Rethinking
CS After AI
Are Gen Z Students Turning Away from Computer Science?
While rise of AI tools has been nothing short of of explosive, and so has our reliance on them. Just this week, while searching for design inspiration for a website I was building, I refreshed my browser to find a banner announcing the launch of ChatGPT-5. It was a reminder of how quickly the technology is evolving, and it made me wonder: what does this AI boom mean for students mapping out their futures?
Across campuses, majors like Computer Science have long been seen as the golden ticket to a stable, high-paying career. But with AI now able to write code, debug programs, and even design systems in seconds, some students are questioning whether the field still offers the same promise. Are we seeing the first signs of a shift in what Gen Z chooses to study? And if so, where are they headed next?
Since its public launch in November 2022, ChatGPT has grown into one of the fastest-adopted technologies in history. By mid-2025, it was handling 2.5 billion prompts every day, more than double the volume from just seven months earlier, and attracting 700 million weekly active users. Combined traffic to the world’s top AI chatbots surged from 30.5 billion visits in 2023 to 55.2 billion in 2025, an 81% increase.
Google Search still dominates with over 8 billion searches daily and a global market share near 90%, but that share has dipped below the 90% mark for the first time in a decade. In parallel, usage of Google News fell by 5% between 2024 and 2025, while ChatGPT’s news-related prompts jumped 212%.
OpenAI, valued at an estimated $86 billion in late 2024, has rapidly expanded its AI product suite — including the launch of ChatGPT-5 — and is directly positioning itself as not just a productivity tool, but a direct competitor to search engines.
Stanford students are quietly rebalancing
Undergraduate degrees conferred by major. CS holds steady, Economics and Symbolic Systems climb, EE declines.
Stanford’s post-AI graduation data shows no collapse in Computer Science; in fact, the field is up slightly, averaging 317 bachelor’s degrees in 2023–24 compared to 299 from 2018–22. CS’s share of all bachelor’s degrees also inched higher from 17.3% to 17.8%.
The real shifts are happening around it, with Economics (+31.6%) and Symbolic Systems (+21.1%) seeing strong gains, along with a modest rise in Human Biology (+10.6%). Meanwhile, traditional engineering-heavy programs like Electrical Engineering (–25.2%), Management Science & Engineering (–24.3%), and Mathematical & Computational Science (–21.1%) declined sharply.
Together, the numbers suggest a subtle restructuring as students blend technical foundations with interdisciplinary, human-focused, and design-oriented paths.
Winners and decliners after the AI boom
Average annual graduates before AI (2018–2022) compared with after AI (2023–2024).
Where this is going next
Emerging career paths vs environmental footprint indicators for large AI models.
The shift in Stanford majors looks driven by tech, coursework, and culture. As tools like ChatGPT, Claude, and Copilot automate routine coding, some students question whether pure programming will keep the same premium. Many now try to pair CS with economics, cognition, design, or policy to stand out in hiring and grad school.
Andrew Chen Williams, a Stanford student and mentor in The Daily’s tech journalism workshop, sees a tension on the ground: “More and more people are becoming CS majors, but many are also talking about how SWE roles may not exist in the near future. It seems contradictory, but these tech jobs and internships are so idealized that students are simply trying even harder to land them.” His read is not retreat but rebalancing, planning for careers that mix code with domain context and adaptability.
Workload and culture matter too. Students cite heavy problem sets, competitive atmospheres, and a desire for creative or socially useful work as reasons to choose interdisciplinary tracks.
Looking ahead, CS stays strong, while growth likely comes from hybrids such as AI and Environment, Sustainable Computing, or Computational Climate Systems. These would prepare students for AI sustainability, green data center design, and tech policy. The environmental stakes are real. Large models can draw power comparable to tens of thousands of homes per year and significant cooling water, and data centers are estimated around 3 percent of global emissions. The next wave of Stanford grads may be the ones who make AI faster and cleaner, balancing computational power with planetary limits.
Built with Chart.js. Data summarized from Stanford degrees conferred (2003–2024) and public AI usage reports. by Sagar Shabbir