Computer science was supposed to be the golden ticket. For nearly two decades, universities couldn’t build computing programs fast enough to accommodate students flooding into what appeared to be the safest career bet in higher education. But something fundamental is shifting on college campuses across America: traditional computer science enrollment is declining while artificial intelligence majors are exploding in popularity.
The irony is brutal. The same technology that promised to revolutionize software development is now driving students away from the very degree that would teach them to build it.
The Numbers Tell a Dramatic Story
This fall, 62% of traditional computer science programs reported enrollment declines, according to the Computing Research Association’s October report. At Stanford—widely considered one of the nation’s premier CS programs—enrollment has stalled after years of relentless growth. The University of Minnesota’s Department of Computer Science and Engineering is seeing similar drops after 15 consecutive years of increases.
Meanwhile, AI-specific programs are swelling with students:
- The University of South Florida’s new College of Artificial Intelligence and Cybersecurity enrolled over 3,000 students in its inaugural semester
- UC San Diego’s brand-new AI major attracted 150 first-year students immediately
- MIT’s “Artificial Intelligence and Decision-Making” program now ranks as the university’s second-largest major with nearly 330 enrolled students—second only to traditional computer science
- SUNY Buffalo’s AI master’s program grew twentyfold from 2020 to 2024, jumping from 5 students to 103
As of 2025, the United States now offers 193 bachelor’s degree programs in artificial intelligence and 310 different AI master’s programs—numbers expected to climb as universities race to capture student demand.
This represents more than a trend. It’s a wholesale reimagining of what computing education means in the age of generative AI.
The Paradox: AI Creates Both the Fear and the Solution
Students are responding to a grimly ironic reality: artificial intelligence tools are simultaneously threatening entry-level coding jobs while creating intense demand for AI specialists. The very technology transforming software development has made students question whether traditional programming skills will remain valuable—then redirected them toward degrees focused on building and managing that transformative technology.
The employment concerns driving this shift are concrete:
Mass Tech Layoffs: Over 100,000 tech workers lost jobs in 2025, following more than 150,000 layoffs in 2024. These weren’t just startup casualties—major players like Amazon, Meta, Google, and Microsoft eliminated thousands of positions, many held by early-career engineers.
AI-Driven Displacement: Companies like Amazon now require software engineers to use AI coding tools like GitHub Copilot. Coinbase CEO Brian Armstrong reportedly fired engineers who refused to adopt AI assistants like Cursor after giving them just one week to comply. The message to students is unmistakable: AI tools are reducing demand for junior programmers who write routine code.
Saturated Entry-Level Markets: Recent CS graduates describe job searches taking six months to a year, submitting hundreds of applications, and competing with experienced developers who were laid off. Data from SignalFire shows that graduates from elite engineering programs (MIT, Stanford, Carnegie Mellon, Berkeley) employed as engineers at major tech companies dropped from 25% in 2022 to just 11-12% currently—a greater than 50% decline in two years.
Skills-Based Hiring: Major firms including Google, IBM, and Accenture have dropped degree requirements for many technical roles, prioritizing demonstrated skills over credentials. This shift disadvantages new graduates competing against bootcamp grads and self-taught developers with stronger portfolios.
Compare this to fields tech leaders spent years mocking: philosophy majors have 3.2% unemployment, art history graduates just 3%, and even journalism majors clock in at 4.4%. The “learn to code” promise has become darkly ironic.
What Makes AI Majors Different—And Why Students Think They’re Safer
AI-focused degrees aren’t simply rebranded computer science programs. Universities are designing curricula that reflect how artificial intelligence is reshaping not just technology, but society broadly.
At MIT, students in the AI and Decision-Making program learn to develop AI systems while studying how technologies like robotics interact with humans and the environment. The program attracts students interested in applying AI across disciplines—biology, healthcare, policy, environmental science.
“Students who prefer to work with data to address problems find themselves more drawn to an AI major,” explains Asu Ozdaglar, deputy dean of academics at MIT’s Schwarzman College of Computing.
UC San Diego’s AI major, housed in the Department of Computer Science and Engineering, developed two new foundational courses in AI and machine learning. But the curriculum goes beyond technical skills, requiring advanced mathematics and coursework addressing the social impacts of emerging technologies.
SUNY Buffalo took this interdisciplinary approach further, creating a standalone “Department of AI and Society” offering degrees like “AI and Policy Analysis”—explicitly training students to navigate the intersection of technology and governance.
This broader framing appeals to students who want technical expertise without limiting themselves to pure software engineering. An AI degree signals flexibility—the ability to apply machine learning techniques in healthcare, finance, climate science, education, or public policy. In a volatile job market, that versatility looks like insurance.
Leena Banga, an 18-year-old first-year student at UC San Diego, chose the AI major after participating in a summer AI program at the University of Pennsylvania. “This is so cool to me to have the opportunity to be at the forefront of this,” she said. Her siblings were initially skeptical—”They were like: ‘What? There’s a major called AI? No way!'”—but her father, who works in tech, was enthusiastic.
Had AI majors not existed, Banga would have chosen traditional computer science. But the AI-specific degree felt more aligned with where technology is heading.
The Harsh Reality: Specialization Isn’t a Guarantee
Here’s the uncomfortable truth universities aren’t advertising: an AI degree doesn’t guarantee better employment outcomes than traditional CS—and in some ways, it might be riskier.
The Depth vs. Breadth Tradeoff: Computer science programs teach fundamental problem-solving skills, data structures, algorithms, and core programming concepts that remain relevant regardless of technological shifts. AI-focused programs risk teaching students to use current tools without building the foundational knowledge needed to adapt when those tools evolve or become obsolete.
Graduate Degree Competition: A substantial portion of AI professionals hold master’s degrees or higher. Bachelor’s-level AI graduates may find themselves competing for fewer opportunities against candidates with more advanced training. Some experts argue that AI specialization makes more sense at the graduate level after students have established CS fundamentals.
Curriculum Quality Concerns: With enrollment booming, some academics worry that programs are being built faster than universities can ensure quality, rigor, or experienced faculty. The pressure to appear cutting-edge can lead to shallow curricula focused on flashy research rather than fundamentals students actually need.
Employer Skepticism: Many hiring managers remain unclear about what AI majors actually learn compared to CS graduates. Degrees matter less than demonstrated skills—portfolio projects, GitHub contributions, real-world applications. A newly minted AI degree from a program launched last year may carry less weight than an established CS program’s reputation.
The AI Bubble Question: If AI development hits technical limitations or economic headwinds—as some researchers suggest it might—demand for AI specialists could decline precisely as the first cohorts of AI majors graduate. Students betting their entire education on one technology face concentration risk.
Loren Terveen, a University of Minnesota CS professor, notes that students are developing “a more realistic attitude” about tech employment: “They no longer expect to have multiple job offers just being almost thrown to them when they graduate.”
That realism should extend to understanding that no degree—CS or AI—guarantees immunity from market volatility.
The Industry Perspective: What Employers Actually Want
While students flock to AI majors hoping to position themselves for the future, employers are sending mixed signals about what they value.
Core Skills Still Matter: Companies consistently emphasize they need engineers who understand fundamental computing concepts, can debug complex systems, adapt to new technologies quickly, and work effectively in teams. These skills come from rigorous CS education, not just AI-focused coursework.
Experience Trumps Credentials: Job listings increasingly treat degrees as optional, preferring candidates who have deployed models in production environments, contributed to open-source projects, or built demonstrable applications. An AI degree means little without a portfolio proving competence.
Specialized Roles Require Specialized Knowledge: Fields like cloud computing, cybersecurity, and data engineering command premium salaries because they demand domain expertise AI tools can’t easily replace. Students pursuing AI without complementary specializations may find themselves competing in oversaturated generalist markets.
Cross-Functional Skills: The most valuable AI professionals aren’t just technically proficient—they understand business context, can communicate with non-technical stakeholders, and recognize ethical implications of their work. Pure technical training without these softer skills leaves graduates underprepared.
The H-1B Wild Card
Adding another layer of complexity: in 2025, tech companies applied for thousands of H-1B visa slots while conducting layoffs. Microsoft alone applied for 4,712 H-1B visas in fiscal year 2025 while simultaneously eliminating thousands of domestic positions.
This creates a paradoxical situation where American students struggle to find entry-level positions while companies claim they can’t find qualified domestic talent. Whether this reflects genuine skills gaps or cost optimization through visa programs, it means students can’t assume their degrees—CS or AI—guarantee access to tech employment simply because openings exist.
What Students Should Actually Do
Given this uncertain landscape, students interested in computing careers should think strategically:
Build Broad Foundations First: Strong fundamentals in mathematics, statistics, algorithms, and core CS concepts provide flexibility regardless of how AI technology evolves. Specialization works best after establishing this base.
Prioritize Programs with Rigor: Choose universities with established computing departments, experienced faculty, and strong industry connections. Brand-new AI programs may lack these advantages despite trendy marketing.
Develop Tangible Skills: Focus on building real projects, contributing to open-source software, participating in research, and creating a portfolio demonstrating capabilities. These matter more than degree titles when employers evaluate candidates.
Pursue Internships Aggressively: Work experience—even unpaid—provides crucial connections, skills, and credibility that classroom education alone can’t deliver. Students who graduate with multiple internships dramatically outperform peers with stronger academic records but no practical experience.
Consider Interdisciplinary Combinations: Pairing computing skills with domain expertise in healthcare, finance, environmental science, or other fields creates differentiation. Students who understand both the technical and application sides of AI become more valuable than pure technologists.
Stay Realistic About Timelines: Industry experts predict the entry-level tech job market may improve within 12-18 months, but recovery will be uneven. Students should plan for potentially challenging job searches rather than assuming automatic placement.
Don’t Ignore Traditional CS: For many students, a traditional computer science degree combined with AI coursework, electives, or a minor provides better preparation than a specialized AI major. The established curriculum, faculty expertise, and industry recognition of CS programs often outweigh the appeal of newer AI-specific degrees.
The Bigger Question: Is Higher Education Keeping Pace?
This enrollment shift raises uncomfortable questions about how universities respond to technological change. Are schools genuinely preparing students for the future, or simply rebranding existing programs to capitalize on AI hype?
Tracy Camp, executive director of the Computing Research Association, calls this shift “a new era for computing degrees becoming more specialized.” But specialization cuts both ways. Students gain depth in specific domains while potentially sacrificing the broad adaptability that has historically made computing graduates valuable.
The risk is that universities create degrees optimized for today’s technology landscape that become outdated before students even graduate. AI is evolving so rapidly that curriculum designed in 2023 may teach tools and techniques already obsolete by 2027.
Meanwhile, the fundamentals of computer science—how computers process information, how algorithms solve problems, how systems are designed and maintained—remain constant. Students betting on specialized AI degrees may be sacrificing enduring knowledge for trendy credentials.
The Bottom Line: Uncertainty Is the Only Certainty
The exodus from traditional computer science toward AI majors reflects profound anxiety about technology’s impact on technology careers. Students are making rational decisions based on visible market signals: layoffs, AI displacement concerns, difficult job searches for recent graduates.
But the shift also reveals how poorly equipped students are to navigate rapidly changing technical landscapes. Chasing the hottest major rarely produces optimal outcomes. The students who will thrive in tech careers—regardless of whether they study CS or AI—will be those who build strong fundamentals, develop practical skills, stay adaptable, and recognize that no degree guarantees immunity from market forces.
Universities bear responsibility for providing honest guidance rather than simply launching trendy programs to capture enrollment. Students deserve clarity about what AI degrees actually prepare them for, how they differ from CS programs, and what employment prospects realistically look like.
What’s clear is that the computing education landscape is undergoing its most significant transformation in decades. Whether this shift produces a generation of students better prepared for AI-driven futures or leaves them holding specialized degrees in an oversaturated market remains to be seen.
For now, students are voting with their enrollment decisions: AI majors are the future, and traditional computer science feels like the past. Only time will reveal whether they’re right.
