p;
The benefit of edge AI is low latency: Local hubs allow information to move more quickly than when it has to cross the entire internet. Nvidia's partnership uses T-Mobile's existing infrastructure to support that for the development of physical AI. The company said utility and operations companies are already using physical AI agents, systems, and digital twins across this infrastructure for use cases like optimizing traffic light timing or fixing transmission lines.
In another announcement, Nvidia also nodded to space computing. The company said its new platforms, including Vera Rubin, are "unlocking a new era of space innovation, bringing AI compute to orbital data centers (ODCs), geospatial intelligence and autonomous space operations."
What that means in practice: Nvidia is on the way to AI applications that can operate between Earth and space, as well as between space and space. Nvidia said its IGX ThorTM and Jetson OrinTM platforms offer the energy-efficient inference and data processing required to do anything in orbit -- which is edge AI, functioning as a local hub in space, outside the cloud.
"As we deploy satellite constellations and explore deeper into space, intelligence must live wherever data is generated," Huang said in the release.
But orbital data centers are still theoretical -- not impossible, but not yet a full reality. While Nvidia's IGX Thor and Jetson Orin platforms are available today, the Vera Rubin Space-1 component of the company's space initiative, announced today, will be "available at a later date."
A new 'factory' for physical AI data
Physical AI lives in robotics, autonomous vehicles, and other real-world applications, which can mean higher stakes if something goes mechanically or computationally wrong. That problem is best avoided with high-quality training data that prepares physical AI systems for as many situations as possible to ensure they take safer, more predictable, and more effective action.
To accompany its focus on physical AI, Nvidia also announced its Physical AI Data Factory Blueprint, an "open reference architecture that unifies and automates how training data is generated, augmented and evaluated, reducing the costs, time and complexity of training physical AI systems at scale."
Set to be available next month on GitHub, Blueprint lets companies use Nvidia's Cosmos family of world foundation models to process real-world data and generate synthetic data at scale to train physical AI systems. It also supports reinforcement learning and testing processes for autonomous vehicles and other physical AI systems. According to Nvidia, Blueprint ensures datasets are diverse by including synthetic examples of edge cases and other infrequent scenarios that are harder or expensive to document in the real world.
While it won't be available widely until April, Nvidia said Uber is already using Blueprint to develop autonomous vehicles, and Skild AI is using it for general-purpose robotics.
The big picture
Advancements in physical AI have consumer applications, like Waymo cars and the viral house chore robots you've likely come across, but are most immediately relevant to industrial engineering. More capable, autonomous robots will have the biggest impact on our public and industrial landscapes: on roads, in factories, and, evidently, walking across theme parks.
Originally published on March 16, 2026 by ZDnet.
