Hey dev.to community,
I've been thinking about this a lot lately. We run a small AI-first dev studio, and we use Claude Code, Cursor, and other AI tools daily. We can scaffold an entire MVP in a weekend now.
So the question is real: If a founder can just prompt their way to a working app, why would they ever pay a dev agency?
Here's what we've observed (both from our own builds and helping others):
Where AI Coding Tools Shine
- Rapid prototyping and UI generation
- Boilerplate code and CRUD operations
- Learning new frameworks fast
- Writing tests and documentation
- Debugging and explaining code
No doubt about it — AI tools are incredible for these tasks.
Where Things Still Break Down
1. Architecture & Scaling Decisions
AI will give you 5 different ways to structure your app. But which one won't bite you in 6 months when you have 10k users?
AI doesn't have opinions about trade-offs — it just generates options.
2. Integration Complexity
Connecting Stripe + Supabase + Resend + a webhook + proper error handling?
AI handles each piece individually, but the orchestration between them is where subtle bugs hide. We've seen this repeatedly.
3. The "Works Locally, Breaks in Production" Gap
AI writes code that works in isolation. But production has:
- Concurrent users hitting the same endpoint
- Rate limits and API quotas
- Edge cases in authentication
- Database connection pooling
- Memory leaks from unoptimized queries
AI doesn't think about these unless you explicitly ask — and most founders don't know to ask.
4. Security Blind Spots
AI-generated code often misses:
- Input validation edge cases
- Proper rate limiting
- Secure token handling
- SQL injection prevention in complex queries
5. Technical Debt Accumulation
We've seen AI suggest 3 different state management patterns in the same codebase because each file was generated in isolation.
The code "works" but it's unmaintainable.
Real Example
A friend built a SaaS MVP with AI tools in 2 weeks. Looked great. But when they tried to launch:
- Database queries were 10x slower than needed (no indexes)
- Authentication had a race condition that only appeared with 50+ concurrent users
- The AI had introduced conflicting dependencies
- No error handling for API failures
They spent another 3 weeks fixing critical issues before they could safely launch.
So Why Do Agencies Still Matter?
Not because AI is bad — it's incredible. But because:
- AI makes good developers 2-3x faster — We use these tools internally, so we ship faster than traditional agencies
- Architecture expertise — Knowing what NOT to build is as important as knowing what to build
- Production readiness — The gap between "demo" and "production" is still massive
- Integration reliability — Making 5+ services work together without breaking
- Focus — Founders should be talking to customers, not debugging AI-generated code
The Real Shift
AI hasn't killed dev agencies. It's changed what agencies need to be good at:
| Less | More |
|---|---|
| Writing boilerplate code | Architecture, security, integration |
| Hours of development | Strategic technical decisions |
| Manual testing | AI-assisted code reviews |
| Basic CRUD | Complex integrations |
Curious to Hear From the Community
I'm genuinely interested in the discussion:
- Founders who've built with AI tools — where did you hit walls?
- Other devs — how are you using AI in your workflow?
- Anyone else seeing the "works locally, breaks in production" problem?
Drop your thoughts in the comments! Let's have an honest conversation about where AI coding tools are actually at in 2025.
P.S. If you want to see what we're building, check out our site https://qbitlabs.tech/ . But mostly here for the conversation!
What's your take?
Let me know in the comments:
- Are you using AI coding tools?
- What's been your biggest win?
- What's been your biggest fail?
Let's discuss!
Top comments (0)