Emerging Frontier: Physical AI x Web3

How Machine Intelligence and Decentralized Networks Are Building the Future of Autonomous Value Creation -– Eugene Tsai, Venture Investor at Decasonic 

The intersection of Physical AI x Web3 is redefining how machines interact with the physical world and exchange value. Far from theory, this new paradigm is being implemented by pioneering projects and rapidly drawing venture capital at record levels. In this post, we will map the real-world use cases, data-driven trends, and core opportunities at this high-impact intersection.

What is Physical AI?

Physical AI, often termed embodied AI or edge robotics, represents the next revolution of agentic AI, where intelligent software transitions into physical systems capable of sensing, navigating, and acting in the real world. Unlike purely digital AI, physical AI integrates advanced algorithms with hardware like sensors and actuators, enabling robots, drones, and autonomous vehicles to learn from their environments and make real-time decisions.

This fusion allows systems to adapt dynamically, performing complex tasks autonomously. Notable projects at this intersection include: PlaiPin and OpenGraph, which leverage decentralized data frameworks to enhance physical AI capabilities, which aligns with Web3’s ethos of distributed systems.

Recent advancements highlight the sector’s growth. Figure AI secured a $1.5B Series C round in 2025, led by Parkway Venture Capital and Align Ventures, which valued the company at $39.5B. Unitree Robotics, a China-based leader, showcased its G1 humanoid at a 2024 conference attracting over 40 companies, with its $16,000 model signaling affordability and scalability. Unitree’s rumored IPO plans reflect a valuation surge, driven by a 90%+ annual sales growth rate in the embodied AI market. Amazon’s fleet metrics further underscore this trend, with increased robot deployment boosting operational efficiency.

As the CEO of NVIDIA, Jensen Huang has mapped, the transition from software-based AI agents to physical AI is accelerating, with companies like Physical Intelligence raising $400 million to develop versatile robotic software, positioning physical AI as a transformative force across industries.

Interaction with Web3 Primitives

In the emerging machine economy, physical AI intersects with Web3 to allow embodied AI systems to operate autonomously. These systems encompass a wide range of devices, from industrial robots to consumer devices such as smart eyewear, augmented reality headsets, and home assistants.

Smart contracts empower these systems to transact autonomously, manage logistics, and verify task completion. This fosters a decentralized ecosystem where machines coordinate and exchange value without intermediaries. For instance, a delivery drone could use smart contracts to confirm package delivery and trigger payments, which enhance efficiency in logistics networks.

Decentralized compute and storage networks, such as those provided by Web3 platforms, offer secure, distributed resources for training and running AI models, as well as storing vast amounts of sensory data generated by embodied AI devices. These networks ensure scalability and privacy for devices like consumer wearables or industrial sensors.

Tokenized coordination further enhances this ecosystem by rewarding contributors, managing access rights, and governing machine networks to enable swarm-like coordination where diverse AI-driven hardware collaborates seamlessly in a decentralized, trustless environment, which optimizes collective tasks like traffic management or distributed manufacturing.

Why This Convergence Matters Now

The convergence of Physical AI x Web3 is driven by the Web3 x AI supercycle, where these rapidly evolving technologies are creating unprecedented opportunities at their intersection.Distributed networks can now coordinate thousands of physical AI agents at global scale using programmable incentives and trustless interactions. The foundational infrastructure provided by Web3 removes many of the bottlenecks seen in siloed or centrally managed robotics fleets, making true decentralized automation possible for the first time.

Opportunities and Societal Shifts

• Networks of robots and sensors, which are enhanced by tele-operations, can form distributed, peer-to-peer automation systems for logistics, delivery, urban management, or environmental monitoring.

• Community ownership and coordination of autonomous systems are now possible, offering new models for labor, asset sharing, and data markets.

• Secure, transparent marketplaces for real-world data, compute and physical work, as well as opportunities for individuals to invest and participate directly in the value generated by AI-driven automation.

From Abstract to Actual: Real-World Use Cases

To illustrate the power of Physical AI x Web3, let us spotlight projects and ecosystems setting the pace:

Spexi: Utilizes drone-based imagery to create decentralized geospatial data networks, enabling community-driven mapping and earth observation for urban planning and agriculture, integrated with Web3 for transparent data sharing.

Geodnet:  A blockchain-based network of GNSS receivers providing centimeter-accurate location data for autonomous vehicles and surveying, rewarding contributors with tokens for maintaining global reference stations.

Frodobots: Deploys autonomous robots for last-mile delivery and urban services, leveraging Web3 for decentralized coordination and tokenized incentives to optimize fleet operations and data sharing.

Natix Network: Harnesses AI-powered dashcams and vehicle cameras to collect real-time road data, rewarding drivers with tokens for contributing to decentralized mapping and urban planning datasets.

Hivemapper: A decentralized mapping network using dashcams to gather road-level imagery, rewarding contributors with HONEY tokens for data used in navigation, logistics, and autonomous driving.

ROVR: Crowdsources 3D geospatial data via vehicle-mounted devices like TarantulaX and LightCone, rewarding drivers with tokens for high-precision mapping data used in autonomous vehicles and spatial AI.

375 AI: Develops embodied AI for industrial automation, integrating Web3 to enable secure, decentralized data processing for robotics in manufacturing and supply chain management.

IoTeX: A Web3 platform powering DePIN projects, enabling secure IoT device connectivity and data exchange for physical AI applications like smart cities and logistics.

Peaq: A layer-1 blockchain for DePIN, facilitating decentralized coordination of physical AI devices like robots and sensors for applications in mobility and urban infrastructure.

Auki: Combines spatial AI with Web3 to create augmented reality ecosystems, enabling decentralized, interactive 3D environments for gaming, education, and virtual collaboration.

OpenMind: Leverages Web3 to coordinate distributed AI agents for real-world tasks like environmental monitoring, using tokenized incentives to align decentralized computation and data collection.

Neuron: Powers machine-to-machine commerce by enabling the development, deployment, and scaling of fully autonomous AI applications.

ReBorn: Build a decentralized ecosystem for embodied AI and robotic intelligence, leveraging a token called Reborn (RBN) for intelligence licensing, data exchange, and project sustainability. 

PrismaX: Advances tele-operations for physical AI, enabling remote control of robotic fleets for logistics and urban management, integrated with Web3 for secure, decentralized operations.

In the first half of 2025, over 80 Web3 and AI integration projects secured funding, with notable rounds exceeding $30 million which reflects intense venture interest and rapid growth in this sector. 

Designing for Autonomous Coordination

Token-based systems are critical for incentivizing and coordinating distributed networks of physical AI agents, enabling a machine-to-machine economy where humans, AI agents, and physical AI devices collaborate seamlessly. Physical agents participate in proof-of-physical-work schemes, earning tokens for verifiable completion of real-world tasks such as autonomous deliveries, infrastructure inspections, environmental data collection, or training physical AI devices.

For example, drones can earn tokens by delivering packages with verifiable GPS-tracked routes, while robots receive rewards for collecting high-quality sensory data or completing training cycles for embodied AI systems. These tasks, validated on-chain, ensure transparency and trust in a decentralized ecosystem. 

GaiaNet exemplifies this model, distributing token rewards to participants hosting and managing AI services on edge devices, including their recently launched AI-powered smartphone, which integrates decentralized compute and data processing for physical AI applications. Data contributors, compute providers, and robot operators receive fair, algorithmically controlled rewards, with roles and incentives managed transparently on-chain, fostering scalable coordination across logistics, smart cities, and environmental monitoring.

Limitations, Risks, and Tradeoffs

Several issues must be resolved for the Physical AI x Web3 fusion to move from prototypes to robust deployments:

• Latency and throughput challenges in blockchain protocols can hinder real-time physical agent decisions, pressing the need for hybrid on-chain and off-chain logic.

• Integrating diverse hardware and sensor platforms is an ongoing technical challenge that limits interoperability and slows broader adoption.

• Privacy and data sovereignty issues are significant, especially as decentralized robots collect and trade real-world, potentially sensitive data.

Projections for Success

• Large-scale decentralized fleets of robots, sensors, and edge nodes are poised to transform autonomous supply chains, micromobility, and peer-owned infrastructure in major cities by 2030, with the global DePIN market projected to reach $3.5 trillion by 2028, growing at a CAGR of 62.3%.

• Proof-of-physical-work models, enabling verifiable task completion for activities like deliveries and data collection, are expected to standardize by 2027, with marketplaces for task assignment and completed work projected to handle over $500 million in transactions annually by 2030.

• Venture activity in Physical x AI integration is surging; in Q1 and Q2 2025, over 80 cross-sector projects in logistics, mapping, IoT, and healthcare secured funding, attracting institutional investments from firms like Hack VC and a16z

• Open protocols and regulatory frameworks are anticipated to emerge by 2026, enabling liability sharing, safety assurance, and equitable value distribution in decentralized physical networks. 

• Society will see expanded access to automation’s benefits, with both individuals and communities earning directly by coordinating and deploying physical AI at scale, increasing resilience and productivity in ways not possible with centralized models.

Physical AI x Web3 represents a step forward in merging programmable, trust-minimized economics with real-world adaptability and intelligence. As these systems mature, they will distribute the ownership, operation, and rewards of the automation revolution across a much broader societal base than ever before

The convergence of Physical AI x Web3 signals a new era, not only for robotics and crypto, but for the organization of labor, value and intelligence at global scale. If you’re a founder building on Physical AI x Web3, exploring autonomous robotics with blockchain integration, or developing decentralized physical infrastructure networks, we want to hear from you. The future of intelligent, autonomous systems is being written today—let’s build it together.

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