

AI tokens are blockchain-based assets that serve as the economic foundation for AI-driven ecosystems. Rather than being AI-powered entities themselves, these tokens function as a medium of exchange, facilitating transactions, incentivizing participation and supporting governance within AI-integrated blockchain platforms.

AI tokens enable access to AI-driven services, incentivize data sharing and contribute to decentralized governance. Unlike AI agents, which autonomously execute tasks and make decisions, AI tokens provide the financial and incentive mechanisms that enable human users to interact with these ecosystems.

AI token applications

• Payment for AI services: many blockchain platforms that integrate AI function as decentralized marketplaces where users can access AI models, machine learning tools or computational resources. AI tokens typically serve as the primary medium of exchange for these services.
• Data sharing incentive: AI tokens incentivize data contributors - such as individuals, developers and organizations - who provide valuable datasets for AI training. This helps facilitate a decentralized supply of training material for AI algorithms within blockchain-based AI platforms. For example, SingularityNET (AGIX) enables users to purchase AI services using AGIX tokens, and it is set to merge into the Artificial Superintelligence (ASI) initiative.
• Governance and decision-making tool: Some AI tokens serve as governance tokens, allowing holders to vote on protocol upgrades, funding allocations and project roadmaps. The planned ASI token - formed from the anticipated merger of FET, AGIX, and OCEAN - is designed to facilitate decentralized governance within the Superintelligence Alliance.
• Staking mechanism in AI ecosystems: Some AI tokens are used in staking mechanisms to incentivize model accuracy and enhance security. Numerai (NMR), for instance, requires participants to stake tokens on their predictions, rewarding or penalizing them based on performance.

Next-gen NFTs and AI-generated digital assets

Next-gen NFTs and AI-generated content AI are also making an impact in the NFT space.  Using machine learning techniques like generative models and neural networks, AI can create unique artworks, intelligent NFTs (iNFTs) and evolving digital assets that change based on user interactions. Projects like Alethea AI (ALI) are pioneering this concept, enabling NFTs that adapt and respond in real time.

Beyond collectibles, AI is expanding NFTs into Web3 gaming and the metaverse. AI-generated in-game assets, procedurally generated storylines and interactive NPCs are transforming digital worlds where players can trade, own and monetize AI-created items as NFTs. While not all AI-generated assets are tokenized, blockchain enables verifiable ownership and provable scarcity, key factors in maintaining digital asset value.

However, challenges remain. The rise of AI-generated art and NFTs raises ethical and legal questions about ownership, authenticity and copyright. Should the creator of the AI algorithm, the user who trained it, or the marketplace where it was sold own the rights to an AI-generated NFT? As AI-driven creativity becomes more widespread, regulatory and industry standards will need to address these challenges.

Challenges and future outlook

While AI and blockchain together offer enormous potential, significant hurdles remain. Running AI on-chain presents computational and security challenges. Advanced cryptographic techniques, such as zero-knowledge proofs (ZK-proofs) for privacy and verification, and off-chain AI oracles for efficient processing, are being explored to balance decentralization, security and scalability.  

Scalability remains a major concern, as most blockchain networks are not optimized for AI’s real-time processing demands. Layer 2 scaling solutions and specialized AI-focused blockchains are being developed to address these limitations. Additionally, AI models are still prone to hallucination risks, where incorrect or misleading outputs could result in financial losses.

Regulatory uncertainty also poses a challenge. AI-powered financial agents operating autonomously introduce legal and ethical complexities, including issues of accountability and compliance. Governments and regulatory bodies will need to develop frameworks for responsible AI use in blockchain, addressing concerns such as compliance risks in DeFi, algorithmic bias in financial decision-making and ethical AI deployment.

Despite existing challenges, AI-driven crypto applications are set to drive the next wave of Web3 innovation, unlocking new possibilities in decentralized finance, gaming and digital ownership.

Stay tuned for more insights from 1inch as we explore the latest trends in DeFi!