There's a lot of hype around AI-assisted development. Most of it focuses on what changes — the speed, the output volume, the ability to build without knowing every syntax. Less of it is honest about what doesn't change, and why that matters more.
After shipping 50+ products with AI-assisted workflows, here's how we actually think about it.
What Vibe Coding Actually Means
Vibe coding is a colloquial term for AI-assisted software development — building products using AI tools (Cursor, Claude, Copilot) as active collaborators rather than passive references. The developer describes intent; the AI produces implementation; the developer reviews, refines, and directs.
The "vibe" part is not random. It reflects that the developer's primary job shifts from syntax recall to judgment: what to build, in what order, and whether what was built is correct.
What it is not: fully automated software generation. The AI does not understand your business, your users, your edge cases, or your security requirements. A senior developer directing AI is dramatically more productive. An inexperienced developer trusting AI unsupervised is a liability.
What Changes
Time to first working version
This is the biggest change, and it's real. Scaffolding a Next.js app with auth, a database schema, and basic CRUD operations used to take 2–3 days for an experienced developer starting from scratch. With AI tooling, it takes 4–6 hours.
The gains compound across a project. API routes that follow standard patterns get written in minutes. TypeScript interfaces get generated from a description. Boilerplate that used to require context-switching to documentation gets produced inline.
Lookup and reference time
We don't Google "how to set up Supabase realtime subscriptions" anymore. We describe what we want in Cursor and get working code. For well-documented APIs and frameworks with strong training data, AI removes the lookup tax almost entirely.
This is particularly valuable for less-used parts of a stack. Every project involves integrating with an API you haven't touched in a year. AI collapses the ramp-up time.
Iteration speed on UI
Describing a UI in natural language and getting a working Tailwind component in seconds is genuinely useful. The output usually needs refinement, but starting from something beats starting from nothing. Figma-to-code pipelines via tools like v0 have made design handoff faster than it's ever been.
Volume of code reviewed per hour
This is the downside that doesn't get talked about enough. AI generates code fast. Reviewing it is still a human job. If you're using AI heavily and shipping fast, you're also reading more code per hour than you ever have. The cognitive load shifts from writing to evaluating. That's a different skill set, and it's one not every developer has developed yet.
What Doesn't Change
Architectural judgment
AI will build whatever structure you point it toward. It will scaffold a 2,000-line page component if you let it. It will put business logic in the wrong layer. It will create circular dependencies and miss the right abstraction until you give it one.
Architecture is still entirely a human responsibility. The data model, the service layer boundaries, the component hierarchy — you design these and direct AI to implement them. Developers who skip this step and let AI drive structure produce codebases that work initially and fall apart under maintenance.
Security
This cannot be overstated: AI-generated code is not safe by default. It writes code that functions, but it routinely produces authorization logic that can be bypassed, validation that can be circumvented, and SQL that can be injected.
Our process: every API route that touches user data is read line by line before it ships. Every auth flow is tested with a second account to verify isolation. Every input that comes from user-controlled sources is validated server-side — not just client-side.
AI helps us move fast. Human review is what makes that speed safe.
Understanding the business problem
The AI has no idea what your product is supposed to do. It doesn't know what your users need, what the edge cases in your domain are, or why you're building this feature instead of another one.
Product judgment remains 100% human. The developer's job is to translate business problems into technical decisions clearly enough that the AI can implement them correctly.
Testing and QA
AI can write tests — but tests written by AI tend to test the implementation rather than the behavior. We write our own test cases for critical paths. We do manual QA on every feature before shipping. We check mobile, check Safari, check empty states and error states.
The audit still happens. The audit still catches things. The audit is not skippable.
A Direct Comparison
| Dimension | Traditional Dev | AI-Assisted Dev | |---|---|---| | Boilerplate time | High | Near zero | | Architecture design | Human | Human | | Security review | Required | Required (more critical) | | Time to working MVP | Weeks | Days | | Code review volume | Normal | Higher | | Lookup/reference time | High | Low | | Business logic judgment | Human | Human | | Bug introduction rate | Lower | Higher without review | | Iteration speed on UI | Moderate | High | | Appropriate for solo devs | Limited | Yes, with discipline |
The gains are real. The responsibilities don't go away.
How We Use It at Greta Agency
Our model: AI writes the first draft. An engineer reads it. We ship the second draft.
The ratio varies by code type. Boilerplate, CRUD routes, TypeScript interfaces, UI components — AI writes most of it, we review quickly. Auth flows, payment logic, data access patterns — we write the structure, AI fills the implementation, we audit the result.
Speed comes from eliminating the drafting time. Quality comes from not eliminating the review.
Frequently Asked Questions
Do I need to know how to code to use vibe coding tools?
Some basic coding knowledge makes a significant difference. You don't need to be able to write everything from scratch, but you need to be able to read what AI produces and recognize when it's wrong. Developers who can't read the code they're shipping are flying blind — and AI makes mistakes that only become visible under load or in edge cases.
Is vibe coding just for MVPs, or can it scale to production?
It works at production scale — our SEO Pilot tool is fully AI-assisted and runs in production. The key is that the workflow has to mature as the product does. Production code needs more careful review, better test coverage, and clearer architectural boundaries than an MVP. AI-assisted development can match traditional development's quality ceiling; it just requires the same discipline.
How do you handle it when the AI generates incorrect code?
We read it, catch it, and fix it. This happens regularly — AI generates plausible-looking code that doesn't handle edge cases, makes incorrect assumptions about data shape, or misses an authorization check. The fix is a review culture where generated code is treated as a draft, not a finished implementation. If you treat AI output as reviewed, you'll ship bugs. If you treat it as a starting point, you ship fast and safely.
What's the learning curve for switching to AI-assisted development?
For experienced developers: 1–2 weeks to develop effective prompting patterns and learn where to trust the AI versus where to be skeptical. The biggest shift is psychological — moving from writing code to directing code production. For less experienced developers: longer, because you need the baseline to evaluate what the AI produces.
Written by
Alex Chen
Founder & Strategy Lead, Greta Agency
Alex has spent 10+ years building growth engines for companies from seed to Series C. He founded Greta Agency to prove that great software can ship in days, not months.