AI is eliminating the tedious parts of programming. Here's how the developer role is shifting from writing code to directing AI and ensuring quality.
The Death of Boilerplate: How AI is Changing What Developers Actually Do
A senior developer spends roughly 30% of their time writing actual novel code. The rest?
- Setting up projects
- Writing configuration files
- Implementing authentication (again)
- Building CRUD operations (again)
- Debugging typos
- Reading documentation
- Writing tests for obvious cases
This is boilerplate. It's the tax you pay to build something new.
AI is eliminating that tax.
The Old Model
For the past 40 years, programming worked like this:
- Understand the problem
- Design a solution
- Write code to implement it
- Debug
- Test
- Deploy
Step 3 was the job. The better you were at translating ideas into syntax, the more valuable you were.
We optimized everything around this: IDEs with autocomplete, frameworks that reduced repetition, Stack Overflow for copy-paste solutions, coding bootcamps that taught syntax.
The New Model
With AI assistants, the workflow becomes:
- Understand the problem
- Design a solution
- Describe the implementation
- Review AI-generated code
- Refine through conversation
- Test and deploy
Step 3 changed from "write" to "describe and review." This is what's known as vibe coding—a fundamental shift in how developers work.
This sounds like a small shift. It's not.
What Actually Changed
Before: You Write, Computer Executes
You → Code → Computer
Your skill was translation: taking ideas and converting them to syntax the computer understood.
After: You Direct, AI Writes, You Verify
You → Instructions → AI → Code → You (review) → Computer
Your skill is now direction and verification: knowing what to ask for and recognizing when the AI got it right.
The Tasks That Disappeared
1. Project Setup
Setting up a new React project with TypeScript, ESLint, Prettier, testing framework, CI/CD... used to take hours of configuration. Modern AI coding assistants handle this instantly.
Now: "Set up a React project with TypeScript, ESLint, Prettier, Vitest for testing, and GitHub Actions for CI"
Done in minutes.
2. Authentication Flows
Every app needs login. Every developer has written user auth multiple times.
Now: "Implement JWT authentication with refresh tokens, email verification, password reset, and rate limiting"
The AI has seen thousands of auth implementations. It generates a solid one instantly.
3. CRUD Operations
Create, Read, Update, Delete. The skeleton of every data-driven app.
Now: "Create API routes for a blog post resource with pagination, filtering, and soft delete"
This used to be a day of work. Now it's a sentence.
4. Test Writing
Testing obvious functionality is tedious but necessary.
Now: "Write unit tests for this user service, covering success cases, validation errors, and database failures"
The AI writes comprehensive tests faster than you can type them manually.
5. Documentation
Nobody likes writing docs. AI doesn't mind.
Now: "Generate API documentation for these endpoints in OpenAPI format with examples"
Documentation that matches the code, generated in seconds.
The Tasks That Remain (And Grew More Important)
1. System Design
AI can implement solutions. It cannot design systems that scale, that are maintainable, that will survive real-world conditions.
Questions like:
- How should data flow through this system?
- What happens when this service fails?
- How do we handle 100x traffic?
- What are the security implications?
These require human judgment.
2. Problem Definition
AI generates code for what you ask. Knowing what to ask for—that's the hard part.
"Build a user notification system" gets you something. "Build a notification system with preference management, rate limiting to prevent spam, cross-channel delivery (email, SMS, push), with read receipts and a 30-day retention policy" gets you what you actually need.
The ability to fully specify requirements matters more than ever.
3. Code Review
AI code works, but is it good?
- Is it secure?
- Will it scale?
- Is it maintainable?
- Does it follow your team's patterns?
- Are there edge cases it missed?
Review skills that senior developers took years to develop are now essential for everyone.
4. Integration
AI writes individual pieces. Connecting them—making different systems talk to each other, handling edge cases at boundaries, managing state across services—still requires human orchestration.
5. Novel Problem Solving
AI is pattern matching at scale. When you need a genuinely new approach—an algorithm that doesn't exist in training data—you're on your own.
This is rare in most business software. But when it matters, it really matters.
How Developer Roles Are Shifting
Junior Developers
Old role: Write straightforward code, learn patterns through repetition.
New role: Direct AI for straightforward code, learn patterns by reviewing what AI generates. For tool recommendations, see our vibe coding tools comparison.
The learning path changed. Instead of struggling through syntax, juniors now struggle through specification and review. Some argue this is worse (no fundamentals). Others argue it's better (faster exposure to patterns).
Mid-Level Developers
Old role: Implement features independently, handle complexity.
New role: Direct AI for implementation, focus on integration and architecture.
This level benefits most. The tedious work that consumed their time is gone. They can focus on more interesting problems.
Senior Developers
Old role: Design systems, mentor juniors, handle the hardest problems.
New role: Design systems, review AI output at scale, handle problems AI can't.
Seniors become force multipliers. One senior can now effectively direct 10x more implementation work through AI.
Staff/Principal Engineers
Old role: Technical strategy, cross-team architecture.
New role: Same, but with AI tooling strategy added.
Which AI tools? What guardrails? How to maintain code quality with AI generation? New responsibilities emerge.
The Uncomfortable Questions
Will There Be Fewer Developer Jobs?
Short term: No. There's infinite demand for software.
Long term: The mix changes. Fewer people writing boilerplate, more people designing systems and directing AI.
The "entry-level coder" job is shrinking. The "senior developer who can leverage AI" job is growing.
Does Code Quality Suffer?
It depends. AI generates verbose, safe code. Without review, codebases can bloat.
But AI also generates consistent code. No more wildly different styles across files. No more forgotten error handling.
Net quality depends entirely on review practices.
What Should Developers Learn Now?
Double down on:
- System design and architecture
- Security fundamentals
- Performance understanding
- Communication and specification
- Code review skills
Less emphasis on:
- Memorizing syntax
- Framework-specific tricks
- Boilerplate patterns
- Typing speed
Should I Be Worried?
If your job is primarily typing code that already exists in patterns: maybe.
If your job is understanding problems, designing solutions, and ensuring quality: no.
The developers who thrive will be those who view AI as a tool that handles the boring parts, freeing them for the interesting parts.
The Practical Takeaway
Boilerplate is dying. The tax is being eliminated.
If you're a developer:
- Learn to use AI tools effectively
- Focus on what AI can't do
- Develop strong review instincts
- Get comfortable with specification over implementation
If you're hiring developers:
- Value system thinking over syntax knowledge
- Test for problem specification skills
- Evaluate code review ability
- Look for adaptability to new tools
The fundamental job—solving problems with software—isn't changing.
The implementation method is transforming completely.
Adapt accordingly.
Frequently Asked Questions
What is boilerplate code and why does it matter?
Boilerplate code is repetitive, standardized code that every project needs but doesn't provide unique value—like authentication systems, CRUD operations, configuration files, and basic error handling. It matters because developers historically spent 60-70% of their time on boilerplate rather than solving novel problems. AI now handles most boilerplate automatically.
How is AI changing what developers actually do?
AI is shifting developers from writing code to directing AI and reviewing outputs. Instead of typing syntax, developers now describe requirements, evaluate AI-generated code for quality and security, design system architecture, and handle novel problems AI can't solve. The role becomes more about judgment and design than implementation.
Will AI replace software developers?
No, but it will change what developers do. AI eliminates routine coding tasks but can't handle system design, security review, novel problem-solving, or business logic decisions. Demand for developers who can leverage AI effectively is actually growing. Entry-level "code typing" jobs are shrinking, while senior roles focused on architecture and quality are expanding.
What should developers learn now that AI handles boilerplate?
Double down on system design and architecture, security fundamentals, performance optimization, clear technical communication, and code review skills. Reduce emphasis on memorizing syntax, framework-specific tricks, and typing speed. The valuable skill is knowing what to build and recognizing quality, not typing implementation details.
Does AI-generated code have good quality?
It depends on review practices. AI generates consistent, working code but tends toward verbose, safe solutions. Without human review, codebases can bloat. With proper review, AI code can match or exceed human-written code for routine tasks. Critical systems still need experienced developers reviewing every line.
Should junior developers be worried about AI?
Junior developers need to adapt, not worry. The traditional path of "learn by typing boilerplate repeatedly" is disappearing, but the need for people who understand software is not. Juniors should focus on learning through review of AI code, understanding system design, and developing judgment about quality rather than just syntax proficiency.
The Practical Takeaway
The death of boilerplate isn't the death of programming. It's the death of the tedious parts. What remains is the interesting work: architecture, design, review, and solving novel problems.
Embrace it. The best developers of the next decade won't be the fastest typists. They'll be the best directors and reviewers.
Need help leveraging AI in your development workflow? Cedar Operations helps teams adopt modern development practices. Let's discuss your needs →
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