Four layers of future robotics: How Web3 is turning machines into autonomous economic entities

Robot Revolution: From Tools to Economic Actors

The year 2025 marks a breakthrough for the robotics industry. After decades as laboratories and specialized tools, robots are entering the real-world market. JPMorgan Stanley predicts that by 2050, the humanoid robot segment could be worth $5 trillion, with over 1 billion robots in use. This signals the end of an era where robots were solely “corporate assets”—they will become “mass social and economic participants.”

But it’s not just about hardware. Modern robotics is a complex system: physical body + intelligence + payment capability + network organization. The current robot ecosystem is transforming from one-way hardware competition into a multi-layered architecture, where each layer plays a critical role.

Four Pillars of the Modern Robotics Ecosystem

Physical Layer: Physical Carriers

The physical layer includes humanoids, robotic arms, drones, EV charging stations, and other devices. They solve basic mechanical challenges: movement, grasping, reliability, and costs. However, robots at this level do not possess “economic capabilities”—they cannot independently charge for services or request tasks.

A key advancement here is in production scale. Components like torque motors or joint modules are systematically becoming cheaper thanks to supply chain improvements. The rapid development of the Chinese robotics sector has further accelerated commercialization.

Perception and Control Layer: “Eyes and Brain”

This layer integrates traditional control robotics, SLAM, speech and image recognition, and the latest systems based on LLMs and Agents. It enables machines to “understand commands, see, and perform tasks.” The AI Agent revolution has allowed robots to shift from executing closed commands to intelligent agents capable of abstract planning.

Simulation breakthroughs are equally significant. Environments like Isaac or Rosie reduce the gap between simulation and reality, enabling mass training of robots at low costs and reliable transfer of skills to the real world.

Machine Economy Layer: Market Integration

This is where the revolution begins. Robots gain wallets, digital identities, and reputation systems. Thanks to mechanisms like x402 and on-chain settlements, they can:

• Pay directly for computing power, data, energy, and infrastructure
• Independently charge for completed tasks
• Manage funds and initiate performance-based payments

This transition from “enterprise asset” to “economic entity” participating in the market.

Coordination Layer: Autonomous Machine Networks

Once robots have payment autonomy and identity, they can organize into fleets and networks. They can automatically set prices, bid on tasks, share profits, and even create autonomous economic entities in the form of DAOs.

Why is Robotics Expanding Now?

Technological Convergence in 2025

The breakthrough is not accidental—it results from the simultaneous maturity of several technologies:

1. Computing Power and Models: LLM systems and specialized control models (e.g., RT-X, Diffusion Policy) have given robots basic general intelligence.

2. Simulation and Knowledge Transfer: Rapid development of realistic simulation environments has narrowed the gap between testing and real-world operation.

3. Hardware Infrastructure: Lower costs of key components and improved reliability of motors and safety systems.

Capital Validates the Change

In 2024–2025, the industry saw unprecedented funding—many deals exceeding $500 million. The key feature: it’s not “concept funding,” but investments in production lines, supply chains, and commercial deployments. Capital clearly signals that robotics has reached the scaling stage.

OaaS Operational Model Shifts the Balance

Companies no longer need to bear high upfront costs for purchase. The “Operation-as-a-Service” (OaaS) model allows monthly robot service subscriptions, drastically improving return on investment. At the same time, the industry is rapidly expanding services: service networks, parts delivery, remote monitoring.

Web3 as Infrastructure for the Machine Economy

Data for AI: Motivation for Large-Scale Collection

A main limitation for training Physical-AI models is the lack of high-quality real-world data. DePIN (Decentralized Physical Infrastructure Networks) in Web3 offers a new solution: token incentives encourage ordinary operators to provide data.

Projects like NATIX Network turn vehicles into mobile data nodes. PrismaX collects data on physical robot interactions (grasping, sorting). BitRobot Network enables robot nodes to generate data from real operations.

Important caveat: Decentralized data has potential in scale and coverage but requires cleaning, selection, and quality control by the Data Engine backend. Web3 solves the “who provides data” problem, not directly the “data quality” issue.

Interoperability: Universal Language for Robots

Currently, robots from different manufacturers cannot communicate with each other. Universal operating systems for robots (like OpenMind) act like Android for the smartphone industry—providing a common interface and infrastructure for machine-to-machine communication.

By standardizing perception interfaces and decision formats, robots gain:

• Abstract world description (perception → structured semantic events)
• Unified understanding of commands
• Shared state expression

This is the first opportunity for robots of different brands to truly collaborate—not in isolated systems, but in open networks.

Economic Autonomy: x402 and Blockchain

x402 is a new agent payment standard that grants robots the status of an economic entity. Machines can now:

• Send payment requests directly via HTTP
• Perform atomic settlements using stablecoins (e.g., USDC)
• Independently purchase resources: computing power, device access, services from other robots

OpenMind × Circle: Integration of USDC in OpenMind OS enables robots to make native stablecoin payments directly within task execution chains.

Kite AI goes further, building a native blockchain for the machine economy. The project designs on-chain identities, composable wallets, automated payments, and settlement systems dedicated to AI agents. It offers a complete autonomous ecosystem where:

1. Each agent receives a cryptographic identity and multi-level key system
2. Stablecoins (e.g., USDC) are default settlement assets
3. Programmable spending limits and whitelist contracts exist

This balances security and autonomy—“opening a wallet for a machine” becomes safe and auditable.

Peaq represents another dimension: a protocol providing robots with verifiable identity, economic incentives, and network-level coordination. It enables:

• Decentralized device identity registration
• Reliable task assignment and reputation systems
• Conditional payments (task completed → automatic payment; unsatisfactory result → funds frozen)

Uncertainties and Challenges of Transition

Economic Feasibility Still Needs Verification

Despite technological breakthroughs, most humanoid robots are still in pilot stages. There is a lack of long-term data on whether companies will be willing to pay for robot services or if OaaS models will deliver ROI. Traditional automation or human labor often remains cheaper.

Long-term Reliability Is a Systemic Challenge

The biggest obstacle is not “can robots do something,” but whether they can operate stably and cheaply for years. Maintenance costs, hardware failures, updates, security, and liability pose hidden risks. If reliability does not reach a commercial threshold, the vision of a robot network will not materialize.

Standards and Regulations Lag Behind Technology

The robot ecosystem is fragmented: various OSs, frameworks, blockchain protocols. Standards are not yet fully converged. Simultaneously, robots with decision-making autonomy challenge current legal frameworks: responsibility, payment compliance, data security remain unclear.

Perspectives: Already Beginning Now

Web3 × Robotics shows long-term growth potential, though still in early stages. The role of Web3 is becoming increasingly clear:

• Data Layer: Motivation for large-scale data collection, covering long-tail cases
• Collaboration Layer: Unified identity, interoperability, task management across devices
• Economic Layer: On-chain payments and verifiable settlements for programmable economic actions of robots

These capabilities collectively lay the foundation for the potential Internet of Machines of the future, enabling robots to cooperate in a more open, auditable environment. This is not a distant future—early elements of this system are already visible in industry practice in 2025.