Alex K. Jones is the Klaus Schroder Endowed Professor and Chair of Electrical Engineering and Computer Science at Syracuse University. He previously served as a Program Director and Deputy Division Director at the National Science Foundation and was a professor for over two decades at the University of Pittsburgh. Since joining Syracuse in 2024, he has helped secure $4.5 million in research funding in AI hardware acceleration, semiconductor design, and workforce development. He is a Fellow of the IEEE Computer Society. He thanks Paulo Shakarian, the KG Tan Professor of AI at Syracuse University, for his comments on this piece.
For years, the formula seemed simple: get a computer science (CS) degree, walk into a high-paying job, and watch your career take off. It was the kind of promise parents could get behind and students could count on, but that story is changing. Uncertainty in the job market is partially to blame, but we also cannot ignore the rise of AI. The cumulative result has made it more difficult for new graduates to land the kinds of coding positions that used to feel almost guaranteed.
The hiring slowdown is real, and it is not just because companies are cutting budgets. Large language models and other AI tools can now do much of the work that used to go to entry-level programmers. In the past, the tech industry could absorb wave after wave of new CS grads because every product and service seemed to need more software. When that demand dipped, we could point to earlier disruptions like the switch from mainframes to personal computers, the outsourcing rush after the dot-com bust, and the rise of cloud computing and say, “We’ve been through this before.” But this time it feels different. The disruption is arriving alongside something new: a flood of people who can code.
Over the past decade, universities have expanded CS programs at a record pace. Bootcamps promised to turn novices into developers in months. Coding became part of the standard toolkit for engineers, scientists, and researchers. That was good for broad technical literacy, but it also meant the job market was suddenly saturated with candidates with programming skills. Now that AI can write, debug, and optimize code on demand, that skill is no longer a differentiator. Yet the big companies are not finding that AI solutions are revealing a substantial cost savings. Why?
Computer science has never just been about coding. AI itself is a product of computer science. It is a discipline built on algorithms, data structures and the architecture of complex systems. People who understand these foundations are in a position not only to use AI tools but to improve them, adapt them, and build the next generation of breakthroughs. Computer science also intersects with other technical areas like quantum technologies, semiconductors, the Internet of Things, and wireless communications. Technologies succeed where theory, software and hardware meet, where computer science principles beyond coding are necessary, and the demand for talent is still strong.
Wireless communications is a great example, blending theory, protocols, and real-world system design. Fifth-generation wireless, or 5G, is an enabler of AI’s computing power in the cloud to connect instantly to the phone in your pocket. The wireless network is not just a backdrop for AI; it is the stage. Building it, securing it, and making it faster takes expertise that cannot be replaced with a few lines of generated code and is a place where job opportunities remain plentiful.
Deep domain knowledge can even make AI better. Some researchers are combining symbolic reasoning with machine learning to preserve complex concepts that traditional AI tends to lose. Others are adding “sanity checks” so systems can spot their own bad logic. And there are big, open problems on which only humans can lead the charge: reducing the energy footprint of large models, addressing bias in training data, and determining when and where AI should be trusted at all.
The problem is that current employers have created a false equivalency between computer science and coding. This is why the design of a CS program matters. Degrees that focus narrowly on coding risk leaving graduates exposed to job insecurity. A better approach is to combine a broad foundation across computing with the chance to go deep in a field where demand will last, like AI, wireless, cybersecurity, quantum computing and others. At Syracuse University, students have the full range of computer science at their fingertips with the opportunity to explore one of these high-impact areas. This breadth gives them flexibility. The depth gives them an edge.
A good degree program is not just a hedge against automation. It is a way to shape the future. The fundamentals of understanding context, solving problems, and thinking critically still determine who succeeds. “Just learn to code” is not a recipe for career security anymore. You can get that here at Syracuse, or almost anywhere in the country, if you want it. But, that’s not the CS degree we strive for or recommend to our students. We teach the discipline, for sure, but that’s not our target differentiator. We want our graduates to see a pathway to success through skills like coupling their foundational knowledge with adaptability, continuous learning, and how to see opportunities where others see threats.
The value proposition for computer science has not vanished. It has evolved. It is not something coding bootcamps can replicate, nor is it something AI can replace. A good CS degree does not just prepare students for the jobs of today, it provides them the skills to be ready for the jobs of tomorrow. A great CS degree prepares their graduates to design the jobs of tomorrow.
