Web3 Empowers the Robot Economy: From Industrial Tools to Autonomous Systems

Four-Dimensional Upgrade Framework of the Robotics Industry

The robotics industry is at a dual critical point—technological breakthroughs and business model innovations are arriving simultaneously. Traditionally, robots are viewed as centrally dispatched “tools,” lacking autonomous collaboration capabilities and an economic主体 position. However, with the integration of new technologies such as AI Agents, on-chain payments, and Machine Economy, the robot ecosystem is evolving from single-dimensional competition to a multi-layered coupled system of “hardware-intelligence-payment-organization.”

The potential of this transformation has been priced into global capital markets. JPMorgan predicts that by 2050, the humanoid robot market could reach $5 trillion, driving growth in supply chains, operations, services, and related industries. By then, the number of active humanoid robots is expected to exceed 1 billion, transforming robots from industrial equipment into “large-scale social participants.”

Understanding this evolutionary path, the entire ecosystem can be deconstructed into four progressive levels:

Level 1: Physical Carrier Layer — All embodied systems such as humanoid robots, robotic arms, drones, charging stations, etc. This layer addresses basic movement and operational capabilities (walking, grasping, reliability, cost), but machines remain “lacking economic主体” and cannot autonomously perform actions like charging, payments, or procurement.

Level 2: Perception and Control Layer — From traditional control theory, SLAM, visual recognition to current LLM+Agent and adaptive planning systems like ROS. This layer enables machines to “understand, see, execute,” but payments, contracts, and identity verification still require manual backend processing.

Level 3: Machine Economy Layer — The site of fundamental change. Machines acquire wallets, digital identities, and reputation systems, enabling direct payments for computing power, data, energy, and road rights via mechanisms like x402 and on-chain settlement; they can autonomously charge fees, escrow funds, and initiate pay-for-performance. Machines upgrade from “enterprise assets” to “market participants.”

Level 4: Machine Coordination Layer — Large numbers of autonomous payment-enabled and identity-verified machines can organize into fleets and networks (drone swarms, cleaning robot networks, power grids), automatically adjust prices, schedule, bid for tasks, and distribute profits, even forming autonomous economies in DAO structures.

This architecture reveals a key fact: the future robot ecosystem is not just a hardware revolution but a systemic reshaping of “physical + intelligence + finance + organization,” redefining the boundaries of machine capabilities and value capture.

Why Will the Robotics Industry Explode by 2025?

Over the past decades, robotics technology has been confined to laboratories, showrooms, and specific industrial scenarios. But after 2025, this threshold is being crossed. From capital markets and technological maturity to industry observers like NVIDIA CEO Jensen Huang, all signals point to the same conclusion: “The ChatGPT moment for general-purpose robots is imminent.”

This is not hype but based on three core industry signals:

Signal 1: Infrastructure for compute, models, simulation, perception, and control is maturing simultaneously

High-performance chips, large model engineering, high-fidelity simulation environments (Isaac, Rosie), and next-generation control algorithms (RT-X, diffusion strategies) are advancing in tandem. These previously bottlenecked areas now form a usable engineering foundation.

Signal 2: Robot intelligence shifting from closed-loop control to LLM/Agent-driven open decision-making

Multimodal perception and new control models enable robots to possess near-general intelligence capabilities—evolving from “only executing preset instructions” to “understanding, decomposing tasks, and reasoning via vision and touch.”

Signal 3: Single-machine capabilities leap to system-level capabilities

Robots evolve from “capable of action” to “able to collaborate, understand, and participate in economic activities” within a complete closed loop.

Jensen Huang predicts humanoid robots will enter widespread commercial use within 5 years, resonating strongly with capital market behaviors and industry deployments around 2025.

Dual-Driven by Capital and Technology

Capital Validation: Record-high funding density

In 2024-2025, the scale and frequency of funding in the robotics industry are unprecedented. In 2025 alone, multiple Series funding rounds have exceeded $500 million. Common features of these financings include:

• Not “concept funding” but focused on production lines, supply chains, general intelligence, and commercial deployment
• Not dispersed projects but integrated hardware-software full-stack architectures and full lifecycle services

Capital does not invest hundreds of millions without reason; behind it is a confirmation of industry maturity.

Technological breakthroughs: Convergence of multiple technologies

Breakthroughs in AI Agents and large models elevate robots from “manipulable devices” to “understandable intelligent agents.” Multimodal perception combined with new control algorithms enables robots to possess near-general intelligence capabilities for the first time.

Mature simulation and transfer learning technologies significantly narrow the virtual-real gap. High-fidelity simulation environments allow large-scale, low-cost training in virtual settings, then reliably transfer to reality. This addresses the fundamental bottleneck of slow learning, expensive data, and high environmental risks.

On hardware, core components like torque motors, joint modules, and sensors continue to decrease in cost due to supply chain scale. China’s accelerated rise in the global robot supply chain further boosts industry capacity. Multiple companies announce mass production plans, giving robots a “reproducible, scalable” industrial foundation.

Improvements in reliability and energy consumption structures enable robots to meet the minimum thresholds for commercial applications—better motor control, redundant safety systems, real-time operating systems—allowing long-term stable operation in enterprise scenarios.

Overall, the robotics industry now has the complete conditions to transition from “lab demonstrations” to “large-scale real deployment” for the first time.

Clearer Commercialization Path

2025 marks the first year when the commercialization path of robots takes shape. Leading companies like Apptronik, Figure, and Tesla’s Optimus have announced mass production plans, signaling humanoid robots moving from prototypes to industrialized, replicable stages.

Simultaneously, pilot deployments in high-demand scenarios such as warehousing, logistics, and factory automation validate robots’ efficiency and reliability in real environments.

As hardware mass production capacity improves, Operation-as-a-Service (OaaS) models are gaining market validation. Companies can subscribe to robot services monthly without large upfront costs, significantly optimizing ROI. This innovative business model is becoming a key driver for large-scale robot adoption.

Meanwhile, maintenance networks, spare parts supply, remote monitoring, and operation platforms—previously missing—are rapidly developing. As these capabilities mature, robots are equipped with the full conditions for continuous operation and a complete commercial closed loop.

Web3 Empowerment in the Robot Ecosystem: Three Dimensions

As the robot industry fully ignites, blockchain technology finds a clear role, complementing key capabilities across three dimensions:

First Dimension: Data Layer — Distributed Incentives Drive Multi-source Training Data for Physical AI

The core bottleneck in training Physical AI models lies in the scale, scene coverage, and high-quality interaction data scarcity of real-world data. DePIN/DePAI paradigms via Web3 offer new solutions: who contributes data, and how to incentivize contributions.

The key point: Distributed data, while larger and more diverse, does not automatically guarantee high-quality training data. Backend data engines still need filtering, cleaning, bias control to truly serve large model training.

Web3 addresses the “motivation problem,” not directly the “quality problem.”

Traditional robot training data mainly come from labs, small fleets, or internal corporate collection, far from sufficient. Web3’s DePIN/DePAI models incentivize ordinary users, device operators, and remote controllers with tokens, greatly expanding data sources’ scale and diversity.

Typical projects include:

• NATIX Network: Converts many vehicles into mobile data nodes, collecting video, geographic, environmental data
• PrismaX: Uses remote-controlled marketplaces to gather high-quality robot interaction data (grasping, classification, moving objects)
• BitRobot Network: Enables robot nodes to perform verifiable tasks, generating real operational, navigation, and collaboration behavior data

Academic research shows that crowdsourcing/distributed data often suffers from “insufficient accuracy, high noise, and bias.” Data must undergo a full process: collection → quality review → redundancy alignment → data augmentation → long-tail completion → label consistency correction, rather than “use immediately.”

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

Second Dimension: Coordination Layer — Unified OS and Distributed Identity Enable Cross-Device Collaboration

The industry is moving from single-machine intelligence to group collaboration, but a key bottleneck remains: different brands, forms, and tech stacks of robots cannot share information or interconnect, lacking a unified communication medium. This limits scalable deployment of multi-robot systems relying on proprietary systems.

Recently, general robot operating systems like OpenMind provide new solutions. These are not traditional “control software” but cross-platform intelligent OS, akin to Android in mobile industry, providing common infrastructure for communication, cognition, understanding, and collaboration among robots.

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

• Abstract descriptions of the external environment (visual/sound/touch → structured semantic events)
• Unified understanding of commands (natural language → action planning)
• Shared multimodal state representations

This effectively endows robots with cognition at the foundational level. Robots are no longer “isolated actuators” but possess a unified semantic interface, enabling integration into larger-scale robot collaboration networks.

The biggest breakthrough of universal OS is “brand-agnostic compatibility”—robots of different brands and forms can now “speak the same language.” All types of robots can connect via the same OS to a unified data bus and control interface.

This allows the industry to genuinely discuss multi-robot collaboration, task bidding and scheduling, perception sharing, and joint execution across spaces.

In cross-device collaboration systems, peaq represents another key infrastructure: a foundational protocol providing verifiable identities, economic incentives, and network-level coordination for machines.

Its core designs include:

1. Machine Identity: peaq offers decentralized identity registration for robots, devices, sensors, enabling them to access any network as independent entities and participate in trust-based task allocation and reputation systems—prerequisites for machines to become “network nodes.”

2. Autonomous Economic Accounts: Machines gain economic autonomy. With native support for stablecoin payments and automatic reconciliation logic, they can settle automatically without human intervention, including:

• Sensor data billing by volume
• Compute and inference services paid per use
• Instant settlement for services provided between machines (transport, delivery, inspection)
• Autonomous charging, space leasing, and infrastructure calls

Machines can also use conditional payments: task completion → automatic payment; unmet conditions → funds freeze or refund. This makes machine collaboration trustworthy, auditable, and automatically arbitrated—key for large-scale commercial deployment.

Income generated from providing services and resources can be tokenized and mapped onto the chain, presenting value and cash flow transparently, traceably, tradably, and programmably.

3. Multi-device Task Coordination: peaq offers frameworks for machines to share status and availability, participate in task bidding and matching, resource scheduling (compute, mobility, perception). Machines can collaborate like network nodes rather than isolated entities.

Once language and interfaces are unified, machines can truly enter collaboration networks rather than being confined to their ecosystems. Standards like OpenMind for cross-platform OS have standardized how machines “understand the world and instructions”; peaq’s Web3 coordination network explores paths for different devices to gain verifiable collaboration capabilities within larger networks.

Third Dimension: Economic Layer — On-Chain Payments and Verifiable Settlement Make Machines Economic Entities

If the cross-device OS solves “how machines communicate,” and the coordination network addresses “how they collaborate,” then the core of the Machine Economy network is transforming robot productivity into sustainable capital flows, enabling machines to self-operate and form closed loops.

The long-standing missing piece in robotics is “autonomous economic capability.” Traditional robots can only execute preset instructions, unable to independently allocate external resources, price, or settle costs for their services. In complex scenarios, they rely heavily on manual backend approval and scheduling, severely impairing collaboration efficiency and complicating large-scale deployment.

x402: Endowing machines with “economic主体” status

As a new generation of Agent payment standard, x402 fills this foundational gap. Machines can directly initiate payment requests via HTTP, using programmable stablecoins like USDC for atomic settlement. This means machines can not only complete tasks but also autonomously purchase all resources needed:

• Compute resources (LLM inference/control models)
• Scene access and device rentals
• Labor services provided by other machines

From now on, machines can act as autonomous economic agents—consuming and producing independently.

In recent years, collaborations between robot manufacturers and crypto infrastructure have begun to emerge, indicating that the Machine Economy network is moving from concept to implementation.

OpenMind × Circle: Enabling native stablecoin payments for machines

OpenMind integrates its cross-device robot OS with Circle’s USDC, allowing machines to directly use stablecoins for payments and settlements within task execution chains. This marks two breakthroughs:

1. The robot task execution chain can natively connect to financial settlement systems, no longer relying on backend systems
2. Machines can achieve “borderless payments” across platforms and brands

For robot collaboration, this is a foundational capability toward autonomous economic entities.

Kite AI: Designing native blockchain infrastructure for the Machine Economy

Kite AI further refines the underlying architecture of the Machine Economy: a blockchain designed specifically for AI Agents, with on-chain identities, composable wallets, and automatic payment/settlement systems, enabling Agents to autonomously execute on-chain transactions. It provides a complete “autonomous Agent economic environment” for robots to participate independently in markets.

Its core modules include:

1. Agent/Machine Identity Layer (Kite Passport): Issues cryptographic identities and multi-layer keys for each AI Agent (future mapping to specific robots), allowing fine-grained control over “who spends” and “who represents whom,” supporting revocation and accountability. This is a prerequisite for treating Agents as independent economic actors.

2. Native stablecoins + built-in x402 primitives: Integrates x402 payment standards at the chain layer, defaulting to USDC or similar stablecoins, enabling Agents to perform intent-based authorization for send/receive and reconciliation. Optimized for high-frequency, small-value M2M payments (sub-second confirmation, low fees, auditable).

3. Programmable constraints and governance: On-chain policies allow setting payment caps, merchant/contract whitelists, risk controls, and audit trails, balancing security and autonomy when “giving machines wallets.”

In essence, if OpenMind’s OS enables machines to “understand the world and collaborate,” Kite AI’s blockchain infrastructure enables machines to “exist within the economic system.”

Through these technologies, the Machine Economy network establishes “collaborative incentives” and a “value loop,” not only allowing machines to “pay” but more importantly:

• Earn income based on performance (result-based pay)
• Purchase resources on demand (autonomous cost structure)
• Participate in markets with verifiable reputation (performance verification)

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

Prospects and Challenges

Future of Ecosystem Integration

Looking across these three dimensions, the role of Web3 in the robotics industry is becoming clearer:

• Data dimension: Providing scalable, multi-source data collection incentives and improving long-tail scene coverage
• Coordination dimension: Introducing unified identity, interoperability, and task governance mechanisms for cross-device collaboration
• Economic dimension: Offering programmable economic behavior frameworks via on-chain payments and verifiable settlement

These capabilities lay the foundation for future Machine Internet prototypes, enabling machines to collaborate and operate in a more open, auditable technological environment.

Remaining uncertainties

Although the robotics ecosystem is set for rare breakthroughs around 2025, the journey from “technological feasibility” to “scalable and sustainable” deployment faces multiple uncertainties—not due to a single technical bottleneck but due to complex coupling at engineering, economic, market, and institutional levels.

Is true commercial viability established?

While breakthroughs in perception, control, and intelligence are achieved, large-scale deployment ultimately depends on genuine business demand and economic returns. Most humanoid and general-purpose robots are still in pilot and validation stages, lacking long-term data to confirm whether companies are willing to pay long-term for robot services, and whether OaaS/RaaS models can reliably deliver ROI across industries. Additionally, the cost-effectiveness of robots in complex unstructured environments is not yet fully established; in many scenarios, traditional automation or manual labor remains cheaper and more reliable.

This indicates that technological feasibility does not necessarily translate into economic inevitability, and uncertainties in commercialization will directly impact the industry’s expansion speed.

Systemic challenges in engineering reliability and operational complexity

The biggest practical challenge is not “can the task be completed,” but “can it be operated reliably, stably, and at low cost over the long term.” Large-scale deployment risks include hardware failure rates, maintenance costs, software upgrades, energy management, safety, and liability—factors that can quickly evolve into systemic risks.

Even if OaaS reduces initial capital expenditure, hidden costs such as operations, insurance, liability, and compliance may erode overall business models. If reliability cannot meet the minimum thresholds for commercial scenarios, the vision of robot networks and the Machine Economy will be hard to realize.

Ecosystem collaboration, standard convergence, and regulatory adaptation

The robotics ecosystem is rapidly evolving at the OS, Agent framework, blockchain protocol, and payment standard levels but remains highly fragmented. Cross-device, cross-vendor, and cross-system collaboration costs are high, and universal standards have not yet fully converged, risking ecosystem fragmentation, redundant development, and efficiency losses.

Meanwhile, robots with autonomous decision-making and economic autonomy challenge existing regulatory and legal frameworks: responsibility attribution, payment compliance, data security, and safety boundaries remain unclear. If regulations and standards do not keep pace with technological evolution, the Machine Economy networks face uncertainties in compliance and implementation.

Overall, the conditions for large-scale robot deployment are gradually forming, and the prototype of the Machine Economy system is emerging in industry practice. Although Web3 × Robotics is still in early stages, it has demonstrated promising long-term development potential worth关注.