AI May Be Scaring Students Away From Coding

A survey suggests student interest in computer science and AI majors is falling, potentially due to the rise of AI tools.

A small but potentially telling survey suggests student interest in computer science is falling sharply. According to data from academic mentoring platform Nova Learning, fields like Computer Science and AI are "losing ground fast" among prospective students. The report highlights a significant shift in academic preferences, with the most traditional "learn to code" pathway, Software & Data Science, seeing a 44% proportional drop in interest for the class of 2025. The AI major itself saw the largest absolute decline of any single subject. In stark contrast, general Engineering has nearly doubled its share of student interest. While the data comes from a single platform and a limited sample size, it points to a possible reversal of a decade-long trend that saw computer science as the dominant and most desirable STEM degree. This early signal suggests the rapid rise of generative AI may be fundamentally altering perceptions about the future of software development as a career path.

A likely driver behind this shift is the widespread availability of powerful AI coding assistants. Tools like GitHub Copilot and large language models capable of generating functional code have changed the nature of software development in a remarkably short time. This has created a perception that the core skill of writing code, once the primary barrier to entry and the main focus of CS programs, is becoming a commodity. Prospective students may be asking themselves why they should invest years and significant tuition fees to learn a skill that an AI can perform instantly. This questions the fundamental value proposition of a traditional computer science education. The fear is that a degree focused on programming languages and syntax could become obsolete, pushing students toward disciplines where human ingenuity and physical interaction, like in mechanical or civil engineering, are perceived as less vulnerable to automation.

This potential decline in CS interest doesn't exist in a vacuum. It aligns with a broader cooling of the tech industry's "growth at all costs" era. High-profile layoffs across major tech companies over the past two years have tarnished the image of software engineering as a guaranteed path to a stable, high-paying career. When combined with the existential threat of AI, it's understandable that students might reconsider their options. The corresponding surge in interest for broader Engineering fields is particularly noteworthy. It indicates that students are not abandoning STEM, but rather reallocating their focus toward areas where AI is seen as an assistive tool rather than a direct replacement. They may be betting that a degree in electrical or aerospace engineering, which combines software with hardware and physics, offers a more durable long-term career advantage in an AI-driven world.

For founders, CTOs, and engineering leaders, this trend is a critical signal for future talent strategy. If it continues, the pipeline of traditionally trained software engineers could shrink, potentially driving up salaries and increasing competition for a smaller pool of candidates. Companies may need to prepare for a new type of entry-level engineer—one who is highly skilled at prompting, guiding, and debugging AI-generated code but may lack a deep understanding of foundational computer science principles. Hiring processes and onboarding programs will need to adapt to this new skill profile. The immediate next step is to monitor official university enrollment statistics over the next one to two years to see if this small survey's findings are reflected in larger, more definitive datasets. Furthermore, tech leaders should watch how university curricula evolve in response, as they will likely shift from teaching pure coding to emphasizing systems design, problem-solving, and effective human-AI collaboration.

This trend could shrink the future pool of software engineers, making it harder and more expensive to hire traditional developers. CTOs and engineering leads may need to rethink hiring strategies and focus more on upskilling non-traditional talent.

A smaller pipeline of CS graduates could drive up engineering salaries and force companies to invest more in AI-native development tools. Businesses may need to adapt their long-term talent strategy, anticipating a workforce with different, more AI-centric skills.