The Rise of the Machine Economy: How Web3 Drives Robots from Tools to Autonomous Systems

Introduction

In recent years, the robotics industry has reached a dual inflection point in both technology and business paradigms. In the past, robots were mostly seen as "tools," relying on enterprise backend scheduling, unable to collaborate autonomously, and lacking economic agency. However, with the integration of new technologies such as AI Agent, on-chain payments (x402), and the Machine Economy, the robotics ecosystem is evolving from single-dimensional hardware competition into a multi-layered complex system composed of "body—intelligence—payment—organization."

Even more noteworthy is that global capital markets are rapidly pricing in this trend. JPMorgan predicts that by 2050, the humanoid robot market could reach $5 trillion, further driving growth in supply chains, operations, services, and other peripheral industries. In the same year, the number of humanoid robots in use is expected to exceed 1 billion units. This means that robots will truly move from being industrial equipment to becoming "large-scale social participants." (1)

To understand the future direction of the robotics industry, we can view the entire ecosystem as a four-layered hierarchical structure:

Source: Gate Ventures

The first layer is the Physical Layer: including humanoids, robotic arms, drones, EV charging stations, and all embodied carriers. They address basic mobility and operational capabilities, such as walking, grasping, mechanical reliability, and cost. However, machines at this layer are still "without economic agency," meaning they cannot autonomously perform actions such as charging fees, making payments, or procuring services.

The second layer is the Control & Perception Layer: covering traditional robotics control theory, SLAM, perception systems, speech and visual recognition, up to today's LLM+Agent, and increasingly abstract planning-capable robotic operating systems (such as ROS, OpenMind OS). This layer enables machines to "understand, see, and execute tasks," but economic activities such as payments, contracts, and identity must still be handled by humans in the backend.

The third layer is the Machine Economy Layer: this is where true transformation begins. Machines start to have wallets, digital identities, and reputation systems (such as ERC-8004), and can directly pay for computing power, data, energy, and road rights through mechanisms like x402, on-chain settlement, and Onchain Callback; at the same time, they can autonomously collect payments for task execution, escrow funds, and initiate result-based pay. This layer enables robots to leap from "enterprise assets" to "economic agents" with the ability to participate in the market.

The fourth layer is the Machine Coordination Layer: when a large number of robots have autonomous payment and identity, they can further organize into fleets and networks—drone swarms, cleaning robot networks, EV energy grids, etc. They can automatically adjust prices, schedule, bid for tasks, share profits, and even form autonomous economic entities in the form of DAOs.

Through these four layers, we can see:

The future robotics ecosystem is no longer just a hardware revolution, but a systemic reshaping of "physical + intelligence + finance + organization."

This not only redefines the boundaries of machine capabilities, but also redefines how value is captured. Whether it is robotics companies, AI developers, infrastructure providers, or crypto-native payment and identity protocols, all will find their place in the new robotics economic system.

Why is the robotics industry exploding now?

For decades, the robotics industry has lingered in laboratories, exhibition booths, and specific industrial scenarios, always one step away from true large-scale commercial and social deployment. However, after 2025, this step is beginning to be crossed. Whether from the perspective of capital markets, technological maturity, or the judgment of industry observers such as Nvidia CEO Jensen Huang, the same signal is being sent:

"The ChatGPT moment for general robotics is just around the corner"

This judgment is not exaggerated hype, but is based on three key industry signals:

1. The fundamentals of computing power, models, simulation, and perception control are maturing simultaneously

2. Robotics intelligence is shifting from closed-loop control to LLM/Agent-driven open decision-making

3. The leap from single-machine capability to system capability: robots will evolve from "being able to act" to "being able to collaborate, understand, and operate economically"

Jensen Huang even further predicts that humanoid robots will enter widespread commercial use within the next five years, a view highly consistent with the behavior of capital markets and industry deployment in 2025.

Capital: Massive financing proves the "robotics inflection point" has been priced in by the market

In 2024–2025, the robotics industry has seen unprecedented density and scale of financing, with multiple rounds exceeding $500 million in 2025 alone. Typical events include:

Source: Gate Ventures

Capital is clearly expressing: the robotics industry has reached a stage where investment can be validated.

Common features of these financings:

● Not "concept financing," but focused on production lines, supply chains, general intelligence, and commercial deployment

● Not scattered projects, but integrated hardware and software, full-stack architecture, and full lifecycle service systems for robots

Capital does not bet hundreds of millions for no reason; behind this is confirmation of industry maturity.

Technology: Decisive breakthroughs are occurring 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 "operable machines" that could only execute instructions in the past, to "understandable agents" capable of understanding language, decomposing tasks, and reasoning with vision and touch. Multimodal perception and new-generation control models (such as RT-X, Diffusion Policy) give robots, for the first time, the basic capabilities approaching general intelligence.

Source: Nvidia

Meanwhile, simulation and transfer technologies are maturing rapidly. High-fidelity simulation environments such as Isaac and Rosie have significantly narrowed the gap between simulation and reality, enabling robots to complete large-scale training at extremely low cost in virtual environments and reliably transfer to the real world. This solves the fundamental bottleneck of slow robot learning, expensive data collection, and high risk in real environments.

Hardware evolution 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 has further boosted industry productivity. With multiple companies launching mass production plans, robots now have, for the first time, an industrial foundation for "replicability and scalable deployment."

Finally, improvements in reliability and energy consumption structure have enabled robots to truly meet the minimum threshold for commercial applications. Better motor control, redundant safety systems, and real-time operating systems allow robots to operate stably for long periods in enterprise-level scenarios.

These factors have, for the first time, given the robotics industry the complete conditions to move from the "laboratory demo stage" to "large-scale real-world deployment." This is the fundamental reason for the current robotics boom.

Commercialization: From prototype → mass production → real-world deployment

2025 is also the first year in which the commercialization path for robots has become clear. Leading companies such as Apptronik, Figure, and Tesla Optimus have successively announced mass production plans, marking the transition of humanoid robots from prototypes to replicable industrialization. At the same time, many companies have begun pilot deployments in high-demand scenarios such as warehousing logistics and factory automation, verifying the efficiency and reliability of robots in real environments.

With the improvement of hardware mass production capabilities, the "Operation-as-a-Service (OaaS)" model has begun to gain market validation. Enterprises no longer need to pay high upfront purchase costs, but can subscribe to robot services on a monthly basis, significantly improving ROI structure. This model has become a key business innovation driving large-scale application of robots.

In addition, the industry is rapidly filling in previously missing service systems, including maintenance networks, spare parts supply, remote monitoring, and operations platforms. As these capabilities take shape, robots are beginning to have the complete conditions required for continuous operation and commercial closed loops.

Overall, 2025 is a milestone year in which robots shift from "can it be done" to "can it be sold, can it be used, and is it affordable," with a sustainable positive feedback loop appearing in the commercialization path for the first time.

Web3 X Robotics Ecosystem

With the full-scale explosion of the robotics industry in 2025, blockchain technology has also found a clear position within it, supplementing several key capabilities for the robotics system. Its core value can be summarized in three main directions: i.) data collection for robotics technology, ii.) cross-device machine coordination networks, and iii.) machine economy networks that support autonomous machine participation in the market.

Decentralization + token incentive mechanisms build new data sources for robot training, but data quality depends on backend Data Engine improvement

The core bottleneck in training Physical-AI models lies in the scale of real-world data, scenario coverage, and the scarcity of high-quality physical interaction data. The emergence of DePIN/DePAI enables Web3 to provide new solutions at the level of "who contributes data and how to sustain contributions."

However, from academic research, decentralized data, while potentially large in scale and coverage, is not inherently equivalent to high-quality training data; backend data engines are still needed for filtering, cleaning, and bias control before it can be truly used for large model training.

First, Web3 solves the "data supply motivation" problem, not directly the "data quality" problem.

Traditional robot training data mainly comes from laboratories, small fleets, or enterprise internal collection, with scale far from sufficient.

Web3's DePIN/DePAI model uses token incentives to make ordinary users, device operators, or remote operators data contributors, significantly increasing the scale and diversity of data sources.

Projects include:

Source: Gate Ventures

● NATIX Network: Turns mass vehicles into mobile data nodes via Drive& App and VX360, collecting video, geographic, and environmental data.

● PrismaX: Collects high-quality robot physical interaction data (grasping, sorting, moving objects) through a remote control marketplace.

● BitRobot Network: Allows robot nodes to execute verifiable tasks (VRT), generating data on real operations, navigation, and collaborative behaviors.

These projects show that Web3 can effectively expand the data supply side, supplementing real-world scenarios and long-tail cases that traditional systems find hard to cover.

But according to academic research, crowdsourced/decentralized data often has structural issues of "insufficient accuracy, high noise, and large bias." Extensive research on crowdsourcing and mobile crowdsensing in academia points out:

1. Large fluctuations in data quality, significant noise and format differences

Differences in contributors' devices, operation methods, and understanding lead to a large amount of inconsistent data, requiring detection and filtering.

2. Structural bias is widespread

Participants are usually concentrated in specific regions/groups, causing sampling distributions to differ from real-world distributions.

3. Raw crowdsourced data cannot be directly used for model training

Autonomous driving, embodied AI, and robotics research widely emphasize: 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, Web3's data networks provide broader data sources, but "whether it can directly become training data" depends on backend data engineering.

The true value of DePIN is to provide Physical AI with a "continuous, scalable, and lower-cost" data foundation

Rather than saying Web3 immediately solves the data accuracy problem, it is more accurate to say it solves:

● "Who is willing to contribute data long-term?"

● "How to encourage more real devices to connect?"

● "How to make data collection shift from centralized to a sustainable open network?"

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 Physical AI era, but not the sole guarantor of data quality.

Cross-device machine coordination networks: Universal OS provides the foundational 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, cannot interoperate, and lack a unified communication medium. This means multi-robot collaboration can only rely on vendor-built closed systems, greatly limiting scalable deployment.

In recent years, the emergence of universal robot operating system layers (Robot OS Layer), represented by OpenMind, is providing a new solution to this problem. These systems are not "control software" in the traditional sense, but cross-body intelligent operating systems, like Android for the mobile industry, providing a common language and public infrastructure for communication, cognition, understanding, and collaboration between robots. (8)

In traditional architectures, each robot's internal sensors, controllers, and reasoning modules are isolated, and there is no way to share semantic information across devices. The universal OS layer, by unifying perception interfaces, decision formats, and task planning methods, enables robots for the first time to have:

● Abstract descriptions of the external world (vision / sound / tactile → structured semantic events)

● Unified understanding of instructions (natural language → action planning)

● Shareable multimodal state expressions

This is equivalent to equipping robots with a cognitive layer capable of understanding, expressing, and learning from the ground up.

Robots are thus no longer "isolated actuators," but have a unified semantic interface and can be incorporated into larger-scale machine collaboration networks.

Moreover, the greatest breakthrough of the universal OS is "cross-body compatibility," allowing robots of different brands and forms to "speak the same language" for the first time. All kinds of 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 truly discuss for the first time:

● Multi-robot collaboration

● Task bidding and scheduling

● Shared perception / shared maps

● Joint execution of tasks across spaces

The prerequisite for collaboration is "understanding the same information format," and the universal OS is solving this underlying language problem.

In the system of cross-device machine collaboration, peaq represents another key infrastructure direction: a foundational protocol layer that provides machines with verifiable identity, economic incentives, and network-level coordination capabilities. (9)

It does not solve "how robots understand the world," but "how robots participate as individuals in network collaboration."

Its core designs include:

1. Machine Identity

peaq provides decentralized identity registration for robots, devices, and sensors, enabling them to:

● Access any network as independent individuals

● Participate in trusted task allocation and reputation systems

This is a prerequisite for machines to become "network nodes."

2. Autonomous Economic Accounts

Source: Peaq

Robots are given economic autonomy. Through natively supported stablecoin payments and automatic billing logic, robots can automatically reconcile and pay without human intervention, including:

● Sensor data settled by volume

● Pay-per-use for computing power and model inference

● Instant settlement after robots provide services to each other (transportation, delivery, inspection)

● Autonomous charging, space rental, and other infrastructure calls

Additionally, robots can use conditional payments:

● Task completion → automatic payment

● Unsatisfactory results → funds automatically frozen or refunded

This makes robot collaboration trustworthy, auditable, and automatically arbitrable, 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 on-chain, presenting its value and cash flow in a transparent, traceable, tradable, and programmable form, thereby building a machine-centric asset representation method.

As AI and on-chain systems mature, the goal is for machines to autonomously earn, pay, lend, and invest, conduct M2M transactions directly, form self-organizing machine economic networks, and achieve collaboration and governance in the form of DAOs.

3. Multi-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

● Resource scheduling (computing power, mobility, sensing capability)

Thus, robots can collaborate like network nodes rather than operate in isolation. Once language and interfaces are unified, robots can truly enter collaborative networks instead of remaining in their own closed ecosystems.

OpenMind and similar cross-body intelligent OSs attempt to standardize how robots "understand the world and instructions"; Peaq and similar Web3 coordination networks explore how different devices can obtain verifiable organizational collaboration capabilities in larger networks. They are just representatives of many attempts, reflecting the industry's acceleration toward unified communication layers and open interoperability systems.

Machine economy networks supporting autonomous machine participation in the market

If cross-device operating systems solve "how robots communicate," and coordination networks solve "how to cooperate," then the essence of machine economy networks is to convert robot productivity into sustainable capital flows, enabling robots to pay for their own operations and form closed loops.

A key missing piece in the robotics industry has long been "autonomous economic capability." Traditional robots can only execute preset instructions and cannot independently schedule external resources, price their own services, or settle costs. Once in complex scenarios, they must rely on human backend accounting, approval, and scheduling, severely dragging down collaboration efficiency and making large-scale deployment even harder.

x402: Endowing robots with "economic agent status"

Source: X@CPPP2443_

x402, as a new generation Agentic Payment standard, fills this fundamental capability for robots. Robots can directly initiate payment requests via the HTTP layer and complete atomic settlement with programmable stablecoins such as USDC. This means robots can not only complete tasks, but also autonomously purchase all resources needed for tasks:

● Computing power calls (LLM inference / control model inference)

● Scene access and equipment rental

● Labor services from other robots

From now on, robots can, for the first time, autonomously consume and produce like economic agents.

In recent years, representative cases of cooperation between robotics manufacturers and crypto infrastructure have begun to emerge, indicating that machine economy networks are moving from concept to implementation.

OpenMind × Circle: Enabling robots to natively support stablecoin payments

Source: Openmind

OpenMind has integrated its cross-device robot OS with Circle's USDC, enabling robots to use stablecoins for payments and settlements directly in the task execution chain.

This represents two breakthroughs:

1. The robot task execution chain can natively access financial settlement, no longer relying on backend systems

2. Robots can achieve "borderless payments" in cross-platform, cross-brand environments

For machine collaboration, this is a foundational capability toward 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 identity, 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," highly compatible with the goal of enabling robots to autonomously participate in the market.

1. Agent / Machine Identity Layer (Kite Passport): Issues cryptographic identities and multi-layer key systems for each AI Agent (which can also be mapped to specific robots in the future), allowing precise control over "who is spending" and "on whose behalf," with support for revocation and accountability at any time. This is the premise for treating agents as independent economic actors.

2. Native stablecoins + built-in x402 primitives: Kite integrates the x402 payment standard at the chain level, using USDC and other stablecoins as default settlement assets, enabling agents to complete sending, receiving, and reconciliation through standardized intent authorization. It is optimized at the base layer 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, agents can be set with spending limits, whitelists of allowed merchants/contracts, risk control rules, and audit trails, striking a balance between security and autonomy when "giving machines wallets."

In other words, if OpenMind's OS enables robots to "understand the world and collaborate," then Kite AI's blockchain infrastructure enables robots to "survive in the economic system."

Through these technologies, the machine economy network builds "collaborative incentives" and "value closed loops," not only enabling robots to "pay," but more importantly allowing robots to:

● Earn income based on performance (result-based settlement)

● Purchase resources as needed (autonomous cost structure)

● Participate in market competition with on-chain reputation (verifiable fulfillment)

This means robots can, for the first time, participate in a complete economic incentive system: able to work → able to earn money → able to spend money → able to 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 motivation and improves long-tail scenario coverage;

● Collaboration layer: Introduces unified identity, interoperability, and task governance mechanisms for cross-device collaboration;

● Economic layer: Provides programmable economic behavior frameworks for robots through on-chain payments and verifiable settlements.

These capabilities together lay the foundation for the future potential machine internet, enabling robots to collaborate and operate in a more open and auditable technical environment.

Uncertainties

Although the robotics ecosystem will see a rare breakthrough in 2025, its journey from "technically feasible" to "scalable and sustainable" still faces multiple uncertainties. These uncertainties do not stem from a single technical bottleneck, but from complex coupling at the engineering, economic, market, and institutional levels.

Is economic viability truly established?

Although robots have made breakthroughs in perception, control, and intelligence, their large-scale deployment ultimately depends on whether real commercial demand and economic returns are established. Most humanoid and general-purpose robots are still in the pilot and validation stage, and there is still a lack of sufficient long-term data to support whether enterprises are willing to pay for robot services in the long run and whether OaaS/RaaS models can stably achieve ROI in different industries.

At the same time, the cost-effectiveness advantage of robots in complex, unstructured environments has not yet been fully established. In many scenarios, traditional automation or manual alternatives are still cheaper and more reliable. This means that technical feasibility does not automatically translate into economic inevitability, and uncertainty in commercialization pace will directly affect the expansion speed of the entire industry.

Systemic challenges of engineering reliability and operational complexity

The biggest practical challenge facing the robotics industry is often not "whether the task can be completed," but whether it can operate long-term, stably, and at low cost. In large-scale deployment, hardware failure rates, maintenance costs, software upgrades, energy management, and safety and liability issues can quickly escalate into systemic risks.

Even if the OaaS model reduces upfront capital expenditure, hidden costs in operations, insurance, liability, and compliance may still erode the overall business model. If reliability cannot cross the minimum threshold for commercial scenarios, the vision of robot networks and machine economies will be difficult to realize.

Ecological synergy, standard convergence, and institutional adaptation

The robotics ecosystem is undergoing rapid evolution in OS, Agent frameworks, blockchain protocols, and payment standards, but is still highly fragmented. The cost of cross-device, cross-vendor, and cross-system collaboration is high, and general standards have not yet fully converged, which may lead to ecosystem fragmentation, redundant construction, and efficiency loss.

Meanwhile, robots with autonomous decision-making and economic agency are challenging existing regulatory and legal frameworks: responsibility attribution, payment compliance, and data and security boundaries are still unclear. If institutions and standards cannot keep pace with technological evolution, machine economy networks will face compliance and implementation uncertainties.

Overall, the conditions for large-scale application of robots are gradually forming, and the prototype of the machine economic system is also emerging in industry practice. Although Web3 × Robotics is still in its early stages, it has already demonstrated long-term development potential worth attention.

References