The Internet Is Fighting About Vibe Coding. They're Missing the Point.
Every few months, the developer community finds a new thing to panic about. First it was no-code platforms. Then it was GitHub Copilot. Now it's "vibe coding" — the practice of describing what you want in natural language and letting AI generate the code.
The reactions are predictable. Senior engineers scoff: "This was always possible with Stack Overflow and Google." Managers get starry-eyed: "We don't need as many developers anymore!" And somewhere in between, the actual truth gets lost.
At Gerus-lab, we've shipped 14+ production projects using AI-assisted development. Not prototypes. Not demos. Real systems handling real users and real money. And after two years of integrating AI into our engineering workflow, here's what we've learned: vibe coding isn't the problem. Your engineering culture is.
The Real Problem Nobody Talks About
Let's be honest about what's actually happening in most companies right now.
A C-level executive attends a conference. They see a demo where someone builds a landing page in 90 seconds using Claude or GPT. They come back to the office and announce: "We're going AI-first." Engineers are told to use Copilot. Metrics are invented — lines generated, tokens consumed, "AI adoption rate." Nobody asks the only question that matters: are we shipping better products faster?
This is not an AI problem. This is a management problem wearing an AI costume.
We've seen this pattern with every technology wave. Blockchain in 2017. Microservices in 2019. Now AI in 2025-2026. The technology itself is genuinely useful. But the way organizations adopt it is almost always broken.
What "Vibe Coding" Actually Looks Like in Production
Here's what our engineering team at Gerus-lab actually does with AI tools — and what we absolutely don't do.
What We Do
1. Rapid Prototyping for Client Validation
When a client comes to us with a vague idea — "I want something like Uber but for pet grooming" — we used to spend 2-3 weeks building a clickable prototype. Now we can generate a working MVP in 2-3 days. Not a Figma mockup. A functional prototype with real API calls, real database operations, real user flows.
This isn't replacing engineering. It's compressing the feedback loop. The client sees their idea alive faster, gives feedback sooner, and we avoid building the wrong thing for six months.
2. Boilerplate Elimination
Every experienced developer knows that 60-70% of any project is boilerplate. CRUD operations. Authentication flows. API endpoint scaffolding. Form validation. This stuff isn't intellectually challenging — it's just time-consuming.
AI handles this brilliantly. Our developers describe the data model, the business rules, and the edge cases. AI generates the repetitive code. Developers review, refine, and focus on the 30% that actually requires human judgment.
3. Cross-Stack Translation
Our team works across Web3 (TON, Solana), traditional SaaS, GameFi, and enterprise automation. When a Solidity developer needs to understand a Rust smart contract, or when a React developer needs to debug a Python ML pipeline, AI acts as a universal translator.
This doesn't make everyone a full-stack expert. It makes everyone less blocked by technology boundaries.
What We Don't Do
We don't let AI make architectural decisions. Ever. The choice between a monolith and microservices, the database selection, the caching strategy — these decisions have consequences that outlive any individual feature. They require understanding of business context, team capabilities, and future growth patterns that no AI model currently possesses.
We don't skip code review for AI-generated code. In fact, we review it more carefully. AI-generated code has a dangerous property: it looks correct. It's syntactically perfect, well-formatted, and plausible. But "plausible" and "correct" are very different things, especially when you're handling financial transactions on a blockchain or processing sensitive user data.
We don't use AI to replace understanding. If a developer can't explain what the AI-generated code does, line by line, it doesn't go into production. Period.
The Habr Debate Gets It Half Right
There's a popular article making rounds on Russian tech forums arguing that vibe coding is nothing new — that everything AI does was possible before with pandas, CMS templates, and Stack Overflow.
They're half right. The individual capabilities aren't new. What's new is the integration speed.
Yes, you could always Google "how to read Excel in Python" and find the pandas one-liner. But could you, in the same session, also:
- Design the data visualization?
- Write the API endpoint to serve the results?
- Generate the frontend component to display it?
- Write the unit tests?
- Create the Docker configuration?
- Draft the documentation?
Each of these was individually easy. Doing all of them in a single flow, maintaining context across layers — that's genuinely new. Not revolutionary. Not AGI. But meaningfully different from having 47 browser tabs open.
The Four Types of Engineering Teams (And How They Handle AI)
After consulting with dozens of companies at Gerus-lab, we've identified four patterns:
Type 1: The Deniers
"AI is just hype. We'll keep doing things our way."
These teams will be fine for 1-2 years. Then they'll notice their competitors shipping faster. Not because AI replaced their developers, but because AI eliminated the boring parts that were slowing everyone down.
Type 2: The Cargo Culters
"Everyone must use Copilot! Track your AI usage! Report your token consumption!"
These teams create the most damage. They optimize for AI adoption instead of product quality. They celebrate when a junior developer generates 500 lines of code in an hour without noticing that 200 of those lines contain subtle bugs.
Type 3: The Pragmatists
"AI is a tool. Use it when it helps. Don't when it doesn't. Judge by output quality."
This is where most good engineering teams land eventually. It's fine. It's safe. But it's also leaving value on the table.
Type 4: The Architects
"AI changes what we build, not just how we build it."
This is where we operate at Gerus-lab. When AI handles the implementation details, engineers can think bigger. Instead of spending two sprints building a recommendation engine, you spend two sprints designing the right recommendation strategy and let AI handle the implementation. The cognitive budget gets reallocated from "how" to "what" and "why."
A Real Case: How We Built a GameFi Platform in 6 Weeks
One of our recent projects was a GameFi platform on TON blockchain. The client wanted:
- NFT-based game assets
- Token economics with staking
- Real-time multiplayer mechanics
- Telegram Mini App integration
- Admin dashboard with analytics
Two years ago, this would have been a 4-6 month project for a team of 5-6 developers. We did it in 6 weeks with 3 developers.
Here's the breakdown:
- Week 1-2: Architecture design, smart contract logic, game mechanics design — 100% human work
- Week 3-4: Smart contract implementation, API development, frontend scaffolding — 60% AI-assisted
- Week 5-6: Integration, testing, optimization, deployment — 30% AI-assisted
The AI didn't design the token economics. It didn't figure out the game balance. It didn't decide on the smart contract architecture. But it did write the boilerplate Solidity, generate the API endpoints, scaffold the React components, and produce the initial test suites.
The result? Same quality. 60% less time. Not because AI replaced engineers, but because it amplified them.
The Uncomfortable Truth About "Prompt Engineering"
Here's something the vibe coding evangelists don't want to hear: writing good prompts requires the same skills as writing good code.
To get useful output from an AI, you need to:
- Understand the problem domain deeply
- Specify requirements precisely
- Anticipate edge cases
- Decompose complex systems into manageable pieces
- Validate the output against your mental model
Sound familiar? That's literally software engineering. The people who are best at "vibe coding" are experienced developers who know what good code looks like. The people who are worst at it are non-technical managers who think describing something vaguely is the same as specifying it precisely.
As we always tell our clients at Gerus-lab: a sufficiently detailed specification IS code. If you can describe every edge case, every error handling path, every data transformation in natural language — congratulations, you've just written pseudo-code. You might as well have written real code.
What Comes Next
The vibe coding debate will die down, just like every other tech culture war. What will remain is this:
AI-assisted development is real and useful. Not transformative in the way VCs want you to believe, but genuinely useful.
The bottleneck was never code generation. It was always understanding, design, and communication. AI doesn't fix those.
Engineering culture determines AI effectiveness. The same teams that wrote good code before AI will use AI well. The same teams that wrote bad code will use AI to write bad code faster.
The winners will be teams that use AI to think bigger, not teams that use AI to think less.
At Gerus-lab, we're not AI optimists or AI pessimists. We're AI pragmatists who've shipped enough real projects to know what works and what's marketing.
Ready to Build Something Real?
If you're tired of the hype and want a team that actually knows how to leverage AI-assisted development for production systems — let's talk.
We build Web3 platforms, SaaS products, GameFi systems, and enterprise automation. With or without AI assistance — whatever ships the best product.
Gerus-lab — Engineering studio that ships. 14+ production projects. AI-augmented, human-driven.
What's your experience with AI in production engineering? Are you a Denier, Cargo Culter, Pragmatist, or Architect? Let us know in the comments.
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