At CES 2025, Nvidia CEO Jensen Huang made an exciting claim about the future of robotics. He said, “The ChatGPT moment for general robotics is right around the corner.” To illustrate this, he highlighted three types of robots that can function right away:
- AI agents: They can perform tasks just like any other office worker.
- Self-driving vehicles: They can navigate existing roads safely.
- Humanoid robots: They can sense and interact with their environment as humans do.
But there’s also a fourth type that deserves attention:
- Industrial automation systems: These systems are becoming more robotic, allowing businesses to rethink and optimize large-scale operations.
The combination of robotics, operational technologies (OT), and IT is vital. Merging these areas creates a robust system that helps businesses make better decisions in real time. This integration turns reactive processes into proactive ones, making physical AI a key player in industrial automation and digital transformation.
However, Huang’s timeline for physical AI remains uncertain. This ambiguity stems from two main challenges. First, developing new models for real-world physics is complex. Second, much of the operational data generated is still not accessible to IT systems, making it difficult to leverage AI applications effectively.
Nonetheless, the push for innovation is strong. Companies eager for change are actively seeking ways to connect OT with IT systems. Nvidia has developed tools to help close this gap, which brings us to recent advancements in bridging the physical AI divide.
Bridging the Physical AI Gap
Nvidia introduced Cosmos at CES 2025 as a platform designed for physical AI development. Unlike traditional large language models like ChatGPT, Cosmos focuses on understanding the physical world. It aims to collect an extensive amount of training data to create accurate models for robots and industrial systems. The platform has been trained on around 20 million hours of video, making it a powerful resource for developing physical AI applications.
Cosmos works in conjunction with Omniverse, Nvidia’s graphics platform. Together, they create realistic 3D simulations. This allows developers to model real-world environments and scenarios that a robot or AI system may encounter. By generating diverse training scenarios, developers can optimize their models more effectively.
Nvidia’s platform operates on three main types of systems:
- AI model training — using Nvidia’s DGX supercomputer.
- Physical AI development and visualization — using Nvidia OVX servers.
- Deployment solutions — using Nvidia AGX robotics computers.
These systems require optimized AI capabilities, which traditional CPUs cannot provide. Therefore, Nvidia has tailored its tools specifically for these advanced platforms, enhancing the development workflow.
As for the deployment of AI applications, the choice of platform should depend on the specific use case rather than a general product selection. This flexibility is crucial for businesses with varied operational needs.
Closing the OT-IT Data Gap
Even though the integration of AI shows great promise, many operational systems remain disconnected from mainstream IT. This gap—often referred to as the OT-IT gap—creates challenges. To overcome this, suppliers are shifting to a “data first” mindset, facilitating smoother connections between OT and IT.
Instead of forcing IT solutions into the OT landscape, businesses are focusing on simplifying data access. This new approach allows for better integration without overhauling existing systems. By gathering operational data as it is, companies can reduce costs and streamline processes.
Recent announcements from major technology firms indicate a collective move toward better OT-IT integration. The aim is clear: to access operational data more effectively without intrusive modifications to IoT devices.
What Lies Ahead for Robotic Enterprises
The concept of creating digital twins—virtual replicas of physical systems—is becoming a reality. When physical AI is paired with operational data, these models can accurately simulate complex scenarios, leading to improvements in efficiency, safety, and decision-making.
While physical AI tools like Nvidia Cosmos are promising, the timeline for widespread adoption remains uncertain. The company has a strong foundation and significant resources, which bodes well for innovation. There are already examples of enterprises working on solutions using these new tools, indicating a positive trend.
However, challenges remain in connecting operational data sources to these advanced models. Although progress is being made, the diversity of OT systems complicates integration. Bridging the OT-IT gap with straightforward interfaces will be key. With increased focus on physical AI, we can expect advancements in connectivity that will support the growing robotic enterprise movement.
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