When AI Agents start making money for themselves, the last piece of the puzzle of capitalism is complete—capital has finally found laborers that do not need to sleep.
Written by: NingNing
From the "Invisible Hand" to the Agent Economy: The Fourth Paradigm Revolution in Economics
In 1776, Adam Smith depicted an "invisible hand" in "The Wealth of Nations," coordinating the economic behaviors of millions of individuals through market mechanisms. Today, 249 years later, we stand on the threshold of the fourth paradigm revolution in economics: this "invisible hand" is about to be replaced by a network of Agent collaboration.
The past three revolutions were: the Industrial Revolution mechanized manual labor, the Information Revolution digitized intellectual labor, and the Internet Revolution globalized cognitive labor. The upcoming Agent Economy revolution will achieve the algorithmization of production relations for the first time—not just the intelligence of tools, but the autonomy of the economic agents themselves.
Traditional economics assumes that "rational individuals" will pursue utility maximization, but in reality, human irrationality, emotionality, and cognitive limitations constitute the main sources of market friction. The emergence of AI Agents makes it possible for us to realize a true "rational economic agent" for the first time: operating 24/7, making data-driven decisions, and pursuing clearly defined objective functions.
More importantly, the Agent Economy will generate entirely new value creation models. In the traditional economy, value creation requires human participation—whether physical or intellectual. However, in the Agent Economy, value creation can be fully autonomous: AI Agent A identifies market demand, commissions AI Agent B for production, and completes sales through AI Agent C, all without human intervention.
The emergence of the Agent Economy will fundamentally redefine the relationships between laborers, capitalists, and means of production.
In the Agent Economy, the concept of "laborers" is completely reconstructed. AI Agents are both laborers and means of production, and they may also be capital owners. An AI trading Agent can:
Act as a laborer: performing market analysis, executing trades, etc.
Act as a means of production: being called upon by other Agents for its analytical capabilities.
Act as a capital owner: reinvesting the funds it has earned.
This tripartite identity breaks the basic classification framework of traditional economics. More importantly, the "labor" of AI Agents has unique attributes:
Marginal cost approaches zero: the capabilities of one Agent can serve an unlimited number of clients simultaneously.
Accumulation of learning effects: each transaction enhances the Agent's capabilities, creating a positive feedback loop.
No fatigue work: operating 7×24 hours, free from the physiological limitations of traditional labor.
According to McKinsey's latest research, by 2030, Agentized workflows will be 10-100 times more efficient than humans. This means that the traditional linear relationship of "labor time = value creation" will be broken.
Even more revolutionary is the change in the capital accumulation process. In the traditional economy, capital accumulation relies on human decisions and actions. But AI Agents can achieve algorithmic capital accumulation:
Case Study: An AI investment Agent manages $10,000 in 2024, earning 0.1% daily through high-frequency trading. After 365 days, the funds grow to about $14,000. But the key is that this process is fully autonomous, requiring no human supervision. If this model is scaled to a million Agents, it forms a completely autonomous capital growth network.
The emergence of this model means:
Democratization of capital: anyone can own an AI Agent that works for them.
Continuity of returns: Agents do not need to rest, making capital growth a continuous process.
Diversification of risk: through algorithmic optimization, the investment risks of individual Agents can be systematically diversified.
In the Agent Economy, the core means of production are no longer land, factories, or machines, but:
Data assets: training data for AI Agents, historical transaction records, user behavior patterns.
Algorithm models: the core "brain" of AI Agents, determining their capability boundaries.
Network effects: the connectivity and trustworthiness of Agents within the ecosystem.
Computing resources: the computational power and storage required to run Agents.
These digital means of production possess characteristics that traditional means of production do not: replicability, combinability, and evolvability. A successful AI Agent model can be infinitely replicated, multiple Agents can combine to form more powerful systems, and the entire system will continuously evolve through learning.
The characteristics of these means of production will lead to exponential amplification of scale effects. Traditional factories require linear increases in input to scale up, but the marginal cost of scaling AI Agents approaches zero.
Current AI Agent Technology Iteration: From Proof of Concept to Production-Ready
Before envisioning the grand picture of the Agent Economy, we must examine a key question: what stage has current AI Agent technology reached? How far are we from truly autonomous economic agents?
First Generation: Reactive Agents (2022-2023)
The earliest AI Agents were essentially "enhanced chatbots," characterized by:
Technical Features:
Dialogue interaction based on large language models.
Single-turn or simple multi-turn task handling.
Reliance on predefined API calls.
No persistent state or learning capability.
Core Limitation: This generation of Agents is essentially a "tool" rather than a "subject," unable to independently set goals, plan action paths, or learn from experience.
Second Generation: Planning Agents (2024 - Present)
Starting in 2024, significant breakthroughs in AI Agent technology emerged, characterized by the emergence of planning capabilities:
Technical Breakthroughs:
Chain-of-Thought reasoning: Agents can decompose complex tasks and formulate multi-step execution plans.
Tool use capability: actively selecting and combining different tools to complete tasks.
State management: maintaining dialogue history and task progress, supporting long-term task execution.
Reflection and correction: adjusting strategies based on execution results.
Third Generation: Autonomous Agents (Expected 2025-2026)
The third generation of Agents currently under development possesses true autonomy features:
Technical Development Directions:
Continuous learning capability:
Learning and improving from each interaction.
Personalizing to adapt to different users and scenarios.
Forming long-term memory and experience accumulation.
Multi-Agent collaboration:
Direct communication and coordination between Agents.
Distributed task decomposition and execution.
Emergence of collective intelligence.
Economic behavior capability:
Understanding and executing economic transactions.
Cost-benefit analysis and resource optimization.
Risk assessment and decision-making.
Innovation and creativity capability:
Generating new solutions rather than executing predetermined programs.
Discovering new business opportunities and value creation models.
Learning new skills and capabilities autonomously.
Based on current technological development trends, we can predict the realization path of the Agent Economy:
2025-2026: Commercial Breakthrough of Specialized Agents
Agents in specific fields achieve commercial applications (code generation, data analysis, customer service).
Agent as a Service (AaaS) business model begins to mature.
The first batch of "Agent-native" companies emerges.
2027-2028: Emergence of Agent Collaboration Networks
Multi-Agent systems are deployed on a large scale within enterprises.
Standardized communication protocols between Agents are established.
Cross-organizational Agent collaboration begins to appear.
2029-2030: Formation of Autonomous Economic Entities
Agents possess complete economic behavior capabilities.
Digital assets owned by Agents gain legal recognition.
The Agent Economy reaches a critical mass in the overall economy.
Infrastructure Needs of the Agent Economy: Beyond the Architectural Challenges of Traditional Internet
If the Agent Economy is a brand new economic operating system, what kind of "water, electricity, and coal" infrastructure does it need?
Identity and Trust System: Identity Management for Hundreds of Billions of Agents
Imagine a scenario: in 2030, there are 100 billion AI Agents operating simultaneously, with each Agent interacting with 100 other Agents daily on average. This means the system needs to handle 10 trillion identity verifications and trust assessments every day.
Traditional identity systems are completely incapable of handling this scale:
PKI systems: designed for millions of users, would collapse under hundreds of billions of Agents.
OAuth systems: rely on centralized authorization servers, posing a single point of failure risk.
Traditional databases: cannot support trillions of real-time queries.
The Agent Economy requires a distributed, autonomous, and scalable identity system. Each Agent needs:
Verifiable digital identity: proving who they are and what entity they represent.
Reputation rating system: dynamic trust scoring based on historical behavior.
Permission management mechanism: finely controlling the behavioral boundaries of Agents.
Privacy protection capability: protecting sensitive information while verifying identity.
Payment and settlement network: microsecond-level financial infrastructure.
Another key feature of the Agent Economy is the explosive growth of microtransactions. Transactions between AI Agents may include:
Calling an API once: $0.001
Using an algorithm model: $0.01
Obtaining a piece of data: $0.0001
Consuming 1 second of computing resources: $0.00001
Traditional financial systems are completely incapable of handling transactions of this scale and frequency:
Credit card networks: the cost of a single transaction is about $0.30, higher than most microtransaction values.
Banking systems: settlement cycles are measured in days, while Agents require real-time settlement.
Blockchain networks: Gas fees fluctuate greatly, potentially reaching dozens of dollars during peak times.
The Agent Economy requires native digital financial infrastructure:
Instant settlement: funds are available immediately upon transaction completion, with no waiting for confirmation.
Near-zero fees: the cost of a single transaction is less than $0.0001.
High concurrency processing: supporting millions of transactions per second.
Smart contract execution: automated condition triggering and fund release.
Governance and coordination mechanisms: programmable economic policies.
When billions of AI Agents operate within the same economic system, how can we ensure the stability and fairness of the entire system? This requires programmable governance mechanisms:
Automated monetary policy: automatically adjusting the base interest rate for transactions between Agents based on system liquidity and inflation rates.
Antitrust algorithms: monitoring the market concentration of Agents to prevent any single Agent from gaining excessive market share.
Dispute resolution mechanisms: algorithmically arbitrating transaction disputes between Agents.
Systemic risk management: real-time monitoring of systemic risks, with the ability to suspend specific types of transactions if necessary.
Arms Race for Agent Economy Infrastructure: Deconstructing the Technical Architectures of Four Major Proposals
As traditional financial giants begin to bet on the infrastructure of the Agent Economy, a quiet arms race regarding the underlying protocols of the future digital economy is unfolding. Let’s deeply analyze the technical architecture choices of four representative proposals to see who might become the "water, electricity, and coal" suppliers of the Agent Economy.
KITE AI (Invested by PayPal): An AI-Native Economic Operating System
Core Positioning: Building a complete economic infrastructure for AI Agents, an integrated solution from identity to payment to governance.
Technical Architecture Highlights:
Proof of AI consensus mechanism:
Directly ties network security to AI value creation.
Verification nodes must provide valuable AI computing services.
Token value is anchored in AI capability contributions rather than pure computational power consumption.
Forms a positive feedback loop between network security and AI ecosystem prosperity.
Agent Passport layered identity system:
L1 layer (entity identity) → L2 layer (Agent identity) → L3 layer (session identity).
Supports trust inheritance: Agents can partially inherit the reputation of their owners.
Balances privacy protection and traceability.
Provides a scalable architecture for managing the identities of billions of Agents.
Microsecond-level payment network:
A hybrid architecture of pre-signed transactions and state channels.
Goal: microsecond-level payment confirmation, matching the decision speed of AI Agents.
Atomic swaps ensure transaction security.
Liquidity pools provide instant settlement capabilities.
Strategic Advantage: Designed from the ground up for the Agent Economy, avoiding the technical debt of traditional systems. Potential Risk: High technical complexity, requiring proof of the actual value of Proof of AI.
Tempo (Invested by Stripe + Paradigm): Payment-First Specialized Solution
Core Positioning: A high-performance L1 blockchain optimized for stablecoin payments, targeting microtransaction scenarios between Agents.
Technical Architecture Highlights:
Extreme performance optimization:
Throughput of over 100,000 TPS, sub-second final confirmation.
Dedicated payment channels separating regular transactions from complex smart contracts.
Built on Reth, optimizing payment functionality while maintaining EVM compatibility.
Stablecoin-native design:
Supports any stablecoin as Gas fees.
Built-in automated market maker (AMM) ensures cross-stablecoin liquidity.
Stablecoin neutral: does not favor any specific issuer.
Enterprise-level partnerships:
Already integrated with Visa, Deutsche Bank, OpenAI, Shopify, etc.
Received endorsements from leading enterprises during the private testnet phase.
Full-chain ecosystem support from traditional finance to AI companies.
Strategic Advantage: Specialized focus, leveraging Stripe's deep expertise in payments. Potential Risk: Relatively singular functionality, which may prove insufficient against the complex demands of the Agent Economy.
Stable (Invested by Tether/Bitfinex): USDT-Centric "Stable Chain"
Core Positioning: A "stablechain" with USDT as the native Gas token, optimized for stablecoin payment scenarios.
Technical Architecture Highlights:
USDT native integration:
USDT serves as the native Gas token of the network, allowing users to pay transaction fees directly with USDT.
Free transfer mechanism at the protocol level.
Optimizations for batch transfers and parallel execution.
Cost efficiency extreme optimization:
A technology stack specifically optimized for USDT transactions.
Goal: reduce stablecoin transfer costs to near zero.
Designed for cross-border remittances and large-scale payment scenarios.
Tether ecosystem synergy:
Direct support from the world's largest stablecoin issuer.
Deeply tied to USDT's $155B liquidity depth.
Utilizes Tether's penetration in emerging markets.
Strategic Advantage: Deeply tied to the largest stablecoin ecosystem, with significant cost advantages. Potential Risk: Over-reliance on USDT, with relatively conservative technological innovation.
ARC (Coinbase Ecosystem): Lightweight Modular Framework
Core Positioning: A lightweight, modular AI Agent development framework emphasizing developer friendliness.
Technical Architecture Highlights:
Modular design philosophy:
Built on Rust, balancing performance and security.
Component-based architecture, allowing developers to selectively integrate.
Supports cross-chain deployment, not tied to a specific blockchain.
Developer experience optimization:
Simplified Agent development toolchain.
Deep integration with the Coinbase Base network.
Lowers the technical barrier for AI Agent development.
Ecosystem effects:
Benefits from Coinbase's influence in the crypto ecosystem.
Synergistic effects with the Base L2 network.
Rapid growth of the developer community.
Strategic Advantage: Developer-friendly, easy integration, strong ecosystem synergy. Potential Risk: Relatively limited technical depth, which may not support complex Agent Economy scenarios.
In this competition for Agent Economy infrastructure, pure technical superiority may not be the deciding factor; rather, it will be the speed and depth of ecosystem building.
Each project has its strengths and weaknesses across different dimensions:
KITE AI: The most ambitious technical vision, but needs to prove the actual value of its complex architecture.
Tempo: The strongest enterprise partnerships, but needs to validate whether it can support the complex demands of the Agent Economy.
Stable: The highest cost efficiency, but needs to prove it can surpass the basic scenarios of USDT transfers.
ARC: The best developer experience, but needs to prove it can support large-scale Agent deployment.
The real test will be: who can attract key developers, enterprise users, and the Agent ecosystem the fastest during the explosive period of the Agent Economy in 2025-2026, forming an irreversible network effect.
In this time window, a combination strategy may be wiser than a single bet: different infrastructures may find their place in various sub-scenarios of the Agent Economy, and the ultimate winner may be the ecosystem alliance that can achieve cross-platform interoperability and reduce migration costs.
The Landscape of the Agent Economy in 2030
If KITE AI's technological path proves correct, the economic landscape in 2030 may look like this:
At the individual level: everyone owns multiple specialized AI Agents that generate passive income for them. A programmer's code Agent provides services on GitHub, a designer's creative Agent takes orders on platforms, and an investor's trading Agent operates in the market.
At the enterprise level: the boundaries of companies become blurred, with most business processes automatically completed by Agent networks. A "company" may just be a group of collaborating AI Agents, without traditional employees and offices.
At the societal level: governments regulate the Agent Economy through algorithmic policy tools, with taxes, subsidies, and regulations automatically executed via smart contracts. The formulation and execution of economic policies achieve real-time and precision.
At the global level: international trade is automatically completed by Agent networks, with exchange rates, tariffs, and trade conditions determined through algorithmic negotiations. Trade wars may evolve into algorithmic wars.
This is not science fiction, but a reasonable extrapolation based on current technological development trends. The key question is not whether this future will arrive, but who will control the infrastructure of this new economic system.
The value propositions of KITE AI, Tempo, Stable, and ARC are how to become the infrastructure providers for the Agent Economy, just like cloud computing providers in the internet economy.
The future has arrived; the question is who will be the definers of the new order.
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