A few days ago, I came across an article titled “Do You Really Want That Computer-Science Degree?“ The article sparked a lively discussion online, and one theme appeared repeatedly throughout the comments: there was a time when computer science was the degree to have.
For many people who entered the workforce in the 1990s and early 2000s, computer science occupied a position similar to what AI and data science occupy today. Demand for technical talent was growing rapidly, salaries were rising, and a degree in computer science was often viewed as a reliable path toward a stable and well-paying career.
Fast forward to today, and the conversation sounds very different.
News headlines frequently report layoffs in the technology sector. Entry-level software engineering positions have become increasingly competitive. Artificial intelligence tools are now capable of generating code, debugging programs, and assisting with tasks that once required junior developers. It is understandable why some students question whether a computer science degree still provides the same value it once did.
As someone who completed a Master of Science in Computer Science in 2024 with a concentration in Data Science, I have mixed feelings about the question.
One of my frustrations during the program was that I often felt like I wasn’t becoming an expert in any single area. My coursework ranged from data structures, algorithms, operating systems, and digital forensics to machine learning and deep learning. After graduation, I further expanded my technical knowledge through a cybersecurity boot camp focused on network security and penetration testing.
Looking back, however, I think that feeling was actually the point.
Computer science is not designed to make someone an expert software engineer, cybersecurity analyst, machine learning researcher, database administrator, or game developer. Rather, it provides a broad technical foundation that allows graduates to move into any of those specialties later.
In many ways, computer science resembles an engineering discipline more than a vocational training program. Students learn the principles behind computing systems, how software interacts with hardware, how data is stored and processed, how networks communicate, and how algorithms solve problems. Those concepts remain relevant even as technologies come and go.
What surprised me after graduation was how often I found myself drawing upon knowledge from multiple areas simultaneously.
A modern data scientist benefits from understanding algorithms and software architecture. A cybersecurity professional benefits from understanding operating systems and networking. An AI practitioner benefits from understanding statistics, databases, and programming fundamentals. The boundaries between disciplines are becoming increasingly blurred.
Artificial intelligence has accelerated this trend even further.
For example, I built the website you are reading in only a few days with the assistance of AI tools. Ten years ago, accomplishing the same task would have required significantly more time spent searching documentation, troubleshooting code, and learning unfamiliar technologies. AI did not eliminate the need for technical knowledge. Instead, it amplified the value of the knowledge I already possessed.
The more I use AI, the more I realize that these tools are most effective when the user already understands the underlying concepts. AI can generate code, explain frameworks, summarize documentation, and accelerate experimentation. However, it still requires someone who can evaluate whether the output is correct, understand the tradeoffs, and connect ideas across different domains.
That is where I believe a computer science education continues to provide value.
The technology industry today is far broader than software development alone. It includes artificial intelligence, cybersecurity, cloud computing, data science, analytics, digital forensics, enterprise systems, automation, robotics, and countless other specialties. Each of these fields is large enough to support an entire graduate degree program by itself.
A computer science degree does not teach everything. In fact, one of its greatest strengths may be that it cannot. Instead, it teaches students how to learn technical subjects, how to think computationally, and how to adapt as technology evolves.
Do I feel threatened by AI?
Not particularly.
Some tasks will undoubtedly become automated. Some job roles will change dramatically. The technology landscape will continue to evolve, just as it always has.
What I see instead is a powerful tool that allows people with a strong technical foundation to accomplish far more than they could before. The future may require fewer people who simply write code, but it will likely require more people who understand systems, data, security, automation, and how these pieces fit together.
In that sense, I no longer view my computer science degree as training for a specific job.
I view it as a foundation that enables me to continue learning, building, and adapting in a world where technology changes faster than ever.
— Linden Lake
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