Nvidia CEO Jensen Huang: Physical Sciences Should Be Focused On By Young Students

Nvidia CEO Jensen Huang says he would focus on physical sciences if he were a 20-year-old student today. He believes studying physical sciences is crucial for the next wave of AI, known as "Physical AI" or "Reasoning AI," which requires understanding laws of physics, friction, and cause and effect. Huang co-founded Nvidia in 1993 and under his leadership, the chipmaker has become the world's most valuable company.

Nvidia CEO Jensen Huang recently expressed his belief that studying physical sciences is crucial for the next wave of AI, which he refers to as "Physical AI" or "Reasoning AI." This perspective underscores the growing intersection between artificial intelligence and the physical sciences, a trend that is transforming various industries and scientific domains.

Huang, who co-founded Nvidia in 1993, has led the company to become one of the world's most valuable. In a recent statement, he highlighted the importance of understanding the laws of physics, friction, and cause and effect for the development of advanced AI systems. This sentiment aligns with the emerging field of Physics AI, where AI algorithms are integrated with rigorous physical methodologies to solve complex problems.

The integration of AI with physics is revolutionizing various sectors, from healthcare and finance to climate science and astrophysics. For instance, AI is being used to accelerate simulations, interpret experimental data, and discover new physical laws. In quantum physics, AI is helping to solve complex equations and design quantum circuits, while in high-energy particle physics, it aids in filtering and analyzing massive datasets generated by particle accelerators.

Huang's emphasis on physical sciences is not just theoretical; it is backed by practical applications. For example, AI is being used to predict material properties and design new molecules, thereby accelerating the discovery of new materials. In climate and atmospheric physics, AI is aiding in weather forecasting and disaster prediction. Even in plasma and fusion research, AI is optimizing control algorithms and simulating plasma behavior.

The importance of AI in these fields is underscored by the increasing use of machine learning techniques, such as supervised learning for particle classification, unsupervised learning for pattern discovery, and reinforcement learning for experiment automation. Moreover, AI is being used to automate and optimize experimental processes, reducing the number of iterations and handling noisy data effectively.

Despite the promising advancements, there are challenges and limitations to consider. These include the need for large datasets, computational resources, and the potential for AI to introduce biases. However, these challenges are being actively addressed by researchers and developers, ensuring that AI continues to make significant strides in the field of physical sciences.

In conclusion, Nvidia CEO Jensen Huang's emphasis on physical sciences for the next wave of AI reflects a broader trend in the industry. As AI continues to intersect with physics, it promises to unlock new possibilities and drive innovation across various sectors. This shift underscores the importance of interdisciplinary collaboration and the need for a robust understanding of both AI and physical sciences.

References:
[1] https://www.cnbc.com/2025/07/16/jensen-huang-china-ai-models-nvidia-gears-up-to-resume-chip-exports-.html
[2] https://sensor1stop.com/knowledge/physics-ai/