You've seen the posts. Someone builds a SaaS in a weekend using Claude or ChatGPT. A founder ships their MVP without writing a line of code themselves. The demos are impressive. The promise is real.
But so are the support tickets, security vulnerabilities, and 3am debugging sessions that don't make it into the Twitter threads.
I've spent the last year helping founders navigate this new landscape—some building from scratch, others rescuing projects that hit walls. Here's what I've learned about when AI-assisted development works, when it doesn't, and how to avoid the expensive mistakes.
What "vibe coding" actually means
The term comes from Andrej Karpathy's observation that you can now build software by describing what you want in natural language. You "vibe" with the AI, iterating through prompts until the code does what you need.
This is genuinely revolutionary. Tasks that required years of programming experience can now be accomplished by someone who understands their problem well enough to describe it clearly.
But there's a catch. Understanding your problem and understanding software architecture are two different skills. The AI will give you code that works for your test case. Whether it works for the thousand edge cases you haven't thought of yet is another question entirely.
Where vibe coding genuinely works
Let me be clear: AI-assisted development is not a gimmick. I use it daily. It's transformed how quickly I can ship solutions. Here's where it excels:
Prototypes and proof of concepts
If you need to validate an idea quickly, vibe coding is perfect. Build the thing, show it to users, learn. The code quality doesn't matter if you're going to throw it away anyway.
Internal tools
Tools that only your team uses have lower stakes. If something breaks, you fix it. There's no angry customer waiting. The cost of imperfect code is manageable.
Automations and scripts
One-off scripts, data transformations, workflow automations—these are ideal. Clear inputs, clear outputs, limited scope.
Learning and exploration
Want to understand how something works? Ask the AI to build it and explain it. You'll learn faster than reading documentation.
Where vibe coding falls apart
Now the uncomfortable part. These are the patterns I see repeatedly in projects that need rescuing:
Authentication and payments
"Claude helped me build a login system" is a sentence that makes security professionals nervous. Authentication has decades of edge cases baked into best practices. AI will give you something that works in the happy path. The unhappy paths—session hijacking, token refresh races, password reset vulnerabilities—require experience to anticipate.
Same with payments. The Stripe integration looks simple until you need to handle webhooks reliably, manage subscription edge cases, or deal with your first chargeback dispute.
Anything that scales
AI-generated code optimises for "it works," not "it works when 10,000 people use it simultaneously." Database queries that seem fine with test data crawl to a halt with production volumes. Memory usage patterns that are invisible with small datasets become server-crashing problems at scale.
Complex integrations
Connecting to one API is straightforward. Connecting to five APIs that need to stay in sync, handle failures gracefully, and retry intelligently? That's where AI-generated code starts producing spaghetti.
Long-term maintenance
Here's the hidden cost nobody talks about: six months from now, when you need to add a feature, will you understand the code well enough to modify it safely? If the AI wrote it and you don't fully grasp how it works, you're in a risky position.
The expensive middle ground
The worst outcome isn't building with AI and failing. It's building with AI, partially succeeding, and then getting stuck.
You've got a working product. Users are paying. But:
- •Adding new features breaks existing ones
- •You're afraid to touch certain parts of the codebase
- •Every change takes longer than the last
- •You can't hire a developer because they'd need weeks just to understand what's there
This is technical debt, and AI can accumulate it faster than any junior developer ever could. At least a junior developer writes consistent bad code. AI writes inconsistent code—different patterns in different places, solutions that don't compose well together.
I've seen founders spend more money rescuing these projects than they would have spent building properly from the start.
The honest framework
Here's how I advise founders to think about this:
Build with AI if:
- •You're validating an idea (prototype phase)
- •The cost of failure is low
- •You have technical judgment to evaluate the output (even if you can't write it yourself)
- •You're building something with clear boundaries and limited scope
Hire a developer if:
- •Users will depend on this working correctly
- •Security or payments are involved
- •You're building something that needs to scale
- •The project will need ongoing development and maintenance
- •You don't have someone technical to evaluate AI output
Work with a developer who uses AI if:
- •You want speed AND quality
- •You have budget constraints but can't afford to build twice
- •You want to learn and potentially take over maintenance later
- •You need someone who can rescue a vibe-coded project that's hit its limits
What I actually do
My approach combines the speed of AI-assisted development with the judgment that comes from building production systems for years.
When I work on a project, AI handles the boilerplate—the repetitive code that would be boring to write manually. But I make the architectural decisions. I choose the patterns that will make the code maintainable. I anticipate the edge cases that AI doesn't know to worry about.
The result is faster delivery without the technical debt. You get working software that a developer can understand and extend later—whether that's me, someone you hire, or you learning to code yourself.
Questions to ask yourself
Before you start building, be honest about these:
1. What's the cost if this breaks? If it's embarrassment, build fast and iterate. If it's lost revenue, angry customers, or security breaches, invest in doing it right.
2. How will this grow? If the scope is fixed, AI-generated code might be fine forever. If you're planning to add features regularly, you need a foundation that supports change.
3. Who will maintain this? If it's you, and you understand the code, great. If you're planning to hand it off, whoever inherits it needs to be able to work with it.
4. What's your timeline? Vibe coding is fastest in week one. Proper architecture is fastest in month six. Choose based on your actual timeline, not your optimistic one.
The bottom line
AI has genuinely changed what's possible. Founders can build things that would have been impossible five years ago. That's exciting and worth embracing.
But AI hasn't eliminated the need for software engineering judgment. It's shifted where that judgment gets applied—from writing code to evaluating code, from implementation to architecture, from syntax to systems thinking.
The founders who win in this environment will be the ones who understand what AI is good at, what it's not, and how to combine it with human expertise effectively.
If you're building something that matters, that combination is worth investing in.