Yann LeCun's advice for young students wanting to go into AI

Yann LeCun said if computer science majors don't spend their time wisely, they may find out their degree doesn't add up.

"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," LeCun said in an email to Business Insider.

LeCun, who teaches computer science at NYU, said during a recent podcast appearance that he jokes that he's "a computer science professor arguing against studying computer science" based on his push on where students should focus their time.

"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," he told Business Insider.

The former chief AI scientist at Meta said his advice is that students "learn things with a long shelf life." Depending on the computer science program, not all of these skills may be baked into a degree.

"What we should do is learn kind of basic things in mathematics, in modeling, mathematics that can be connected with reality," LeCun said on "The Information Bottleneck" podcast. "You tend to learn this kind of stuff in engineering in some schools that's linked with computer science, but sort of electrical engineering, mechanical engineering, et cetera."

Universities and computer science students continue to grapple with how to adapt their programs to the age of generative and increasingly agentic AI. Earlier this year, UC Berkeley professor Hany Farid described the struggle students face in finding jobs, compared to how graduates used to have "the run of the place."

Leaders in the field, including OpenAI's Bret Taylor, have stressed that computer science is about so much more than simply learning to code. Others, including Nobel Laureate Geoffrey Hinton, have stressed that learning critical thinking is what's the key to staying ahead of AI's advancements.

"Some skills that are always going to be valuable, like knowing some math, and some statistics, and some probability theory, knowing things like linear algebra that will always be valuable," Hinton recently told Business Insider. "That's not knowledge that's going to disappear."

LeCun jokingly pointed out that he did not initially study CS. He studied electrical engineering at ESIEE in Paris before earning a Ph.D. CS from the renowned Sorbonne Université in 1987. LeCun said some CS schools are linked to engineering programs, which tend to require more advanced math.

"Engineering disciplines, you know, in the US, you learn Calculus 1, 2, 3 that gives you a good basis, right?" he said. In computer science, you can get away with just Calculus 1. That's not enough, right?"

Engineering also exposes students to concepts like control theory and signal processing, which LeCun said are "really useful for things like AI."

All of this isn't to say basic programming should be thrown out, LeCun said. Vibe coding is nice, but it's not a substitute for fundamental knowledge.

"Obviously, you need to learn enough computer science to kind of program and use computers," he said. "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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