By CNBC News Staff — July 18, 2025

Jensen Huang, the co-founder and CEO of Nvidia, made headlines this week in Beijing by offering rare insights into his perspective on education and the next wave of artificial intelligence (AI). In a candid exchange with journalists, Huang remarked that if he were a new university graduate today, he would be more inclined to study the physical sciences—a shift from his own history in electrical engineering and software.

The Logic Behind Physical Sciences

“For the young, 20-year-old Jensen, he probably would have chosen more of the physical sciences than software sciences,” Huang told reporters. This is especially notable given his instrumental role in propelling Nvidia from a graphics chip startup to a $4 trillion behemoth now driving much of global AI innovation.

Huang’s advice comes at a pivotal time as the boundaries between software, hardware, and the physical sciences blur amid the rapid evolution of AI and robotics. He holds a bachelor’s degree in electrical engineering from Oregon State University (awarded when he was just 20) and a master’s from Stanford University, earned in 1992—credentials that formed the groundwork for Nvidia’s founding in 1993 over a breakfast meeting at a San Jose Denny’s.

Physical Sciences and the Next AI Epoch

Why the pivot to physical sciences now? According to Huang, the coming AI revolution won’t only rely on data and code, but also on a deep understanding of physics, chemistry, inertia, and causality. Huang refers to this fusion of disciplines as “Physical AI.”

Over the last 15 years, AI has progressed through several hallmark phases. Huang describes the first as “Perception AI,” catalyzed by breakthroughs in deep learning and computer vision technologies such as AlexNet. Introduced in 2012, AlexNet’s performance at the ImageNet competition proved that machines could recognize images—and, by extension, patterns in massive datasets—at superhuman speed.

The second era, which dominates today, is “Generative AI.” As evidenced by large language models like OpenAI’s GPT-4 and Google’s Gemini, AI can understand, synthesize, and generate text, images, code, and more. These generative systems are transforming industries, from education and design to medicine and enterprise IT.

“We’re now in this age called ‘Reasoning AI’… where you now have AI that can understand, it can generate, [and] solve problems and recognize conditions that we’ve never seen before,” said Huang. AI agents—sometimes known as agentic AI or digital workforce robots—are increasingly being adopted by major tech firms including Microsoft and Salesforce.

Physical AI: Robotics and the Digital Workforce

The next leap, says Huang, will require infusing AI with physical reasoning—the capability to grasp laws of motion, friction, inertia, and cause-effect relationships. “Physical AI” will form the backbone for robotics and autonomous systems, enabling machines to predict, interact with, and safely manipulate the physical world. This shift has enormous implications for manufacturing, logistics, healthcare, and beyond.

For instance, robotics powered by physical AI may soon manage hazardous warehouse operations, manufacture electronics with sub-millimeter precision, or assist surgeons by anticipating human motion and intent. Already, Nvidia’s robotics simulation platforms and its Omniverse ecosystem are being deployed in smart factories across the globe, including the United States, Europe, and Asia. According to consulting firm McKinsey, the global industrial robot market is forecast to reach $85 billion by 2030, catalyzed by tightening labor pools and the drive toward automation.

“This is really, really important for us now, because we’re building plants and factories all over the United States,” Huang noted. “Hopefully, in the next 10 years, as we build out this new generation of plants and factories, they’re highly robotic and helping us deal with the severe labor shortage that we have all over the world.”

AI’s Influence on the Global Economy

Nvidia’s rise demonstrates just how lucrative the intersection of AI, semiconductors, and advanced computing has become. After surpassing Apple and Microsoft to become the world’s most valuable company in June 2025, Nvidia now plays a central role in fueling global AI infrastructure. In the previous fiscal year, Nvidia’s data center revenue soared 80%, driven by demand for its GPU accelerators used in training and deploying leading AI models.

Industry watchers note that the race for “Physical AI” will likely intensify competition among semiconductor giants, robotics startups, and tech conglomerates—all vying for dominance as more sectors rely on intelligent automation. According to a recent IDC report, global spending on AI systems, including robotics, is projected to exceed $500 billion by 2027.

Implications for Students and Future Professionals

Huang’s comments are prompting educators and students alike to reconsider the composition of a future-proof STEM (science, technology, engineering, mathematics) curriculum. While coding and software engineering remain valuable, the edge may increasingly go to those who combine computational skills with expertise in physics, materials science, or mechanical engineering.

“The next wave requires us to understand things like the laws of physics, friction, inertia, cause and effect,” Huang emphasized. As AI continues to permeate manufacturing, transportation, medicine, and everyday life, the ability to bridge real-world phenomena and digital intelligence could define the coming workforce.

The Road Ahead

Looking forward, Huang’s vision positions Nvidia in the vanguard of “Physical AI”—a space where semiconductor design, simulation, robotics, and core science will converge to define the next era of innovation. For young professionals and students, the message is clear: mastering the fundamentals of mathematics and core sciences may well be the ticket to thriving in an AI-augmented future.

As industries from automotive to pharmaceuticals invest billions into autonomous systems and intelligent robotics, the leaders of tomorrow will need to speak a language that is equal parts programming, lab science, and industrial engineering. Jensen Huang’s advice could shape this new blueprint—and, for Nvidia and the broader technology sector, the revolution has just begun.