From Smart Devices to AI Teammates: Building the Infrastructure for Physical AI

At CES 2025 in Las Vegas, Jensen Huang delivered a clear message in his keynote: AI has outgrown the screen. It no longer lives just on your laptop. Today, it powers machines that see, decide, and act in the physical world. Huang walked us through a curve that maps this shift - starting with pattern recognition, moving through content generation and decision-making, and arriving in the real world.

Not surprisingly, this brave new world of Physical AI demands a different approach to infrastructure – an approach that Nvidia and other players are already shaping. The reason? If we want devices to operate with shared context, and not as separate intelligent endpoints, they need to be able to cooperate, be aware of their environment (and each other), and respond to what’s happening in real time. 

In this article, I’ll break down what the shift to Physical AI looks like, what it means, what it demands from the systems we already rely on, and how Xyte is uniquely positioned to facilitate this shift.

Primer: What Is Physical AI?

‍Physical AI refers to machines that understand the world they’re in. They don’t just get input and respond - they notice what’s around them, make sense of it, and take action through a network of connected devices. The intelligence isn’t off somewhere in the cloud. It’s built into the networked machines, and often supported by the cloud.

This isn’t some distant vision. We already see it around us. A robot changes course to avoid a stack of boxes that wasn’t there a minute ago. A sensor catches movement and sets off a chain reaction across the building. A camera adjusts its zoom to focus on the person speaking in the room, and cars can already drive autonomously. This is just the beginning - we’re about to see many more similar applications all around us in the near future.

Physical AI takes it further. It doesn’t treat machines as simple input-output devices. It enables them to sense their surroundings, interpret what is happening, and act in real time with intent. A robot can navigate an obstacle, a vehicle can respond to traffic conditions, and a camera system can adjust dynamically to capture what matters in the scene. In each case, the intelligence resides in the system itself, allowing it to operate autonomously within the physical world.

That only works if the underlying systems are aligned. Devices need a common frame of reference. They need to understand what they’re doing, how it affects the system, and what’s happening around them. That kind of coordination takes a system that can act locally, stay aware globally, and adapt to context. 

Enabling this level of alignment requires more than device connectivity. It depends on creating context – which gives AI agents the awareness to act with a clear sense of their surroundings.

Creating Context 

Context enables AI agents to make decisions that are meaningful, accurate, and safe.

AI agents depend on context to function effectively. The quality of their output is tied directly to the quality of the context they receive. Much of the innovation ahead will focus on building systems that supply agents with the right context at the right time.

AI models process data, yet they do not understand its relevance without context. A temperature reading of -40 degrees may be acceptable for a freezer but disastrous for a data center. A device alert may be routine or critical depending on the state of the system it belongs to. 

Systems that link technical signals with business logic provide the necessary alignment, connecting devices, workflows, and user roles so AI can interpret events in a way that reflects the environment in which it operates. Xyte’s platform, for example, supports this by giving devices and AI agents a shared frame of reference that turns raw data into actionable insight. With this alignment, connected systems can respond to real conditions, coordinate actions, and maintain awareness across the organization - enabling them to move beyond isolated actions and operate as true teammates.

Device AI and Context: The Making of an AI Teammate

Physical AI works best when intelligence lives where the work happens and stays aligned through context.

Device AI operates at the edge and responds in real time. A smartwatch detects a spike in your pulse and checks in. A conference room system adjusts microphones and cameras as participants join. These devices act locally and keep the environment running.

Context can connect these local actions to the larger workflow. Through the Model Context Protocol (MCP), AI agents gain access to device data, business logic, and operational context in one unified layer. This alignment allows the local AI agent to interpret events, understand their significance, and take appropriate action without manual intervention.

This is an “AI Teammate” - an AI agent that integrates with your tools and connected devices through MCP. It works alongside your team, maintains awareness across systems, and completes tasks without needing direction. Like any teammate, it stays engaged, fills gaps, and keeps the flow of work moving.

Context is what makes this possible. It ensures that every action the AI teammate takes is informed by the environment, the state of the devices, and the business processes around them.

The Future of Intelligent Infrastructure

These capabilities are only the beginning of what Physical AI will deliver. 

Physical AI is changing what we’re asking machines to do. We no longer expect machines to simply receive inputs and return outputs but to notice what’s happening, respond in real time, and remain connected to the bigger picture. That expectation depends on context – the foundation that gives AI agents the awareness they need to function as true teammates.

The infrastructure for this is starting to emerge. Nvidia is delivering the hardware and system tools for distributed intelligence. MCP is creating a common layer for AI agents to access the context they need. And Xyte is bringing these elements together in the connected device ecosystem, helping smart devices align with their environment, act with intent, and integrate into coordinated systems.