Robots "survive" in the economic system: When Web3 changes the game

A Shift from “Static Machines” to “Economic Agents”

In 2025, the robotics industry is experiencing a strange phenomenon: humanoid robot projects, previously considered just hardware breakthroughs, are now valued in a completely different way. Not because robotic arms are raised higher or engines are more powerful, but because a fundamental question has been answered: Can robots manage finances, make payments, and collaborate without human intervention behind the scenes?

The answer is yes, and this is reshaping the entire economic logic of the industry.

Previously, robots were defined as “enterprise assets”—having a body, intelligence (through control algorithms), but lacking “economic personality.” They couldn’t open wallets, sign contracts, or decide to buy resources or sell services. All transactions, payments, and profit distributions had to go through the administrative layer of the owning enterprise.

But as AI Agents, on-chain payments, and blockchain protocols converge, that picture has changed. Robots are no longer just “tools”; they have become “economic entities” capable of participating in markets in ways previously impossible.

Why now, not before?

The robotics industry has been waiting for the “ChatGPT moment” for two decades. Nvidia CEO Jensen Huang stated: “The ChatGPT moment for general robotics is just around the corner”—a statement not just marketing hype, but a reflection of three rare converging events.

First: Technology has matured simultaneously

Multimodal perception (multimodal perception), next-generation control like RT-X and Diffusion Policy, high-fidelity simulation (Isaac, Rosie), and large language models combined with AI Agents—all have reached a point where commercial application in the same phase is possible. Especially, simulation environments are now mature enough for large-scale robot learning at very low cost, then reliably transfer to the real world.

The long-standing “hard to learn, data-expensive, high-risk” problem faced by robots—has now found a way out.

Second: Hardware can now scale mass production

Motors, joints, sensors—core components once produced only in hundreds—are now beginning mass production. The rise of Chinese manufacturing in the global robotics supply chain has significantly lowered costs. As companies plan to produce millions of units, robots are finally on an industrial “copyable” platform.

Third: Reliability has surpassed the minimum threshold

Real-time operating systems, backup safety systems, better control engines—robots can now operate stably over long periods in commercial environments, not just for lab demos.

Result: in 2025, the robotics sector receives unprecedented funding density—over $500 million, focused on manufacturing lines, commercialization deployment, and full-stack hardware-software architectures, not just “idea funding.”

No falsehood here. The market has priced the industry as having shifted from “can we do it” to “can we sell it, can it be used.”

The four-tier economic model of the modern robot ecosystem

To understand Web3’s role, we need to see the structure it is intervening in:

Layer 1 – Physical Layer: Humanoids, robotic arms, drones, EV charging stations. These are the “bodies,” solving movement, manipulation, mechanical reliability. But they still “lack economic behavior”—cannot pay autonomously.

Layer 2 – Perception & Control: From SLAM, traditional sensors, to today’s LLM+Agent, along with robot OS like ROS, OpenMind. This layer allows robots to “hear, see, plan,” but all economic transactions still require human handling.

Layer 3 – Machine Economy Layer: Here is where real change begins. Robots start owning digital identities, e-wallets, on-chain trust systems. Through protocols like x402, on-chain payments, they can:

• Directly pay for computing power, data, energy
• Collect revenue when providing services
• Manage funds and control payments based on outcomes

Robots transition from “enterprise assets” to “economic subjects,” capable of market participation.

Layer 4 – Coordination & Governance: When robots can pay and identify themselves autonomously, they organize into teams, networks—drone swarms, cleaning robot networks, EV energy grids. They self-adjust prices, auction tasks, share profits, even form DAOs.

These four layers are “physical + intelligence + finance + organization,” and Web3 is not just a part—it is the glue connecting them.

Three ways Web3 changes the game

( 1. Data: From “who provides AI?” to “who is ready to provide continuously?”

The hardest bottleneck for Physical AI has always been training data—requiring massive scale, multi-context, many real-world interactions.

Previously, robots learned only from labs, small fleets, or internal company data. The scale was too limited.

Web3’s DePIN/DePAI opens another path: ordinary users, device operators, remote controllers—can become “data providers” and earn tokens as rewards. This decision is not trivial.

NATIX Network enables common vehicles to become mobile data nodes, collecting video, geolocation, environmental data.

PrismaX focuses on high-quality physical interaction data—how robots grasp, sort, transport objects—via remote control marketplaces.

BitRobot Network allows robots to perform verifiable tasks, generating data on manipulation, navigation, cooperative behaviors.

But here’s the subtlety: Web3 addresses the question “is AI ready to contribute?”, not directly ensuring “data quality”. Crowdsourced data is often noisy, inconsistent, biased )bias(. It still needs a data engine behind to filter, clean, audit.

The real value of DePIN is providing a “continuous, scalable, low-cost” data platform—it’s the foundational layer, not a complete solution.

) 2. Collaboration: When robots “speak the same language”

Today’s robots are still stuck in closed ecosystems. A robot arm from Brand A cannot share info with a humanoid from Brand B. No common language, no communication.

This limits large-scale multi-robot cooperation.

OpenMind and other intelligent robot OS are solving the “language” problem. It’s not traditional control software, but an inter-device OS—like Android for phones—offering a common interface for perception, cognition, understanding, and cooperation.

Instead of sensors, controllers, inference modules being isolated in each robot, OpenMind unifies:

• How robots describe the external world (vision/sound/tactile → structured semantic events)
• How robots understand commands (natural language → action plans)
• How robots share states

For the first time, robots of different brands and forms can “speak the same language.”

But OpenMind only solves half the problem: how robots “understand” each other. The other half is how robots “interact” as economic entities.

That’s where Peaq comes in.

Peaq provides a protocol layer for machines with identity recognition, economic incentives, and network-level coordination. It doesn’t solve “how robots understand the world,” but “how robots participate in cooperation as entities in a network”:

1. Identify Peaq: Robots, devices, sensors register with a decentralized identity, can connect to any network as independent entities, joining trust systems.

2. Autonomous economic accounts: Robots can automatically pay stablecoins ###USDC or similar( for sensor data, computing power, services from other robots. Payments are conditioned on:

• “Task completion → automatic payment”
• “Results not met → funds frozen or refunded” This makes cooperation trustworthy, auditable, and self-enforcing.

3. Multi-device task coordination: Robots share states, bid, assemble tasks, coordinate resources as a network node, rather than operate in isolation.

Result: robots gain a unified semantic interface )OpenMind###, cross-device interaction capability (Peaq), and reliable coordination mechanisms. They step into a true cooperative network, not confined within closed ecosystems.

( 3. Economics: When robots “consume and produce” autonomously

The final, and most crucial piece: robots need the ability to participate in a complete economic system—able to work, earn, spend, and optimize behaviors independently.

x402 is the new generation Agentic Payment standard. It allows robots to send direct payment requests via HTTP and complete atomic transactions with USDC or programmable stablecoins.

What does this mean? Robots not only complete tasks—they can autonomously purchase all necessary resources:

• Call computing power )LLM inference, model inference(
• Access contexts, rent equipment
• Buy services from other robots

For the first time, robots can self-consume and self-produce as economic agents.

OpenMind × Circle: OpenMind integrates a multi-device OS with Circle’s USDC, enabling robots to pay and reconcile directly on-chain for task execution. The execution chain no longer depends on human backend systems.

Kite AI: Going further, Kite AI designs a complete “Agent-Native” blockchain platform:

• Kite Passport: Assigns cryptographic identity to each AI Agent )future mapped to robots(, controlling “who pays,” supporting refunds and traceability.
• Native stablecoin + x402: Integrates x402 at blockchain level, optimized for M2M payments )machine-to-machine( high-frequency, small-value transactions.
• Programmable constraints: Set spending limits, whitelist contracts, control risk via on-chain policies.

Kite AI helps robots “live within the economic system”—able to:

• Receive income based on performance )result-based settlement###
• Purchase resources as needed (autonomous cost structure)
• Compete in markets via on-chain reputation (fulfill verifiable commitments)

From classroom to marketplace: practical realities

2025 marks the year when robot commercialization becomes clear. Apptronik, Figure, Tesla Optimus announce mass production plans in turn. Robots shift from prototypes to industrialized phases.

The Operation-as-a-Service (OaaS) model is gaining market acceptance: companies no longer need large upfront capital, just monthly robot service subscriptions. ROI improves significantly.

Meanwhile, maintenance networks, component supply, remote monitoring—previously missing—are rapidly expanding.

As these capabilities mature, robots begin to operate continuously and in closed-loop commercial environments. That’s when the sustainable cycle begins.

Three Web3 layers in the robot ecosystem

Looking at the whole picture:

Data layer: DePIN provides incentives for large-scale, multi-source data collection, improving coverage of long-tail contexts. But raw data needs a data engine behind to filter, clean, audit—not just “collect and use.”

Collaboration layer: OpenMind (OS) + Peaq (protocols) unify recognition, interaction, and task governance for cross-device cooperation. Robots of different brands and forms can “speak the same language” for the first time.

Economics layer: x402 + on-chain stablecoins + Kite AI provide a programmable economic framework. Robots can autonomously pay, receive, manage funds, and execute conditional contracts.

These three layers together lay the foundation for the “Internet of Machines”: cooperative, operable robots in an open, auditable technological environment.

Dark shadow in the sunlight

Despite the technological breakthrough, the journey from “technologically feasible” to “sustainable scale” still faces many uncertainties—not from a single technological flaw, but from complex interactions of technical, economic, market, and institutional factors.

Is the economic viability solid? Although robots have advanced in perception, control, and intelligence, large-scale deployment ultimately depends on real commercial demand and economic profitability. Currently, most humanoid robots are still in testing phases. Are companies willing to pay long-term? Will OaaS/RaaS models operate stably across industries? Long-term data is still lacking. The cost-effectiveness advantage of robots in complex, unstructured environments is not yet fully clear. In many contexts, traditional automation remains cheaper and more reliable.

Reliability and long-term operation challenges: The biggest challenge is not “can it complete tasks,” but “can it operate stably, long-term, at low cost.” Hardware failure rates, maintenance, software upgrades, energy management, insurance, liability—all can amplify systemic risks. If reliability doesn’t surpass a minimum threshold, the robot network will be hard to realize.

Ecosystem cooperation and regulatory adaptation: The robotics ecosystem remains fragmented. Cooperation costs, common standards are not fully converged. Meanwhile, robots with decision-making and autonomous economic capabilities challenge current legal frameworks: liability, payment compliance, safety—are still unclear. If regulations don’t keep pace, the machine economy network faces compliance and deployment uncertainties.

Conclusion

The 2025 robot ecosystem is not just a hardware revolution, but a re-architecture of the entire “physical + intelligence + finance + organization” system.

Web3 is not “the solution for robots,” but part of the four-layer architecture the industry is building. It provides incentives for data (DePIN), language for cooperation (giao thức phối hợp), and a programmable economic framework (on-chain payments).

The conditions for large-scale robot deployment are beginning to form. The embryonic form of the Machine Economy has appeared in the industry.

But from “technologically feasible” to “economically sustainable”—there is still a long, risky, uncertain road ahead. Robots sketch a grand future, but not everything on the blueprint can become reality.