Stop Telling Students Computer Science Is Dying (opinion)

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July 28, 2026

Stop Telling Students Computer Science Is Dying

The data says otherwise.

By

Christine Julien

urbazon/E+/Getty Images

Every computer science department head I know is having a version of the same conversation—with a parent at orientation, with a high school senior at an open house, with the colleague at lunch who asks if it’s true. The headlines say computer science is collapsing. The data shows something else. Failing to make that clear to students and families leaves them with no choice but to decide based on the wrong information.

Surrounded by Literalists

A colleague of mine—not a computer scientist, but someone who works alongside them every day—laughed at me recently after I took something they said a little too literally.

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“I’m surrounded by literalists,” they said.

They meant it as a joke. I received it as a compliment. It was not the first time I’ve had some version of this exchange.

The literalism they described is not a personality quirk. It is a trained habit of mind—one that computer science education cultivates deliberately. A computer does only and exactly what you tell it to do. Not approximately. Not what you meant. What you said. Computer scientists learn to translate underspecified requirements into unambiguous instructions, to anticipate every edge case. We develop, over years of practice, an instinct for exactness.

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I love writing code. So do most of my colleagues. And the particular discipline we love—sitting with a blank screen and producing working code, character by character, from our own reasoning—is becoming less central as AI coding assistants get better. The reasoning is still ours; the classic coding increasingly is not. Pretending otherwise would be naïve.

But learning computer science is less about the code you produce and more about the habits of mind you build. Writing code from scratch teaches you to think in systems, to reason about cause and effect, to hold an entire logical structure in your head and interrogate it. Those capacities do not become obsolete when the tools change.

The Headlines Versus the Data

If you have been paying attention to recent technology news, you have encountered headlines like “Goodbye, $165,000 Tech Jobs” and “Graduates Reset Ambitions in Pursuit of First Jobs.” The message is unmistakable, and for the student considering a computer science major, it is genuinely frightening.

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So I did what a CS education taught me to do automatically: I looked at the data, specifically data from the U.S. Bureau of Labor Statistics’ Current Employment Statistics. And I did the following exercise with my first-year CS majors at Virginia Tech on the last day of the semester, starting with a chart I had recreated from a widely circulated visualization—one that had been shared with me as justification for discouraging students from studying CS. No title, no explanation, just the chart. “What do you see?” I asked them.

Christine Julien

They saw what most people see: The trend since 2022 heads sharply downward. Then I asked how it made them feel. Hopeless, said one. Despair, offered another.

A beat of silence. Then a hand went up. “It looks like the industry was way overhiring around 2022.”

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Right observation, but that cliff still loomed.

I asked the students to look more carefully at the chart. The y-axis shows the change in jobs compared to the same month one year earlier—a choice that virtually guarantees a dramatic visual when growth slows after a boom. It is not lying, exactly. But it is designed to alarm rather than inform.

I then showed my students the same data plotted differently—as total employment rather than year-on-year change.

Christine Julien

The cliff disappears. What you see instead is a line that grew from roughly 900,000 jobs in 1990 to a peak of just over four million in 2023—not 2022, as the first chart might suggest. The peak on that first chart is an illusion created by the choice of what to measure.

But the story doesn’t end there. The dataset behind both charts counts jobs at tech-sector employers.

A second Bureau of Labor Statistics dataset asks not where people work, but what they do. It surveys employers across every sector of the economy and counts how many workers are doing computer and mathematical work, regardless of what industry their employer is in. A software developer at a hospital, a data scientist at a bank, a network engineer at a car manufacturer—all of them appear in this dataset, but not in...