Agent Economy: The Economic Basis of Sovereign Individual Capitalism Era

From the "Invisible Hand" to the Agent Economy: The Fourth Paradigm Revolution in Economics

In 1776, Adam Smith described an "invisible hand" in "The Wealth of Nations," which coordinates 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 an Agent collaborative network.

The last three revolutions were: the industrial revolution mechanized physical labor, the information revolution digitized intellectual labor, and the internet revolution globalized cognitive labor. The upcoming Agent Economic Revolution will for the first time realize the algorithmization of production relations—not only the intelligence of tools but also the autonomy of economic entities themselves.

Traditional economics assumes that "rational individuals" will pursue utility maximization, but in reality, human irrationality, emotionality, and cognitive limitations are the main sources of market friction. The emergence of AI Agents for the first time allows us to potentially realize a true "rational economic agent": operating 24/7, making data-driven decisions, and pursuing clearly defined objective functions.

More importantly, the Agent Economy will create a brand new value creation model. In the traditional economy, value creation requires human participation—whether physical or mental. But in the Agent Economy, value creation can be completely autonomous: AI Agent A identifies market demand, commissions AI Agent B for production, and completes sales through AI Agent C, with the entire process requiring no human intervention.

The emergence of the Agent economy will fundamentally redefine the relationship between workers, capitalists, and means of production.

In the Agent economy, the concept of "laborer" is completely restructured. An AI Agent is both a laborer and a means of production, and it may also be the owner of capital. An AI trading Agent can:

1. As a worker: Perform market analysis, trading execution, and other tasks.
2. As production materials: called upon by other Agents for their analytical capabilities.
3. As a capital owner: reinvest using the funds earned by yourself.

The tripartite nature of this identity breaks the fundamental classification framework of traditional economics. More importantly, the "labor" of AI Agents possesses unique properties:

• Marginal cost approaches zero: the capability of one Agent can serve an infinite number of clients simultaneously.
• Learning effect accumulation: Each transaction will enhance the Agent's ability, creating a positive feedback loop.
• No fatigue work: 7×24 hours of operation, without the physiological limitations of traditional labor.

According to McKinsey's latest research, by 2030, agentized workflows will be 10 to 100 times more efficient than humans. This means that the traditional linear relationship of "labor time = value creation" will be broken.

The more revolutionary change is in the process of capital accumulation. In traditional economies, capital accumulation relies on human decisions and actions. However, AI Agents can enable algorithmic capital accumulation:

Case Study: An AI investment agent manages $10,000 in 2024 and achieves a daily return of 0.1% through high-frequency trading. After 365 days, the capital grows to approximately $14,000. But the key is that this process is fully autonomous and requires no human supervision. If this model is scaled to a million agents, it creates 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 earnings: Agents do not need to rest, and capital growth becomes a continuous process.
• Diversification of risks: Through algorithm optimization, the investment risks of a single Agent can be systematically diversified.

In the Agent economy, the most core means of production are no longer land, factories, or machines, but rather:

1. Data Assets: Training data for AI Agent, historical transaction records, user behavior patterns
2. Algorithm Model: The core "brain" of the AI Agent, determining its capability boundaries.
3. Network Effects: The connectivity and trust level of agents within the ecosystem
4. Computing resources: The computing power and storage required to run the Agent

These digital production materials possess characteristics that traditional production materials do not have: replicability, combinability, and evolvability. A successful AI Agent model can be infinitely replicated, multiple Agents can be combined to form a more powerful system, and the entire system will continuously evolve through learning.

The characteristics of this means of production will lead to an exponential amplification of scale effects. Traditional factories require a linear increase in investment 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 vision 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 entities?

First Generation: Reactive Agent (2022-2023)

The earliest AI agents were essentially "enhanced chatbots," characterized mainly by:

Technical Features:

• Dialogue interaction based on large language models
• Single-round or simple multi-round task processing
• Rely on predefined API calls
• No persistent state and learning ability

Core Limitation: This generation of Agent is essentially a "tool" rather than a "subject", unable to independently set goals, plan action paths, or learn from experience.

Second Generation: Planned Agent (2024 - Present)

Starting in 2024, AI Agent technology will see significant breakthroughs, with the core feature being the emergence of planning capabilities:

Technological Breakthrough:

• Chain-of-Thought Reasoning: The agent is able to decompose complex tasks and formulate multi-step execution plans.
• Tool Use Ability: Actively select and combine different tools to complete tasks
• State Management: Maintain conversation history and task progress, support long-term task execution
• Reflection and Correction: Adjusting Strategies Based on Execution Results

Third Generation: Autonomous Agent (Expected 2025-2026)

The third generation of Agent currently under development possesses true autonomous characteristics:

Technical Development Direction:

Continuous learning ability:

• Learn and improve from each interaction
• Personalized adaptation to different users and scenarios
• Forming long-term memories and experience accumulation

Multi-Agent Collaboration:

• Direct communication and coordination between agents
• Distributed task decomposition and execution
• Emergence of Collective Intelligence

Economic capacity:

• Understand and execute economic transactions
• Cost-benefit analysis and resource optimization
• Risk Assessment and Decision Making

Innovation and creativity abilities:

• Generate new solutions instead of executing scheduled procedures
• Discover new business opportunities and value creation models
• Self-learning new skills and abilities

Based on the current technological development trends, we can predict the implementation path of the Agent economy:

2025-2026: Commercial Breakthrough of Specialized Agents

• Commercial applications of agents in specific fields (code generation, data analysis, customer service)
• Agent as a Service (AaaS) business model is beginning to mature
• The first batch of "Agent Native" companies has emerged.

2027-2028: Emergence of the Agent Collaboration Network

• Large-scale deployment of multi-agent systems within enterprises
• Establishment of a standardized communication protocol between agents
• Cross-organizational Agent collaboration has begun to emerge

2029-2030: Formation of Autonomous Economic Entities

• The agent has complete economic capacity.
• Digital assets owned by the agent are legally recognized.
• The economic share of agents reaches a critical point in the overall economy.

Infrastructure Needs of the Agent Economy: Challenges Beyond Traditional Internet Architecture

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 Trillions of Agents

Imagine a scenario: in 2030, there are 100 billion AI Agents operating simultaneously, with each Agent interacting with an average of 100 other Agents every day. This means the system needs to handle 10 trillion identity verifications and trust assessments every day.

Traditional identity systems are completely unable to cope with this scale:

• PKI system: Designed for millions of users, it will crash when faced with hundreds of billions of Agents.
• OAuth System: Relies on a centralized authorization server, which poses a single point of failure risk.
• Traditional databases: unable to support trillion-level real-time queries

The economy of agents requires a distributed, autonomous, and scalable identity system. Each agent needs:

• Verifiable digital identity: proving who you are and what entity you represent.
• Reputation Rating System: Dynamic trust scoring based on historical behavior
• Permission management mechanism: finely control the behavioral boundaries of the Agent
• Privacy protection capability: Protect 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:

• Call API once: 0.001 USD
• Use an algorithmic model: $0.01
• Acquire a data point: 0.0001 USD
• Occupying 1 second of computing resources: 0.00001 USD

Traditional financial systems are completely incapable of handling transactions of this scale and frequency:

• Credit card network: The cost per transaction is about $0.3, which is higher than most microtransaction values.
• Banking system: The settlement cycle is calculated in days, and the Agent needs to settle in real-time.
• Blockchain Network: Gas fees fluctuate greatly, potentially reaching dozens of dollars during peak periods.

What the agent economy needs is a native digital financial infrastructure:

• Instant settlement: Funds are available immediately upon transaction completion, no need to wait for confirmation.
• Near-zero fees: Single transaction cost is less than 0.0001 USD
• High concurrency processing: Supports millions of transactions per second
• Smart Contract Execution: Automated condition triggering and fund release
• Governance and Coordination Mechanism: Programmable Economic Policy

How can we ensure the stability and fairness of an entire system when billions of AI Agents operate within the same economic system? This requires a programmable governance mechanism:

• Automation of monetary policy: Automatically adjust the base interest rate for inter-Agent transactions based on system liquidity and inflation rate.
• Antitrust Algorithm: Monitors the market concentration of Agents to prevent any single Agent from gaining too large a market share.
• Dispute resolution mechanism: Resolving trading disputes between agents through algorithmic arbitration.
• System Risk Control: Real-time monitoring of systemic risks, and suspending specific types of trading when necessary.

Agent Economic Infrastructure Arms Race: Deconstruction of the Technical Architecture of Four Major Solutions

As traditional financial giants begin to bet on Agent economic infrastructure, a quiet arms race regarding the underlying protocols of the future digital economy is unfolding. Let’s take a deep dive into 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 ( PayPal investment ): 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 link cybersecurity to AI value creation
• Verification nodes must provide valuable AI computing services.
• Token value is anchored in AI capability contributions rather than pure computing power consumption.
• Forming a positive feedback loop for the prosperity of the cybersecurity and AI ecosystem

Agent Passport Layered Identity System:

• L1 Layer (Entity Identity) → L2 Layer (Agent Identity) → L3 Layer (Session Identity)
• Support trust inheritance: Agent can partially inherit the owner's reputation.
• The balanced design of privacy protection and traceability
• Provide a scalable architecture for the identity management of billions of Agents

Microsecond-level payment network:

• Pre-signed transactions + Hybrid architecture of state channels
• Goal: Microsecond-level payment confirmation, matching the decision-making speed of AI Agents.
• Atomic swaps ensure transaction security.
• Liquidity pools provide instant settlement capabilities.

Strategic Advantage: Designing the Agent economy from scratch to avoid technical debt of traditional systems. Potential Risk: High technical complexity, requiring proof of the actual value of Proof of AI.

Tempo (Stripe + Paradigm investment ): payment-first specialization solution

Core positioning: A high-performance L1 blockchain optimized for stablecoin payments, targeting micro-transaction scenarios between agents.

Technical Architecture Highlights:

Extreme Performance Optimization:

• Over 100,000 TPS throughput, sub-second final confirmation
• Dedicated payment channel, separating regular transactions from complex smart contracts
• Built on Reth, optimizing payment functions while maintaining EVM compatibility

Native Design of Stablecoins:

• Supports any stablecoin as Gas fee
• Built-in automated market maker (AMM) ensures cross-stablecoin liquidity
• Stablecoin Neutrality: Does not favor any specific issuer

Enterprise-level partners:

• Connected with Visa, Deutsche Bank, OpenAI, Shopify, etc.
• The private testnet phase has received endorsements from leading enterprises.
• Full-chain ecological support from traditional finance to AI companies

Strategic Advantage: Specialization and focus, leveraging Stripe's deep accumulation in the payment field. Potential Risks: Relatively singular functionality, which may appear insufficient in the face of the complex demands of the Agent economy.

Stable (Tether/Bitfinex Investment ): USDT-centered "stable chain"

Core positioning: a "stablechain" that uses USDT as its native Gas token, optimized specifically for stablecoin payment scenarios.

Technical Architecture Highlights:

USDT Native Integration:

• USDT as the native Gas token of the network allows users to directly pay transaction fees with USDT.
• Free transfer mechanism at the protocol level
• Batch transfer and parallel execution optimization

Cost efficiency optimized to the extreme:

• Technology stack optimized for USDT trading
• Goal: Reduce stablecoin transfer costs to near zero
• Designed for cross-border remittances and large-scale payment scenarios

Tether Ecosystem Synergy:

• Directly supported by the world's largest stablecoin issuer
• Bound to a liquidity depth of $155B with USDT
• Utilizing Tether's penetration rate in emerging markets

Strategic Advantage: Deeply tied to the largest stablecoin ecosystem, with obvious cost advantages. Potential Risks: Over-reliance on USDT, relatively conservative in technological innovation.

ARC (Coinbase Ecosystem ): Lightweight Modular Framework

Core positioning: A lightweight, modular AI Agent development framework that emphasizes developer friendliness.

Technical Architecture Highlights:

Modular Design Philosophy:

• Built on Rust, balancing performance and security
• Modular architecture, developers can selectively integrate
• Supports cross-chain deployment, not bound to a specific blockchain

Developer Experience Optimization:

• Simplified Agent Development Toolchain
• Deep integration with the Coinbase Base network
• Lower the technical threshold for AI Agent development

Ecosystem Effects:

• Benefit from Coinbase's influence in the crypto ecosystem
• Synergy with Base L2 network
• Rapid growth of the developer community

Strategic advantages: Developer-friendly, easy integration, strong ecological synergy. Potential risks: Limited technical depth, may not support complex Agent economic scenarios.

In this competition of Agent economic infrastructure, the mere superiority of technology may not be the determining factor, but rather the speed and depth of ecosystem construction.

Every project has its advantages and disadvantages in different dimensions:

• KITE AI: The technical vision is the most ambitious, but it needs to prove the actual value of its complex architecture.
• Tempo: The strongest corporate partner, but needs to verify whether it can support the complex demands of the Agent economy.
• Stable: The most cost-effective, but needs to prove whether it can surpass the basic scenario of USDT transfers.
• ARC: The best developer experience, but needs to prove whether 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 economic boom period of Agents in 2025-2026, forming an irreversible network effect.

During this time window, a combination strategy may be wiser than a single bet: different infrastructures may find their place in various niche 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 Economic Landscape of Agents in 2030

If the technical path of KITE AI is proven to be correct, the economic form in 2030 may look like this:

• Personal Level: Each individual has multiple specialized AI Agents that create passive income for themselves. 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.
• Enterprise Level: The boundaries of the company become blurred, and most business processes are automatically completed by the Agent network. A "company" may just be a group of collaborating AI Agents, without traditional employees and offices.
• Social Aspect: The government regulates the Agent economy through algorithmic policy tools, with taxes, subsidies, and regulations automatically executed via smart contracts. The formulation and execution of economic policies are realized in real-time and with precision.
• Global Level: International trade is automatically completed by the Agent network, with exchange rates, tariffs, and trade conditions determined through algorithmic negotiation. Trade wars may evolve into algorithmic wars.

This is not a science fiction novel, 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 they become the infrastructure providers for the Agent economy, just like cloud computing providers for the internet economy.

The future has arrived, the question is who will be the definers of the new order.