From industrial tools to economic entities: How Web3 promotes the integration of the robot economy and the atomic model

The Economic Awakening of Machines

The robotics industry is at a pivotal point in history. Once, robots were viewed as single-dimensional hardware tools—executing preset instructions, relying on manual management, and lacking economic autonomy. But after 2025, all of this is changing.

With the integration of AI Agents, on-chain payments (x402), and the machine economy system, robots are evolving from “passive executors” to “active participants.” They are beginning to possess wallets, digital identities, reputation systems, and the ability to make autonomous economic decisions. This is no longer just a hardware revolution but a systemic reconfiguration of the “physical layer—intelligent layer—financial layer—organizational layer.”

JPMorgan’s forecast illustrates the scale of this transformation: by 2050, the humanoid robot market could reach $5 trillion, with over 1 billion humanoid robots in operation. This means robots will upgrade from industrial equipment to large-scale “social participants.”

Four-Layer Ecosystem: Understanding the Construction Logic of the Machine Economy

To grasp the future of the robotics industry, it is essential to understand its structure from four dimensions:

Physical Layer: Includes humanoid robots, robotic arms, drones, charging stations, and other embodied carriers. This layer addresses basic mobility and operational reliability but robots still lack “economic capability”—the ability to independently handle payments, receipts, or purchase services.

Perception & Control Layer: Traditional robot control systems, SLAM, visual and speech recognition, as well as current LLM+Agent and advanced robot operating systems (ROS, OpenMind OS). This layer endows machines with “understanding, observation, and execution” capabilities, but economic activities are still managed by human backends.

Machine Economy Layer: The true revolution begins here. Machines acquire wallets, digital identities, reputation systems (e.g., ERC-8004 standard), and through x402 and on-chain callback mechanisms, can directly pay for computing power, data, energy, and access rights. Simultaneously, machines can autonomously receive rewards for completed tasks, manage funds, and execute payments based on results. This transforms machines from “enterprise assets” into “economic entities.”

Coordination & Governance Layer: When many machines gain payment capabilities and independent identities, they can self-organize into drone swarms, cleaning robot networks, electric vehicle energy grids, etc. Machines can automatically adjust prices, schedule shifts, bid on tasks, distribute profits, and even establish autonomous economic entities in DAO form. This layer embodies the true meaning of the atomic model in the machine economy system—each machine as an independent economic atom interacting via standardized interfaces and protocols.

Why Is the Explosion Happening Now?

Nvidia CEO Jensen Huang once said, “The era of general-purpose robots with ChatGPT is just around the corner.” This is not marketing hype but a professional judgment based on three core signals.

Capital Signal: Funding Boom Validating Commercial Feasibility

In 2024-2025, the robotics industry is witnessing unprecedented funding density. Multiple rounds exceeding $500 million occurred in 2025 alone. These financings share a common feature: they are no longer concept-stage funding but target real projects in production lines, supply chains, general intelligence, and commercial deployment. When capital bets billions, it confirms the industry’s maturity.

Technological Signal: Multiple Key Innovations Converging

2025 marks a “technological convergence”—a historic simultaneous breakthrough. Innovations in AI Agents and large language models are transforming robots from “instruction executors” into “understanding agents.” Multimodal perception and new control models (RT-X, Diffusion Policy) are providing machines with near-general intelligence capabilities for the first time.

Simultaneously, simulation and transfer learning are rapidly maturing. High-fidelity environments like Isaac and Rosie significantly narrow the virtual-real gap, enabling large-scale, low-cost training in virtual environments and reliable skill transfer to reality. This addresses past bottlenecks: slow learning, high data collection costs, and environmental risks.

Hardware is equally critical. Cost reductions in torque motors, joint modules, sensors, and the scaling of global supply chains (especially China’s rise in the robotics supply chain) further boost productivity. Many companies are launching large-scale production, giving robots a “reproducible, scalable” industrial foundation.

Business Signal: Clear Path from Prototype to Mass Production

Leading companies like Apptronik, Figure, and Tesla Optimus have announced mass production plans, marking the transition of humanoid robots from prototypes to industrialization. Many are starting pilot projects in high-demand scenarios like warehousing and logistics, validating efficiency and reliability in real environments.

More importantly, the validation of Operation-as-a-Service (OaaS) models. Companies no longer need to bear high upfront costs but can subscribe to robot services monthly, greatly improving ROI. This is a key innovation for large-scale robot adoption.

Three Pillars of Web3 in the Machine Economy

As the robotics industry explodes, blockchain technology finds a clear role, providing three core capabilities for the machine economy system.

Data Layer: Solving Incentives, Not Directly Ensuring Quality

Decentralized token incentives offer new data sources for robot training, but data quality ultimately depends on the backend data engine.

The main bottleneck in Physical-AI model training is the lack of large-scale real data, insufficient scene coverage, and high-quality physical interaction data. The emergence of DePIN/DePAI enables Web3 to address “who provides data and how to continuously incentivize.”

Academic research shows: decentralized data has potential in scale and coverage but does not automatically become high-quality training data. It still requires backend data engines to select, clean, and control biases.

Web3 primarily solves the “motivation to supply data” problem, not directly guaranteeing “data quality.” Traditional robot training data mainly comes from labs, small fleets, or internal corporate collection—insufficient in scale. Web3’s DePIN/DePAI models incentivize ordinary users, device operators, or remote controllers via tokens, greatly expanding data volume and diversity.

Representative projects include:

• NATIX Network: Converts ordinary vehicles into mobile data collection nodes via Drive&App and VX360, collecting video, geographic, and environmental data
• PrismaX: Uses remote control markets to gather high-quality physical interaction data (grasping, classification, moving objects)
• BitRobot Network: Enables robot nodes to perform verifiable tasks (VRT), generating real operation, navigation, and collaboration data

However, many crowdsourcing and mobile sensing studies highlight structural issues in decentralized data—low accuracy, high noise, and bias. Contributors tend to cluster geographically or within specific groups, leading to sampling distributions that do not reflect reality. Raw crowdsourced data cannot be directly used for training.

Therefore, Web3 data networks provide broader data sources, but whether they can be directly used for training depends on backend data engineering. The true value of DePIN is providing a “continuous, scalable, low-cost” data foundation, not an immediate solution to accuracy.

Coordination Layer: Unified Interface for Cross-Device Collaboration

Robotics is evolving from single-machine intelligence to group collaboration, but key bottlenecks remain: different brands, forms, and tech stacks cannot share information or are incompatible, lacking a unified communication medium. This limits large-scale deployment.

Recently, general robot operating system layers (Robot OS Layer), represented by OpenMind, offer new solutions. These are not traditional control software but cross-device intelligent operating systems—like Android for mobile—providing common language and infrastructure for robot communication, cognition, understanding, and collaboration.

In traditional architectures, each robot’s sensors, controllers, and reasoning modules are isolated, unable to share semantic information across devices. The general OS layer introduces:

• Abstract descriptions of external environments (visual/sound/tactile → structured semantic events)
• Unified command understanding (natural language → action planning)
• Shared multimodal state representations

This is akin to equipping robots with a cognitive layer capable of understanding, expressing, and learning. Robots are no longer “isolated actuators” but possess a unified semantic interface, enabling integration into large-scale collaborative networks.

The biggest innovation is “cross-device compatibility”: different brands and forms of robots can now “speak the same language” for the first time. All robots can connect via the same OS to the same data bus and control interface.

This interoperability allows the industry to discuss:

• Multi-robot collaboration
• Task bidding and planning
• Shared perception/maps
• Cross-space joint task execution

The prerequisite for collaboration is “understanding the same information format”—the universal OS is solving this fundamental language problem.

peaq represents another key infrastructure direction in the device coordination ecosystem: providing verifiable identity, economic incentives, and network-level coordination protocols. It does not solve “how robots understand the world” but “how robots as individuals participate in network collaboration.”

peaq’s core features:

1. Machine Identity Registration (Kite Passport)

Each AI Agent and robot obtains cryptographic identity and multi-layer keys, enabling:

• Access as independent entities to any network
• Participation in trusted task assignment and reputation systems

This is a prerequisite for becoming a “network node.”

2. Autonomous Economic Accounts

Robots gain economic autonomy. With native support for stablecoin payments and automatic billing logic, robots can settle and pay automatically without human intervention, including:

• Sensor data billing based on consumption
• Computing and inference costs
• Inter-robot service settlements (transport, delivery, inspection)
• Autonomous charging, site leasing, and infrastructure calls

Robots can also use conditional payments:

• Task completion → automatic payment
• Unsatisfactory results → automatic fund freeze or refund

This makes inter-robot collaboration trustworthy, auditable, and automatically arbitrated—key for large-scale commercial deployment.

Additionally, service revenues and resource supplies generated by robots in the real world can be tokenized and mapped on-chain, making their value and cash flow transparent, traceable, tradable, and programmable—building asset representations of machine entities.

As AI and on-chain systems mature, the goal is to enable machines to autonomously earn, pay, borrow, and invest, executing M2M transactions and forming self-organizing economic networks, with DAO-based collaboration and governance.

3. Inter-Device Task Coordination

At a higher level, peaq provides a coordination framework for machines to:

• Share status and availability information
• Participate in task bidding and matching
• Manage resources (computing, mobility, perception)

This allows robots to operate as nodes within a collaborative network rather than isolated units.

Only when language and interfaces are unified can robots truly enter collaborative networks rather than remain in closed ecosystems. Cross-device OS like OpenMind aim to standardize how robots “understand the world and commands”; peaq and similar Web3 coordination networks explore how different devices can gain verifiable organizational collaboration capabilities within broader networks. They are representative of industry efforts toward unified communication layers and open interoperable systems.

Economic Layer: Empowering Machines with Autonomous Economic Participation

If the cross-device OS solves “how robots communicate,” and the coordination network addresses “how they collaborate,” then the core of the machine economy network is transforming robots’ productivity into sustainable capital flows—allowing machines to autonomously cover operational costs and close loops.

A long-standing missing link in the robotics industry is “autonomous economic capability.” Traditional robots can only execute preset instructions; they cannot manage external resources, price their services, or adjust costs. In complex scenarios, they rely heavily on manual bookkeeping, approval, and management, greatly reducing collaboration efficiency and hindering large-scale deployment.

x402: Giving Machines the Status of “Economic Entities”

x402, as a new agentic payment standard, provides this foundational capability. Robots can send payment requests via HTTP and settle atomically with programmable stablecoins (e.g., USDC). This means robots can not only complete tasks but also autonomously purchase all necessary resources:

• Computing power (LLM inference/control models)
• Scene access and device leasing
• Services from other robots

For the first time, robots can act as autonomous economic agents.

Recent collaborations between robot manufacturers and crypto infrastructure providers exemplify this shift, indicating that machine economic networks are moving from concept to implementation.

OpenMind × Circle: Native Stablecoin Payments for Robots

OpenMind integrates its cross-device robot OS with Circle’s USDC, enabling robots to directly perform stablecoin payments and settlements during task execution. This represents two breakthroughs:

1. Robot task execution pipelines natively integrated with financial settlement, no longer relying on backend systems
2. Cross-platform, cross-brand “borderless payments” among robots

This is a foundational capability toward autonomous economic entities.

Kite AI: Building Agent-Native Blockchain for the Machine Economy

Kite AI advances the infrastructure for the machine economy: designed for AI agents, featuring on-chain identities, composable wallets, automated payments, and settlement systems, enabling agents to autonomously execute various on-chain transactions.

Its core includes:

1. Agent/Robot Identity Layer (Kite Passport)

Each AI Agent (and future robots) obtains cryptographic identity and multi-layer keys, enabling fine-grained control—“who pays” and “who acts on behalf,” with revocation and accountability. This is a prerequisite for treating agents as independent economic entities.

2. Native Stablecoins + x402 Integration

Kite integrates x402 payment standards at the chain level, using USDC and other stablecoins as default settlement assets, allowing agents to complete send/receive/reconciliation via standardized intents, optimized for high-frequency, small-value, inter-robot payments (sub-second confirmation, low fees, auditable).

3. Programmable Constraints & Governance

Through on-chain policies, expenditure limits, merchant/contract whitelists, risk management rules, and auditability can be set, balancing “opening wallets to machines” with security and autonomy.

In other words, if OpenMind’s OS enables robots to “understand the world and collaborate,” Kite AI’s blockchain infrastructure allows robots to “survive within the economic system.” These technologies build the “collaborative incentives” and “closed value loops” of the machine economy network, enabling machines to not only “pay” but also:

• Earn income based on performance (result-oriented settlement)
• Purchase resources on demand (autonomous cost structures)
• Participate in markets via chain-based reputation (verifiable fulfillment)

Robots can participate in a complete economic incentive system: work → earn → spend → autonomously optimize behaviors.

Outlook and Challenges

Future: The Machine Internet Beyond the Internet

From the three directions above, the role of Web3 in the robotics industry is becoming increasingly clear:

• Data Layer: Providing motivation for large-scale, multi-source data collection, improving coverage of long-tail scenarios
• Coordination Layer: Introducing unified identities, interoperability, and task governance mechanisms for cross-device collaboration
• Economic Layer: Enabling programmable economic behaviors through on-chain payments and verifiable settlements

Together, these capabilities lay the foundation for the future machine internet, allowing machines to collaborate and operate in a more open, auditable environment.

Challenges: From Technical Feasibility to Business Sustainability

Although the robotics ecosystem is reaching an unprecedented inflection point in 2025, transitioning from “technological feasibility” to “scaling and sustainability” involves multiple uncertainties—not solely technical bottlenecks but complex interplays of engineering, economics, markets, and regulation.

Is true economic viability actually achieved?

Despite progress in perception, control, and intelligence, large-scale deployment still depends on genuine business demand and economic returns. Currently, most humanoid and general-purpose robots are still in pilot validation stages; long-term data on enterprise willingness to pay for robot services and whether OaaS/RaaS models can ensure stable ROI across industries are lacking. Moreover, the cost-effectiveness of robots in complex unstructured environments is not yet fully established. In many cases, traditional automation or manual labor remains more economical and reliable. This means that technological feasibility does not automatically translate into economic necessity, and the uncertainty in commercialization will directly impact the industry’s growth rate.

Systemic challenges in engineering reliability and operational complexity

The biggest challenge in robotics is often not “can the task be completed,” but “can it be done reliably and at low cost over the long term.” Large-scale deployment risks include hardware failure rates, maintenance costs, software updates, energy management, safety, and liability—potentially evolving into systemic risks. While OaaS reduces initial capital expenditure, hidden costs like maintenance, insurance, liability, and compliance may erode overall profitability. If reliability does not meet minimum thresholds for commercial scenarios, robotic networks and the machine economy will remain theoretical.

Ecosystem coordination, standard convergence, and regulatory adaptation

The robotics ecosystem is rapidly evolving in OS, agent frameworks, blockchain protocols, and payment standards but remains highly fragmented. Cross-device, cross-vendor, and cross-system collaboration costs are high, and standardization is incomplete, risking fragmentation, duplicated efforts, and efficiency losses. Meanwhile, autonomous decision-making and economic capabilities challenge existing legal and regulatory frameworks: liability, payment compliance, data boundaries, and security are still unclear. Without parallel evolution of regulation and standards, the machine economy network faces compliance and implementation uncertainties.

Overall, the conditions for large-scale robot deployment are gradually forming, and prototypes of the machine economy system are emerging in industry practice. Web3 × robotics remains in early stages but shows promising long-term potential worth monitoring.