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Your Student Picked Computer Science. AI May Have Already Changed The Career Path They Expected
For years, Computer Science felt like the safest major in America.
Families heard the same message everywhere: learn to code, enter tech, secure your future.
But the labor market many families imagined when their student first became interested in Computer Science may no longer exist in the same way by the time today’s seniors graduate college.
That is the part families need to understand now.
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According to Burning Glass Institute data, unemployment among 22 to 24 year olds with Computer Science degrees reached 10.9% in 2024, the highest level in more than a decade. At the same time, entry level openings in jobs with high AI exposure have steadily declined since 2022.
The first jobs disappearing are often the exact jobs recent graduates once relied on to enter the industry.
That does not mean Computer Science is a bad major.
It does mean AI may already be reshaping the early career ladder students thought they were climbing.
For many families, this creates an uncomfortable question:
If AI can already generate code, automate technical workflows, and complete portions of junior level programming tasks, what exactly will employers want from new graduates five years from now?
The answer is probably not “less technical.”
It is more likely “more differentiated.”
The students most likely to thrive in Computer Science moving forward are probably not the students choosing the major because it feels practical or safe. They are students who genuinely enjoy solving hard technical problems. They like building systems, debugging code, understanding infrastructure, and thinking deeply about how technology works beneath the surface.
Because increasingly, basic familiarity with coding may not be enough.
The market is shifting from rewarding students who simply learned to program toward rewarding students who can apply technical thinking in more specialized, interdisciplinary, and operational ways.
In other words, the degree itself is no longer the differentiator.
This matters in admissions too.
At schools like The University of Texas at Austin, Computer Science admissions have become extraordinarily competitive partly because so many students view CS as the “safe” path. Admissions readers know this. Employers know this too.
The result is an applicant pool filled with students who often sound remarkably similar.
Coding camp. Generic AI app. Hackathon. Robotics club. Vague interest in “technology.”
The strongest applicants increasingly show something more specific and more real.
Some are combining software with hardware through Computer Engineering. Others are exploring cybersecurity, embedded systems, robotics, semiconductor design, or AI infrastructure. Some students are pairing technical skills with logistics, manufacturing, healthcare systems, operations, or business strategy.
That last category may quietly become one of the most important shifts of all.
As AI tools become easier to access, companies still desperately need people who understand how real systems function. Supply chains still need optimization. Infrastructure still needs management. Manufacturing still needs coordination. Businesses still need people who can connect technology to operational problems in the real world.
This is one reason traditional Engineering fields continue to show resilience. Mechanical, Electrical, Civil, and Industrial Engineering all connect to large scale physical systems that are harder to automate quickly and often create broader long term flexibility.