AI systems have access to enormous amounts of information from the internet, but some of the most valuable data simply doesn’t exist online.
The physical world is constantly changing, and capturing that reality at scale is a completely different challenge. Think about what happens around us every day.
Streets change. Shops rearrange their layouts. Objects move. Buildings evolve. People interact with their surroundings. Traffic patterns shift.
Shall we go on? Weather changes environments. Even ordinary places can look completely different from one day to the next.
This creates a problem for the next generation of AI. How do you teach a machine about a world that is constantly moving?
Traditional datasets can provide enormous amounts of information, but they are often static snapshots of reality. So what's the solution?
Physical AI needs something more dynamic: fresh, diverse and geographically distributed data collected from actual environments.
Instead of relying exclusively on expensive and centralized mapping infrastructure, Vangrid is turning smartphones into a distributed network for collecting real-world 3D data.
The smartphone in your pocket is already packed with cameras, sensors and computing power. Most of the time, however, we use all that technology watch videos and argue about crypto.
Vangrid is exploring how that same hardware can become a physical-world data collection node. Physical AI needs machines to understand space.
Contributors can capture environments when fresh data is required, creating a potential bridge between what is happening in the real world and the datasets required to train increasingly capable AI systems.
The important distinction is that this isn’t simply about taking more photographs. Where an object is located? How large it is? What surrounds it?
That means 3D spatial information can become an important component of training robotics, autonomous systems and world models.
Unlike a conventional dataset sitting unchanged on a server, a distributed collection network has the potential to continuously refresh its view of the physical world.
The internet gave AI an incredible foundation. Text, images, video, code and countless other forms of digital information have helped create increasingly capable models.
The main issue? The robots don't live on the internet. They operate in kitchens, warehouses, roads, factories, offices, farms and cities.
A model can know what a chair is from millions of images, but a robot still needs to understand where that chair actually is, how to navigate around it and how the surrounding environment affects its actions.
Building a bigger model doesn’t automatically solve the problem if the model doesn’t have enough high-quality information about the environments it needs to understand.
Vangrid’s approach effectively treats the smartphone as more than a consumer device. It becomes part of a distributed sensing infrastructure.
That combination of deep-tech, infrastructure and Web3 experience is relevant to what Vangrid is attempting to build because the challenge isn't simply collecting data.
The infrastructure needs to handle collection, processing, provenance and ultimately make that information useful for AI applications.
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