Will "Vibe Coding" kill the Software Engineering role? "Vibe coding" has become a popular term in the software community, especially on X, describing a practice where developers write code based on intuition, trial and error, and AI suggestions, often without fully grasping the underlying principles. AI coding assistants like GitHub Copilot and ChatGPT now generate functions, debug errors, and even build entire components with minimal human input. These tools can produce working code from simple text prompts, leading many to question the future role of human programmers. AI is changing all aspects of the world around us. Google search is losing ground to AI search. Logistics companies are going bust due to AI taking over the industry. Low skilled labour is being lost to AI. Google search is losing ground to AI search AI taking over the industry Low skilled labour is being lost to AI And now people are saying that "Vibe Coders" are going to take all of the Software Engineering roles. I disagree. I disagree. I see a different reality forming. AI tools act as amplifiers - they make good engineers better and bad engineers worse. The gap between skilled and unskilled developers isn't shrinking; it's expanding rapidly. Those who rely on AI without foundational knowledge will hit walls they can't overcome, while those who combine technical expertise with AI assistance will become exceptionally productive. The Rise of AI-Assisted Coding The AI coding assistant market has exploded with tools like GitHub Copilot, Amazon CodeWhisperer, and ChatGPT changing how developers write code. These Large Language Models (LLMs) process billions of code samples to predict what you'll type next. Daily coding tasks have changed dramatically. Copilot suggests entire functions as you type. ChatGPT generates complete API endpoints from simple descriptions. These tools are really good at creating boilerplate code, writing documentation, and building repetitive logic patterns that once took hours of manual work. I can't believe I ever had to write my own code mapping tool, or that I generated my own types based on database tables. I used to have to do that by hand (well, usually I'd create some weird Excel formula with string contatenation)! "Vibe coding" - the practice of relying on these AI prompts and suggestions - makes shipping code much faster. Developers can build and deploy features without mastering all the details of their tech stack. You describe what you want, the AI suggests code, and you tweak until it works. The downside? Many developers now ship code they don't fully understand. They can build quickly but struggle to fix complex bugs or explain system architecture when things break. The even worse downside? People who know nothing about engineering are writing code that people are paying for, and even worse, are giving their private details to. I won't name and shame here, but there is a very famous (on X anyway) case of someone who was bragging about having no coding experience at all and had released some basic software that people were now paying him for. A week or so late he had been hacked, his site was down and the clients data was leaked. That's not good, is it. The Illusion of Speed AI tools let engineers produce code at an astonishing rate - 10x faster than manual typing in many cases. But quantity and quality remain very different metrics. These tools generate syntax quickly but lack strategic thinking about system design, scalability, and maintenance. Just as a fast typist isn't automatically a good author, a developer with AI assistance isn't automatically building better systems. AI excels at writing individual functions but falls short on architecture decisions, security considerations, and long-term maintainability. Without proper review, testing, and system knowledge, this "vibed" code becomes tomorrow's technical debt. Imagine a junior developer merging an AI-generated authentication module that passes all unit tests but silently breaks your payment processing service. The code looked perfect in isolation but didn't account for how services interact. It might take senior engineers three days to find and fix the issue. To say that this was coded quicker is a false economy. Fast code creation without deep system knowledge creates an illusion of productivity while building future problems. Why Great Engineers Still Matter AI coding tools write syntax but stop far short of building complete systems. They can't: Design system architecture that scales with user growthBuild deployment pipelines that catch issues before they reach productionPredict edge cases that happen once in a thousand requestsBalance performance needs against cloud computing costsSpot security vulnerabilities that aren't obvious in the code Design system architecture that scales with user growth Build deployment pipelines that catch issues before they reach production Predict edge cases that happen once in a thousand requests Balance performance needs against cloud computing costs Spot security vulnerabilities that aren't obvious in the code The best engineers think beyond individual functions. They create patterns and abstractions that entire teams build upon. They select technologies that match business goals. They plan for failure scenarios and recovery strategies. Software development involves trade-offs that AI doesn't understand: stability vs speed, flexibility vs simplicity, performance vs cost. Making these decisions requires both technical knowledge and business context. In my experience, senior engineers spend most of their time thinking, not typing. They ask questions like: How will this system evolve? What might break under stress? How do we recover when things fail? How will this system evolve? What might break under stress? How do we recover when things fail? How will this system evolve? What might break under stress? How do we recover when things fail? As code generation becomes accessible to everyone, the value shifts from knowing syntax to understanding systems. Your competitive advantage comes from knowing why specific approaches work, when they fail, and how to build solutions that last beyond the first deployment. AI as an Amplifier, Not a Replacement AI tools don't replace engineers - they amplify existing skills and habits. The most skilled developers become dramatically more productive, while less skilled ones produce more problems faster. Strong engineers use AI strategically to: Automate repetitive coding tasksGenerate initial project structuresExplore multiple solutions to a problemGet past mental blocks when stuck Automate repetitive coding tasks Generate initial project structures Explore multiple solutions to a problem Get past mental blocks when stuck Meanwhile, weak engineers hide behind AI-generated code without building core skills. They paste solutions they can't explain or modify. When bugs appear, they lack the knowledge to fix them without asking for more AI help. What Engineers Should Focus On Now As AI handles more coding tasks, successful engineers must shift focus to areas where human judgment adds the most value: Systems thinking stands out as the top skill. You need to see how components interact, predict failure points, and build for scale. This comes from experience and study, not from prompts. Next, master infrastructure and deployment. Code that works locally but fails in production creates no business value. Learn how CI/CD pipelines, monitoring, and cloud services work together. Pay special attention to API design. Well-designed interfaces between systems let teams work independently. Bad contracts create bottlenecks that slow everyone down. Make security a priority from day one. A single oversight can lead to data breaches that damage customer trust and business reputation. Develop clear communication skills, both in writing and speaking. You must explain technical choices to non-technical stakeholders just as well as you document code. Learn how AI tools actually work. Understand their limits and strengths so you can direct them effectively. For senior engineers, mentorship becomes more critical than ever. Junior developers need guidance on using AI responsibly - learning when to trust its output and when to question it. Conclusion The software industry sits at a turning point. AI will generate more code in less time, changing how teams build products. This shift creates both opportunity and risk. The best jobs will go to engineers who master what machines can't do. They will decide what products to build, which architecture patterns to use, and how to balance business needs with technical limits. "Vibe coding" works as a tool for specific tasks - quickly generating standard components or common functions. But it fails as an overall strategy for building complex systems. Strong engineers will pull even further ahead by using AI to handle routine work while they focus on bigger challenges. Weak engineers will fall behind as their lack of fundamentals becomes clear. The future belongs to those who treat AI as a partner, not a replacement. Software remains a human discipline, just with better tools at our disposal. The future belongs to those who treat AI as a partner, not a replacement. Software remains a human discipline, just with better tools at our disposal.

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Vibe Coding: AI's Impact on Software Engineering

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