AI is disrupting fast. It's moving industries at a pace not seen since the industrial revolution.
Google is losing ground to AI search, truckers are going to be replaced by automation and low skilled admin jobs are being made redundant every single month due to AI.
Are Software Engineers doomed to the same fate?
The "Vibe Coding" Trend
The software community has coined a term that's gaining traction on X - "vibe coding." This describes how people write software today: through guesswork, following AI suggestions, and making adjustments until something works, often without knowing the technical details.
AI assistants for coding have made dramatic progress. Tools from GitHub Copilot to ChatGPT can now write full code sections based on simple text descriptions. Many wonder if these tools will replace human programmers completely.
My take differs. These AI systems act as multipliers rather than substitutes. Good programmers become more effective, bad programmers create problems faster. People who lack core knowledge will face limitations they can't fix, while those who mix technical skills with AI help will see their output soar.
How Coding Has Changed
The software development landscape has shifted with AI tools built on large language models that study millions of code examples to predict what comes next.
The daily coding experience looks completely different now:
- AI suggests full functions while you type
- Simple descriptions turn into working endpoints
- Standard code patterns appear in seconds
- Documentation writes itself
- Repetitive tasks finish instantly
This "vibe coding" approach lets teams deliver faster. You can build features without knowing every technical detail. Simply describe what you need, get AI suggestions, and adjust until they work correctly.
The drawback? Many people ship code they can't explain. They work quickly but struggle when systems break or need changes.
An alarming trend has also emerged - complete beginners selling AI-built software. One recent case saw someone with zero programming background launch a paid service, only to suffer a major hack days later because they didn't understand basic security practices.
Fast Does Not Mean Good
AI helps programmers produce more code with less typing. But raw output and quality systems are separate things.
These tools write syntax fast but miss system design, growth planning, and maintenance needs. A faster keyboard doesn't make you a better author; faster code generation doesn't create better software.
AI excels at writing single functions but fails at big-picture decisions about architecture, security, and long-term support. Without proper knowledge, "vibed" code often creates hidden problems.
Picture this real situation: Someone adds AI-generated login code that works in testing but causes subtle payment processing errors. Finding and fixing these issues might take senior staff days. The speed creates false progress while adding hidden costs later.
Human Engineers' Unique Value
AI coding tools write syntax but cannot:
- Design systems that grow with your user base
- Create reliable testing and deployment processes
- Find rare but critical edge cases
- Balance performance against costs
- Spot security weaknesses
The best engineers think at a higher level. They create frameworks for teams, select technologies that match business goals, and plan for both success and failure.
Software involves tradeoffs that AI can't judge: Do we want stability or speed? Flexibility or simplicity? High performance or lower costs? These decisions need both technical expertise and business context.
My top performers spend most time planning, not typing. They ask: How will our needs change? What might break? How do we recover from failures?
As basic code generation becomes available to everyone, your value shifts to understanding complete systems. Your advantage comes from knowing why certain approaches work, when they might break, and how to build solutions that last.
AI Magnifies Existing Skills
These coding tools magnify what already exists - they don't replace skills. The best developers become far more productive, while less skilled ones create problems more quickly.
Strong engineers use AI for:
- Eliminating repetitive tasks
- Starting projects with basic structures
- Testing multiple approaches quickly
- Breaking through mental blocks
Meanwhile, weaker coders use AI to hide knowledge gaps. They implement solutions they don't understand. When these break, they lack skills to fix them.
This widens the skill gap. Good engineers delegate basic work to AI so they can focus on high-value thinking. Poor engineers substitute AI for learning, creating problems when they face tasks beyond AI capabilities.
Key Skills for Success with AI
As AI takes over basic coding, successful engineers must focus on areas where machines still fall short:
Systems thinking tops the list - seeing how parts connect, spotting potential failures, and building for growth. This comes from experience and study, not from prompts.
Learn about infrastructure and deployment next. Code that works on your computer but fails in production adds no value. Study CI/CD pipelines, monitoring systems, and cloud services.
Master API design - the connections between systems. Good interfaces let teams work independently. Poor ones create roadblocks for everyone.
Add security from day one. A single mistake can lead to data breaches that damage trust and business standing.
Build communication skills for both technical and business audiences. You need to explain complex decisions clearly.
Study how AI tools actually work. Know their limits to use them effectively.
For experienced developers, teaching becomes critical. New coders need guidance on using AI wisely - knowing when to trust it and when to verify its work.
Looking Ahead
We're entering a new chapter for software development. AI will write more code faster, changing how teams build products. This creates both opportunities and challenges.
The best jobs will go to those who excel at what machines can't do. These people will decide what products to build, which architectures to use, and how to balance business goals with technical limits.
"Vibe coding" works for specific tasks - building standard components quickly. But it fails as a complete strategy for complex systems.
Good engineers will advance by using AI for routine work while tackling bigger challenges. Less skilled engineers will struggle as their knowledge gaps become obvious.
The future belongs to those who see AI as a partner, not a replacement. Software remains a human field, just with better tools at our disposal.
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