During a recent visit to Beijing, Nvidia co-founder and CEO Jensen Huang was asked what he would study if he were a 20-year-old graduate in 2025. His answer was clear: he’d choose a different academic direction than the one that launched his own career. Though he didn’t explicitly name a single subject, Huang emphasized that he would lean more toward the physical sciences rather than software. This subtle but significant shift points to his evolving view of where the next wave of opportunity lies. His response reflects a broader vision for the future of artificial intelligence—one rooted not just in code, but in physics, robotics , and the mechanics of the real world that AI must increasingly learn to navigate and reason through.
Jensen Huang's evolving view: from engineering to physical AI
Huang, who earned his electrical engineering degree from Oregon State University and a master’s from Stanford, co-founded Nvidia in 1993. Today, the company sits at the forefront of the AI revolution and recently hit a historic $4 trillion market cap. While reflecting on his own path, Huang revealed he had graduated from college at age 20 and, if given the chance again, would pursue physical sciences, fields that deal with non-living systems like physics, chemistry, and astronomy.
Although he didn’t go into specifics about why, Huang’s broader remarks provide insight. Over the years, AI has evolved from “Perception AI,” which focused on recognition tasks like image detection, to “Generative AI,” which includes language models capable of translating, coding, and creating. But the next chapter, according to Huang, is “Reasoning AI,” systems that can understand context, solve novel problems, and act as “agentic AI” or digital robots with reasoning power.
The rise of physical AI and the future of robotics
Looking ahead, Huang predicts the rise of “Physical AI,” where artificial intelligence must grasp real-world physics: friction, inertia, cause and effect. These capabilities go beyond generating text or images—they involve anticipating how objects move, how to grip items with the right force, or how to infer the presence of hidden obstacles like a pedestrian behind a car.
When AI gains this kind of physical reasoning and is embedded into machines, it leads to robotics, a critical component of Huang’s vision. With global labor shortages and the rapid expansion of manufacturing facilities, especially across the United States, Huang sees robotics powered by physical AI as essential. “Hopefully, in the next 10 years... they’re highly robotic and helping us deal with the severe labor shortage,” he said, underscoring the need for innovation that bridges the digital and physical worlds.
Jensen Huang's evolving view: from engineering to physical AI
Huang, who earned his electrical engineering degree from Oregon State University and a master’s from Stanford, co-founded Nvidia in 1993. Today, the company sits at the forefront of the AI revolution and recently hit a historic $4 trillion market cap. While reflecting on his own path, Huang revealed he had graduated from college at age 20 and, if given the chance again, would pursue physical sciences, fields that deal with non-living systems like physics, chemistry, and astronomy.
Although he didn’t go into specifics about why, Huang’s broader remarks provide insight. Over the years, AI has evolved from “Perception AI,” which focused on recognition tasks like image detection, to “Generative AI,” which includes language models capable of translating, coding, and creating. But the next chapter, according to Huang, is “Reasoning AI,” systems that can understand context, solve novel problems, and act as “agentic AI” or digital robots with reasoning power.
The rise of physical AI and the future of robotics
Looking ahead, Huang predicts the rise of “Physical AI,” where artificial intelligence must grasp real-world physics: friction, inertia, cause and effect. These capabilities go beyond generating text or images—they involve anticipating how objects move, how to grip items with the right force, or how to infer the presence of hidden obstacles like a pedestrian behind a car.
When AI gains this kind of physical reasoning and is embedded into machines, it leads to robotics, a critical component of Huang’s vision. With global labor shortages and the rapid expansion of manufacturing facilities, especially across the United States, Huang sees robotics powered by physical AI as essential. “Hopefully, in the next 10 years... they’re highly robotic and helping us deal with the severe labor shortage,” he said, underscoring the need for innovation that bridges the digital and physical worlds.
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