We Shipped an MVP With Vibe-Coding. Here's What Nobody Tells You About the Aftermath

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This one's for mid-to-senior developers wondering where they stand in the age of AI.

There's an endless debate in the IT world: will AI replace developers? I'm a tech lead at AluSoftBel, and I want to share my perspective — both as a developer and as someone who works directly with business stakeholders. Spoiler: the answer might surprise you.

The Uncomfortable Truth About AI-Generated Code

Let me be straight: the quality of code AI models produce is poor. But here's the other side of the coin — since AI arrived, businesses have felt the speed of development increase, and now they want more features, faster.

This creates a classic conflict of interests. Business wants to earn more money. Developers want maintainable, quality code — so we don't shoot ourselves in the foot later. Both sides are right. The question is how to balance them.

How We Actually Use AI: A Real-World Workflow

Let me walk you through the approach we landed on at our company.

Phase 1: MVP First — Quality Later

Step one is always an MVP. Fast, hardcore, zero regard for quality. The only goal: ship a working product that can be sold.

Yes, you end up with technical debt. Yes, it's messy. But the product hits production quickly, it works — somehow — and that's enough to build on. You can show it at a presale, close a deal, get a budget approved.

The key insight: an MVP doesn't just give you a demo. It gives you a real working product where you can see what's strong, what's weak, and course-correct before serious money is invested.

Phase 2: Sell It, Then Plan For Real

Once the product is sold, the tech lead, business analyst, and PM sit down and build a real development plan. I look at technical implementation, the analyst looks at business requirements. We're not guessing anymore — we're working with concrete, real-world cases and filing down what already exists.

Simultaneously, the business side collects real user feedback: what works, what doesn't, what's frustrating. Not assumptions — live cases. From these, the PM and analyst build a proper spec with clear requirements and a real product vision. Only then does full-scale development begin.

Phase 3: Dealing With the Aftermath

Now comes the technical debt cleanup. In my case, the MVP was pure vibe-coding — a term for AI-assisted, low-oversight rapid prototyping. The code was rewritten 3–4 times, leaving behind dead models, orphaned database tables, unused libraries, and broken legacy code.

After the cleanup comes decomposition into a proper tech stack — moving everything into a structured architecture based on actual design patterns. From chaos to order. This is the work AI simply cannot do.

The Real Costs: Pros and Cons

❌ Cons

Overhead on one or two people. Someone has to carry a massive workload alone, fall out of their normal rhythm, work overtime. In my case — that was me.
Technology chosen on the fly. Libraries with a handful of GitHub stars, experiments run directly in production, no upfront research. Everything was battle-tested the hard way — and that costs time and sanity.
The team still pays the price. After the MVP, the whole team has to sit down, read through the messy code, and redo the decomposition. The "fast start" delays the pain — it doesn't eliminate it.

💬 Sound familiar? Drop a comment — I'm curious how your team handles the post-MVP cleanup.

✅ Pros

Business gets a proof of concept for relatively little money. If the idea works, they invest properly and build a real product. If it doesn't — losses are minimal. It's essentially the Lean Startup model in practice.
The team gets a project, investment, and — let's be honest — work. In today's climate of automation anxiety, that last point matters more than people admit.
We cleared almost all of our company's technical debt using this approach. AI accelerated the start, but the people cleaned up and built what lasts.

So, Will AI Replace Developers?

Based on my experience: no — and here's why.

The number of products being built is going to grow. Which means the amount of work is going to grow. The cycle looks like this: AI helps spin up an MVP quickly, it gets validated, it gets launched — and then someone still has to build something that actually works at scale.

Because AI is prone to mistakes when designing databases, choosing indexes, picking architectural patterns, and selecting the right tech stack. These aren't edge cases — they're the core of every serious product. In the end, developers — senior and junior alike — will come in and fix it, extend it, maintain it. AI creates work. It doesn't eliminate it.

Yes, there's a market dip right now, and it will probably continue for a while. But my bet is that the economy will recalibrate — and we're heading toward a genuine boom in software development and technology.

The developers who will thrive aren't the ones who refuse to use AI. They're the ones who know how to wield it — and how to clean up after it.