Introduction
In recent years, the robotics industry has reached a dual turning point in technology and business paradigms. In the past, robots were more often seen as "tools," relying on enterprise backend scheduling, lacking autonomous collaboration, and not possessing economic behavior capabilities. However, with the integration of new technologies such as AI Agents, on-chain payments (x402), and the Machine Economy, the robotics ecosystem is evolving from a single-dimensional hardware competition to a multi-layered complex system composed of "body - intelligence - payment - organization."
What is even more noteworthy is that the global capital market is rapidly pricing this trend. Morgan Stanley predicts that by 2050, the market size for humanoid robots could reach $5 trillion, further driving growth in surrounding industries such as supply chain, operations, and services. In the same year, the number of humanoid robots in use is expected to exceed 1 billion. This means that robots will transition from industrial equipment to true "scale social participants." (1)
To understand the future development direction of the robotics industry, we can conceptualize the entire ecosystem as a four-tier structure:
Source: Gate Ventures
The first layer is the Physical Layer: This includes all embodied carriers such as humanoids, robotic arms, drones, and EV charging stations. They address fundamental issues of movement and operational capability, such as walking, grasping, mechanical reliability, and cost. However, robots at this layer still lack "economic behavior capabilities," meaning they cannot autonomously perform actions like charging, payment, or service procurement.
The second layer is the Control & Perception Layer: This encompasses traditional robotics control theory, SLAM, perception systems, voice and visual recognition, to today's LLM+Agent, and an increasing number of robotic operating systems (like ROS, OpenMind OS) with abstract planning capabilities. This layer enables machines to "understand, see, and execute tasks," but economic activities such as payments, contracts, and identities still need to be handled by humans in the backend.
The third layer is the Machine Economy Layer: The real transformation begins here. Machines start to possess wallets, digital identities, and credit systems (like ERC-8004), and can directly pay for computing power, data, energy, and road rights through mechanisms like x402, on-chain settlement, and Onchain Callback; they can also autonomously collect payments, manage funds, and initiate result-based payments due to task execution. This layer allows robots to leap from being "enterprise assets" to "economic entities," capable of participating in the market.
The fourth layer is the Machine Coordination Layer: Once a large number of robots possess autonomous payment and identity capabilities, they can further organize into fleets and networks—drone swarms, cleaning robot networks, EV energy networks, etc. They can automatically adjust prices, schedule tasks, bid for jobs, share profits, and even form autonomous economic entities in the form of DAOs.
Through this four-layer structure, we can see:
The future robotics ecosystem is no longer just a hardware revolution, but a systematic reshaping of "physical + intelligence + finance + organization."
This not only redefines the capability boundaries of machines but also redefines the ways of value capture. Whether it is robotics companies, AI developers, infrastructure providers, or crypto-native payment and identity protocols, all will find their respective positions in the new robotic economic system.
Why is the Robotics Industry Exploding at This Moment?
For the past few decades, the robotics industry has lingered in laboratories, exhibition halls, and specific industrial scenarios, always a step away from true large-scale commercial use and social deployment. However, after 2025, this step is beginning to be crossed. Whether from the capital market, technological maturity, or from industry observers like Nvidia CEO Jensen Huang, the same signal is being conveyed:
“The ChatGPT moment for general robotics is just around the corner.”
This judgment is not an exaggeration but is based on three key industry signals:
The fundamental skills of computing power, models, simulation, and perception control are maturing simultaneously.
Robotic intelligence is transitioning from closed control to LLM/Agent-driven open decision-making.
The leap from individual capabilities to system capabilities: Robots will evolve from being "active" to being "collaborative, understanding, and economically operational."
Jensen Huang even further predicts that humanoid robots will enter widespread commercial use within the next five years, a view that aligns closely with the behaviors of the capital market and industry deployment in 2025.
Capital Aspect: Massive Financing Proves the "Turning Point" of Robotics Has Been Priced by the Market
In 2024-2025, the robotics industry has seen an unprecedented density and scale of financing, with multiple instances of over $500 million in funding occurring in 2025 alone. Notable events include:
Source: Gate Ventures
Capital clearly expresses: The robotics industry has reached a stage where investment is verifiable.
The common characteristics of these financings are:
● They are not "concept financings," but rather focus on production lines, supply chains, general intelligence, and commercial deployment directions.
● They are not scattered projects, but rather a combination of software and hardware, full-stack architecture, and a complete lifecycle service system for robots.
Capital will not arbitrarily bet on a hundred billion scale; behind it is a confirmation of industry maturity.
Technological Aspect: Decisive Breakthroughs Occur Simultaneously
The robotics industry is experiencing a historically rare "multi-technology convergence" in 2025. First, breakthroughs in AI Agents and large language models have upgraded robots from being "operable machines" that could only execute commands to "understandable agents" capable of comprehending language, breaking down tasks, and reasoning through visual and tactile inputs. Multi-modal perception and new generation control models (like RT-X, Diffusion Policy) have given robots their first foundational capabilities approaching general intelligence.
Source: Nvidia
At the same time, simulation and transfer technologies are rapidly maturing. High-fidelity simulation environments like Isaac and Rosie significantly narrow the gap between simulation and reality, allowing robots to complete large-scale training in virtual environments at very low costs and reliably transfer to the real world. This addresses the fundamental bottlenecks of slow learning speeds, expensive data collection, and high risks in real environments that robots faced in the past.
The evolution of hardware is equally critical. Core components such as torque motors, joint modules, and sensors continue to decrease in cost due to supply chain scaling, and China's accelerated rise in the global robotics supply chain further enhances industry productivity. With multiple companies initiating mass production plans, robots now possess the industrial foundation for "replicability and scalable deployment."
Finally, improvements in reliability and energy consumption structures have enabled robots to truly meet the minimum thresholds for commercial applications. Better motor control, redundant safety systems, and real-time operating systems allow robots to operate stably for extended periods in enterprise-level scenarios.
These factors have provided the robotics industry with the complete conditions necessary to transition from the "laboratory demo stage" to "large-scale real deployment." This is the fundamental reason for the current explosion in robotics.
Commercialization Aspect: From Prototype → Mass Production → Real-World Deployment
2025 is also the year when the commercialization path of robotics first becomes clear. Leading companies like Apptronik, Figure, and Tesla Optimus have successively announced mass production plans, marking the transition of humanoid robots from prototypes to a replicable industrial stage. At the same time, multiple companies are beginning pilot deployments in high-demand scenarios such as warehousing logistics and factory automation, validating the efficiency and reliability of robots in real environments.
With the enhancement of hardware mass production capabilities, the "Operation-as-a-Service (OaaS)" model is beginning to gain market validation. Companies no longer need to pay high upfront costs for purchases but can subscribe to robotic services on a monthly basis, significantly improving ROI structures. This model has become a key commercial innovation driving the large-scale application of robots.
Additionally, the industry is rapidly filling in the previously missing service systems, including maintenance networks, spare parts supply, remote monitoring, and operation and maintenance platforms. As these capabilities take shape, robots are beginning to possess the complete conditions necessary for sustained operation and commercial closure.
Overall, 2025 marks a milestone year for robotics, transitioning from "can it be done" to "can it be sold, can it be used, and can it be afforded," with a sustainable positive cycle in the commercialization path finally emerging.
Web3 X Robotics Ecosystem
With the comprehensive explosion of the robotics industry in 2025, blockchain technology also finds a clear position within it, supplementing several key capabilities for the robotic system. Its core value can be summarized in three main directions: i.) data collection for robotic technology, ii.) cross-device machine coordination networks, and iii.) supporting machine economic networks for autonomous market participation.
Decentralization + Token Incentive Mechanisms Build New Data Sources for Robot Training, but Data Quality Depends on Backend Data Engine Enhancements
The core bottleneck in training Physical-AI models lies in the scale of real-world data, scene coverage, and the scarcity of high-quality physical interaction data. The emergence of DePIN/DePAI allows Web3 to provide new solutions regarding "who contributes data and how to sustain contributions."
However, from an academic research perspective, while decentralized data has potential in scale and coverage, it does not inherently equate to high-quality training data; it still requires backend data engines for filtering, cleaning, and bias control to be truly usable for large model training.
First, Web3 addresses the "data supply motivation" issue rather than directly guaranteeing "data quality."
Traditional robotic training data primarily comes from laboratories, small-scale fleets, or internal company collections, which are exponentially insufficient in scale.
The DePIN/DePAI model of Web3 incentivizes ordinary users, device operators, or remote operators to become data contributors through token rewards, significantly enhancing the scale and diversity of data sources.
Projects include:
Source: Gate Ventures
●NATIX Network: Transforms public vehicles into mobile data nodes through Drive & App and VX360, collecting video, geographic, and environmental data.
●PrismaX: Collects high-quality robotic physical interaction data (grasping, organizing, moving objects) through remote-controlled markets.
●BitRobot Network: Enables robot nodes to perform verifiable tasks (VRT), generating data on real operations, navigation, and collaborative behaviors.
These projects demonstrate that Web3 can effectively expand the data supply side, filling the gaps in traditional systems that struggle to cover real-world scenarios and long-tail situations.
However, according to academic research, crowdsourced/decentralized data often suffers from structural issues such as "insufficient accuracy, high noise, and significant bias." Extensive studies in academia on crowdsourcing and mobile crowdsensing indicate:
1. Data quality varies greatly, with significant noise and format differences.
Differences in devices, operating methods, and understanding among contributors can lead to a large amount of inconsistent data that needs to be detected and filtered.
2. Structural bias is prevalent.
Participants often cluster in specific areas/groups, leading to sampling distributions that do not align with real-world distributions.
3. Raw crowdsourced data cannot be directly used for model training.
Research in autonomous driving, embodied AI, and robotics emphasizes that high-quality training sets require a complete process of: collection → quality review → redundancy alignment → data augmentation → long-tail completion → label consistency correction, rather than "collect and use." (7)
Therefore, while the data network of Web3 provides broader data sources, "whether it can directly become training data" depends on the backend data engineering.
The true value of DePIN lies in providing a "sustainable, scalable, and cost-effective" data foundation for Physical AI.
Rather than saying Web3 immediately solves data accuracy issues, it addresses:
● "Who is willing to contribute data long-term?"
● "How to encourage more real devices to connect?"
● "How to transition data collection models from centralized to sustainable open networks?"
In other words, DePIN/DePAI provides the foundation for data scale and coverage, making Web3 an important piece of the "data source layer" in the era of Physical AI, but not the sole guarantor of data quality.
Cross-Device Machine Coordination Network: General OS Provides the Basic Communication Layer for Robot Collaboration
The current robotics industry is moving from single-machine intelligence to group collaboration, but a key bottleneck remains: robots of different brands, forms, and technology stacks cannot share information, interoperate, and lack a unified communication medium. This forces multi-machine collaboration to rely on closed systems built by manufacturers, greatly limiting scalable deployment.
Recently emerging general robotic operating system layers (Robot OS Layer), represented by OpenMind, are providing new solutions to this problem. These systems are not traditional "control software," but rather cross-body intelligent operating systems that, like Android in the mobile industry, provide a common language and public infrastructure for communication, cognition, understanding, and collaboration among robots. (8)
In traditional architectures, the sensors, controllers, and reasoning modules within each robot are isolated, completely unable to share semantic information across devices. The general operating system layer allows robots to gain:
● Abstract descriptions of the external world (vision/sound/tactile → structured semantic events)
● Unified understanding of instructions (natural language → action planning)
● Shareable multi-modal state expressions
This effectively equips robots with a cognitive layer that can understand, express, and learn from the ground up.
As a result, robots are no longer "isolated executors," but possess a unified semantic interface, enabling them to be integrated into larger-scale machine collaboration networks.
Additionally, the greatest breakthrough of the general OS lies in "cross-body compatibility," allowing robots of different brands and forms to "speak the same language" for the first time. Various robots can connect to a unified data bus and control interface through the same OS.
Source: Openmind
This cross-brand interoperability enables the industry to genuinely discuss:
● Multi-robot collaboration
● Task bidding and scheduling
● Shared perception / shared maps
● Cross-space joint task execution
Collaboration requires "understanding the same information format," and the general OS is addressing this underlying language issue.
In the system of cross-device machine collaboration, peaq represents another key infrastructure direction: providing machines with verifiable identities, economic incentives, and network-level coordination capabilities through a foundational protocol layer. (9)
It does not solve "how robots understand the world," but rather "how robots participate as individuals in collaboration within a network."
Its core design includes:
- Machine Identity
peaq provides decentralized identity registration for robots, devices, and sensors, enabling them to:
● Connect to any network as independent entities
● Participate in trusted task allocation and reputation systems
This is a prerequisite for machines to become "network nodes."
- Autonomous Economic Accounts
Source: Peaq
Robots are endowed with economic autonomy. Through natively supported stablecoin payments and automated billing logic, robots can autonomously reconcile and make payments without human intervention, including:
● Sensor data billed by quantity
● Pay-per-use for computing power and model inference
● Instant settlement after providing services between robots (transportation, delivery, inspection)
● Autonomous charging, leasing space, and other infrastructure calls
Additionally, robots can adopt conditional payments:
● Task completion → automatic payment
● Unsatisfactory results → funds automatically frozen or returned
This makes robot collaboration trustworthy, auditable, and subject to automatic arbitration, which is a key capability for large-scale commercial deployment.
Furthermore, the income generated by robots providing services and resources in the real world can be tokenized and mapped to the blockchain, presenting its value and cash flow in a transparent, traceable, tradable, and programmable form, thereby constructing a machine-centric asset representation.
As AI and on-chain systems mature, the goal is for machines to autonomously earn, pay, borrow, and invest, directly engaging in M2M transactions, forming self-organizing machine economic networks, and achieving collaboration and governance in the form of DAOs.
- Multi-Device Task Coordination
At a higher level, peaq provides a coordination framework between machines, enabling them to:
● Share status and availability information
● Participate in task bidding and matching
● Conduct resource scheduling (computing power, mobility, sensing capabilities)
This allows robots to collaborate like a node network rather than operate in isolation. Once language and interfaces are unified, robots can truly enter a collaborative network instead of remaining in their respective closed ecosystems.
OpenMind, as a cross-body intelligent OS, attempts to standardize how robots "understand the world and instructions"; while Peaq, as a Web3 coordination network, explores how different devices can gain verifiable organized collaboration capabilities within a larger network. They are just representatives among many attempts, reflecting the entire industry’s accelerated evolution towards a unified communication layer and open interoperability system.
Supporting Machine Economic Networks for Autonomous Market Participation
If cross-device operating systems solve "how robots communicate," and coordination networks address "how to collaborate," then the essence of machine economic networks is to transform the productivity of robots into sustainable capital flows, allowing robots to pay for their own operations and form a closed loop.
A key piece long missing in the robotics industry is "autonomous economic capability." Traditional robots can only execute preset instructions but cannot independently schedule external resources, price their own services, or settle costs. Once they enter complex scenarios, they must rely on human backends for accounting, approval, and scheduling, severely hampering collaboration efficiency and making large-scale deployment even more challenging.
x402: Filling the "Economic Entity Qualification" Gap for Robots
Source: X@CPPP2443_
x402, as a new generation of Agentic Payment standard, fills this fundamental capability gap for robots. Robots can initiate payment requests directly through the HTTP layer and complete atomic settlements using programmable stablecoins like USDC. This means robots can not only complete tasks but also autonomously purchase all the resources needed for those tasks:
● Computing power calls (LLM inference / control model inference)
● Scene access and device rentals
● Labor services from other robots
For the first time, robots can autonomously consume and produce like economic entities.
In recent years, representative cases of collaboration between robot manufacturers and crypto infrastructure have begun to emerge, indicating that machine economic networks are transitioning from concept to reality.
OpenMind × Circle: Enabling Robots to Natively Support Stablecoin Payments
Source: Openmind
OpenMind integrates its cross-device robotic OS with Circle's USDC, allowing robots to use stablecoins for payments and settlements directly within the task execution chain.
This represents two breakthroughs:
The robotic task execution chain can natively access financial settlements, no longer relying on backend systems.
Robots can make "borderless payments" in cross-platform, cross-brand environments.
For machine collaboration, this is a foundational capability for moving towards autonomous economic entities.
Kite AI: Building an Agent-Native Blockchain Foundation for the Machine Economy
Source: Kite AI
Kite AI further advances the underlying structure of the machine economy: it designs on-chain identities, composable wallets, and automated payment and settlement systems specifically for AI agents, allowing agents to autonomously execute various transactions on-chain. (10)
It provides a complete "autonomous agent economic operating environment," which aligns closely with the autonomous market participation that robots aim to achieve.
1. Agent / Machine Identity Layer (Kite Passport): Issues cryptographic identities and a multi-layer key system for each AI Agent (which can also be mapped to specific robots in the future), allowing for fine control over "who is spending" and "who is acting on behalf of whom," and supports revocation and accountability at any time. This is a prerequisite for viewing agents as independent economic entities.
2. Native Stablecoin + Built-in x402 Primitives: Kite integrates the x402 payment standard at the chain level, using stablecoins like USDC as the default settlement asset, enabling agents to send, receive, and reconcile through standardized intent authorization. It has made underlying optimizations for high-frequency, small-value, machine-to-machine payment scenarios (sub-second confirmation, low fees, auditable).
3. Programmable Constraints and Governance: Through on-chain policies, it sets spending limits, allowed merchant/contract whitelists, risk control rules, and audit trails for agents, balancing the act of "opening wallets for machines" between security and autonomy.
In other words, if OpenMind's OS allows robots to "understand the world and collaborate," then Kite AI's blockchain infrastructure enables robots to "survive in the economic system."
Through the above technologies, the machine economic network constructs "collaborative incentives" and "value loops," allowing robots not only to "make payments" but, more importantly, to:
● Earn income based on performance (result-based settlement)
● Purchase resources on demand (autonomous cost structure)
● Participate in market competition based on on-chain reputation (verifiable performance)
This means that for the first time, robots can participate in a complete economic incentive system: can work → can earn money → can spend money → can independently optimize behavior.
Summary
Outlook
Looking at the three major directions above, the role of Web3 in the robotics industry is becoming increasingly clear:
● Data Layer: Provides scalable, multi-source data collection power and improves coverage of long-tail scenarios;
● Collaboration Layer: Introduces unified identity, interoperability, and task governance mechanisms for cross-device collaboration;
● Economic Layer: Provides a programmable economic behavior framework for robots through on-chain payments and verifiable settlements.
These capabilities collectively lay the foundation for a potential future machine internet, enabling robots to collaborate and operate in a more open and auditable technological environment.
Uncertainty
Although the robotics ecosystem is expected to experience rare breakthroughs by 2025, it still faces multiple uncertainties in transitioning from "technically feasible" to "scalable and sustainable." These uncertainties do not stem from a single technological bottleneck but arise from the complex coupling of engineering, economic, market, and institutional levels.
Is Economic Feasibility Truly Established?
Despite breakthroughs in perception, control, and intelligence, the large-scale deployment of robots ultimately depends on whether real commercial demand and economic returns are established. Currently, most humanoid and general-purpose robots remain in pilot and validation stages, and there is still insufficient long-term data to support whether companies are willing to pay for robot services long-term and whether OaaS/RaaS models can stably demonstrate ROI across different industries.
At the same time, the cost-effectiveness of robots in complex, unstructured environments has not yet been fully established. In many scenarios, traditional automation or human replacement solutions are still cheaper and more reliable. This means that technical feasibility does not automatically translate into economic necessity, and the uncertainty of commercialization pace will directly impact the expansion speed of the entire industry.
Systemic Challenges of Engineering Reliability and Operational Complexity
The biggest real-world challenge facing the robotics industry often lies not in "whether tasks can be completed," but in whether they can operate long-term, stably, and at low cost. In large-scale deployments, hardware failure rates, maintenance costs, software upgrades, energy management, and safety and liability issues can quickly amplify into systemic risks.
Even if the OaaS model reduces initial capital expenditures, hidden costs in operations, insurance, liability, and compliance may still erode the overall business model. If reliability cannot cross the minimum threshold of commercial scenarios, the vision of a robot network and machine economy will be difficult to realize.
Ecological Synergy, Standard Convergence, and Institutional Adaptation
The robotics ecosystem is simultaneously undergoing rapid evolution in OS, agent frameworks, blockchain protocols, and payment standards, but it remains highly fragmented. The costs of cross-device, cross-manufacturer, and cross-system collaboration are high, and universal standards have not yet fully converged, which may lead to ecological fragmentation, redundant construction, and efficiency losses.
At the same time, robots with autonomous decision-making and economic behavior capabilities are challenging existing regulatory and legal frameworks: issues of liability, payment compliance, and data and security boundaries remain unclear. If institutions and standards cannot keep pace with technological evolution, the machine economic network will face uncertainties in compliance and implementation.
Overall, the conditions for large-scale application of robots are gradually taking shape, and the embryonic form of the machine economic system is also emerging in industrial practice. While Web3 × Robotics is still in its early stages, it has already shown promising long-term development potential worth paying attention to.
References
https://www.morganstanley.com/insights/articles/humanoid-robot-market-5-trillion-by-2050
http://www.xinhuanet.com/tech/20250908/89cc1111e729403ca5af4a397ebd01ce/c.html
https://orbilu.uni.lu/bitstream/10993/39438/1/comst-preprint.pdf?
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