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Atlantic Article Argues Computer Science Degrees Retain Value Despite AI

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#1992Steven Sinofsky@STEVESI

There’s Never Been a Better Time to Study Computer Science: Even as AI progresses, coders aren’t doomed. https://www.theatlantic.com/technology/2026/05/computer-science-major-coding-ai/687279/ // Never thought I'd see this here, but here it is. The article asks and answers the question about the value of a CS degree. I think this isn't quite the right question. In fact we've been asking this since CS departments were created in the 1960s and 70s and evolving ever since. Is Computer Science a discipline to be studied independently or a tool used in every discipline, and what is the ratio between those two. Many CS departments were rooted in math as much as electrical engineering. In the 1960s as many departments were formed the question was always about "affinity"—is CS closer to math or to electrical engineering. Many schools saw the affinity with EE and the major/department was even EECS and course requirements included taking the intro sequences of EE courses. Those students did stuff with wire and multimeters. Where I went to school, Cornell, the department was somewhat conflicted and while it was physically housed in the Engineer Quad, students in the Arts & Sciences school could major in it. Engineering students ended up taking more physics and chemistry than Arts students, and graduates were BS or BA depending on what school they were from. Employers didn't care and we never talked about it. Our common requirements included more abstract theory than other EECS-rooted departments. Other fields like Physics had unique engineering disciplines such as Applied Engineering Physics with CS being unique among those cross-registered majors. Just a few years ago, Cornell created a stand alone "College of Computing" that straddles the entire university. Meanwhile every single university department was "using" computers: Statistics, Physics, and more. At Cornell all the Agriculture majors and even the Hotel School took classes in BASIC or Fortran programming. Universities have long used modifiers on majors to indicate they are "in between" such as Political Science or Food Science (is there a _science_ to politics? is food science a part of chemistry or culinary 'arts'?) To some this signified "soft" or "not really science" while to others this was a signal of interdisciplinary importance. The science modifier often indicates this "softness" of study. It isn't clear to me this has fundamentally changed after almost 60 years. The rise of AI along with the more modern competitive nature of universities is causing a rush to create, new more marketable majors that include AI in the title. Universities move much faster now than they did with the rise of computing. When I was in school many programs were still figuring out what to do to have a computer science major and many (even) new computer science faculty were trained in EE or Math. It isn't nearly as clear what these new majors mean as AI has rapidly diffused to every department. There's a legitimate question right now as to what knowledge is foundational versus tactical or transactional. As the PC and productivity tools like Word and Excel rose—including programming tools like VB and Excel macros—the separation between using a computer and studying computer science became super clear. Taking courses in how to use a spreadsheet or word processor were abundant but not a major in college. Trade skills vs. foundational skills were clear. No one majored in spreadsheets, but you majored in Finance Business, or Economics and used a spreadsheet. No one majored in word processing, but you majored in English, Marketing, or History and used a word processor. In the 1980s **the big question** about studying computer science was "what programming language to learn?" The brand new AP CS test used Pascal even as many departments were not yet teaching that and it was controversial. The field seemed defined by languages. The joke was if you earned a PhD then you probably created a new language. Most every research group developed a language. Writing a compiler was a rite of passage as was fighting over the "best" programming language. Think I'm kidding, this was one of the earliest USENET memes: "Real Programmers Don't Use Pascal" by the legendary Ed Post. I think just about every computer terminal room and grad student office had a line printer version of this posted. https://www.ee.torontomu.ca/~elf/hack/realmen.html Departments hotly debated the choice of programming language and that choice came to define the rigor of a university. Pascal was good for teaching but no one used it in business where COBOL dominated and those building "systems" used C or those doing math used Fortran. If you got a specialized job in industry like building avionics you might use a language like JOVIAL or myriad others you would learn later at a company. It was also, importantly, viewed as the difference between studying "computer science" versus "computer programming." Science was a lifelong discipline. Programming was something like a trade-school skill. My very first day of my very first class began with this very first statement from my professor (and later advisor), "In CS 100 you learn to program _into_ a language not _in_ a language." What he meant was we were learning the abstract skill of programming, not the bothersome syntax and paradigm of any single language. Thus my first programming language in school was not even the obscure language PL/1 (the union of Fortran and COBOL from IBM that mostly never took off) but an obscure research variant PL/CS that presumably made it more academic. When we complained about it not being practical, the department just said it didn't matter. I learned PL/1, Fortran, Ada, LISP, C, Pascal, ASM, and a half dozen other esoteric and forgotten languages, scripts, and libraries in the courses I took as well as COBOL during my internship. In addition we used at least a half dozen operating systems, a different one for each advanced course. So today, this history is pretty important as the entire fields of "EECS" and programming are upended by AI. From 1980-2025 and even today, operating systems seemed to continue on the same path as all the textbooks and certainly whether you use Linux, Mac, Windows, iPhone, Android, or anything else you are using that foundation. But architectures, processors, chips, networking, languages, even LLMs themselves are in an incredible state of flux. They are not improving on linear paths by any stretch. There are people inventing these new paradigms. Their knowledge and skills are rooted in computer science and EE. These skills are hardly going away. In fact the need for this foundational skill set is now greater than ever. At the same time the rapid rise of LLMs and Agents has created an incredible demand for the skills to apply these tools/platforms to all the other work that goes on in society. I double-majored in Chemistry. In the 1980s you didn't actually use a computer to major in chemistry, just goggles and test tubes. When you did use a computing device it was an embedded computer in a machine like a GC/MS. There was literally no programming done as a Chem major. That rapidly changed and in a few specialties—those closest to physics like molecular mechanics or physical chemistry—computing was rapidly becoming core. This was much like how Math was evolving. AI is exactly like this today. I suspect that 2025 was the last year one could graduate college without a mandatory (implied or otherwise) use of AI, much like 1984 was the last year you could graduate college without using a word processor. The question of this article is deep but also has an easy answer. If you want to build the foundational tools for computing then become a computer science major where you'll be working on AI which will perfuse through the field the way programming languages did. If you want to apply AI to other fields then any course you take in those fields will use AI. And that use will look a lot like programming just as majoring in Math or Chemistry transitioned. And most importantly, the specific AI model, user experience, features, and architecture will be wildly different 5, 10, 30 years into your career, whether you create the next foundation or just use it. I promise. Your major is not your lifelong toolset, but a lifelong foundation for learning.

10:35 AM · May 29, 2026

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#1992Steven Sinofsky@STEVESI

The article asks and answers the question about the value of a CS degree. I think this isn't quite the right question. In fact we've been asking this since CS departments were created in the 1960s and 70s and evolving ever since. Is Computer Science a discipline to be studied independently or a tool used in every discipline, and what is the ratio between those two.

Many CS departments were rooted in math as much as electrical engineering. In the 1960s as many departments were formed the question was always about "affinity"—is CS closer to math or to electrical engineering. Many schools saw the affinity with EE and the major/department was even EECS and course requirements included taking the intro sequences of EE courses. Those students did stuff with wire and multimeters.

Where I went to school, Cornell, the department was somewhat conflicted and while it was physically housed in the Engineering Quad, students in the Arts & Sciences school could major in it. Engineering students ended up taking more physics and chemistry than Arts students, and graduates were BS or BA depending on what school they were from. Employers didn't care and we never talked about it. Our common requirements included more abstract theory than other EECS-rooted departments. Other fields like Physics had unique engineering disciplines such as Applied Engineering Physics with CS being unique among those cross-registered majors. Just a few years ago, Cornell created a stand alone "College of Computing" that straddles the entire university.

Meanwhile every single university department was "using" computers: Statistics, Physics, and more. At Cornell all the Agriculture majors and even the Hotel School took classes in BASIC or Fortran programming.

Universities have long used modifiers on majors to indicate they are "in between" such as Political Science or Food Science (is there a _science_ to politics? is food science a part of chemistry or culinary 'arts'?) To some this signified "soft" or "not really science" while to others this was a signal of interdisciplinary importance. The science modifier often indicates this "softness" of study. It isn't clear to me this has fundamentally changed after almost 60 years.

The rise of AI along with the more modern competitive nature of universities is causing a rush to create, new more marketable majors that include AI in the title. Universities move much faster now than they did with the rise of computing. When I was in school many programs were still figuring out what to do to have a computer science major and many (even) new computer science faculty were trained in EE or Math.

It isn't nearly as clear what these new majors mean as AI has rapidly diffused to every department. There's a legitimate question right now as to what knowledge is foundational versus tactical or transactional.

As the PC and productivity tools like Word and Excel rose—including programming tools like VB and Excel macros—the separation between using a computer and studying computer science became super clear. Taking courses in how to use a spreadsheet or word processor were abundant but not a major in college. Trade skills vs. foundational skills were clear. No one majored in spreadsheets, but you majored in Finance Business, or Economics and used a spreadsheet. No one majored in word processing, but you majored in English, Marketing, or History and used a word processor.

In the 1980s **the big question** about studying computer science was "what programming language to learn?" The brand new AP CS test used Pascal even as many departments were not yet teaching that and it was controversial. The field seemed defined by languages. The joke was if you earned a PhD then you probably created a new language. Most every research group developed a language. Writing a compiler was a rite of passage as was fighting over the "best" programming language. Think I'm kidding, this was one of the earliest USENET memes: "Real Programmers Don't Use Pascal" by the legendary Ed Post. I think just about every computer terminal room and grad student office had a line printer version of this posted. https://www.ee.torontomu.ca/~elf/hack/realmen.html

Departments hotly debated the choice of programming language and that choice came to define the rigor of a university. Pascal was good for teaching but no one used it in business where COBOL dominated and those building "systems" used C or those doing math used Fortran. If you got a specialized job in industry like building avionics you might use a language like JOVIAL or myriad others you would learn later at a company. It was also, importantly, viewed as the difference between studying "computer science" versus "computer programming." Science was a lifelong discipline. Programming was something like a trade-school skill.

My very first day of my very first class began with this very first statement from my professor (and later advisor), "In CS 100 you learn to program _into_ a language not _in_ a language." What he meant was we were learning the abstract skill of programming, not the bothersome syntax and paradigm of any single language. Thus my first programming language in school was not even the obscure language PL/1 (the union of Fortran and COBOL from IBM that mostly never took off) but an obscure research variant PL/CS that presumably made it more academic. When we complained about it not being practical, the department just said it didn't matter. I learned PL/1, Fortran, Ada, LISP, C, Pascal, ASM, and a half dozen other esoteric and forgotten languages, scripts, and libraries in the courses I took as well as COBOL during my internship. In addition we used at least a half dozen operating systems, a different one for each advanced course.

So today, this history is pretty important as the entire fields of "EECS" and programming are upended by AI. From 1980-2025 and even today, operating systems seemed to continue on the same path as all the textbooks and certainly whether you use Linux, Mac, Windows, iPhone, Android, or anything else you are using that foundation.

But architectures, processors, chips, networking, languages, even LLMs themselves are in an incredible state of flux. They are not improving on linear paths by any stretch. There are people inventing these new paradigms. Their knowledge and skills are rooted in computer science and EE. These skills are hardly going away. In fact the need for this foundational skill set is now greater than ever.

At the same time the rapid rise of LLMs and Agents has created an incredible demand for the skills to apply these tools/platforms to all the other work that goes on in society.

I double-majored in Chemistry. In the 1980s you didn't actually use a computer to major in chemistry, just goggles and test tubes. When you did use a computing device it was an embedded computer in a machine like a GC/MS. There was literally no programming done as a Chem major. That rapidly changed and in a few specialties—those closest to physics like molecular mechanics or physical chemistry—computing was rapidly becoming core. This was much like how Math was evolving.

AI is exactly like this today. I suspect that 2025 was the last year one could graduate college without a mandatory (implied or otherwise) use of AI, much like 1984 was the last year you could graduate college without using a word processor.

The question of this article is deep but also has an easy answer.

If you want to build the foundational tools for computing then become a computer science major where you'll be working on AI which will perfuse through the field the way programming languages did. If you want to apply AI to other fields then any course you take in those fields will use AI. And that use will look a lot like programming just as majoring in Math or Chemistry transitioned.

And most importantly, the specific AI model, user experience, features, and architecture will be wildly different 5, 10, 30 years into your career, whether you create the next foundation or just use it. I promise. Your major is not your lifelong toolset, but a lifelong foundation for learning.

5:42 PM · May 29, 2026 · 1.4K Views