Vibe coding, the practice of using AI tools to generate code through natural language prompts, is making programming faster and more enjoyable for many developers, but experts caution it has clear limits. While some see it as a game-changer, engineers and computer science professors stress that it cannot replace deep technical understanding or the judgment of experienced software engineers. Kevin Wu, CEO and co-founder of Leaping AI, acknowledges that many developers are using vibe coding, but warns that not everyone is doing it effectively. He shared an example where a newly hired engineer seemed to rely heavily on AI-generated code without truly understanding how it worked. “They didn’t even have any idea what the code does,” Wu said, highlighting a key risk: AI can produce code that appears functional but may be flawed, insecure, or misaligned with project goals. Nenad Medvidovic, chair of computer science at the University of Southern California, compares vibe coding to giving a beginner a high-performance race car—exciting, but dangerous without proper training. He notes that AI excels at handling simple or moderately complex tasks, but struggles with intricate problems where context, logic, and system-level thinking are crucial. The rise of AI is already impacting entry-level software jobs. A Stanford study found a 13% relative decline in employment for workers aged 22 to 25 in AI-exposed fields, with early-career software developers seeing nearly a 20% drop in hiring from late 2022 to mid-2025. Todd Millstein, a computer science professor at UCLA, views AI as a junior engineer rather than a replacement. “You give it a task, review the output, and guide it,” he said. But he emphasizes that engineers remain responsible for the code, especially when dealing with complex logic, security, or system integration. “There are parts too tricky to explain to the AI,” he added. Maintenance, debugging, and long-term scalability remain deeply human tasks. At OpenAI, engineer Jigar Bhati sees AI as a powerful assistant. It’s useful for rapid prototyping, but building a reliable, production-ready product requires human oversight, collaboration across teams, and deep system knowledge. “You’re still in control,” he said. Ritvika Nagula, a software engineer at Microsoft, agrees. The quality of AI output depends heavily on the quality of the prompt. Without proper context, the AI may generate solutions that are technically correct but irrelevant or unsafe. Still, many engineers appreciate the speed and creativity vibe coding enables. Antara Dave, a product designer at Microsoft, finds it fun and efficient—allowing quick experimentation and idea generation. Millstein points out that automating repetitive tasks frees engineers to focus on more meaningful challenges. Professors like Medvidovic and Millstein are adapting curricula to include AI literacy, recognizing that students will use these tools regardless. But they stress that core computer science fundamentals—algorithms, data structures, system design—remain essential. The role of a software engineer is evolving, not disappearing. AI is a tool, not a substitute. The future belongs to those who can wield it wisely, with deep understanding and critical thinking.