This morning, The Economist highlighted two developments that seem contradictory at first glance:
Universities are producing more Computer Science graduates than ever before.
Artificial Intelligence may be reducing demand for some traditional programming roles.
At the same time, companies like Nvidia are investing billions in what comes next: AI-powered personal computing, agentic systems, and new hardware designed specifically for AI workloads.
So which story is true?
Perhaps both.
For decades, Computer Science programs focused heavily on teaching students how to write code. Today, AI can generate code, explain code, debug code, and even build complete applications from natural language prompts.
This has led some observers to ask whether we are witnessing the beginning of the end for Computer Science as a discipline.
I would argue the opposite.
What may be ending is the era in which programming syntax was the primary skill. What is emerging is a new era where problem formulation, data literacy, critical thinking, systems design, and human-AI collaboration become the core competencies.
The question is no longer:
“Can you write Python?”
The question is:
“Can you identify a problem worth solving, structure data appropriately, evaluate AI-generated solutions, and deploy those solutions responsibly?”
As a doctoral candidate at Central Michigan University researching AI-assisted learning and the development of Agentic AI Enhanced Learning (AAEL) environments, I see parallels to previous technological shifts.
Spreadsheets did not eliminate accountants.
Calculators did not eliminate mathematicians.
Search engines did not eliminate researchers.
AI is unlikely to eliminate computer scientists.
Instead, it may redefine what it means to be one.
The future may belong less to coders and more to computational problem solvers: people who understand data, domain knowledge, human behavior, and AI systems well enough to orchestrate solutions.
For students considering Computer Science today, the answer may not be to avoid the field.
The answer may be to expand it.
Learn programming.
Learn analytics.
Learn AI.
Learn how to learn.
Because the most valuable skill in an AI-driven world may not be coding itself. It may be knowing what questions to ask next.