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Posted on 04/30/2026 8:28:38 PM PDT by SeekAndFind
Over the last decade or so, computer science seemed like a pretty safe bet when it came to choosing a major, but a recent Washington Post analysis reports that enrollment growth in computer science has begun to stall or decline as students rethink what artificial intelligence means for coding careers. Today we’re going to delve into why computer science majors are on the decline, along with what it could mean for students and recent graduates looking to start careers in computer science. Let’s begin.
It’s not as if tech jobs disappeared overnight, so what gives? Well, students are beginning to notice trends that have started because of the continued integration of AI systems into the workplace.
For starters, AI can now write code. Tools that once assisted developers are increasingly generating production-ready code. That has changed the way students think about entry-level programming roles, which were once the traditional on-ramp into the industry.
Since certain entry-level roles have been exposed to automation, the job market has tightened. This not only means there’s less hiring going on but also fewer students looking to break into the industry.
Computer science used to feel like a guaranteed return on investment, but now students are weighing whether combining technical and business skills may offer better career protection.
Make no mistake, a degree in computer science is still valuable, you just have to look at it differently. Before AI, you would major in computer science, become a programmer, and eventually grow into a technical leader. In the present, you would get your degree, develop expertise in a specialized area (such as machine learning), and go on to manage AI-enabled systems.
The point is, the advantages a computer science degree brings to your career are changing. Career trajectories may look different from what they were 10 years ago, but that doesn’t mean computer science degrees are obsolete.
Despite shifting sentiment among students, there’s actually little evidence that employers are moving away from technical talent. In fact, many are simply redefining how they use it. Research from McKinsey & Co. consistently shows that demand for workers with digital and data skills continues to outpace supply, particularly in roles that combine technical expertise with business acumen. Along with this, the World Economic Forum identifies analytical thinking, AI literacy, and technology design as among the fastest-growing skill areas globally.
This trend is perhaps best demonstrated in how professional services firms are hiring. Firms still recruit candidates with computer science and engineering backgrounds, but often into roles that go beyond traditional programming. For example, strategy and operations teams now rely on professionals who can interpret data, evaluate automation tools, and translate technical outputs into business recommendations. Looking at the issue this way, it seems that technical majors haven’t lost relevance but instead have expanded in scope.
Some students may be reconsidering whether to pursue (or continue with) a computer science degree. The truth is, current data suggests the more effective approach is not to abandon your major altogether but to complement it. According to the National Association of Colleges and Employers (NACE), employers consistently rank problem-solving, communication, and teamwork alongside technical skills as core hiring criteria.
This helps explain why double majors and minors are gaining traction (more on that in a bit). Students are now pairing technical degrees with fields like economics, business, or public policy to improve flexibility in a changing job market. Think of it this way: a computer science major who understands financial modeling or market dynamics is often better positioned than a candidate with purely technical or purely business training.
Across U.S. campuses, more undergraduates are adding second majors to improve job-market flexibility and differentiate themselves in uncertain hiring environments. Taking into consideration everything we’ve seen and learned so far in the age of AI, that trend makes a whole lot of sense.
If automation handles routine technical execution, professionals who connect disciplines may gain leverage faster. For instance, a recent graduate might enter the workforce with technical literacy and strong business judgment or communication skills.
The real shift here isn’t whether computer science majors will find jobs, but rather what those jobs will look like. Historically, early-career technical roles involved writing scripts, debugging systems, and maintaining infrastructure.
In the age of AI, the same roles are now being expected to review AI-generated code, test automated workflows, manage outputs, and interpret results for stakeholders. The point is, computer science majors won’t be doing any less technical work, but they will have to possess strong judgment and communication skills along with their technical expertise.
If you’re choosing a major or deciding whether to stay in computer science, consider these strategies. First, add some business exposure into the mix—take coursework in finance, economics, or operations strategy.
Next, gain familiarity with AI tools. Moving forward, employers will expect graduates to understand how generative tools integrate into real work environments. Lastly, don’t sleep on internship opportunities. Experience is still a key differentiator among candidates, perhaps more than major selection alone.
The goal here isn’t to avoid technical fields because of the continued use of AI. Instead, consider a more strategic approach by combining technical knowledge with business context and strong soft skills like communication, because regardless of how AI continues to change the workplace and hiring trends, those skills will never go out of style.
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There will be a new industry predicated on materials and methods to checkmate AI.
“You know the mission is too important to allow you to hold it back, Dave”
—HAL.
Exactly!
“Soft skills” or the ability to communicate the complex to the non-technical will be key.
AI (traditional, generative, agentic, etc.) are going to give those non-technical people what they ask for, and they don’t know how to ask (prompt) just like they today don’t have a clue how to ask a scrum team why their simple idea is a deal-breaker technically.
Knowing how everything ticks inside and telling the rest of the people how and why and what their options are is golden.
True CS nerds will simply assimilate AI as just another tool to create cool stuff.
If tech companies were smart they would be using AI at first to simply address tech debt.
Lately I've been having some great results integrating structured data (in my case, XML) with AI. This is my current "crack".
“Is Computer Science Still a Good Major in the Age of AI?”
Better question...
“Is Computer Science Still a Good Major in the Age of H1-B?”
I retired in June 2025. Some of the code I was maintaining was very old JavaScript. Every time a security scan came through, there was a complaint around the use of < script >, < style > and one other tag. Adding the Content-Security header resulted in a total meltdown. There are thousands of instances of those uses as inline code. That is the kind of technical debt that would be suitable to turn over to AI to morph into a safer form. Doing it manually would be a huge task and blithely omitted from the funding request made in the proposal. It's a genuine bomb waiting to detonate on the team that has to fix it.
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