Author: Stacy Muur, Crypto Researcher
Compiled by: Felix, PANews
Based on a16z's three-point investment logic for "AI × Crypto," crypto researcher Stacy Muur published an article stating 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. The details are as follows.
As AI Agents begin to think, act, and transact independently, the core question becomes: how to safely involve AI in economic activities. Blockchain can provide the necessary coordination layer, making autonomous Agents credible economic entities.
This article will analyze a16z's "AI × Crypto" investment thesis: "Know Your Agent" (KYA) and how crypto trust enables AI Agents to collaborate. Additionally, it discusses why micropayments are crucial for a sustainable AI economy and which projects and infrastructure are worth watching.
Thesis 1: Blockchain can serve as an infrastructure layer for efficient collaboration between AI models and Agents.
AI is gradually advancing to solve problems that only a few experts could handle. 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 frequent "errors."
But as AI progresses, these "mistakes" can help it combine ideas and make connections, much like human brainstorming. To unleash this creativity on a large scale, we need to move beyond single models and build hierarchical systems. In such a system, one AI system freely generates ideas, a second system critiques them, a third refines the best parts, and a fourth validates the final result.
However, once multiple AIs operate together, two fundamental problems arise:
- Interoperability
- Accountability
Different models have varied formats, lacking a shared language or control layer, making coordination very difficult. When one AI proposes an idea, another improves it, and a third verifies it, it's hard to determine who deserves credit, who should be paid, and who is accountable.
Cryptocurrency and blockchain can precisely solve this problem. They don't act as intelligent systems but provide infrastructure to record who did what, when it happened, and how much each contributor contributed. Through verifiable logs, hashes, proofs, and automatic incentive mechanisms, crypto technology can serve as a bookkeeping and coordination layer, allowing different AI systems to collaborate.
Watchlist
1. Covalent: Building a modular data architecture that enables AI Agents to collaborate using shared, verifiable blockchain data. Multiple Agents can use its AI Agent SDK and "Zero-Employee Enterprise" workflows to complete complex tasks collaboratively, while Block Specimens and GoldRush API ensure interoperability between blockchains and tools. This makes blockchain the foundation for data availability, verification, and incentive mechanisms.
2. Allora: Developing a decentralized coordination layer that allows multiple models to collaborate on very specific tasks for better results. Allora leverages crypto technology to coordinate participation, verify contributions, and ensure that different AI Agents work together in a way that makes the system smarter over time.
3. Questflow: Building an on-chain orchestration layer where autonomous AI Agents can communicate with each other, coordinate actions, and collectively complete entire workflows, rather than each Agent executing a single task in isolation. Questflow's Multi-Agent Orchestration Protocol (MAOP) allows clusters of Agents to work together, performing reasoning, decision-making, action, and payment settlement.
4. Gaia: Provides routing, load balancing, and request services for a large number of independently operating AI Agents. Through a standardized runtime environment (WasmEdge), OpenAI-compatible APIs, and Agent composition (LLMs + RAG + tools), Gaia solves the problem of large-scale interoperability between heterogeneous Agents. The network boasts over 700,000 nodes and over 29 trillion inference throughputs, demonstrating its practical potential. Instead of relying on provider trust, Gaia uses protocol-level mechanisms (such as on-chain IDs, escrow contracts, and staking) to introduce accountability for AI agent execution.
5. Sentient: Building the GRID Open Intelligence Network, where 100+ models, Agents, data sources, tools, and computing providers collaborate as a single system. GRID routes each query to the most relevant specialized Agent and then merges the output into a coherent result.
The network is live with over 110 partners and uses a token-based model that directs rewards to valuable outputs through staking and actual usage, aligning capital with utility. By enabling Agents to transact directly in $SENT, crypto technology becomes the coordination and incentive layer that makes open, networked intelligence sustainable at scale.
In addition to the above projects, there are two interesting research papers. If you want to learn more and explore these areas in depth, you can also check out:
1. Emergent Knowledge Intelligence Systems (ISEK): ISEK proposes a collaborative structure where humans and AI Agents not only perform tasks but also discover each other, negotiate roles, form ad-hoc teams, and settle results through a native protocol loop (Publish → Discover → Recruit → Execute → Settle → Feedback). Trust, memory, and incentives are primary essentials: Agents have verifiable identities (Agent cards / NFTs), multidimensional reputations, and exchange value through tokenized micropayments based on work performance.
2. LOKA Protocol: A decentralized framework for building a trustworthy and ethical AI Agent ecosystem.
LOKA is an academic proposal aimed at governing large-scale AI Agent ecosystems. It introduces a layered architecture where Agents have autonomous identities (DID + Verifiable Credentials), graph-aware communication, and a decentralized ethical consensus mechanism, enabling Agents to think about what they "should do," not just what they "can do." LOKA explores how to embed accountability and ethics directly into the protocol layer using on-chain logs, reputation-weighted consensus, and even post-quantum cryptography.
Thesis 2: AI Agents need identity, not more intelligence. "KYA" is the missing factor.
AI Agents are already playing a role in the real economy today. They make payments, book services, trade assets, negotiate deals, and operate critical financial infrastructure through APIs, bots, scripts, and automated systems. These Agents are smart enough to function; intelligence is no longer the barrier. Identity and trust are the problems. When an Agent makes a payment, places an order, or signs a contract, no one knows who these actions belong to, what it can do, or who is responsible if something goes wrong. As a result, websites and merchants default to blocking them with CAPTCHAs, IP bans, and bot protection.
The solution is "KYA." Agents need cryptographic identities and verifiable credentials, just as humans need legal identities. Each Agent must have a signing key to prove its creator, who it represents (individual, company, or DAO), its permission limits, and liability in case of damage. These credentials clearly define the Agent's spending, trading, and data access limits, delineating responsibility.
Watchlist
1. Billions is building "KYA." Using the Agent JS SDK, Agents generate their own DIDs (Decentralized Identifiers), prove control through cryptographic signatures, and manage keys through a modular Key Management System (KMS), enabling Agent identity, accountability, and reputation. Over 2,372,153 users have already joined.
In partnership with Privado ID (formerly Polygon ID), Billions leverages zero-knowledge self-custodied identity for privacy-preserving verification across services, devices, and protocols. At its core is $BILL, a fixed-supply ERC-20 utility token that powers the trust economy, with a cycle of: network growth → verification activity → revenue → on-chain buybacks → supply reduction → value appreciation → network growth, combining actual use with long-term value accumulation.
2. cheqd.io: Building trust infrastructure for the Agent economy, turning KYA into a deliverable product. Through Agentic Trust Solutions, AI Agents obtain verifiable DIDs, fine-grained credentials, permissions, and certifications, all anchored in a tamper-proof trust registry.
Through the MCP (Model Context Protocol) server, Agents can read/write identities, issue and present verifiable credentials, and prove their creator, scope of authority, and trustworthiness.
3. Vouched.ID: Building a KYA tech stack focused on security, accountability, and compliance. Through MCP-I (Model Context Protocol—Identity), Agents obtain verifiable cryptographic identities, authorization from humans, context-based operational limits, and a full audit trail.
This stack is paired with knowthat.ai (a public Agent reputation registry) and Vouched Agentic Bouncer (which blocks unauthorized or impostor Agents), ensuring the safe deployment of autonomous AI in regulated real-world environments.
4. ERC-8004 (Trustless Agents): A proposed standard (EIP) for Ethereum, not yet a final protocol. Its main goal is to implement "KYA" at the protocol level. It defines how AI Agents can have verifiable on-chain identities, reputations, and execution proofs. It allows users and services to determine an Agent's authorization and trustworthiness without relying on centralized platforms. This EIP is being actively designed and discussed by the Ethereum Foundation team, with contributors from companies like Coinbase and MetaMask.
Thesis 3: Blockchain enables real-time, usage-based micropayments and nanopayments, automatically compensating creators when AI Agents or tools use content, ensuring fair and transparent revenue distribution.
AI tools like ChatGPT, Claude, and Copilot are convenient for users but are quietly undermining the revenue models of the open web. The web relies on ads, subscriptions, and pay-per-view to sustain operations, yet AI has completely altered the value cycle:
- Before AI: User searches → Clicks on website → Website monetizes.
- Now: User asks AI → It reads websites → Gives answer → Website traffic and revenue decline.
This creates an "invisible tax" where AI consumes information without paying the creators who produce it. If this continues, websites lose traffic, ad revenue plummets, creators stop publishing content, the open web shrinks, and ironically, AI will lack fresh, high-quality data. While the law can intervene, progress is too slow, so there is an urgent need for technical solutions aligned with incentive mechanisms.
A shift to a usage-based compensation model is needed, where creators are automatically paid in real-time each time their information is used by AI. Content will be paid for per AI use (like Spotify pays per stream, YouTube pays per view), rather than through fixed licensing agreements.
This model is enabled by micropayments and nanopayments. AI would attribute answers to multiple sources and use mathematical algorithms to proportionally distribute small payments automatically, rather than manually. For example: Website A contributes 20%, Website B 30%, Website C 50%, pay proportionally.
This is where blockchain and cryptocurrency come in; by embedding automated payments directly into the fabric of the web via smart contracts, AI can continue to provide convenience while fairly compensating the creators it relies on.
Watchlist
1. Catena Labs: Building an AI-native financial institution designed for AI Agents to participate directly 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 autonomously send and receive payments. ACK supports stablecoin payments, micropayments, and inter-Agent transactions on blockchain testnets, allowing Agents to automatically compensate other Agents or human creators when using data, content, or services.
2. x402: Embeds micropayments into standard HTTP requests with near-zero friction, enabling AI Agents to instantly pay for content, APIs, and compute. KITE AI upgrades this payment primitive into a full execution layer, creating a blockchain that enables autonomous AI Agents to reliably settle pay-per-use transactions at scale. Kite allows AI Agents to use x402-formatted flows, Agent-native identities, and stablecoin settlement to automatically pay creators, services, and other Agents when consumption occurs.
3. Alsa: Building a native AI payment and billing layer that enables AI Agents to pay only when actions are performed, using a single account, token, and API. It supports on-demand micropayments measured per token, powered by low-latency blockchain infrastructure and emerging agent-side payment standards.
Having processed over 10.5 million x402 transactions (about 16% of network activity, primarily on Base), with plans to expand to Solana and Polygon, this demonstrates that native AI micropayments can operate reliably at scale.
Related reading: a16z: From Identity to Payments, Five Reasons Blockchain is a Key Piece of the AI Puzzle
