The Future of Decentralized AI in Robotics

In January 2025, Nvidia CEO Jensen Huang designated the next stage in the evolution of artificial intelligence. According to the head of the company, it should be Physical AI (PAI) – a global “brain” for robots and autonomous devices, allowing machines to navigate in the real world. The idea was taken up by representatives of the blockchain industry, proposing its decentralized version. Already in February, analytics platform Messari reported on a new form of interaction between people, machines and Web3 – DePAI. The idea of an infrastructure based on DePin and a stack to develop a future “robot economy” inspired cryptoinfluencer Miles Deutscher. In April, he called DePAI “the biggest trend in cryptocurrency for the next two years.” In a new piece, Coinpaper talks about DePAI’s potential to build a balanced future for people and machines. Physical AI from Nvidia Nvidia CEO Jensen Huang announced at CES 2025 the creation of Cosmos, a platform for developing World Foundation Models (WFM) that aims to revolutionize the way physical AI (PAI) systems are built: robots and autonomous vehicles. Cosmos uses WFM to simulate real-world conditions, enabling the testing of AI systems. The controlled test space reduces costs and speeds up the development process. Huang noted the open nature of the Cosmos platform, whose source code is available on GitHub. “We sincerely hope that the openness of Cosmos will give the world of robotics and industrial AI what Llama 3 did for industrial AI,” he added. According to Nvidia , PAI enables autonomous systems – such as robots, unmanned cars and smart spaces – to perceive, understand and perform complex actions in the real world. Large Language Models (LLMs) like GPT and Llama are capable of generating human language and abstract concepts, but are limited in understanding the laws of the physical world. PAI is able to complement their capabilities by teaching orientation in space. 3D data for AI is generated using high-fidelity simulations that act as a data source and simultaneously as a learning environment. The process starts by creating a digital twin of a space, such as a factory, with the addition of sensors and robots. While sensors capture the dynamics of solids (motion and collisions) or the interaction of light with the environment, real-world scenarios are simulated in the virtual replica. PAI opens up new possibilities for the transformation of many industries. In robotics, such a concept can improve a number of unit characteristics for use in a variety of products: autonomous mobile robots . With data from embedded sensors, the machines can navigate challenging environments and avoid collisions in warehouses; Manipulators on the conveyor . Adjustment of gripping force and position based on object coordinates, demonstrating adaptive motor control; surgical robots . Trained to perform tasks that are difficult for machines, such as threading a needle and suturing; general-purpose humanoid robots . Need both large and fine motor skills. Must perceive, understand and interact with the real world regardless of the task at hand. The PAI model is rewarded for successfully performing the required actions, so it gradually improves. Over time, the machines develop complex skills, including the precise motor actions needed for real-world applications: packing boxes neatly, helping to assemble vehicles, or navigating in space. DePAI is a Web3 stack for intelligent robots The idea of PAI has been picked up by the crypto community, suggesting blockchain as a remedy for AI centralization. Messari analyst Dylan Bain added the prefix decentralized to the acronym in a February report. The new acronym looks like this: DePAI – decentralized physical artificial intelligence networks. DePAI is at the intersection of AI, robotics and Web3. It has the potential to become one of the engines of the future economy, where automation must be balanced with the real needs of humanity. What started as generative AI for content creation is now turning into autonomous agents capable of making decisions on their own. It’s just a matter of equipping them with effective “bodies.” On Messari’s DePAI map, there are six main activity groups. DePIN projects like the GEODNET geodetic network or the Hivemapper alternative to Google maps are naturally lumped into the new DePAI stack. The products of such startups can become the ears and eyes of robots in the real world and time, acting as a data collection layer for them. Startup WeatherXM incentivizes users to host personal weather stations and upload climate data in exchange for tokens. Such data can be utilized by devices in DePAI. For example, integrated systems in smart homes can automatically adjust ventilation or room temperature based on the current weather. In the robotics category, startup Frodobots is using DePIN to deploy low-cost “sidewalk” delivery robots around the world. The data used fixes the complexity of human decisions when navigating in real-world environments. Gamers can practice controlling machines in an accessible format. To this list should be added a company dedicated to creating digital replicas of physical robots on the Polkadot blockchain – Robonomics Network. It is exploring the possibility of connecting the operating system of robots and IoT devices to the blockchain for publishing tasks and transferring services via smart contracts. Blockchain platforms with a focus on DePIN – Peaq and IoTeX – are building machine coordination networks. Their high-speed architecture provides the large number of concurrent transactions required by DePAI. The Posemesh data transfer protocol from Auki is focused on creating a global spatial intelligence network. It should ensure privacy and facilitate the creation of a virtual map for robots. Its functions are not limited to logistics and is also used for AI self-learning in a simulation environment. In the category of investment DAO is Xmaquina . The project focuses on creating a structure for the collective ownership, management and development of high-value AI robots. The organization allows communities and investors to jointly fund and develop technology, distributing the benefits equitably. In a global machine economy, DePAI would hypothetically return the power now held by corporations back to the people. It is already important for users to know who owns the technology, which vendor defines the standards of interaction, and who will ultimately profit. DePAI in the future machine economy On April 14, 2025, renowned crypto analyst Miles Deutscher called DePAI one of the biggest trends in the industry. The image attached to the post illustrates the complexity of the relationships within the DePAI stack. The dynamics reflected in the vectors show the purpose of the actions performed between the infrastructure participants. The Peaq blockchain team, which is actively involved in the development of DePAI, emphasizes seven components that every autonomous system needs: Hardware. Robots that allow AI models to interact with the physical world; software. AI agents – the mind of machines; Data aggregation and distribution for PAI learning and evolution; spatial intelligence. Understanding and interactions of robots with the environment; DePIN networks. Providing resources to store, compute, and power systems; machine economics layer. Unifies all elements through specialized protocols for interaction between DePIN networks, AI and robots;DAOs. Enabling people, communities, and companies to own, manage, and generate revenue on an equal footing; If the average Web3-user has encountered or at least heard about most of the points, the future machine economy requires clarification. By combining all advanced cryptotechnologies, the model has the potential to bring the following positive changes: provide a motivation system, incentivizing robots to act in the interest of humanity – based on decisions made by humans; support global governance and an open model of participation, allowing everyone – not just corporations – to be part of the economy; serve as a global trading platform; establish standards and protocols for interaction between autonomous robots; pave the way for decentralized welfare distribution systems; provide the transactional and application infrastructure to build and connect decentralized applications and machines. The downside of utopia Like all new technologies, DePAI faces significant challenges that must be addressed before mass adoption. The main challenges are: Privacy and legal compliance. The use of real data (faces, voices) raises risks of violating local legal regulations. Even with ZKP, clear anonymization standards and data usage policies are needed; Network and device security. Cyberattacks can lead to physical damage through malicious commands. Need smart-contract protection, encryption, built-in robot security systems; standardization and interoperability. Devices use different protocols – unification at the hardware and software levels is required; Scalability and infrastructure. High bandwidth, reliable blockchain and physical infrastructure (storage, charging stations) are required; management and accountability. The DAO may own the robots, but maintenance requires centralized teams. There is no clarity on how to allocate responsibility in case of incidents or damage. The presented image of a future machine economy with a favorable outcome for humans is certainly alluring and hopeful, but whether robotics giants like Tesla and other corporations will become part of it is a big question. Web3, built on a blockchain foundation, is likely the best solution currently available for implementing decentralized AI. Meanwhile, the DePAI stack clearly outlines the benefits for all parties involved in future progress-but only time will tell who its participants will be. Source: https://coinpaper.com/9047/de-pai-and-web3-the-future-of-decentralized-ai-in-robotics