Is DePAI the next crypto trend?

Translated by: Jinse Finance xiaozou

DePAI (Decentralized Physical AI) is regarded by many crypto people as the next big thing (trend) in crypto, and is one of the few areas that can use blockchain and crypto incentive mechanisms to have a substantial impact on other technology fields. What is it? What is innovative? What potential does it have? Let's take a look at it together.

In short, it is an innovative concept that combines decentralized physical infrastructure network (DePIN) with artificial intelligence (AI) technology. It coordinates the physical hardware facilities of multiple individual units through blockchain technology to establish and maintain the infrastructure network in a permissionless, trustless and programmable manner.

Messari analyst Dylan Bane posted the following on the X platform in support of DePAI:

Decentralized Physical Artificial Intelligence (DePAI) provides an alternative to centralized control of robotics and physical AI infrastructure stacks. From real-world data collection to physical AI agent-operated robots deployed by DePIN,DePAI is developing rapidly.

Nvidia CEO Huang Renxun said: "The 'ChatGPT moment' in the field of general-purpose robotics is coming."

The digital age started with hardware and gradually evolved into the invisible world of software. The era of artificial intelligence began with software and is now seeing the physical world as its ultimate challenge and frontier.

In a world where robots, cars, drones, and bionic people driven by autonomous physical AI gradually replace human labor, the ownership of these machines becomes a core social issue. Decentralized physical artificial intelligence (DePAI) provides an important opportunity to build Web3 physical AI before centralized players dominate.

The decentralized physical artificial intelligence (DePAI) infrastructure stack is developing rapidly. At this stage, the most active layer is the data collection layer, which can provide real-world data for training physical artificial intelligence agents deployed on robots, while transmitting data in real time to navigate the environment and complete tasks.

Real-world data is the main bottleneck for training physical AI. Although Nvidia's Omniverse and Cosmos provide a promising development path through simulated environments, synthetic data is only part of the solution. Remote operation and real-world video data are also indispensable.

In the field of teleoperation, FrodoBots is using the Decentralized Physical Infrastructure Network (DePIN) to deploy low-cost sidewalk delivery robots around the world. This data collection method not only captures the complexity of human navigation decisions in real-world environments, thereby generating high-value data sets, but also effectively overcomes the capital gap problem.

The Decentralized Physical Infrastructure Network (DePIN) can accelerate the deployment of data collection sensors and robots through its token-driven flywheel effect. For robotics companies seeking to accelerate sales and reduce capital expenditures (CapEx) and operating expenses (OpEx), DePIN provides significant practical advantages over traditional approaches.

Decentralized Physical AI (DePAI) can use real-world video data to train physical AI and build a shared spatial understanding of the world. Hivemapper and NATIX Network, with their unique video datasets, may become an important source of this data.

Decentralized Physical AI (DePAI) is able to use real-world video data to train physical AI and build globally shared spatial understanding capabilities. Hivemapper and NATIX Network, with their unique video datasets, are expected to become important sources of data in this field. As Mason Nystrom pointed out, "Data is difficult to monetize at the individual level, but it is easy to realize value transformation after aggregation." Real-world data can be aggregated through the Decentralized Physical Infrastructure Network (DePIN) to form high-value datasets. IoTeX's Quicksilver protocol realizes data aggregation across DePINs, while taking into account data verification and privacy protection, providing key technical support for this ecosystem.

Spatial intelligence/computing protocols are also committed to achieving decentralized control of spatial coordination and real-world 3D virtual twins through decentralized physical infrastructure networks (DePIN) and decentralized physical artificial intelligence (DePAI). Auki Network's Posemesh protocol achieves real-time spatial perception capabilities while protecting privacy and decentralization, providing innovative technical solutions for this field.

The initial application of physical artificial intelligence agents (Physical AI Agents) has also emerged. SAM is connected to Frodobots' global fleet of robots and can infer geographic location. With the help of frameworks such as Quicksilver, future artificial intelligence agents are expected to access data streams provided by the decentralized physical infrastructure network (DePIN) in real time.

The most direct way to access physical AI may be through an investment-based decentralized autonomous organization (DAO).

XMAQUINA provides its members with access to physical AI assets, including machine RWA, the Decentralized Physical Infrastructure Network (DePIN) protocol, robotics companies, and intellectual property (IP), and is supported by internal R&D.

Crypto researcher DeFi Cheetah responded positively to Dylan Bane's remarks on DePAI:

Decentralized Physical AI (DePAI) is the next major development direction in the crypto field. Blockchain and crypto incentive mechanisms will enable spatial intelligence - the ability of robots to perceive the environment, instantly understand surrounding objects or structures, and respond effectively, which is one of the most challenging problems in the field of artificial intelligence robotics.Our industry can help solve the most critical bottleneck in the development of spatial intelligence - obtaining fine-grained, high-quality and continuously updated spatial data.

Achieving powerful spatial intelligence requires massive amounts of data that not only captures visual cues (such as color and texture), but also contains deep geometric context (such as polygons, point clouds, topological representations) and physical properties (angles, distances, friction, material types, etc.). Traditional 2D images or basic GPS coordinates, while valuable, are often oversimplified for training advanced models designed to operate in dynamic, complex and unpredictable real-world environments.

● Complexity of 3D Mapping

Projects such as Google Street View or dedicated LiDAR scans provide high-resolution 3D maps, but they are expensive and produce relatively sparse datasets. For example, a high-resolution LiDAR device can cost more than $50,000, and a city-wide scan can easily cost hundreds of thousands of dollars. This cost complexity often results in infrequent updates, rendering maps outdated within months.

● Limitations of Centralized Data Pipelines

In many countries, most spatial data is controlled by national agencies or large companies. Because these centralized entities only collect data in specific areas, large swaths of the world—especially rural or underdeveloped regions—remain unmapped or have outdated data. In addition, proprietary data restrictions can lead to market fragmentation and hinder innovative research.

● Lack of annotated 3D datasets

While annotated 2D image datasets (such as ImageNet, which contains over 14 million annotated images) have exploded, annotated 3D datasets remain scarce. Creating such datasets requires a combination of sensor fusion techniques (such as LiDAR + vision + IMU readings) and extensive manual annotation. This process is extremely time-consuming and costly, slowing down the development of robotics and machine learning applications.

Driven by the popularity of mobile devices, the crowdsourcing model recognizes that billions of smartphone and wearable device users around the world can collectively provide a huge amount of location-based data. Modern smartphones are equipped with a variety of sensors—accelerometers, gyroscopes, magnetometers, cameras, GPS chips, etc.—that can capture spatiotemporal data far beyond simple latitude and longitude. This model helps achieve the following goals:

● Real-time data collection

Imagine commuters capturing 3D scans of urban infrastructure on their daily commute, or residents of remote villages recording paths, building outlines, and farmland boundaries using only their phone cameras. Over time, these seemingly small contributions will accumulate into a global, comprehensive spatial database.

● Diverse environmental coverage

Since mobile devices are nearly ubiquitous, their data naturally covers a wider range of geographic regions, terrains, and cultural environments. This geographic diversity is critical for robust AI models that must learn to adapt to variable climates and community layouts.

● Democratization of data collection

By lowering the barrier to participation, the crowdsourcing model disrupts the traditional centralized model. Individuals around the world can easily contribute data and share in improvements to maps, navigation apps, and AI innovations without a single entity having to shoulder the expensive costs of large-scale data collection.

Blockchain plays a key role as an incentive and validation layer:

● Trust and data integrity

Distributed ledger technology ensures that every contribution—whether it’s a geotagged photo, a small photogrammetric scan, or a sensor log—is stored in a tamper-proof manner. Because every data submission is hashed and recorded on a public or private blockchain, users and researchers can trace the origin and authenticity of spatial data.

● Tokenized incentives

Blockchain-based tokens can provide micro-rewards based on the quality, quantity, and relevance of submitted data. Contributors are compensated through smart contracts, which automatically distribute tokens to participants when data meets certain criteria (such as clarity, geospatial accuracy, novelty). By providing fair and transparent incentives, the platform encourages continuous high-quality data contributions - a key requirement for building large-scale and updated datasets.

● Open Ecosystem for Spatial Data

Decentralized ecosystems are less susceptible to single points of failure or data monopolies. Tokens give rise to a micro-economy that encourages diverse entities such as professional cartographers, AI labs, enthusiasts, startups, and smartphone users to collaborate, thereby enhancing the quantity and reliability of data flows.

Decentralized Physical Artificial Intelligence (DePAI) is one of the few areas that I believe can use blockchain and crypto incentive mechanisms to have a substantial impact on other technology fields.