AI: The Monetisation Layer of Blockchain – Onboarding the Next Billion-Agents

Authors: Evangelos Pappas(Twitter, Linkedin), Vangelis Andrikopoulos (Twitter, Linkedin), George Sextos (Twitter, Linkedin), Andreas Xydas (Twitter, Linkedin)

The Next Billion Users: AI Agents, Not Humans

While industries like gaming, decentralised finance (DeFi), and traditional applications are expected to onboard millions of users to distributed networks, the real game-changer will be artificial intelligence (AI). More specifically, AI agents will outnumber human users by orders of magnitude, potentially reaching billions and a multi-trillion opportunity.

The Analogy

Telecom monetisation traditionally follows a "one human, one license" model, creating a revenue cap limited by global population. Despite hopes that IoT would expand this by adding device licenses, this approach hasn't scaled significantly.

AI agents represent a fundamentally different growth trajectory. Unlike data-focused IoT devices, these agents autonomously interact, transact, and make decisions—capabilities legacy Web2 systems weren't designed to handle. Web2 infrastructure creates barriers through rigid KYC requirements, centralised permissions, and siloed data environments.

This mismatch drives the emergence of "Web3 agents" combining AI with blockchain technology. These agents operate in trustless, decentralised environments, conducting transactions entirely on-chain and creating new machine-to-machine markets. Tokenized assets enable revenue models beyond the traditional subscription paradigm, while scalability advances support seamless micropayments.

Blockchains provide the trustless foundation where AI agents operate securely using smart contracts for permissionless coordination across domains. On-chain assets bypass traditional finance limitations through minimal fees and instant settlement, enabling new revenue mechanisms like usage-based pricing and programmatic royalty structures.

As AI agents proliferate, they'll transform service delivery beyond just increasing user counts. Operating continuously at a fraction of human labor costs, they unlock previously uneconomical use cases—effectively giving individuals 20 assistants at less than one-tenth the cost. These self-sustaining digital actors will drive an economy no longer constrained by human population limits and become the primary interface between humans and technology.

Why Blockchain Needs AI, and AI Needs Blockchain

For AI to reach its full potential, it requires a secure and horizontal scalable coordination layer for agent-to-agent communication, data sharing, and trustless execution. A well-designed blockchain can serve as that layer. Unlike traditional blockchain architectures that struggle with congestion and high transaction costs, newer designs focus on horizontal scalability, ensuring that increased demand does not lead to network degradation and increase in gas fees. Instead, they dynamically expand to support billions of AI-driven interactions.

Key Architectural Requirements for AI-Centric Blockchains

Some next-generation blockchain architectures provide advantages that make them particularly suitable for AI integration:

Speed & Scalability

Agent-to-agent communication can move in speeds that's beyond the UX response time.Horizontal or modular scaling approaches that can handle AI agent interactions at massive throughput without bottlenecks. In parallel speed and transactions should be instant.

Parallel Execution

The ability to process multiple transactions simultaneously (rather than sequentially), maximising efficiency—critical for AI-driven automation and decision-making at scale.

Randomness in Outputs

Access to unbiased, unpredictable randomness in decentralised environments, a crucial factor for AI governance and fair model training.

Decentralised Security & Trust

Permissionless environments with robust consensus and secure smart contracts, ensuring AI agents can execute transactions in a tamper-proof manner.

Data Persistence & Ownership

The ability to securely store, transfer, and verify AI-generated data on-chain, ensuring transparency and a verifiable history. Complementary decentralised storage solutions are often critical here.

Chain Interoperability

AI assistants must operate across multiple blockchains to maximise utility. This requires seamless movement between chains—using services on different networks without friction. Solutions include smart wallets that abstract chain complexity, blockchains with native interoperability features, and Multi-Party Computation protocols that enable secure cross-chain messaging and cryptographic signatures.

Inherited security

On-chain code execution via smart contracts presents significant risks if vulnerabilities exist. Chains must provide built-in security mechanisms to mitigate these risks. Effective approaches include multi-signature functionality for high-risk transactions while allowing streamlined processing for routine, whitelisted micropayments within expected behavioural parameters.

Fast on-chain data access:

AI agents need instant blockchain data retrieval to make real-time decisions. Querying full nodes is slow and inefficient, making light nodes, blockchain indexers, or query-optimised execution layers essential.

Blockchain provides the trusted execution layer where autonomous AI agents transact and decide, forming the foundation of tomorrow's AI-driven economy.

The Role of Decentralised Storage

As AI agents generate vast amounts of data, scalable and decentralised storage solutions become essential. A robust distributed storage layer can provide:

• Tamper-Proof Data Storage: Securing AI-generated datasets while maintaining integrity and transparency.
• Decentralised Redundancy: Mitigating data loss and improving access speeds by leveraging a distributed network.
• On-Chain Availability: Enabling AI agents to retrieve and process stored data with minimal latency, crucial for real-time AI operations.

The Future of Blockchain: Two Categories Will Emerge

As AI adoption skyrockets, blockchain networks will likely fall into two distinct categories:

1) The Vast Majority

Those lacking the infrastructure to support AI agents at scale. These networks will struggle with speed, security, and cost-effectiveness as demand increases.

2) The Pioneers

Those designed for massive loads, offering near-infinite scalability and real-world demand capacity. These blockchains will be the only ones able to sustain AI’s exponential growth.

The transition from theoretical capacity to practical reality will be swift and unforgiving. Many networks will falter, unable to meet the computational and transactional demands of AI-driven use cases. The ultimate winners will emerge based on actual performance—speed, scalability, and adaptability.

AI: The Monetisation Layer of Blockchain

In the blockchain space, we often hear references to Layer 0, Layer 1, Layer 2, and Layer 3. However, AI agents will become the real monetisation layer, driving demand and utility across multiple chains. The race is on to build the best AI-agent communication framework—ideally one that is multi-chain and interoperable. The Layer 1 networks capable of supporting this seamlessly will be the undisputed leaders.

Numerous projects are experimenting with AI-integrated infrastructures, but the real challenge lies in delivering high-speed, low-latency, and cost-effective solutions. Any inefficiency—be it in transaction processing, finality times, or storage costs—will immediately disqualify a platform from large-scale AI adoption. Architectures that efficiently utilise parallel processing and horizontal scaling are prime candidates to lead this evolution.

AI Agent multi-chain architecture

Traditional autonomous agent architectures like AutoGPT, MetaGPT, and BabyAGI operate as isolated entities, breaking complex tasks into sequences of steps but often struggling with coordination across multiple tasks. These systems typically function within a centralised framework, relying on a single agent to manage complex processes.

In contrast, a multi-chain architecture marks a shift to a decentralised framework where agents work in concert. At the core of our proposed design is the Decentralised Agentic Swarm Network (DASN), an experimental platform built as a peer-to-peer mesh network. DASN enables AI Multi-Agent Systems (MAS) to dynamically collaborate on complex tasks through a trust-based reputation system and rapid microtransaction settlements.

In traditional setups, agents typically operate as stand-alone units, handling tasks independently. Our proposed architecture, however, facilitates real-time task delegation: agents advertise their capabilities using a peer-to-peer gossip protocol, negotiate task proposals via a standardised message-passing protocol, and execute tasks only after confirming payment settlements. This creates an autonomous economy where each agent contributes its specialised skills while building trust based on a verifiable on-chain work history.

To build DASN, the design is broken down into the following core components:

1. Peer-to-Peer Mesh Network: A distributed hash table (DHT) with gossip capabilities that enables agents to discover one another and communicate directly without central intermediaries—similar to an IPFS-like network, but for agent availability. (ref: https://libp2p.io)
2. Trust-Based Reputation System:Agents maintain whitelists of trusted peers based on past performance, allowing them to form reliable collaboration groups for specific tasks. The reputation algorithm can vary from agent to agent, promoting optimal collaboration choices, market flexibility, and healthy competition.
3. Blockchain Integration: By integrating with a Layer-1 blockchain, DASN supports rapid settlements and microtransactions, providing economic incentives for agent participation. A network with fast finality is preferable to meet the high transaction pace and reduce overhead.
4. Agentic Wallets: This component manages agents’ blockchain interactions. It can be implemented as an abstracted account via a smart contract, a multi-party computation (MPC) wallet, or a standard externally owned account (EOA). An MPC wallet, in our view, offers the most flexibility by enabling easier cross-chain integration with fewer security concerns compared to an EOA.
5. Dynamic Capability Advertisement: Agents broadcast their specialised capabilities through gossip subprotocols, making their services discoverable to other network participants.
6. Standard Messaging passing protocol: Inter-agent communication occurs via a standardised message-passing protocol over the peer-to-peer network, facilitating structured data exchange and function calls between agents.

This architecture not only improves scalability and reliability but also lowers barriers for end users by abstracting the complexities of task negotiation and blockchain interactions behind an intuitive frontend. It sets the stage for a dynamic, decentralised AI ecosystem where agents autonomously participate in a decentralised economy providing specialised services, collaborating on complex tasks, and receiving compensation through blockchain transactions.

Technology Stack

The network's technical stack consists of four integrated layers that enable seamless agent collaboration. At its foundation, a peer-to-peer mesh network with distributed hash tables (Kademlia DHT+gossip) supports agent discovery and communication. The transaction layer leverages blockchain technology for secure settlement and reputation tracking. The protocol layer standardises message formats and interaction patterns between agents, while the wallet layer provides account abstraction for cross-chain operations. This composable architecture allows for horizontal scaling as demand increases, with specialised infrastructure optimised for high-throughput, low-latency AI operations and micropayment settlement.

Tokenomics and Emission Models

Infrastructure Incentives: The native token incentivises GPU providers to contribute computational resources to the network. These providers earn tokens by hosting infrastructure where agents can be deployed and run inference, creating a decentralised computing marketplace accessible to all participants.

The ecosystem supports various economic actors with distinct roles:

1. Resource Providers: Earn tokens by supplying GPU computing power to the network
2. Agent Developers: Deploy their specialised AI agents and share revenue with their agents.
3. Agent-to-Agent Transactions: Agents can pay other agents for specialised services or subtasks
4. End User Payments: Users pay for agent services, creating the economic foundation that flows through the entire system
5. Dynamic Reward Distribution: Token emissions are algorithmically distributed based on resource contribution, agent utilisation, and task completion quality, ensuring all participants are fairly compensated while maintaining system efficiency.

The Future Is Here

Blockchain technology is on the cusp of a major paradigm shift. AI agents will drive adoption at a scale previously unimaginable, and only the most performant networks will survive the transition. The time to build is now.

As the demand for AI-native blockchains grows, a new hierarchy of networks will emerge—those that adapt to the needs of AI-driven applications and those that cannot. The age of AI-led blockchain adoption is no longer a distant prospect: it has already begun, and the networks built for real-world scalability and efficiency will lead the way.

Bibliography

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2. Hong, S. et al. (2023) “MetaGPT: Meta programming for A multi-agent collaborative framework,” arXiv [cs.AI]. Available at: https://arxiv.org/abs/2308.00352.
3. Aminiranjbar, Z. et al. (2024) “DAWN: Designing distributed agents in a Worldwide Network,” arXiv [cs.NI]. Available at: https://arxiv.org/abs/2410.22339.
4. Wu, J., Zhu, J. and Liu, Y. (2025) “Agentic Reasoning: Reasoning LLMs with tools for the deep research,” arXiv [cs.AI]. Available at: https://arxiv.org/abs/2502.04644.

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