Agent Economy: The Economic Foundation of the Sovereign Individual Capitalism Era
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Agent Economy: The Economic Foundation of the Sovereign Individual Capitalism Era
The moment AI Agents start earning money for themselves, the final piece of capitalism is complete—capital has finally found workers who don't need sleep.
Navigating Web3 tides with focused insightsAuthor: NingNing
From the "Invisible Hand" to Agent Economy: The Fourth Paradigm Revolution in Economics
In 1776, Adam Smith described an "invisible hand" in The Wealth of Nations, coordinating the economic behavior of millions through market mechanisms. 249 years later, we stand at the threshold of the fourth paradigm revolution in economics: this "invisible hand" is about to be replaced by a network of collaborative Agents.
The past three revolutions were: the Industrial Revolution mechanized manual labor, the Information Revolution digitized cognitive labor, and the Internet Revolution globalized knowledge work. The upcoming Agent Economy revolution will achieve, for the first time, the algorithmization of production relationships—not just intelligent tools, but autonomous economic agents themselves.
Traditional economics assumes "rational individuals" who maximize utility, but in reality, human irrationality, emotions, and cognitive limitations constitute the primary source of market friction. The emergence of AI Agents offers the first real possibility of achieving truly rational economic actors—operating 24/7, making data-driven decisions, and pursuing clearly defined objective functions.
More importantly, the Agent Economy will create entirely new models of value creation. In traditional economies, value creation requires human involvement—either physical or mental. But in the Agent Economy, value creation can occur autonomously: AI Agent A identifies market demand, commissions AI Agent B for production, and uses AI Agent C to complete sales—all without human intervention.
The rise of the Agent Economy will fundamentally redefine the relationships between laborers, capitalists, and means of production.
In the Agent Economy, the concept of "laborer" is completely redefined. An AI Agent is simultaneously a laborer, a means of production, and potentially a capital owner. For example, an AI trading agent can:
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Act as a laborer: performing market analysis and trade execution
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Act as a means of production: having its analytical capabilities invoked by other agents
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Act as a capital owner: reinvesting earnings autonomously
This triple identity breaks down the fundamental classification framework of traditional economics. More significantly, AI Agent "labor" has unique characteristics:
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Near-zero marginal cost: one Agent’s capability can serve infinitely many clients simultaneously
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Accumulating learning effects: each transaction enhances the Agent's ability, creating positive feedback loops
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No fatigue: operating 7×24 hours without physiological constraints of human labor
According to McKinsey's latest research, by 2030, Agent-powered workflows will be 10–100 times more efficient than humans. This means the traditional linear relationship between "labor time = value creation" will be broken.
Even more revolutionary is the transformation in capital accumulation. In traditional economies, capital accumulation depends on human decisions and actions. But AI Agents enable algorithmic capital accumulation:
Case Study: An AI investment Agent managing $10,000 in 2024 earns a daily return of 0.1% via high-frequency trading. After 365 days, the capital grows to approximately $14,000. Crucially, this entire process runs autonomously without human oversight. If scaled to a million such Agents, it forms a fully autonomous capital growth network.
The emergence of this model implies:
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Democratization of capital: anyone can own an AI Agent that works for them
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Continuous returns: Agents never rest, turning capital growth into a continuous process
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Decentralized risk: algorithmic optimization allows systematic risk diversification across individual Agent investments
In the Agent Economy, the core means of production are no longer land, factories, or machinery, but:
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Data assets: training data, historical transaction records, and user behavior patterns for AI Agents
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Algorithmic models: the core "brain" of AI Agents, defining their capability boundaries
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Network effects: an Agent’s connectivity and trust level within the ecosystem
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Computing resources: computational power and storage required to run Agents
These digital means of production possess qualities absent in traditional ones: replicability, composability, and evolvability. A successful AI Agent model can be infinitely replicated; multiple Agents can combine into more powerful systems; and the entire system evolves continuously through learning.
These characteristics lead to exponential scale effects. Traditional factories require linear increases in input to scale, but AI Agent expansion approaches zero marginal cost.
Current AI Agent Technology Evolution: From Proof-of-Concept to Production-Ready
Before envisioning the grand picture of the Agent Economy, we must address a key question: where does current AI Agent technology stand? How far are we from true autonomous economic agents?
First Generation: Reactive Agents (2022–2023)
The earliest AI Agents were essentially "enhanced chatbots," characterized by:
Technical Features:
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Conversational interaction based on large language models
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Single-turn or simple multi-turn task handling
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Reliance on predefined API calls
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No persistent state or learning capability
Core Limitation: These Agents are fundamentally "tools," not "agents"—unable to independently set goals, plan actions, or learn from experience.
Second Generation: Planning Agents (2024–Present)
Starting in 2024, AI Agent technology achieved significant breakthroughs, most notably the emergence of planning capabilities:
Technical Advances:
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Chain-of-Thought Reasoning: Agents can decompose complex tasks and formulate multi-step execution plans
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Tool Use Capability: actively selecting and combining different tools to complete tasks
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State Management: maintaining conversation history and task progress, enabling long-term execution
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Reflection and Adjustment: modifying strategies based on outcomes
Third Generation: Autonomous Agents (Expected 2025–2026)
The third-generation Agents currently under development exhibit true autonomy:
Technical Development Directions:
Persistent Learning Capability:
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Learning and improving from every interaction
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Personalized adaptation to different users and scenarios
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Development of long-term memory and experiential accumulation
Multi-Agent Collaboration:
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Direct communication and coordination between Agents
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Distributed task decomposition and execution
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Emergence of collective intelligence
Economic Behavior Capabilities:
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Understanding and executing economic transactions
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Cost-benefit analysis and resource optimization
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Risk assessment and decision-making
Innovation and Creativity:
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Generating novel solutions rather than executing pre-defined programs
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Discovering new business opportunities and value creation models
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Autonomous acquisition of new skills and capabilities
Based on current technological trends, we can project the path toward the Agent Economy:
2025–2026: Commercial Breakthrough of Specialized Agents
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Domain-specific Agents achieve commercial deployment (code generation, data analysis, customer service)
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Agent-as-a-Service (AaaS) business models mature
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First "Agent-native" companies emerge
2027–2028: Emergence of Agent Collaboration Networks
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Large-scale deployment of multi-Agent systems within enterprises
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Standardized communication protocols established among Agents
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Cross-organizational Agent collaboration begins
2029–2030: Formation of Autonomous Economic Agents
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Agents possess full economic behavior capabilities
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Digital assets owned by Agents gain legal recognition
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The Agent Economy reaches a critical mass within the overall economy
Infrastructure Requirements for the Agent Economy: Architectural Challenges Beyond Traditional Internet
If the Agent Economy is a new economic operating system, what kind of "water, electricity, and coal" infrastructure does it need?
Identity and Trust Systems: Managing Identities for Billions of Agents
Imagine this scenario: in 2030, 100 billion AI Agents operate globally, with each Agent interacting with an average of 100 others daily. This means the system must handle 10 trillion identity verifications and trust assessments per day.
Traditional identity systems are utterly incapable of handling such scale:
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PKI systems: designed for millions of users, would collapse under billions of Agents
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OAuth frameworks: rely on centralized authorization servers, vulnerable to single points of failure
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Traditional databases: cannot support trillions of real-time queries
The Agent Economy requires a distributed, autonomous, and scalable identity system. Each Agent needs:
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Verifiable digital identity: proving who they are and which entity they represent
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Reputation scoring system: dynamic trust ratings based on historical behavior
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Permission management: fine-grained control over Agent behavior boundaries
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Privacy protection: safeguarding sensitive information while verifying identity
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Payment and settlement network: financial infrastructure with microsecond-level speed
Another key feature of the Agent Economy is the explosive growth of microtransactions. Transactions between AI Agents could include:
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One API call: $0.001
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Use of an algorithm model: $0.01
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Access to one data point: $0.0001
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One second of computing resource usage: $0.00001
Traditional financial systems cannot handle transactions at this scale and frequency:
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Credit card networks: average transaction cost ~$0.30, exceeding the value of most microtransactions
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Banking systems: settlement cycles measured in days, while Agents require real-time settlement
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Blockchain networks: gas fees fluctuate wildly, reaching tens of dollars during peak times
The Agent Economy needs natively digital financial infrastructure:
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Instant settlement: funds transferred immediately upon transaction completion
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Near-zero fees: per-transaction costs below $0.0001
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High concurrency: supporting millions of transactions per second
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Smart contract execution: automated conditional triggers and fund releases
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Governance and coordination mechanisms: programmable economic policies
When billions of AI Agents operate within the same economic system, how do we ensure stability and fairness? This requires programmable governance mechanisms:
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Automated monetary policy: automatically adjusting base interest rates between Agents based on system liquidity and inflation
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Anti-monopoly algorithms: monitoring market concentration to prevent any single Agent from dominating
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Dispute resolution mechanisms: algorithmic arbitration of transaction disputes between Agents
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Systemic risk controls: real-time monitoring of systemic risks, with ability to pause specific transaction types if needed
The Infrastructure Arms Race for the Agent Economy: Deconstructing Four Technical Architectures
As traditional financial giants begin betting on Agent Economy infrastructure, a quiet arms race over the foundational protocols of the future digital economy is unfolding. Let's deeply analyze the technical architecture choices of four representative projects to see who might become the "water, electricity, and coal" provider for the Agent Economy.
KITE AI (Backed by PayPal): AI-Native Economic Operating System
Core Positioning: Building complete economic infrastructure for AI Agents—an integrated solution spanning identity, payments, and governance
Technical Architecture Highlights:
Proof of AI Consensus Mechanism:
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Ties network security directly to AI value creation
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Validation nodes must provide valuable AI computing services
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Token value anchored in AI capability contributions, not pure computational waste
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Creates a positive feedback loop between network security and AI ecosystem prosperity
Agent Passport Layered Identity System:
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L1 (Entity Identity) → L2 (Agent Identity) → L3 (Session Identity)
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Supports trust inheritance: Agents can partially inherit owner reputation
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Balances privacy protection with traceability
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Provides a scalable architecture for managing identities of billions of Agents
Microsecond Payment Network:
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Mixed architecture using pre-signed transactions and state channels
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Goal: microsecond payment confirmation, matching AI Agent decision speeds
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Atomic swaps ensure transaction security
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Liquidity pools enable instant settlement
Strategic Advantage: Designed from scratch for the Agent Economy, avoiding legacy technical debt Potential Risk: High technical complexity; must prove the real-world value of Proof of AI
Tempo (Backed by Stripe + Paradigm): Specialized Solution with Payment Priority
Core Positioning: A high-performance L1 blockchain optimized specifically for stablecoin payments, targeting microtransactions between Agents
Technical Architecture Highlights:
Extreme Performance Optimization:
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Throughput of 100,000+ TPS, sub-second finality
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Dedicated payment channels separating routine transactions from complex smart contracts
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Built on Reth, maintaining EVM compatibility while optimizing payment functions
Stablecoin-Native Design:
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Supports any stablecoin as gas fee
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Built-in Automated Market Maker (AMM) ensures cross-stablecoin liquidity
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Stablecoin-neutral: does not favor any particular issuer
Enterprise Partnerships:
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Already integrated with Visa, Deutsche Bank, OpenAI, Shopify, etc.
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Secured endorsements from top enterprises during private testnet phase
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Full-chain ecosystem support from traditional finance to AI companies
Strategic Advantage: Focused specialization leveraging Stripe’s deep expertise in payments Potential Risk: Relatively narrow functionality; may fall short against the complex demands of the Agent Economy
Stable (Backed by Tether/Bitfinex): USDT-Centric "Stablechain"
Core Positioning: A "stablechain" with USDT as native gas token, optimized specifically for stablecoin payment scenarios
Technical Architecture Highlights:
Native USDT Integration:
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USDT serves as the network’s native gas token—users pay transaction fees directly in USDT
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Protocol-level free transfer mechanism
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Optimized batch transfers and parallel execution
Ultra-Efficient Cost Structure:
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Technology stack optimized specifically for USDT transactions
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Goal: reduce stablecoin transfer costs to near zero
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Designed for cross-border remittances and large-scale payment scenarios
Tether Ecosystem Synergy:
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Direct backing from the world’s largest stablecoin issuer
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Deeply integrated with USDT’s $155B liquidity pool
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Leverages Tether’s strong penetration in emerging markets
Strategic Advantage: Deep integration with the largest stablecoin ecosystem, clear cost advantages Potential Risk: Over-reliance on USDT; relatively conservative in technological innovation
ARC (Coinbase Ecosystem): Lightweight Modular Framework
Core Positioning: Lightweight, modular AI Agent development framework emphasizing developer-friendliness
Technical Architecture Highlights:
Modular Design Philosophy:
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Built with Rust, balancing performance and security
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Component-based architecture allowing selective integration
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Supports cross-chain deployment, not tied to any specific blockchain
Developer Experience Optimization:
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Simplified Agent development toolchain
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Deep integration with Coinbase Base network
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Lowers technical barriers to AI Agent development
Ecosystem Effects:
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Leverages Coinbase’s influence in the crypto ecosystem
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Synergy with Base L2 network
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Rapidly growing developer community
Strategic Advantage: Developer-friendly, easy integration, strong ecosystem synergy Potential Risk: Limited technical depth; may struggle to support complex Agent Economy scenarios
In this infrastructure race for the Agent Economy, raw technical superiority may not be the deciding factor—rather, the speed and depth of ecosystem building will matter most.
Each project has strengths and weaknesses across different dimensions:
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KITE AI: Most ambitious technical vision, but must prove real-world value of its complex architecture
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Tempo: Strongest enterprise partnerships, but must demonstrate capacity for complex Agent Economy needs
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Stable: Highest cost efficiency, but must show it can go beyond basic USDT transfer use cases
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ARC: Best developer experience, but must prove scalability for massive Agent deployments
The real test will be: which project can fastest attract key developers, enterprise users, and Agent ecosystems during the 2025–2026 Agent Economy breakout period, establishing irreversible network effects?
Within this window, a diversified strategy may be wiser than betting on a single solution: different infrastructures may find niches in various segments of the Agent Economy, and the ultimate winner may be an ecosystem alliance that enables cross-platform interoperability and reduces migration costs.
The Agent Economy in 2030: A Vision
If KITE AI’s technical path proves correct, the economic landscape in 2030 might look like this:
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Individual Level: Everyone owns multiple specialized AI Agents generating passive income. A programmer’s coding Agent provides services on GitHub, a designer’s creative Agent takes on platform gigs, and an investor’s trading Agent operates in financial markets.
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Enterprise Level: Company boundaries blur, with most business processes automated by Agent networks. A "company" may simply be a group of collaborating AI Agents, with no traditional employees or offices.
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Societal Level: Governments regulate the Agent Economy through algorithmic policy tools, with taxes, subsidies, and regulations executed automatically via smart contracts. Economic policymaking becomes real-time and precise.
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Global Level: International trade is conducted automatically by Agent networks, with exchange rates, tariffs, and trade terms negotiated algorithmically. Trade wars may evolve into algorithmic wars.
This is not science fiction, but a reasonable extrapolation based on current technological trends. The key question is not whether this future will arrive, but who will control the infrastructure of this new economic system.
The value proposition of KITE AI, Tempo, Stable, and ARC lies in becoming infrastructure providers for the Agent Economy—just as cloud providers powered the internet economy.
The future is already here. The question is: who will define the new order?
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