For years, we told young people that learning to code was a golden ticket. From 2005 to 2023, computer science (CS) enrollment in the U.S. exploded—quadrupling in size. It made sense: every company was becoming a tech company, and software was eating the world.
But now, the story is shifting.
This year, Computer Science enrollment nationwide grew by just 0.2%. At places like Stanford and Princeton, the number of CS majors has stopped growing—or is outright declining. At Duke, enrollment in intro CS courses dropped 20% in just one year.
So what’s going on?
AI Is Changing the Game—Faster Than Anyone Expected
Let’s be honest: we’re seeing the consequences of AI scaling up faster than traditional career paths can adjust.
At some of the companies I’ve worked with or advised, AI now assists or even fully writes a growing share of the codebase. It’s not just chatbots and copy. AI tools are changing how software is designed, built, and shipped.
One tech leader recently admitted that junior engineers are increasingly being replaced—not by new grads, but by AI. If that’s happening at scale, we’re not just facing a tech hiring slowdown. We’re looking at a full rewrite of the traditional “start-at-the-bottom-and-work-your-way-up” career model.
And it’s not just software roles. A recent Axios article outlines how generative AI is poised to transform or eliminate millions of entry-level white-collar jobs, from customer service to paralegals to marketing analysts.
Why the Decline in CS Majors Might Actually Make Sense
When I speak with students and recent grads, I hear a lot of anxiety. They’re not afraid of working hard—they’re afraid of putting in the time, money, and effort only to find out that the rules of the game have changed by the time they graduate.
And they’re not wrong.
Generative AI has become so good at certain entry-level tasks—writing code, creating content, crunching numbers—that the first rung of the career ladder is starting to disappear. Some call it “automation.” Others call it “progress.” But for people just entering the workforce, it can feel like the floor is moving beneath their feet.
But This Isn’t the End of Tech Careers—It’s the Beginning of Something New
I don’t think this means people should stop studying computer science.
What it does mean is that students (and honestly, all of us) need to think differently about how we build value. If AI can do the repetitive tasks, what uniquely human skills do we bring to the table?
From my vantage point, here’s what I believe the next generation of great tech leaders will need:
- Systems thinking: not just how to code, but why systems behave the way they do.
- Creativity: the ability to connect dots in unexpected ways—especially across disciplines.
- Empathy and ethics: understanding the impact of tech on people, not just markets.
- Communication: translating technical complexity into simple, clear, human language.
AI can automate knowledge. It can’t replicate judgment, emotional intelligence, or vision.
So What Should Students (and Career-Changers) Do Right Now?
Here are a few suggestions I often share with younger professionals, based on what I’m seeing in the market:
1. Combine disciplines
If you’re studying Computer Science, pair it with psychology, design, philosophy, or business. The future is interdisciplinary.
2. Learn to use AI as your co-pilot
Prompt engineering. Workflow automation. Rapid prototyping. You don’t have to build the AI—but you do need to know how to drive it.
3. Focus on outcomes, not titles
Companies are looking less at what your degree says and more at what you can do. Show them.
4. Build a portfolio, not just a resume
Whether it’s open-source projects, a Substack newsletter, or a TikTok channel—create something that shows your thinking and your edge.
What Educators and Employers Need to Rethink
This shift isn’t just a student problem—it’s a challenge for universities and employers, too.
If you’re a professor or university leader, this is the moment to rethink curriculum design. AI literacy, real-world application, soft skills, and cross-disciplinary thinking are no longer “nice to haves.” They’re essential job skills for the future of work.
If you’re an employer, it’s time to rebuild the ladder. Don’t just cut junior roles and call it AI efficiency. Find new ways to bring in talent and help them grow—maybe through apprenticeships, cohort-based learning, or rotational programs.
Is This a Bubble? Or a Real Correction?
Some call this a “computer science bubble.” Maybe. Maybe not.
Like past bubbles—dot-com, crypto, blockchain—there’s definitely hype. But there’s also real value. We’re not just watching a correction. We’re watching a recalibration. The same way the invention of the calculator didn’t kill math, AI won’t kill coding. But it will change the way we think about value and mastery.
Final Thoughts on Computer Science Bubble: Your Edge Is You
To anyone worried about the future of work: don’t bet against yourself. Bet on your ability to learn, adapt, and stay curious.
Yes, the game is changing. But you have something no AI will ever have: perspective, imagination, and heart.
The most powerful combination in the coming years? AI fluency + human intuition. If you can bring both to the table, you won’t just survive this shift—you’ll shape it.
👉 Want to level up your AI skills? Here’s a practical guide to building AI fluency in the workplace.
