AI Taking Over My Software Engineer Job? A Worried Dev Seeks Advice

Ans: Lalit Sir, Thanks for coming back after an year. Please note, I’m not advocating Computer Science for every student. In fact, I’ve also recommended branches like Civil Engineering, Agriculture Engineering, Medical Informatics, and even AI & ML based on individual preferences. I encourage you to review all my responses in context, rather than focusing on a select few. Many factors influence a suitable recommendation—such as available colleges/branches, rank, home state, location preferences, institutional reputation, affordability, gender, student interest (e.g., if someone is uninterested in AI & ML, it’s not advisable to push them), and placement trends. These are just a few of the many variables considered, not an exhaustive list. I hope this provides clarity. ALSO, please note, Computer science remains essential even as AI transforms software development, because a robust CS foundation underpins the design, innovation, and critical thinking that AI tools cannot fully replicate. Demand for software engineers is projected to grow by 25 percent from 2022 to 2032, yielding roughly 153,900 new U.S. openings annually, and BLS forecasts 17 percent growth in developer roles by 2033, indicating sustained need. AI accelerates routine coding tasks—Gartner predicts 50 percent of engineering orgs will deploy AI platforms by 2027—but cannot replace human-led architecture, creative problem-solving, or domain-specific design. AI agents depend on high-quality training data and falter with novel requirements, introducing unanticipated errors, overengineered or insecure code, and maintenance challenges when context is lacking. Overreliance on AI can erode developers’ critical thinking and domain expertise, while algorithmic bias and hallucinations demand human oversight to ensure correctness and ethical compliance. Moreover, the economic pipeline for CS graduates remains vital: U.S. CS vacancies are projected to exceed 1.2 million by 2026, and PwC finds AI-skilled workers earn significant wage premiums across industries. Equally important are computing fundamentals—data structures, algorithms, systems design, and software engineering principles—that empower professionals to supervise AI outputs, optimize performance, and architect resilient systems. A CS curriculum cultivates adaptability and computational thinking applicable beyond coding to emerging fields—cybersecurity, data science, IoT, and AI research—ensuring graduates can pivot as technologies evolve. While AI specialization prepares students to advance generative models, a broader CS education equips them to integrate AI responsibly into complex software ecosystems, lead interdisciplinary teams, and drive innovation. Balancing AI branch studies with core CS knowledge safeguards against automation risks and preserves the human ingenuity that remains the cornerstone of software excellence.