Vibe Code Caution
“Vibe Code” and the Mirage of Momentum: Lessons from Building Four Apps Twice
We’re living in an extraordinary moment for software creation. AI-assisted builders promise a future where anyone can develop complex applications at speed-no Git, no DevOps, no terminal, no deployment pipeline.
Some platforms market this as “build fully-functional apps in minutes with just your words. No coding necessary”.
And at first, it feels miraculous.
You say “build a user dashboard” and suddenly you have one. You ask for “add authentication” and the screen updates. You request a database table and the schema appears. Within hours, you might have a working prototype.
For founders, charities, agencies, and experimentation?
This is thrilling, and genuinely useful.
But after building four production-grade apps twice - first in vibe-code platforms, then migrating them to stable architectures - one pattern becomes clear:
What feels effortless on day one becomes painfully expensive by day one hundred. Let’s unpack why.
The Core Problem: Real Software Outgrows “Vibes”
Early-stage AI builders trade precision for acceleration.
The initial velocity isn’t the danger - it's the stall.
Key Failings of Vibe-Code Environments
1. Live Development Is the Default
Changes apply directly to production.
Real users. Real data. Real risk.
No staging.
No branch.
No sandbox with parity.
All learning happens on the live system - the most expensive classroom imaginable.
2. Limited Context = Fragile Code
The AI only “remembers” part of the application.
As systems grow, requests become:
Misinterpreted
Half-updated
Inconsistent between modules
Dependent on your prompt phrasing rather than architecture
You don’t realise where the weak joints are until something breaks.
3. Debugging Becomes an Archaeological Dig
When features fail, you have:
No version history
No commit diff
No reproducible environment
No reliable logs
No ability to isolate the regression
Bug fixing becomes prompting.
Troubleshooting becomes guesswork.
4. No Predictable Deployment = Developer Anxiety
Most platforms offer:
No pre-deployment build
No preview URL
No rollback
No test harness
No migration control
No database branching
It’s like replacing the engine while driving at motorway speed… and hoping.
5. Trapped Data and Environment Bleed
Sharing data across environments is either:
Impossible
Manual and error-prone
Automated in ways you can’t audit
This makes GDPR, audit, QA, and investor confidence… shaky.
6. The Hidden Cost: Cognitive Load on the Founder
You become:
Product manager
Developer
QA team
Release engineer
Infrastructure risk-bearer
Not by choice - by necessity.
And because changes are “just a prompt”, internal stakeholders unintentionally devalue the engineering discipline.
Perception: “It only takes a minute.”
Reality: “We need DevOps, RLS, migrations, version control, CI/CD, and rollback.”
The Amateur Trap—and Why It’s Not Their Fault
Platforms make it easy to start building without learning:
Deployment pipelines
Data separation
Security models
Governance
Versioning
Reproducible builds
Rollback process
Testing strategies
New builders unknowingly create systems that can never leave the prototype phase safely.
Then, as complexity grows…
They don’t have a bad system.
They have an unmaintainable one.
And investors should pay attention.
What Professional Development Looks Like
High-quality engineering environments provide:
Local dev environment
Staging/preview environments
Production environment
Version control (Git)
Rollback capability
Continuous deployment pipelines
Database migrations
Audit trails
Error logs
Security policy control
Rehearsed disaster recovery
It’s not bureaucracy.
It’s the difference between:
Shipping software and Running a live experiment on paying customers
Who Should Care About This?
Charity trustees
Relying on an experimental environment for sensitive user services is not governance.
SMEs and agencies
Speed is good. Vendor lock-in + fragile architecture + no rollback is not.
Investors and boards
Ask founders:
Do you have staging?
Can you roll back releases?
How do you test database migrations?
What happens if your builder platform goes offline or is acquired?
If the answer is “the AI handles it”, ask again.
So What Is the Solution?
AI acceleration yes.
But coupled with proper software discipline.
Ideal stack for stability + speed:
AI-assisted code, not opaque generation
Supabase / Postgres
Vercel / Render
GitHub
Branching & Review
Automated deploy previews
CI/CD
Error monitoring
Use vibe-code systems to explore, shape, and iterate on the idea.
But move to a real environment before the system becomes mission-critical.
What Should Be In Place Before Building
Data model spec - Avoid schema drift
User roles & permission matrix - Avoid retrofitting RLS
Environment plan (dev/staging/prod) - Safe deployment
Git & versioning setup - Audit trail and rollback
AI prompt discipline - Deterministic code generation
Backup & export policy - Avoid platform lock-in
Migration plan - Know your exit before you enter
Final Thought
Vibe-code platforms are wonderful accelerators.
They are not software delivery pipelines.
If you’re a founder, charity leader, or investor, the question isn’t:
“Can this build fast?”
It’s:
“Can this scale without breaking—and who is accountable when it does?”
One day, AI-native dev platforms may mature into enterprise-grade pipelines.
Today, they are prototype engines.
Respect them for that, and they’re brilliant.
Confuse them for mature engineering tools, and costs appear suddenly and painfully just at the moment success begins.