Let AI Write the Obvious: Human Skill in the Age of Codegen Hype
A deep dive into the real limitations of today’s AI coding assistants, the myth of total replacement, and why strong human engineering skills matter more than ever.
Kade Powell
Let AI Write the Obvious: Human Skill in the Age of Codegen Hype
Introduction
AI-powered coding tools like GitHub Copilot, OpenAI’s ChatGPT, and Amazon CodeWhisperer are increasingly woven into developers’ workflows. These tools promise productivity boosts — for example, GitHub claims Copilot helps programmers code 55% faster (GitHub, 2023).
However, emerging research and developer experience show that over-reliance on these tools can lead to unintended negative outcomes. This article explores the implications of heavy AI use in development, outlines where these tools fall short, discusses the physical and economic limits of the current AI hype cycle, and concludes with guidelines for responsible, effective adoption.
Part 1: The Drawbacks of Heavy AI Use
❌ Code Duplication & Churn
- Studies like GitClear's 2024 report found an 8x rise in duplicate code blocks (GitClear, 2024).
- Code churn — code that is added then removed or rewritten shortly after — has also increased.
🔐 Security Vulnerabilities
- Up to 30% of Copilot's output in some studies contained vulnerabilities (Pearce et al., 2022).
❎ Inefficiency Despite Speed
- AI generates plausible but not always optimal code.
- Extra time is often spent debugging, refactoring, or rewriting AI output.
Part 2: Where AI Falls Short
🚫 New Technologies & APIs
- AI often suggests outdated libraries or misuses recent APIs.
❓ Domain-Specific Logic
- AI lacks contextual understanding of business logic.
🤔 Novel Problems
- AI struggles with novel or previously unsolved problems.
Guiding Principle: How likely is it that this problem has been solved before and appeared in the AI's training data?
Part 3: Long-Term Risk — Skill Erosion
⚠️ Shallow Understanding
- Heavy AI reliance can weaken fundamentals.
❌ Over-Dependence
- Devs may default to copy-paste without critical thinking.
ℹ️ Professional Risk
- AI won’t replace strong engineers — but it may expose weak ones.
Part 4: The AI Bubble (and No-Code Déjà Vu)
🧠 Functional Fixedness and AI
- AI can reinforce or break default thinking patterns.
🔍 Hype Comparison
Like Web3, NFTs, and no-code/low-code platforms, this AI moment shows signs of bubble dynamics:
- Overpromising automation.
- Underestimating real-world complexity.
The difference: AI is embedded deeper in developer tooling — making blind trust more dangerous.
Part 5: Where AI Tools Are Genuinely Useful
| Safe Use Case | Why It Works |
|---|---|
| Boilerplate code | Pattern-heavy, repetitive |
| Project scaffolding | Fast setup, easy to verify |
| Unit test generation | Repetitive logic, low business risk |
| Code translation/refactoring | Deterministic transformations |
| Documentation generation | Natural language summaries |
| Communication & onboarding | LLMs rated more persuasive than Reddit users (Reynolds & McDonell, 2023) |
| Admin task offloading | Meeting summaries, email chains, updates |
Use AI for speed, not strategy.
Part 6: Best Practices
- Treat AI like a junior dev. Review everything.
- Automate quality gates: linters, security scanners, test coverage.
- Limit AI use to known-safe tasks.
- Don't skip real learning.
- Use prompting wisely.
Final Takeaway
AI coding tools are powerful but blunt instruments. In my workflow, AI handles boilerplate, drafts, scaffolding, and admin summaries — but never without my review.
Assume it’s wrong until proven otherwise.
Let AI write the obvious, so you can focus on the essential. That’s how it becomes a partner — not a liability.