During a visit to Beijing on July 18, 2025, Nvidia CEO Jensen Huang discussed his educational preferences if he were a student today.

He said that he would choose to study physical sciences over software sciences, referencing his early graduation at age 20.

Huang, who earned a degree in electrical engineering from Oregon State University in 1984 and a master’s degree from Stanford University in 1992, co-founded Nvidia in 1993.

Under his leadership, the company has become a global leader in chipmaking and recently reached a market capitalization of US$4 trillion.

Although Huang did not provide specific reasons for his preference for physical sciences, he has frequently addressed the future of AI.

He identified “Physical AI” as a critical next step in AI development, following advancements such as Perception AI and Generative AI.

This new phase focuses on understanding physical principles including friction, inertia, and cause-and-effect relationships.

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🔗 Source: CNBC

🧠 Food for thought

1️⃣ AI’s evolution mirrors the increasing integration with physical reality

Huang’s focus on physical sciences reflects the natural progression of AI development through distinct waves over decades.

The AI field has historically evolved from purely computational systems to increasingly physical interactions, with the Stanford AI report documenting how early AI systems like Logic Theorist (1955) focused exclusively on abstract reasoning rather than physical world interaction 1.

This progression aligns with Huang’s characterization of AI waves: from Perception AI (starting with the AlexNet breakthrough in 2012) to today’s Reasoning AI, which can solve novel problems but still lacks physical understanding.

The investment landscape confirms this direction, with companies like Apptronik raising substantial funding to develop humanoid robots that combine AI with physical capabilities 2.

The integration of AI with physical systems is already showing practical impact in sectors like healthcare, where surgical robots have seen procedures increase from 570,000 in 2014 to over 2.6 million in 2024, demonstrating the tangible value of combining AI with physical world interaction 3.

2️⃣ Physical AI represents a trillion-dollar economic frontier addressing global challenges

Huang’s advice to study physical sciences connects directly to massive economic opportunities in combining AI with robotics and physical systems.

McKinsey projects AI could contribute an additional $13 trillion to the global economy by 2030, with a significant portion coming from automation and physical world applications 4.

This economic potential is driving a 92% increase in AI investments across companies, with particular focus on applications that bridge the digital-physical divide 5.

The global AI market is projected to reach $826.70 billion by 2030, with robotics and physical systems representing a substantial portion of this growth as they address critical labor shortages 6.

The economic imperative is particularly strong given global demographic challenges. Huang specifically mentions using highly robotic factories to address “the severe labor shortage that we have all over the world,” a problem confirmed by multiple economic analyses in the research 4.

3️⃣ The convergence of AI and physical sciences is creating new skills requirements and educational priorities

Huang’s educational advice reflects the changing skills landscape that multiple labor market analyses have identified.

Workers with AI skills already command a 56% wage premium, but the integration with physical systems is creating demand for hybrid expertise combining digital and physical world understanding 7.

The speed of skill changes in AI-exposed jobs is accelerating by 66% compared to previous years, indicating that education must increasingly bridge computational and physical domains 7.

Industries at the forefront of AI-physical integration, like healthcare and manufacturing, are experiencing the fastest growth in specialized roles that combine these domains, with positions like AI/Machine Learning Engineer seeing 41.8% year-over-year growth 8.

This transformation explains why Huang, despite his own software background, now recommends physical sciences. He is identifying the educational path that will best position students for the next wave of innovation where AI capabilities must be grounded in physical world understanding.

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