Is Vibe Coding ruining a generation of engineers?

Revolutionize AI tools Software development by automating repetitive tasks, refactoring bloated code, and identifying errors in real-time. Developers can now generate well-structured code from plain text prompts, saving hours of manual effort. These tools learn from large codebases and provide contextual recommendations that increase productivity and reduce errors. Instead of starting from scratch, engineers can prototype quickly, iterate faster, and focus on solving increasingly complex problems.

As code generation tools become more popular, they raise questions about the future size and structure of development teams. Earlier this year, Garry Tan, CEO of startup accelerator Y Combinator, noted that about a quarter of his current customers are using AI to write 95% or more of their software. In an interview with CNBCTan said: “For founders, this means you don’t need a team of 50 or 100 engineers and you don’t have to raise as much. The capital lasts much longer.”

AI-powered coding may offer a quick fix for companies under budget pressure – but the long-term impact on the industry and labor pool cannot be ignored.

As AI-powered coding increases, human expertise may decrease

In the It was from youThe traditional path to coding competence that has long supported experienced developers could be at risk. Easy access to large language models (LLMs) allows junior programmers to quickly identify problems in code. While this speeds up software development, it can distance developers from their own work and delay the growth of core problem-solving skills. This allows them to avoid the focused and sometimes unpleasant hours required to build expertise and progress toward becoming successful senior developers.

Consider Claude Code from Anthropica terminal-based wizard based on Claude 3.7’s Sonnet model that automates error detection and resolution, test creation, and code refactoring. Using natural language commands reduces repetitive manual work and increases productivity.

Microsoft has also released two open source frameworks – AutoGen and Semantic Kernel — to support the development of agent AI systems. AutoGen enables asynchronous messaging, modular components, and distributed agent collaboration to create complex workflows with minimal human effort. Semantic Kernel is an SDK that integrates LLMs with languages ​​such as C#, Python and Java, allowing developers to create AI agents to automate tasks and manage enterprise applications.

The increasing availability of these tools from Anthropic, Microsoft and others could reduce opportunities for programmers to refine and deepen their skills. Instead of “bashing your head against the wall” to debug a few lines or pick a library to unlock new features, junior developers can simply turn to the AI ​​for assistance. This means that experienced programmers with problem-solving skills honed over decades may become an endangered species.

Over-reliance on AI to write code risks compromising developers’ practical experience and understanding of key programming concepts. Without regular practice, they may find it difficult to debug, optimize, or design systems independently. Ultimately, this loss of skills can undermine critical thinking, creativity and adaptability – qualities that are essential not only for coding but also for assessing the quality and logic of AI-generated solutions.

AI as a mentor: Turning code automation into hands-on learning

While there are legitimate concerns that AI will impact the capabilities of human developers, companies should not reject AI-powered coding. They just need to think carefully about when and how to use AI tools in development. These tools can be more than just productivity boosters; They can act as interactive mentors, guiding programmers in real-time with explanations, alternatives, and best practices.

If youAs a training tool, AI can reinforce learning by showing programmers why code is broken and how to fix it – rather than simply applying a solution. For example, a junior developer using Claude Code could receive immediate feedback on inefficient syntax or logic errors, along with suggestions linked to detailed explanations. This allows for active learning, not passive correction. It’s a win-win: speed up project timelines without doing all the work for junior programmers.

Additionally, coding frameworks can support experimentation by allowing developers to prototype agent workflows or integrate LLMs without requiring upfront expertise. By observing how AI creates and refines code, junior developers who actively engage with these tools can internalize patterns, architectural decisions, and debugging strategies – reflecting the traditional learning process of trial and error, code reviews, and mentoring.

However, AI coding assistants should not replace real mentoring or pair programming. Pull requests and formal code reviews remain essential for guiding new, less experienced team members. We are still far from getting to the point where AI can single-handedly train a young developer.

Companies and educators can create structured development programs around these tools that emphasize code understanding to ensure AI is used as a training partner rather than a crutch. This encourages programmers to question AI outputs and requires manual refactoring exercises. In this way, AI becomes less a replacement for human ingenuity and more a catalyst for accelerated, experience-based learning.

Bridging the gap between automation and education

When used in a targeted manner, AI doesn’t just write code; It teaches coding and combines automation with education to prepare developers for a future where deep understanding and adaptability remain essential.

By using AI as a mentor, as a programming partner, and as a development team that we can focus on the problem at hand, we can bridge the gap between effective automation and education. We can empower developers to grow alongside the tools they use. We can ensure that as AI evolves, so do human capabilities, fostering a generation of programmers who are both efficient and knowledgeable.

Richard Sonnenblick is chief data scientist at Plan view.