Geoffrey Hinton, widely recognized as the “godfather of AI,” has weighed in on the ongoing debate about the future of computer science degrees in an era where artificial intelligence is rapidly transforming the coding industry. In an interview with Business Insider, Hinton emphasized that CS degrees remain valuable despite AI’s growing capabilities in programming tasks.
Hinton acknowledged that “obviously, just being a competent mid-level programmer is not going to be a career for much longer, because AI can do that.” However, he stressed that many people misunderstand what a computer science degree truly encompasses. “Many people think a CS degree is just programming or something,” Hinton explained, noting that the degree offers far more than coding skills alone. He believes a CS degree will be valuable for quite a long time because it teaches critical thinking, systems thinking, and foundational knowledge that extends beyond mere code writing.
This perspective aligns with other prominent figures in the AI and tech industries. OpenAI chairman Bret Taylor, who holds both a BS and MS in computer science from Stanford University, called a CS degree “extremely valuable” earlier this year. Taylor emphasized that “there’s a lot more to coding than writing the code” and described computer science as “a wonderful major to learn systems thinking.”
Google’s head of Android, Sameer Samat, suggested that CS programs should evolve to focus on “the science of solving problems” rather than just programming. Meanwhile, UC Berkeley professor Hany Farid argued that the most exciting opportunities for CS graduates now lie at the intersection of computing and other fields, including computational drug discovery, medical imaging, computational neuroscience, finance, digital humanities, and social science—not just at traditional Silicon Valley tech giants.
For younger students, Hinton compared learning to code to studying Latin in a humanities education: valuable as an intellectual exercise even if not directly used in daily work. He advised aspiring AI researchers and engineers to focus on developing critical thinking skills and foundational knowledge in mathematics, statistics, probability theory, and linear algebra—skills that “will always be valuable” and won’t be easily replaced by AI.
Key Quotes
Obviously, just being a competent mid-level programmer is not going to be a career for much longer, because AI can do that.
Geoffrey Hinton, the renowned AI pioneer known as the ‘godfather of AI,’ made this stark assessment about the future of programming careers, acknowledging AI’s growing capability to handle routine coding tasks while emphasizing that CS degrees offer much more than just programming skills.
There’s a lot more to coding than writing the code. Computer science is a wonderful major to learn systems thinking.
OpenAI chairman Bret Taylor, who holds advanced CS degrees from Stanford, explained why computer science education remains valuable beyond just the technical skill of writing code, emphasizing the broader analytical frameworks students develop.
I think learning to code is, it is maybe a bit like learning Latin is if you’re in the humanities or something, you’re never going to speak Latin, but it’s still useful learning Latin.
Hinton drew this analogy to explain why he believes middle and high school students should still learn to code even if AI eventually handles most programming tasks, framing it as a valuable intellectual exercise that develops critical thinking skills.
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. That’s not knowledge that’s going to disappear.
Hinton outlined the foundational skills he believes will remain essential for aspiring AI researchers and engineers, emphasizing mathematical and analytical capabilities over any single technical skill that could be automated.
Our Take
Hinton’s intervention in the CS degree debate is particularly significant given his unique position as both an AI pioneer and educator. His nuanced view—acknowledging AI’s disruptive potential while defending the value of formal CS education—reflects a mature understanding of how technology transforms rather than simply eliminates professions. The comparison to Latin is especially apt: it signals that coding may become less about production and more about literacy and understanding. What’s most striking is the consensus among AI leaders that foundational knowledge trumps specific skills. This suggests the AI revolution will reward those who understand systems deeply rather than those who simply know current tools. Universities and students should take note: the future belongs to critical thinkers who can work alongside AI, not compete with it on routine tasks. The shift from “learning to code” to “learning to think computationally” represents a fundamental evolution in how we prepare for an AI-augmented workforce.
Why This Matters
This statement from one of AI’s most influential pioneers carries significant weight as the tech industry grapples with agentic AI’s disruption of traditional programming jobs. Hinton’s perspective offers reassurance to students and educators concerned about the relevance of computer science education in an AI-dominated future. His emphasis on critical thinking over specific technical skills signals an important shift in how we should approach tech education.
The convergence of opinions from Hinton, Taylor, and other industry leaders suggests a consensus is forming: while AI will automate routine coding tasks, the analytical and problem-solving foundations taught in CS programs remain essential. This has major implications for universities, which must balance teaching practical coding skills with deeper theoretical knowledge. For businesses, it reinforces the need to hire candidates who can think critically about complex systems rather than just write code. The message is clear: adaptability and foundational knowledge will be the differentiators in an AI-augmented workforce, making comprehensive CS education more relevant than ever, even as the nature of programming work fundamentally changes.