GOT KIDS WONDERING WHAT TO FOCUS ON IN COLLEGE?

If Nvidia CEO Jensen Huang were a college student today, he says he’d focus on physical sciences—fields like physics, chemistry, astronomy, and earth sciences—rather than software engineering.

His reasoning? Huang believes the next wave of artificial intelligence will be “Physical AI”, where machines must understand real-world concepts like friction, inertia, and cause and effect. This shift moves beyond generative models and into reasoning and robotics, enabling AI to interact with the physical world in meaningful ways—gripping objects, predicting movement, and navigating unseen obstacles.

He’s essentially saying: if you want to build the future, don’t just learn to code (which is increasingly being done by AI now anyways)—learn how the world works.

If Huang were a student today, he says he’d focus on physical sciences—fields like physics, chemistry, astronomy, and earth sciences—rather than software engineering2. His reasoning? He believes the next wave of AI will be “Physical AI”, where machines must understand real-world dynamics like friction, inertia, and cause and effect.

Here’s how he breaks it down:

🔄 The Evolution of AI (According to Huang)

1. Perception AI – Recognizing images and sounds (think: AlexNet, 2012)

2. Generative AI – Creating text, code, and visuals (ChatGPT, DALL·E, etc.)

3. Reasoning AI – Solving problems and making decisions

4. Physical AI – Interacting with the real world through robotics and simulation

He sees robotics and autonomous systems as the next trillion-dollar frontier, especially as countries face labor shortages and build out smart factories. Huang is so convinced of this vision of the future that he has even launched platforms like Nvidia Cosmos to simulate physics-based environments for training robots.

So if you’re advising your pre-college kids—or your college kid who is uncertain what to major in—Huang’s message is clear: master the real world, not just the digital one.

Career Search: Entering the Physical AI Frontier

🔍 Fields to Explore

• Robotics Engineering – Designing autonomous machines and humanoids

• Computer Vision & Sensor Fusion – Teaching machines to “see” and interpret the world

• Physics Simulation & Modeling – Building digital twins and training environments

• Embedded Systems & Edge AI – Powering real-time decision-making in physical devices

🎓 Recommended Skills & Education

• Programming: Python, C++, ROS (Robot Operating System)

• Simulation Tools: NVIDIA Isaac Sim, MuJoCo, Gazebo

• AI Frameworks: PyTorch, TensorFlow, NVIDIA Cosmos

• Degrees: Mechanical Engineering, Physics, Robotics, AI/ML

🚀 Potential Career Paths