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This week, there were two important events – a keynote at CES and a Q&A with CES financial analysts – where Jensen Huang shared his vision for the future. It’s clearly a union of Agentic AI and robotics, or as he calls it, Physical AI. In today’s episode of our Agentic series, I want to focus on Huang’s perspective on Agentic AI, the progression toward physical intelligence, and the actionable insights we should keep in mind as this shift unfolds. He predicts that within 10 years, robots might reach a level of capability that surprises even skeptics. And as Jensen Huang sees it, it’s a multi-million industry. Once again, I highly recommend watching his entire presentation (or should I say, show?) from CES – you’ll be looking into the future. Ready? Let’s peek together.

Jensen Huang, NVIDIA’s charismatic and visionary CEO, who is not afraid to be a little goofy at his keynotes, has long been at the forefront of defining the cutting edge in AI. In his most recent talk at CES, Huang articulated a transformative vision for the evolution of AI – a world where digital agents seamlessly execute complex tasks, and physical AI systems fundamentally reshape our interactions with the real world. 

That’s how his roadmap for AI’s evolution looks like:

Huang thinks human robotics is a multi-billion-dollar industry, but it’s impossible without working agentic systems. So, let’s take a closer look what gets us to it.

The Age of Agentic AI

So many people in their predictions proclaimed 2025 The Year of Agents. Huang is fascinated with it as well, describing these systems as a “new digital workforce” with the ability to reason about missions, breaking them into actionable subtasks, retrieve relevant data, and using tools to generate high-quality outcomes. Unlike traditional software systems, agentic AI adapts to dynamic contexts and operates autonomously or semi-autonomously, augmenting human capabilities across industries.

What Makes Agentic AI Unique?

What are Real-World Applications according to Huang?

Huang envisions Agentic AI as ubiquitous, spanning industries and use cases. Examples include:

These examples highlight how agentic AI can streamline and improve tasks that have typically relied on human effort.

“The next  frontier of AI is Physical AI”

While agentic AI focuses on digital tasks, the next frontier lies in physical AI. Huang describes physical AI as systems capable of understanding and interacting with the real world. This shift involves developing AI models that comprehend physical dynamics, spatial relationships, and environmental nuances – enabling them to operate in unstructured, real-world environments.

What is Physical AI?

Physical AI builds on the foundation of agentic systems but incorporates an understanding of the physical world. It requires models to grasp:

Is Physical AI a new concept?

Yes and no. Robotics industry is a well established industry, with a lot of fascinating research around it. You might remember that even OpenAI once had a robotics research arm. Back in 2019, its Rubik’s Cube-solving robotic hand captured headlines, demonstrating how reinforcement learning could teach machines to handle complex physical tasks. It felt like the beginning of something transformative – a seamless convergence of AI and robotics.

But in 2021, OpenAI quietly stepped away from robotics. The decision wasn’t due to failure but to a strategic pivot. Robotics demanded hardware integration, resource-heavy infrastructure, and solutions to the stubborn gap between simulated environments and the real world. For OpenAI, the returns didn’t justify the effort. They decided to double down on large language models like GPT – in 2021 GPT-3 was already showing a lot of potential for broader impact.

Thanks to that decision, we now have ChatGPT and the surge of Generative AI, which has elevated agentic systems to a completely new level (agents, as you might suspect, also carry a phenomenal history of development on their shoulders).

By enabling this leap in Agentic AI, OpenAI set the stage for others to reimagine what’s possible. Jensen Huang, inspired by this shift, envisioned the future of robotics enabled by advanced AI = Physical AI. Working backwards, he saw how NVIDIA could play a critical role in bringing that future to life, building the tools and infrastructure to make it happen.

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“Working backwards” approach

Jensen Huang’s "working backwards" approach begins with envisioning the future of AI as a foundational layer across industries, where AI agents and physical systems act autonomously and intelligently. From this vision, he identifies the necessary elements – scalable architectures, robust training systems, and tools that Nvidia must develop to enable this future. Working backwards, Huang outlined Three Key Computers – the essential computing architectures pivotal for modern AI and robotics systems:

Why Robotics become so important now? Huang thinks they might become the largest computer industry ever

The global workforce population is declining, and in some manufacturing-heavy countries, this decline is particularly significant. For these nations, ensuring that robotics becomes a productive and integral part of their industries has become a strategic imperative. With no foreseeable growth in their labor force, the urgency to develop and deploy advanced robotics systems has never been greater.

Opportunities

NVIDIA’s Strategy: Building an Industry, Not Just Products

One of the defining aspects of NVIDIA’s strategy, as articulated by Jensen Huang, is its commitment to shaping and enabling entire industries – not just creating individual products. NVIDIA operates like a tank with a built-in 3D printer: relentless in its progress, constantly constructing new realities in computation and AI. With every advance, NVIDIA reshapes the landscape, crafting frameworks that others rely on to innovate. Its vast resources drive simultaneous advances in hardware, software, and ecosystems, empowering industries to evolve and scale around its orbit. At the core of its strategy lies an ecosystem that equips developers, researchers, and enterprises with the tools to build on NVIDIA’s foundation, remaining the de facto choice for innovation.

They might or might not build their own robots, NVIDIA positions itself as a catalyst for scaling Physical AI across industries.

How will the economics work?

The economics of human robotics, as described by Jensen Huang, revolve around ease of deployment, scalability, and leveraging economies of scale:

Nvidia predicts that within 10 years, these robots will reach a level of capability that surprises even skeptics. And as Jensen Huang sees it, it’s a multi-million industry.

The Long Game

NVIDIA’s investments in foundational technologies, such as Blackwell GPUs and AI simulation platforms, are designed to support industries for decades to come. This long-term perspective ensures that NVIDIA remains indispensable as AI adoption grows exponentially. By aligning its strategy with broader technological trends, NVIDIA ensures it’s not just part of the AI revolution but a leader in defining its direction.

And guess what? In January 2025, OpenAI announced its return to robotics. Caitlin Kalinowski shared job listings for hardware engineers and robotics specialists with experience in areas like autonomous cars, drones, wheeled robots, humanoids, and soft robots. The team aims to tackle the challenges that once led OpenAI to step back from robotics, now leveraging years of AI advancements and deeper industry insights. This shift highlights the growing potential for embodied AI.

Conclusion

So, where does that leave us? This year, AI agents are set to take off – starting with software engineering, digital marketing, and customer service. But Jensen Huang envisions a future where agentic and physical AI converge to redefine industries, from manufacturing to healthcare. For many countries with declining populations, relying on robots becomes imperative to maintain productivity and economic growth. From autonomous vehicles to industrial robotics, AI is quickly becoming the backbone of future industries.

These millions of new robots and autonomous vehicles will drive billions of dollars into supporting data centers. AI is no longer just a tool; we’re moving toward agents and assistants that use tools – a whole new layer above the computing stack. Every factory will have a digital twin, mirroring operations to optimize KPIs before real-world deployment. Huang’s warning is clear: if your enterprise isn’t leveraging AI assistants yet, you’re already falling behind.

OpenAI’s renewed focus on robotics only reinforces this shift. Their return signals that embodied intelligence is becoming a priority for the entire field, marking a critical step forward in the evolution of AI.

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