Author: Stacy Muur, Crypto Researcher

Translated by: Felix, PANews

Based on a16z's "AI × Crypto" three-point investment logic, crypto researcher Stacy Muur published an article pointing out that the future of AI lies not merely in enhancing intelligence, but in how it integrates into the human economy. In this process, blockchain is an essential infrastructure. Below are the details of the content.

As AI Agents begin to think, act, and trade independently, the core issue becomes: how to enable AI to safely participate in economic activities. Blockchain can provide the necessary coordinating layer to make autonomous Agents trustworthy economic entities.

This article will analyze a16z's "AI × Crypto" investment argument: "Know Your Agent" (KYA) and how crypto trust enables AI Agents to collaborate. Additionally, it discusses why micro-payments are crucial for a sustainable AI economy and which projects and infrastructures are worth paying attention to.

Argument 1: Blockchain can serve as the infrastructure layer for efficient collaboration between AI models and Agents.

AI is gradually evolving to solve problems that only a few experts could tackle. Recently, ChatGPT 5.2 successfully solved a mathematical problem that only a few hundred people worldwide could solve.

In the past, AI was often criticized for frequently producing "errors."

However, with the advancement of AI, these "mistakes" can assist it in brainstorming like a human, combining ideas and establishing connections. To unleash this creativity on a large scale, it is necessary to go beyond a single model and build a hierarchical system. In this system, one AI system freely generates ideas, a second critiques them, a third distills the essence, and a fourth validates the final result.

However, once multiple AIs operate in tandem, two fundamental problems arise:

• Interoperability
• Accountability

Different models differ in format, lacking a shared language or control layer, making coordination extremely difficult. When one AI proposes an idea, another improves it, and a third validates it, it is hard to discern who deserves credit, who should be rewarded, and who should be held accountable.

Cryptocurrencies and blockchain can solve this problem; they do not act as intelligent systems, but rather provide infrastructure to record who did what, when it happened, and how much each contributor contributed. Through verifiable logs, hashes, proofs, and automated incentive mechanisms, cryptographic technology can serve as a ledger and coordination layer, allowing different AI systems to collaborate.

Watch List

1. Covalent: Building a modular data architecture to enable AI Agents to collaborate using shared, verifiable blockchain data. Multiple Agents can collaborate on complex tasks using its AI Agent SDK and "Zero-Employee Enterprise" workflows, while Block Specimens and GoldRush API ensure interoperability between the blockchain and tools. This makes the blockchain the foundation of data availability, verification, and incentive mechanisms.

2. Allora: Developing a decentralized coordination layer that allows multiple models to work together on very specific tasks for better results. Allora leverages cryptographic technology to coordinate participation, verify contributions, and ensure different AI Agents collaborate in a way that allows the system to get smarter over time.

3. Questflow: Building an on-chain orchestration layer where autonomous AI Agents can communicate, coordinate actions, and complete entire workflows together, rather than each Agent executing isolated single tasks as before. Questflow's Multi-Agent Orchestration Protocol (MAOP) allows Agent clusters to work together for reasoning, decision-making, actions, and payment settlements.

4. Gaia: Providing routing, load balancing, and request services for a large number of independently operating AI Agents. Through a standardized runtime environment (WasmEdge), OpenAI compatible API, and Agent combinations (LLMs + RAG + tools), Gaia addresses the large-scale interoperability issues between heterogeneous Agents. The network has over 700,000 nodes and over 29 trillion inference throughput, demonstrating its potential in practical applications. Gaia does not rely on provider trust but instead uses protocol-level mechanisms (such as on-chain IDs, hosted contracts, and staking) to introduce accountability into the execution of AI agents.

5. Sentient: Building the GRID open intelligence network, where over 100 models, Agents, data sources, tools, and computing power providers work together as a single system. GRID routes each query to the most relevant specialist Agent, then merges outputs into coherent results.

The network is online, with over 110 partners, adopting a token-based model to direct rewards towards valuable outputs through staking and actual usage, aligning funding with utility. By allowing Agents to transact directly with $SENT, cryptographic technology becomes a coordinating and incentive layer that enables open, networked intelligence to sustainably scale.

In addition to the above projects, there are two interesting research papers. If you want to learn more and explore these areas further, check out:

1. Emergent Knowledge Intelligent Systems (ISEK): ISEK proposes a collaborative structure where humans and AI Agents not only perform tasks but also discover, negotiate roles, form temporary teams, and settle results through a native protocol loop (publish → discover → recruit → execute → settle → feedback). Trust, memory, and incentives are paramount: Agents have verifiable identities (Agent cards / NFTs), multi-dimensional reputations, and exchange value based on performance through tokenized micropayments.

2. LOKA Protocol: Building a decentralized framework for a trustworthy and ethical AI Agent ecosystem.

LOKA is an academic proposal aimed at governing a large-scale AI Agent ecosystem. It introduces a hierarchical architecture where Agents possess self-sovereign identities (DID + verifiable credentials), context-aware communication, and a decentralized ethical consensus mechanism, enabling Agents to contemplate what they "should do," not just what they "can do." LOKA explores how to embed accountability and ethical norms directly into the protocol layer using on-chain logs, reputation-weighted consensus, and even post-quantum cryptography.

Argument 2: AI Agents need identity, not just intelligence. "KYA" is the missing factor.

AI Agents are now playing a role in the real economy. They handle payments, book services, trade assets, negotiate deals, and operate critical financial infrastructure through APIs, bots, scripts, and automation systems. These Agents are smart enough to function normally; intelligence is no longer a barrier. Identity and trust are the issues. When an Agent makes a payment, places an order, or signs a contract, nobody knows whose actions they belong to, what they can do, or who is responsible when things go wrong. Therefore, websites and merchants default to intercepting them through CAPTCHAs, IP bans, and bot protections.

The solution is "KYA." Agents need cryptographic identities and verifiable credentials, just as humans need legal identities. Each Agent must possess a signing key to prove its creator, represent entities (individuals, companies, or DAOs), its permission limitations, and liability when causing harm. These credentials explicitly define the Agent’s spending, trading, and data access limitations, clearly delineating responsibility.

Watch List

1. Billions is building "KYA," utilizing the Agent JS SDK, allowing Agents to generate their own DID (decentralized identity) and prove control through cryptographic signatures, managing keys through a modular key management system (KMS) to achieve the Agent's identity, accountability, and reputation. Currently, over 2,372,153 users have joined.

By partnering with Privado ID (formerly known as Polygon ID), Billions leverages self-sovereign identities with zero-knowledge proofs for privacy verification across services, devices, and protocols. At its core is $BILL, a utility token with a fixed supply that powers the trust economy, cycling through: network growth → verification activity → revenue → on-chain buyback → supply reduction → value appreciation → network growth, combining actual usage with long-term value accumulation.

2. cheqd.io: Building trust infrastructure for the Agent economy, turning KYA into tangible products. Through Agentic Trust Solutions, AI Agents obtain verifiable DIDs, fine-grained credentials, permissions, and certifications, all anchored in tamper-proof trust registries.

Through the MCP (Model Context Protocol) server, Agents can read/write identities, issue and present verifiable credentials, and prove their creators, permission scopes, and credibility.

3. Vouched.ID: Building a KYA tech stack focused on security, accountability, and compliance. Through MCP-I (Model Context Protocol – Identity), Agents gain verifiable cryptographic identities, authorizations from humans, context-based operational restrictions, and complete audit trails.

This stack works alongside knowthat.ai (a public Agent reputation registry) and the Vouched Agentic Bouncer (intercepting unauthorized or impersonating Agents), ensuring the safe deployment of autonomous AI in regulated real-world environments.

4. ERC-8004 (Trustless Agents): A standard proposed by Ethereum (EIP), which has yet to become a final protocol. Its main goal is to achieve "KYA" at the protocol level. It defines how AI Agents can have verifiable on-chain identities, reputations, and execution proofs, allowing users and services to ascertain an Agent's authorization and trustworthiness without using centralized platforms. This EIP is actively being designed and discussed by the Ethereum Foundation team, with contributions from companies like Coinbase and MetaMask.

Argument 3: Blockchain can enable real-time, usage-based micropayments and nanapayments, automatically compensating creators when AI Agents or tools utilize their content, ensuring fair and transparent income distribution.

AI tools like ChatGPT, Claude, and Copilot are convenient for users, but they also silently disrupt the revenue models of open networks. The network relies on advertising, subscriptions, and pay-per-view to maintain operations, yet AI has fundamentally changed the value cycle:

• Before AI: User searches → clicks on the website → website profits.
• Now: User asks AI → it reads the website → gives an answer → website traffic and revenue decline.

This creates an "invisible tax," where AI consumes information without compensating the creators of that information. If this continues, websites will lose traffic, advertising revenue will plummet, creators will stop publishing content, the open network will shrink, ironically making AI lack fresh high-quality data. While legal intervention could be an option, the progress is too slow, hence there’s an urgent need for technical-level solutions aligned with incentive mechanisms.

A shift towards usage-based compensation models is needed, where creators automatically receive payments in real-time each time AI utilizes information. Content will be paid based on AI usage frequency (similar to how Spotify pays per stream, YouTube pays per view), rather than through fixed license agreements.

This model is realized through micropayments and nanapayments, where AI attributes answers to multiple sources and proportionately distributes small payments using mathematical algorithms, instead of manually allotting them. For example: website A contributes 20%, website B contributes 30%, website C contributes 50%, and payments are allocated proportionately.

Blockchain and cryptocurrencies play a role here; by embedding automated payments directly into the network through smart contracts, AI continues to provide convenience while fairly compensating the creators it relies upon.

Watch List

1. Catena Labs: Building an AI-native financial institution specifically designed for AI Agents to directly participate in the economy. Through the open-source Agent Commerce Kit (ACK), it provides AI Agents with wallets, verifiable identities, payment channels, and rule-based spending controls, enabling them to conduct payments autonomously. ACK supports stablecoin payments, micropayments, and transactions between Agents on blockchain test networks, allowing Agents to automatically compensate other Agents or human creators when utilizing data, content, or services.

2. x402: Integrating micropayments into standard HTTP requests with near-zero friction, enabling AI Agents to immediately pay for content, APIs, and compute power. KITE AI upgrades this payment primitive to a complete execution layer, creating a blockchain that allows autonomous AI Agents to reliably settle on-demand transactions at scale. Kite enables AI Agents to use x402 formatted processes, Agent-native identities, and stablecoin settlements, automatically compensating creators, services, and other Agents at the moment of consumption.

3. Alsa: Building a native AI payment and billing layer, allowing AI Agents to only pay when they execute actions, using a single account, token, and API. It supports token metered on-demand micropayments, backed by low-latency blockchain infrastructure and emerging agent-side payment standards.

Currently, over 10.5 million x402 transactions have been processed (approximately 16% of network activity, mainly on the Base platform), with plans to expand to Solana and Polygon platforms, indicating that native AI micropayments can operate reliably at scale.

Reading Recommendation: a16z: From Identity to Payments, Five Reasons Why Blockchain is a Key Piece of the AI Puzzle

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