For years, computer science was marketed as one of the safest degrees in higher education. Strong demand, high wages and a seemingly endless need for technical talent made CS feel like a guaranteed return on investment. Today, that certainty is being questioned. Headlines warn of a “bursting” computer science bubble, students worry that AI will replace entry-level developers and colleges are hearing growing skepticism from prospective learners and families. For colleges, this shift is creating real pressure on enrollment, program strategy and how computer science is positioned to prospective students.
Those concerns are grounded in real changes, but they are often overstated. Recent reporting from the New York Times shows unemployment among new computer science graduates at about 6.1 percent, compared to roughly 4.8 percent for recent college graduates overall. In practical terms, that means out of every 100 CS graduates, about one additional student is unemployed relative to the average graduate. That difference matters, but it is a far cry from a collapse. The vast majority of CS graduates are still finding work, and often in roles with substantially stronger long-term earning potential than many other fields.
What has changed is not the relevance of computer science, but the ease of entry into the field.
A tougher entry point, not a disappearing field
The early-career tech market has undeniably tightened. Entry-level hiring is more competitive than it was just a few years ago, and routine technical tasks are increasingly supported by automation and AI-enabled tools. For new graduates, the first step into a software career now requires stronger signals of readiness than it once did.
It is tempting to attribute this shift entirely to generative AI, but that explanation misses important context. As On EdTech by Phil Hill & Associates has argued, the downturn in new-graduate hiring began before the widespread adoption of generative AI. Much of what we are seeing reflects a cyclical correction after an unprecedented pandemic-era hiring surge, combined with broader economic uncertainty. AI may be accelerating change, but it is not the sole or even primary driver of today’s entry-level slowdown.
That distinction matters. Treating the current moment as evidence that AI is “killing” computer science risks drawing the wrong conclusions and pursuing the wrong solutions.
Despite the tighter market, opportunity remains. Tens of thousands of entry-level software and computing roles are still posted each year across a wide range of industries. For graduates who secure these roles, coding careers continue to offer strong compensation and advancement potential relative to many other disciplines.
Put simply, computer science is no longer a golden ticket, but it is far from obsolete.
Why AI does not make computer science irrelevant
A common fear among students is that AI will write all the code, leaving little need for human developers. History suggests otherwise. New tools rarely eliminate disciplines entirely. Instead, they change where human expertise creates the most value.
AI is transforming how software is built, but more importantly it is increasing demand for higher-order skills: system design, architectural decision-making, security, integration, experimentation and translating real-world problems into technical solutions. The professionals best positioned to succeed will be those who understand how to use AI effectively as part of the development process.
This shift places a premium on applied skills and on graduates who can demonstrate how they work with modern tools and theoretical knowledge.
The real risk for colleges and students
A major risk facing higher education institutions is that computer science programs fail to evolve alongside the field.
Enrollment data shows signs of hesitation, with growth in CS majors slowing at some institutions. If colleges respond by retreating from computer science altogether, they risk leaving students unprepared for an economy that is becoming more software-driven, not less.
The moment calls for modernization. Computer science education must reflect how the field is practiced today. That means integrating AI literacy into the curriculum, emphasizing applied problem-solving, and helping students build portfolios that demonstrate real-world capability. Career preparation matters more than ever in a competitive early-career market.
How institutions can respond
At Rize Education, we work with 135+ colleges facing these exact challenges.
Rize’s Applied AI program is designed to integrate directly into existing Computer Science and Data Science pathways, either as a concentration or a combined major. The five-course sequence emphasizes hands-on experience, giving students opportunities to build custom AI systems, design neural architectures, fine-tune models, engineer effective prompts and communicate the value of their work through data and visualization. The goal is simple: help students graduate ready to contribute in an increasingly AI-driven workforce.
At the same time, many institutions are recognizing that AI fluency cannot be limited to technical majors alone. For colleges looking to broaden AI readiness across campus, Rize’s AI Literacy program offers a flexible, non-technical pathway that equips students in any discipline to understand, apply and work responsibly with AI tools in their future careers
AI is reshaping computer science, but it’s not diminishing its relevance. The core question is increasingly whether colleges will adapt how they teach, position, and support it. Institutions that modernize thoughtfully can preserve trust with students, protect enrollment, and ensure their CS programs remain aligned with the realities of today’s labor market.
