AI Won't Replace Developers (But It Will Change How We Work)
Everyone's either panicking that AI will take our jobs or hyping it as the solution to everything. After actually using it daily, the truth is more nuanced.
What AI Is Actually Good For
1. Boilerplate Code
I used to spend 20 minutes writing a CRUD API. Now I ask Claude:
"Create a Next.js API route for managing appointments with GET, POST, PATCH, DELETE"
And it gives me a solid starting point. Then I modify it for my needs.
Time saved: ~15 minutes per endpoint
2. Learning New Libraries
Before AI:
- Read docs for 30 minutes
- Google examples
- Copy-paste from StackOverflow
- Debug for hours
With AI:
- "How do I use Zod to validate this data structure?"
- Get working example in 2 minutes
- Adjust as needed
This is huge for learning.
3. Explaining Code
Got someone else's messy code?
// What does this even do?
const x = data.reduce((a, b) => ({
...a,
[b.id]: [...(a[b.id] || []), b]
}), {})
Ask AI: "Explain this code"
"This groups an array of objects by their id property into an object where keys are ids and values are arrays of objects with that id."
Game changer for working with legacy code.
4. Debugging
I paste my error message and relevant code. AI often spots the issue faster than I would.
Not because it's smarter, but because it's not tired or biased by what I think should work.
5. Writing Tests
I hate writing tests. AI makes it bearable.
Me: "Write tests for this function"
function calculateDiscount(price: number, isReturning: boolean) {
return isReturning ? price * 0.9 : price
}
AI gives me test cases I wouldn't have thought of:
- Normal cases
- Edge cases (price = 0, negative price)
- Type edge cases
I still review and adjust, but it's a great starting point.
What AI Sucks At
1. Understanding Context
AI doesn't know:
- Your business requirements
- Why you chose this architecture
- What "fast enough" means for your app
- Your users' actual needs
I asked it to optimize a function. It made it 10x faster but also 10x more complex. For my use case (runs once per page load), simpler was better.
AI optimizes for the wrong thing if you don't guide it.
2. System Design
Try asking AI to design your entire app architecture. You'll get generic advice that doesn't account for:
- Your scale (100 users vs 100,000)
- Your team size
- Your deployment constraints
- Your actual problems
It'll suggest Kubernetes when you need a $12 DigitalOcean droplet.
3. Product Decisions
AI can't tell you:
- What features to build
- What users actually want
- How to prioritize
- When "good enough" is good enough
These require talking to actual users and making judgment calls.
4. Debugging Complex Issues
Simple bugs? AI is great.
Complex bugs involving:
- Race conditions
- Memory leaks
- Weird browser behavior
- Production-only issues
AI guesses. You need to actually understand your code.
5. Knowing What to Build
The hardest part of development isn't writing code—it's knowing what to build.
AI can help you build faster. It can't tell you what's worth building.
How I Actually Use AI
Morning Workflow
- Planning - I decide what to build (AI can't do this)
- Research - Ask AI about libraries/approaches
- Scaffold - AI generates boilerplate
- Build - I write the actual logic (with AI help for tricky parts)
- Debug - AI helps spot issues
- Test - AI helps write tests
AI is my junior developer who's really fast at typing but needs direction.
Real Example
Task: Add SMS notifications to appointment booking
Without AI: ~4 hours
- Research SMS providers (30 min)
- Read Twilio docs (30 min)
- Write integration code (1 hour)
- Handle errors (1 hour)
- Write tests (1 hour)
- Debug weird issues (30 min)
With AI: ~2 hours
- "Which SMS provider works in Nepal?" (5 min)
- "Show me Twilio integration in Next.js" (5 min)
- Modify their code for my use case (30 min)
- "Write error handling for this" (10 min)
- "Write tests for SMS sending" (10 min)
- Debug (still takes time) (1 hour)
Time saved: 50%
But I still needed to:
- Decide we needed SMS
- Choose when to send messages
- Design the user experience
- Understand the integration
- Debug production issues
The Real Shift
AI isn't replacing developers. It's changing what we spend time on.
Before AI:
- 20% thinking
- 80% typing/googling/debugging
With AI:
- 40% thinking
- 60% typing/debugging
This is good. More time for:
- Understanding the problem
- Designing solutions
- Talking to users
- Making good decisions
Less time for:
- Boilerplate
- Googling syntax
- Copy-pasting from docs
For New Developers
Concern: "Should I learn to code if AI can do it?"
Answer: Absolutely yes.
AI is a tool, like Stack Overflow or Google. It makes you faster, but you need to know:
- What questions to ask
- How to evaluate answers
- When the answer is wrong
- How to fix it when it breaks
Analogy: Calculators didn't make math useless. They made mathematicians more productive.
For Companies
If you're hiring, don't ask:
- "Can they code without AI?"
Ask:
- "Can they build the right thing?"
- "Do they understand the tools?"
- "Can they debug when things break?"
- "Do they make good decisions?"
The best developers are those who use every tool available—including AI.
My Prediction
In 5 years:
- AI will be like Google—everyone uses it, nobody thinks twice
- Junior devs will be 2x more productive
- Senior devs will be 1.5x more productive (they're already fast)
- Companies will expect faster delivery
- The skill gap will be in decision-making, not coding
What won't change:
- Understanding users
- Making trade-offs
- Debugging complex systems
- Knowing what to build
- Working with people
Hot Take
AI might actually make developers more valuable, not less.
Why?
Because now the bottleneck isn't coding speed—it's knowing what to build and why.
Companies that thought they just needed "coders" will realize they need people who can:
- Understand business problems
- Design solutions
- Make good trade-offs
- Ship reliable software
These skills are harder to replace than code generation.
Bottom Line
Use AI. It's a productivity multiplier.
Don't rely on AI. You still need to understand what you're building.
Embrace it. Developers who use AI will outperform those who don't.
But remember: AI is a tool. Your judgment, creativity, and understanding of users—that's still on you.
The future isn't "AI or developers." It's "AI-empowered developers building better software, faster."
And honestly? That's pretty exciting.