Robotics isn’t moving forward just because the machines are getting better, it’s evolving because robots are starting to work together.
They’re becoming part of a shared operational layer for the physical world, similar to how operating systems once standardized computing.

Global robotics spending hit between $75 and $90 billion in 2024, depending on how you count.
Industrial robots still make up most of the installed base, but the fastest growth now comes from service robotics: automated logistics, inspection drones, mobile manipulators, healthcare assistants, and autonomous vehicles.

Looking ahead to 2030, forecasts point to a market exceeding $250 billion annually.
But the real value is shifting away from hardware margins and toward software, autonomy, and data.

As motors, sensors, cameras, LiDAR, and onboard compute become cheaper and more standardized, the real challenges move upstream, toward machine coordination, learning in unpredictable environments, scaling deployments, and sustainable economic models for autonomous systems.

The Bottleneck Moved From Hardware to Coordination

Modern robots rely on three key inputs that don’t scale well under centralized models:

• Real-world data gathered from diverse, uncontrolled environments
• Control policies that adapt beyond lab or simulation conditions
• Operational coordination across manufacturers, operators, and end users

Traditional, centralized robotics development struggles to cover this complexity.
Collecting real-world data remains slow and fragmented, and once robots leave the lab, deployment incentives often drift out of alignment.

Unlike Web2, which scaled through centralized control of digital systems, robotics faces physical constraints that demand a different approach. One that depends on:

• Shared technical standards
• Verifiable machine behavior
• Predictable economic flows
• Coordination between untrusted parties

Shared Robotics Infrastructure Is Emerging

A growing set of tools and frameworks is now being reused across industries, from operating systems and middleware to perception, navigation, fleet management, data pipelines, and access controls.

These layers abstract away hardware differences and allow robotics teams to focus on higher-order problems.

Instead of rebuilding full stacks for each device, companies increasingly rely on shared components that define how machines perceive the world, make decisions, communicate, and get updated over time.

That common foundation now powers a wide range of deployments:

• Warehouse and logistics robots, coordinating tasks across large facilities
• Drones, operating beyond line of sight and across jurisdictions
• Humanoids and mobile manipulators, sharing learned behaviors across environments
• Autonomous vehicles, integrating perception, planning, and economic constraints

In this environment, economic coordination matters as much as technical innovation.
Who controls access to data, how capabilities are shared, how usage is priced, and how responsibility is enforced all shape whether systems can scale sustainably.

OpenMind and Open Robotics Software

OpenMind is creating open, interoperable operating systems for robotics.
Its core goal is to separate intelligence from hardware, allowing robotic autonomy to be developed once and deployed across many platforms.

Today, each new robot platform typically requires custom integrations: distinct codebases, drivers, and interfaces.

OpenMind breaks this cycle by introducing a shared operating layer that abstracts hardware complexity, similar to how operating systems allowed software to run across PCs and mobile devices without being rewritten for each one.

1. Decoupling intelligence from hardware

By standardizing the execution layer:

• Autonomy stacks are built independently of specific robot form factors
• The same intelligence can run on drones, service robots, warehouse manipulators, or mobile assistants
• Deployment no longer requires rebuilding systems from scratch

It reduces friction between research, development, and real-world deployment.

2. Faster experimentation, more adaptable fleets

With a common operating layer in place:

• Developers can test autonomy across heterogeneous fleets
• New behaviors and policies propagate quickly across devices
• Fleets become software-upgradable systems, not fixed hardware assets

3. Interoperability across manufacturers and operators

OpenMind’s open architecture enables:

• Robots from different manufacturers to operate in a shared software environment
• Real-time data sharing, cooperative mapping, and coordinated task execution
• Collaboration across fleets owned and operated by different parties

Innovation shifts away from closed ecosystems and toward shared infrastructure, with competition focused on intelligence quality, usability, and outcomes.

4. Avoiding centralized control planes

Rather than imposing a single proprietary coordination layer, OpenMind:

• Keeps coordination decentralized
• Allows integration with external systems handling identity, trust, or economic transactions
• Preserves long-term flexibility as robotics markets and standards evolve

This process helps to avoid repeating the lock-in patterns of centralized platforms while still enabling large-scale coordination.

Peaq and Machine-Centric Economics

While OpenMind focuses on unifying how robots think and communicate, Peaq builds the economic layer that allows them to act as independent entities within the world.

Peaq provides core primitives for machine identity, autonomous settlement, and direct machine-to-machine transactions.

Rather than treating robots as passive assets fully dependent on human intermediaries, peaq reframes them as economic agents capable of creating, delivering, and capturing value.

Verifiable machine identity

Through unique on-chain identities, robots registered on Peaq can prove who they are independently of manufacturers.
This makes ownership, responsibility, and accountability transparent across multiple stakeholders, especially in distributed settings such as logistics networks, shared fleets, or infrastructure inspection systems.

Once identity is verifiable, machines can operate across organizations and jurisdictions without relying on proprietary control layers.

Robots as economic participants

With identity established, robots can directly participate in economic activity:

• A delivery drone can log completed routes and settle payments automatically
• Energy usage, wear, or service costs can be accounted for programmatically
• Maintenance or services can be requested from other autonomous systems without manual coordination

Autonomous settlement and programmable revenue

Peaq embeds settlement into infrastructure itself.
A robot-as-a-service can charge based on work completed or time in operation. Fleet revenue can be distributed automatically among operators, owners, or investors through smart contracts.

Scheduling, billing, and reconciliation move from centralized systems into shared infrastructure.

New deployment and ownership models

With identity, settlement, and coordination handled natively:

• Robots can move fluidly across owners, locations, and applications
• Usage, performance, and compensation remain transparent and auditable
• Robotics economics begin to resemble the internet’s architecture: distributed, programmable, and composable

This time, applied to physical assets.

OpenMind and Peaq outline a shift from isolated robotic products toward connected machine ecosystems.

OpenMind standardizes intelligence and execution.
Peaq standardizes identity, coordination, and economics.

The result is an environment where intelligence, coordination, and economic logic operate together, enabling robots to collaborate safely, efficiently, and sustainably at global scale.

Data Networks and Learning Outside the Lab

Robotics learning thrives on real-world exposure.
Simulations help shape early models, speeding up iteration and prototyping, but true autonomy depends on large volumes of human-influenced perception and motion data gathered from unpredictable, uncontrolled environments.

Web3-native data networks are changing how that data is collected and shared.
Rather than relying on centralized labs or institutional datasets, these networks introduce open, incentive-driven collection models:

• Contributors gather real-world data using common technical standards and open protocols
• Ownership and provenance remain verifiable and auditable on-chain
• Incentive mechanisms reward coverage, diversity, and data quality
• Buyers and developers access global datasets without exclusive control

That framework shifts data collection from an institutional bottleneck to a distributed, participatory process.
Robotics teams can train and test systems on richer, more varied inputs, from household clutter to changing outdoor conditions, while contributors capture economic value without surrendering their rights.

The structure closely mirrors open-source software economics, adapted to physical intelligence.
Just as open code accelerated computing innovation, open data participation can accelerate robotics learning, spreading both progress and value more evenly across contributors and developers.

From Ownership to Participation

In the traditional model, robotic deployments revolve around centralized operators who control fleets, data, and maintenance cycles.

While effective at small scale, this structure becomes a bottleneck globally: limiting flexibility, responsiveness, and collaboration.

Networked robotics systems introduce a different model, one where control gives way to participation.

• Independent operators deploy and manage machines
• Incentives and tokenized rewards replace rigid bilateral service contracts
• Transparency and shared verification replace implicit trust assumptions
• Participation, not ownership, drives network growth

Robotics as Economic Infrastructure

Robotics is now maturing into shared physical infrastructure, much like the internet matured into shared digital infrastructure. Machines act as nodes, data becomes flow, and software defines the coordination logic that glues everything together.

Web3 technologies do not control motors or perception loops directly, what they provide is the economic and coordination substrate.

Through identity, incentives, accounting, and settlement, they enable diverse participants to operate, collaborate, and transact with predictability and trust minimization.

The coming phase of robotics adoption will depend less on breakthroughs in motors or sensors, and more on economic design, and scalable access to real-world data.

It marks a shift from building smarter robots to connecting autonomous systems into cohesive, incentive-aligned networks, an economy of machines learning, earning, and evolving together.