Nvidia CEO Jensen Huang says he’d study physics over coding if he were a student today, echoing Elon Musk’s advice to focus on real-world sciences for the AI-driven future.
In a world increasingly shaped by artificial intelligence and automation, some of the most influential tech leaders are urging a surprising pivot. Instead of doubling down on software and coding, Nvidia CEO Jensen Huang and Tesla CEO Elon Musk want today’s students to focus on the fundamentals—particularly physics and mathematics.
Jensen Huang: The case for physical sciences
During a recent event in Beijing, Nvidia’s Jensen Huang was asked what he would study if he were a 22-year-old graduate in 2025. His response caught many off guard. “I probably would have studied physical sciences,” Huang said, prioritizing physics over computer science. Despite building Nvidia into the world’s most valuable chipmaker, Huang believes that the next frontier in AI is not just software-driven but rooted in understanding the physical world.
Huang stressed that future AI systems—especially those operating in robotics and real-world environments—will require deep knowledge of physics. “The next wave requires us to understand friction, inertia, and cause and effect,” he noted, referring to what he calls “Physical AI.” As AI moves beyond perception and reasoning into real-world interaction, skills in physics, mechanics, and materials science will become increasingly valuable.
To that, the Nvidia CEO said: “For the young, 20-year-old Jensen, that’s graduated now, he probably would have chosen ... more of the physical sciences than the software sciences,” adding that he actually graduated two years early from college, at age 20.
Physical science, as opposed to life science, is a broad branch that focuses on the study of non-living systems, including physics, chemistry, astronomy and earth sciences.
Huang got his electrical engineering degree from Oregon State University in 1984 before earning his master’s degree in electrical engineering from Stanford University in 1992, according to his LinkedIn profile.
About a year later, in April 1993, Huang co-founded Nvidia with fellow engineers Chris Malachowsky and Curtis Priem over a meal at a Denny’s restaurant in San Jose, California. Under Huang’s leadership as CEO, the chipmaker has now become the world’s most valuable company.
Nvidia also became the world’s first company to hit a $4 trillion market cap last week.
Although Huang didn’t explain why he says he’d study the physical sciences if he were a student again today, the tech founder has been very bullish on “Physical AI” or what he calls the “next wave.”
Over the past decade and a half, the world has moved through multiple phases of artificial intelligence, he explained in April at The Hill & Valley Forum in Washington, D.C.
“Modern AI really came into consciousness about 12 to 14 years ago, when AlexNet came out and computer vision saw its big, giant breakthrough,” Huang said at the forum.
AlexNet was a computer model unveiled during a 2012 competition that demonstrated the ability of machines to recognize images using deep learning, helping spark the modern AI boom.
This first wave is called ‘Perception AI,’ Huang said.
Then, came the second wave called “Generative AI,” “which is where the AI model has learned how to understand the meaning of the information but [also] translate it” into different languages, images, code and more.
Elon Musk and the math-physics mantra
Elon Musk, known for companies like Tesla and SpaceX, has long championed physics as the foundation of all serious problem-solving. In response to a viral post by Telegram CEO Pavel Durov urging students to master mathematics, Musk simply added: “Physics (with math).” He has consistently said that understanding first principles—fundamental truths derived from physics—is key to building scalable innovation.
Musk’s endorsement aligns with his real-world projects. From rockets to autonomous cars, the challenges he tackles demand a mastery of physics far beyond software logic.
Why this shift matters
The advice from Huang and Musk marks a broader shift in thinking. While coding remains a critical skill, these leaders are advocating a return to the scientific roots that power real-world innovation. Physical AI and robotics are seen as the future of human-machine collaboration, and success in these fields depends less on writing code and more on understanding how the world works
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