AI Godfather Advises CS Majors to Learn Math and Physics

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“AI Godfather” Yann LeCun advised students in Computer Science Majors should focus far more on mathematics and physics if they are aiming for a future in AI.

What Yann LeCun Wants CS Majors to Know

Coding skills alone are not enough, nor just learning the new programming languages or technologies of today. Rather, learn foundational subjects.

Just the basic math course as part of your computer science degree is not enough for the technology changes coming ahead.

Yann LeCun told Business Insider:

“If you are a CS major and take the minimum required math courses for a typical CS curriculum, you might find yourself unable to adapt to major technological shifts.”

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And it is not limited to maths.

“My recommendation was not to avoid CS as a major but to take the maximum number of courses on foundations (e.g. math, physics, or EE courses) rather than take courses on the trendy technology du jour.”

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Basically, Yann is saying, don’t study only the tech trends of today. Tech changes fast. If you spend most of your time learning tools, that knowledge can become outdated pretty quickly. 

Math, physics, and electrical engineering are different. They teach you how things actually work under the hood. 

Once you understand concepts like probability, statistics, signals, control theory, or systems, learning new tools becomes much easier. These skills also tend to remain relevant longer.

In his own academic journey, LeCun also began in electrical engineering before earning a Ph.D. in computer science. He believes this background gave him critical mathematical tools that many CS students today might miss. 

This led to work on convolutional neural networks in the late 1980s that became foundational to modern AI. Yann LeCun was later given the title of one of three “Godfathers of AI”. He is currently a professor at New York University and also serves as Chief AI Scientist at Meta.

So What About Coding?

However, this doesn’t mean that core CS subjects aren’t important. Yann explains in a podcast:

“Obviously, you need to learn enough computer science to kind of program and use computers, and even though AI is going to help you be more efficient at programming, you still need to know how to do this.”

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What he means is that AI won’t replace the need to understand programming basics. Yes, AI tools can now write code, but that only helps if you already know what the code is supposed to do.

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LeCun’s views aren’t isolated. Another “Godfather of AI” is Geoffrey Hinton, who also says a CS degree remains highly valuable.

“Many people think a CS degree is just programming or something. A CS degree will be valuable for quite a long time.” ‍ 

But according to him, computer science should not be limited to writing code. Some courses teach you how to think and break down problems. He also encourages students to spend more time on skills like math, statistics, probability, and linear algebra. 

Bottom Line

Such advice reflects a broader shift in the tech world. As generative AI and LLMs automate many routine coding tasks, simply knowing how to type code will no longer guarantee a successful career. Instead, understanding the principles that make AI systems work is likely to set future innovators apart.

Universities right now are also grappling with how to adapt their curricula to the age of AI. The challenge is balancing practical coding skills that students need for immediate employment with the foundational knowledge that will serve them long-term.

Some are adding AI ethics courses, expanding mathematics requirements, and creating hybrid programs that combine computer science with engineering disciplines. 

But change is happening slowly, and so you need to act fast. If you are a student right now, don’t chase the latest tools. Spend more time learning the math and physics that power AI systems.