Only as long as you have no clients, payments, or responsibility
Artificial intelligence has already changed software development significantly. Today you can open an AI tool, describe a task in plain language, and get a working project. This approach is often called vibe coding: instead of writing code by hand, you manage it through AI prompts.
At first glance, it seems like anyone can now build a product without programmers. That's partly true — the barrier to entry in development has dropped dramatically.
But there's an important nuance: building a demo has become easy. Building a product that works reliably, accepts payments, stores user data, evolves over time, and doesn't break with every change — that's still an engineering challenge.
The real question isn't whether AI can write code. The real question is: how much will a mistake in that code cost you?
I'd divide projects into three levels:
The first level covers projects where the cost of failure is low. This includes landing pages, simple corporate websites, parsers, utilities, automation scripts, Telegram bots, internal tools, and prototypes for testing an idea.
For example, you have a hypothesis. You want to test it on yourself, friends, or a small group of users. You don't have payments, complex auth, sensitive data, or scaling concerns yet. In that situation, you can genuinely try to build the first release yourself with AI.
"Build with AI" doesn't always mean "done in 15 minutes with zero knowledge."
AI can write code, but you still need to understand the basics: what stack the project uses, where the code lives, how to set up deployment, how to connect a domain, and what to do when something breaks after your latest change.
You'll probably manage to build a landing page. But under the hood it might be sloppy: heavy images, unoptimized code, poor responsiveness, slow loading. For a quick offer test that's often fine — but it doesn't mean the site is well-built.
If you don't understand what GitHub, deployment, hosting, frontend and backend mean, I wouldn't recommend jumping straight into code. For a landing page, use Framer, Webflow, or Tilda.
I also wouldn't recommend building an online store from scratch with AI, especially without technical experience. Better to use Shopify, WooCommerce, or similar platforms that already have products, cart, orders, payments, and an admin panel. You focus on content and sales, not code.
AI can still help: preparing site structure, writing copy, product descriptions. But the technical foundation should be ready-made.
If you understand how web development works, the situation is different. You can use Claude, Cursor, or another AI tool — show it competitor designs, load project rules, describe architectural patterns, and ask it to build a site on React or Next.js.
Store the code in GitHub, deploy through Vercel, and for simple scenarios use Supabase as a ready-made database and auth layer. If you don't want to deal with any of that, pick a no-code platform and focus on the business.
The next level is projects where real users, commercial logic, databases, auth, subscriptions, payments, or custom business processes appear.
For example, you built a simple auto-posting tool for yourself — it runs locally, no users, no payments. That's level one. But if you decide to turn it into a SaaS with registration, pricing plans, and payment processing — that's a completely different level of responsibility.
This is where you need engineering oversight. You don't necessarily need a full team yet: one experienced developer who helps choose the architecture, set up the project, review pull requests, and point out where to rewrite and where to leave things as-is.
The main danger of AI-generated code is that it can look finished before it's actually reliable.
The interface opens, buttons click, forms submit. Everything seems fine. But underneath there may be bugs that only surface later.
A user accidentally accesses someone else's data. Subscription cancellation doesn't work. A payment goes through but the database status doesn't update. Or there's a vulnerability you don't even know about.
In a commercial product, it's not just the surface that matters. Data, security, predictability, and architecture matter too. AI helps you move faster, but the responsibility must stay with the engineer.
The third level covers complex products already in production: SaaS with an active user base, marketplaces, CRM, ERP, fintech, healthcare, B2B systems, and complex mobile apps with many roles, integrations, and business logic.
In these projects, vibe coding in the form of "build me a new section" no longer works. AI is still useful, but it's used differently: not as a replacement for a programmer, but as a programmer's tool. The difference is in who sets the task.
A non-technical person writes to AI: "Build me a dashboard with payments." An engineer writes: "Add a new field to this model, create a migration, update the API, maintain backward compatibility, add permission checks, cover with tests."
AI doesn't replace the programmer. AI becomes the programmer's tool. The difference is enormous.
The developer sets the technical task, understands the project context, reviews the result, refines the code, and takes responsibility for the final quality. In my team this is already standard practice — AI handles the routine, the engineer makes the decisions.
A small commercial product used to require a mini-team: backend dev, frontend dev, tester, manager. Tasks in Jira, syncing work, reviews, rework. Now, at an early stage, one strong full-stack developer with AI tools can cover most of it.
That doesn't mean teams are no longer needed. But at the early stages of a commercial product, development costs and time to market have changed dramatically. As the product grows, a team will likely be needed anyway.
The simplest criterion is the cost of a mistake. If a mistake costs nothing — experiment on your own. If it could cost money, user data, or reputation — you need at least engineering oversight.
If the product is already generating revenue, has active users, and is scaling — you need a full-time developer or team.
The path is straightforward: first you test the hypothesis yourself, then bring in an engineer when there's commercial potential, then hire full-time when the product starts earning and growing. Don't build perfect architecture for an unvalidated idea. But don't launch a service with payments on code you don't understand.
Vibe coding is a great way to start. But the closer a project gets to money, users, and responsibility, the more important engineering thinking becomes — not the speed of code generation.
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