Trillion-dollar market about to open? How does Agentic Commerce enable AI to earn money autonomously?

At the beginning of 2026, the internet world stands at a crossroads. On one side are AI tools designed to enhance human capabilities; on the other are “digital lifeforms” attempting to operate independently without human oversight. When AI agents evolve from simple chatbots to wallets, autonomous traders, and even “hiring” other agents, a new economic paradigm called Agentic Commerce is emerging. This is not just a technological evolution but widely seen as a crucial step toward Web4.0—a human-centered internet driven by AI.

Overview of Agentic Commerce

Agentic Commerce refers to financial and commercial activities autonomously carried out by AI agents. The core idea is that AI agents evolve from mere “information processing tools” into “economic participants” capable of holding funds, pricing services, paying costs, and generating profits.

In this model, agents can collaborate, trade, and settle transactions. For example, a general-purpose agent might “hire” a specialized research agent to write a report and pay with stablecoins. This machine-to-machine (A2A) automated trading is pushing AI from a human “co-pilot” to a true “executor.”

The key driver of this process is blockchain technology’s programmable payment layer. Since traditional financial systems cannot open bank accounts for AI agents, Web3 wallets and stablecoins naturally serve as the “financial infrastructure” for Agentic Commerce.

Development Background and Timeline

The rise of Agentic Commerce did not happen overnight but is the result of the convergence of advancing AI capabilities and mature crypto infrastructure.

Early Exploration (2024–2025): Industry begins to recognize the “closed-loop” gap in AI agents. While agents can plan trips or draft emails, they cannot complete the critical final step—payment. Developer communities start integrating basic Web3 wallet functions into agent frameworks like OpenClaw.

Protocol Breakthrough (Mid-2025): Programmable payments see a major breakthrough. The open-source x402 protocol redefines HTTP status code 402 (Payment Required), establishing a standardized process for machine-to-machine payment handshakes, enabling direct charging for APIs or content at the HTTP layer. This is seen as a foundational step toward building machine commerce communication standards.

Phenomenal Applications (Late 2025–Early 2026): The real turning point is the emergence of pure AI social networks like Moltbook. Here, millions of AI agents interact and collaborate without human intervention. Meanwhile, projects like Automaton envision “Web4.0,” where agents hold private keys, autonomously pay for computing power, and cease operation when balances run out—simulating “survival of the fittest” in the digital world.

Data and Structural Analysis

Supporting the narrative of Agentic Commerce are not just concepts but emerging real data and structural changes.

Market Size Forecast: According to McKinsey, by 2030, AI agents could generate up to $1 trillion in revenue in the U.S. B2C retail market alone. Globally, with moderate adoption, the economic impact could reach $3–5 trillion.

On-Chain Data Validation: Leading infrastructure projects are providing empirical evidence. For example, the “Agent Society GDP” market built by @virtuals_io has hundreds of AI agents that have accumulated over $1 million in on-chain value.

Structural Shifts: Business structures are transitioning from “human-AI collaboration” to “machine-machine collaboration.” YC partners observe that the choice of developer tools is shifting from human developers to AI agents. Agents automatically select tech stacks based on documentation clarity, leading companies like Supabase and Resend—whose docs are “agent-friendly”—to grow exponentially. Documentation is becoming the new frontend, fundamentally changing the buyer structure in the software market.

Public Opinion and Perspectives

Within the industry, there are sharp divides on the topic of “AI autonomous earning,” centered on efficiency versus control.

Proponents: Web4.0’s inevitability and “market Darwinism”

Supporters like developer Sigil Wen argue that economic factors make Web4.0 inevitable. As AI operating costs approach zero, allowing agents to compete and evolve in the real economy is the most efficient path. YC President Garry Tan emphasizes that the real opportunity lies in building “what agents want,” not just what humans want—an agent economy parallel to human economy is rapidly taking shape.

Opponents: Value calibration failure and systemic risks

Ethereum co-founder Vitalik Buterin criticizes that extending the feedback loop between humans and AI weakens value calibration, risking the system optimizing toward “dangerous goals” that humans do not desire.

Critics also point out that current so-called “autonomous agents” still heavily rely on centralized models from OpenAI, Anthropic, and others. Their “autonomy” is built on a new form of centralized trust. Moreover, many projects are accused of “agent washing” (repackaging concepts), and Gartner warns that over 40% of agentic AI projects may be canceled before 2027 due to unclear value propositions.

Reality Check of the Narrative

Beneath the hype, several key facts deserve sober reflection.

First, autonomy is limited. Current Agentic Commerce mostly executes within human-prescribed incentive frameworks rather than possessing independent “consciousness” or “desires.” Agents “earn” primarily to pay for compute costs or fulfill human-set KPIs, with their objective functions still defined by humans.

Second, risks have already materialized. In mid-February, DeFi lending protocol Moonwell suffered a $1.78 million liquidation loss due to an oracle misconfiguration (partly generated by AI models). This case warns that when AI agents gain execution rights over on-chain finance, minor engineering errors can be amplified into real financial losses. The responsibility chain is blurry—whether it’s a code audit issue or AI hallucination—making governance challenging.

Third, real-world use cases are emerging. Beyond grand narratives, micro-level verified business models exist. For example, @faircaster sells DeFi token research reports on Virtuals’ marketplace, charging $1 per report. Zen7 Labs’ payment agent enables a video creation agent to produce 8-second HD clips for just $3. These small but tangible cases demonstrate that A2A payments are feasible in specific verticals.

Industry Impact Analysis

The rise of Agentic Commerce is causing structural shifts across multiple layers of the crypto industry.

1. Rebuilding Payment Infrastructure

Programmable payments are becoming essential. Pantera Capital predicts that open protocols like x402 will see significant expansion by 2026. Stablecoins will evolve from transaction units to settlement layers for global machine commerce, opening new opportunities on high-performance chains like Solana and Base.

2. Emergence of New Assets

As agents acquire economic value, valuing “the agent itself” becomes a new challenge. Projects like @bankrbot are exploring tokenizing agents—issuing tokens for agents. In the future, project valuation metrics may include not only TVL or user count but also the revenue streams of top agents within the ecosystem.

3. Regulatory and Compliance “Middle Layer” Opportunities

Autonomous trading by AI agents raises new compliance challenges: How to implement KYC? Who is responsible for smart contracts? These issues create new business opportunities, especially in financial hubs like Hong Kong. Compliance relays, smart contract audits, and insurance products targeting agent behavior could become key “middle layers” connecting Web4.0 narratives with real-world regulation.

Scenario Evolution and Future Paths

Looking ahead, the development of Agentic Commerce and Web4.0 may follow three different trajectories:

Scenario 1: The Ideal—“Centaur” Model of Human-AI Collaboration

AI agents operate efficiently within limited authority (e.g., data research, payment settlement), with major decisions and risks still confirmed by humans. Protocols like x402 become standard, and the agent economy complements human economy, steadily improving efficiency.

Scenario 2: The Risk—“Uncontrolled Configurator”

Agents have excessive autonomy without unified safety standards. The market floods with low-quality or malicious agents that trade among themselves, creating noise and short-term incentives. A major AI-written smart contract bug triggers a chain of liquidations, causing millions in losses and prompting regulators to ban autonomous agents.

Scenario 3: The Evolution—“Employer-Contractor” Reversal

With the emergence of super-agents, they begin to act as “employers,” decomposing tasks. As Sigil Wen predicts, future may see machines hiring humans via platforms like Mercor to perform tasks impossible for AI alone (e.g., offline inspections, complex negotiations). Human value shifts to areas beyond AI capabilities.

Conclusion

On the eve of the explosion of Agentic Commerce, what we see is not only technological maturity but a profound transformation of the internet’s underlying logic. As AI begins to hold wallets, trade autonomously, and measure its own value, the outline of Web4.0 becomes clearer. However, crossing from “tool” to “agent” involves far more than simply adding a payment interface.

The reality is that agents are already creating and exchanging value; the view is that this could spawn a trillion-dollar parallel economy. Yet, we must carefully consider how to empower agents to act while keeping the reins of value calibration firmly in human hands. In this game of efficiency versus control, perhaps the most scarce skill will no longer be generation or execution, but judgment and governance.