Compiled: Golden Finance xiaozou

DePAI (Decentralized Physical AI) is regarded as the next encryption by many cryptographers Major events are "one of the few areas that can use blockchain and crypto incentive mechanisms to have a substantial impact on other technical fields." what is it? What are the innovations? What kind of potential does it have? Next, let’s find out.

Simply put, it is an innovative concept that combines decentralized physical infrastructure networks (DePIN) with artificial intelligence (AI) technology, through Blockchain technology coordinates physical hardware facilities of multiple individual units to build and maintain infrastructure networks in a licensing, trustless and programmable way.

Messari analyst Dylan Bane supports DePAI on the X platform as follows:

Go Centralized physical artificial intelligence (DePAI) provides an alternative to centralized control of robotics and physical artificial intelligence infrastructure stacks. DePAI is developing rapidly, from real-world data collection to physical artificial intelligence agent operating robots deployed by DePIN.

NVIDIA CEO Jensen Huang said: "The 'ChatGPT moment' in the field of general robots is coming soon. ”

The digital age starts with hardware and gradually evolves into the invisible world of software. The era of artificial intelligence began with software, and now it is seeing the physical world as its ultimate challenge and cutting-edge field.

In a robot, automobile, powered by autonomous physical artificial intelligence (Physical AI), In a world where drones and bionics gradually replace human labor, the issue of ownership of these machines becomes the core social issue. Decentralized Physical Artificial Intelligence (DePAI) provides an important opportunity to establish Web3 physical AI before centralized players dominate.

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

Real world data is the main bottleneck in training physical artificial intelligence (Physical AI). Although Nvidia's Omniverse and Cosmos provide a promising path through simulated environments, synthetic data is only part of the solution. Remote operation is also indispensable as real-world video data.

In the field of remote operations, FrodoBots is leveraging the Decentralized Physical Infrastructure Network (DePIN) ) Deploy low-cost sidewalk delivery robots worldwide. This data collection method not only captures the complexity of human navigation decisions in the real environment, thereby generating high-value data sets, but also effectively overcomes the capital gap problem.

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

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

Decentralized physical artificial intelligence (DePAI) can use real-world video data to train Physical artificial intelligence and build global shared spatial understanding capabilities. With their unique video datasets, Hivemapper and NATIX Network are expected to be important data sources in this field.As Mason Nystrom pointed out, “The data isIt is difficult to monetize at the individual level, but it is easy to achieve value conversion after aggregation. "Real world data can be aggregated through the decentralized physical infrastructure network (DePIN), thus forming high-value data sets. IoTeX's Quicksilver protocol realizes data aggregation across DePINs, while taking into account data verification and privacy protection, for this The ecosystem provides key technical support.

Space Intelligence/Computing Protocol is also committed to passing Decentralized Physical Infrastructure Network (DePIN) and Decentralized Physical Artificial Intelligence (DePAI) to achieve spatial coordination and decentralized control of real-world 3D virtual twins. The Posemesh protocol of Auki Network protects privacy and decentralized Under the premise, real-time spatial perception capabilities are realized, providing innovative technical solutions for this field.

The initial application of Physical AI Agents has also emerged. SAM has connected to the fleet of robots across the world, and can infer geographic locations. With the help of frameworks such as Quicksilver, AI agents are expected to be accessed in real time in the future. Data flow provided by the Decentralized Physical Infrastructure Network (DePIN).

Touch Physics The most direct way of AI may be through investment-based decentralized autonomous organizations (DAOs).

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

Crypto researcher DeFi Cheetah responded positively to Dylan Bane’s remarks about DePAI:

Decentralized Physical Artificial Intelligence (DePAI) is the next major development in the field of encryption Direction, blockchain and crypto incentive mechanisms will empower spatial intelligence—that is, the ability of robots to perceive the environment, understand surrounding objects or structures instantly and respond effectively, which is one of the most challenging problems in the field of artificial intelligence robots. Our Industry can help solve developmentThe most critical bottleneck of spatial intelligence - obtain fine-grained, high-quality and continuously updated spatial data.

Implementing powerful spatial intelligence requires massive amounts of data that not only capture visual cues (such as colors and textures), but also contains deep geometric contexts (such as Polygons, point clouds, topological representations) and physical properties (angle, distance, friction, material type, etc.). While traditional 2D images or basic GPS coordinates have some value, they are often oversimplified for training advanced models designed to run in dynamic, complex, and unpredictable real-world environments.

●Complexity of 3D map construction

Projects such as Google Street View or dedicated LiDAR scanning Although it can provide high-resolution 3D maps, it is expensive and the generated data sets are relatively sparse. For example, a high-resolution LiDAR device can cost more than $50,000, while a city-wide scan operation can easily cost hundreds of thousands of dollars. This cost complexity often leads to low update frequency, making maps obsolete within months.

●Limitations of centralized data pipelines

In many, most spatial data is made by Institutions or large enterprises control. Since these centralized entities collect data only in specific regions, large areas around the world—especially rural or underdeveloped—are still in a state of unmapping or outdated data. In addition, proprietary data restrictions may lead to market fragmentation and hinder innovative research.

●The lack of labeled 3D data sets

Although labeled 2D image data sets (e.g. ImageNet, which contains more than 14 million annotated images, has seen an explosive growth, but annotated 3D datasets are still scarce. Creating such datasets requires the combination of sensor fusion techniques such as LiDAR + Vision + IMU readings and a lot of manual annotations. This process is extremely time-consuming and expensive, which slows down the research and development process of robotics and machine learning applications.

Powered by the popularity of mobile devices, the crowdsourcing model recognizes that billions of smartphone and wearable devices worldwide can jointly provide massive amounts of location-based data . Modern smartphones are equipped with a variety of sensors—accelerometers, gyroscopes, magnetometers, cameras, GPS chips, etc.—those sensors can capture spatiotemporal data far beyond simple latitude and longitude. This model helps achieve the following goals:/p>

●Real-time data acquisition

Imagine that commuters capture urban infrastructure on their daily commutes 3D scan data, or residents of remote villages only use their mobile phone camera to record the path, building profile and farmland boundaries. Over time, these seemingly tiny contributions will accumulate into a global integrated spatial database.

●Diverable environment coverage

Because mobile devices are almost everywhere, their data is natural It covers a wider geographical area, topography and cultural environment. This geographical diversity is crucial for the robust AI model that must be learned to adapt to variable climates and community layouts.

●Democratization of data collection

The crowdsourcing model subverts the Traditional centralized model. Individuals around the world can easily contribute data while sharing improvements in maps, navigation applications and AI innovations without the cost of costly large-scale data acquisition costs for a single entity.

Blockchain, as an incentive and verification layer, plays the following key role:

● Trust and Data Integrity

Distributed ledger technology ensures that every contribution—whether it is geotagged photos, small photogrammetry scans or sensor logs—is in Storage in a tamper-proof manner. Since each submitted data is hashed and recorded on a public or private blockchain, both users and researchers can trace the source and authenticity of spatial data.

●Tokenization incentive mechanism

Blockchain-based tokens can be submitted according to submission The quality, quantity and relevance of data provide a micro reward. Contributors are compensated through smart contracts, and when the data meets certain criteria (such as clarity, geospatial accuracy, novelty), the smart contract automatically distributes tokens to participants. By providing fair and transparent incentives, the platform encourages continuous high-quality data contributions—a key requirement for building large-scale and maintained updated data sets.

●Open ecology of spatial data

Decentralized ecosystems are not susceptible to single point of failure or data monopoly. Tokens have spawned a micro-economic system that encourages diversification such as professional cartographers, AI labs, enthusiasts, startups and smartphone users. Entities participate in collaboration to enhance the quantity and reliability of data flows.

Decentralized Physical Artificial Intelligence (DePAI) is one of the few that I think can leverage blockchain and One of the areas where the crypto incentive mechanism has a substantial impact on other technical fields.